From de7531ba7b89c5b1fb37afcf1d81fd336e5e04ce Mon Sep 17 00:00:00 2001 From: Jonah Larson Date: Wed, 27 May 2026 15:56:05 -0500 Subject: [PATCH 01/43] Initial Driver System (#1335) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Refactored `transformers` so that bridge configuration code is source-agnostic * Initial breakup of Bridge into core and bridge only components. Setup remote driver in prep * Initial setup of the vllm source Cherry-picked from 98bbb835 on feature/vllm-integration. Resolved against dev-4.x's Phase A type split: - bridge.py: kept the shim (TransformerBridge content lives in transformer_bridge.py now). - sources/transformers.py: accepted the scaffold's package restructure (transformers.py → transformers/{__init__,helpers,source}.py + _hf_format.py). - sources/transformers/source.py: re-applied Phase A's explicit TransformersDriver construction in boot(). sources/vllm/source.py's boot_vllm currently sets bridge._forward_impl, which Phase A removed. The function is dead until the next chunk lifts it onto VLLMDriver + RemoteBridge — landing as-is so the scaffold tree is on dev-4.x for incremental Phase B work. Co-Authored-By: Claude Opus 4.7 (1M context) * Revision round 1 of the vLLM system * Full vLLM integration initial completion * jupiter patch * dynamo patching * pass through batch size * add max model length to remote bridge boot * Updating config construction to properly pass through to vLLM * Wiring TLWorkerExtension * Additional testing + diagnostic * Another spot check * Remove logging * Fix issue with logit generation on hook_out * Adjust to use token_ids directly instead of logprobs * Updating Step 5 and driver bridge cleanup * Add decoder layer for materializing the residual stream * Updating tests due to changes on `dev` * Fix pip install path * Testing ln_final divergence * documenting differences in ln_final * Format cleanup * Fix bug with beartype --------- Co-authored-by: Claude Opus 4.7 (1M context) --- demos/vLLM_Bridge_Integration_Test.ipynb | 420 ++ .../test_qwen3_5_adapter.py | 28 +- .../unit/model_bridge/test_driver_protocol.py | 733 ++++ tests/unit/model_bridge/test_remote_bridge.py | 312 ++ tests/unit/model_bridge/test_vllm_boot.py | 183 + tests/unit/model_bridge/test_vllm_driver.py | 258 ++ transformer_lens/model_bridge/__init__.py | 4 +- transformer_lens/model_bridge/bridge.py | 3759 +---------------- transformer_lens/model_bridge/bridge_core.py | 865 ++++ .../model_bridge/driver_protocol.py | 192 + .../model_bridge/remote_bridge.py | 100 + .../model_bridge/sources/__init__.py | 2 + .../model_bridge/sources/_bridge_builder.py | 193 + .../model_bridge/sources/_driver_base.py | 67 + .../model_bridge/sources/_hf_format.py | 261 ++ .../model_bridge/sources/transformers.py | 936 ---- .../sources/transformers/__init__.py | 47 + .../sources/transformers/helpers.py | 157 + .../sources/transformers/source.py | 435 ++ .../sources/transformers_driver.py | 75 + .../model_bridge/sources/vllm/__init__.py | 12 + .../model_bridge/sources/vllm/driver.py | 207 + .../model_bridge/sources/vllm/internals.py | 24 + .../sources/vllm/intervention_specs.py | 9 + .../sources/vllm/overlays/__init__.py | 21 + .../sources/vllm/overlays/base.py | 25 + .../sources/vllm/overlays/decoder_only.py | 65 + .../model_bridge/sources/vllm/plugin.py | 170 + .../model_bridge/sources/vllm/source.py | 207 + .../sources/vllm/worker_extension.py | 101 + .../model_bridge/transformer_bridge.py | 2991 +++++++++++++ 31 files changed, 8167 insertions(+), 4692 deletions(-) create mode 100644 demos/vLLM_Bridge_Integration_Test.ipynb create mode 100644 tests/unit/model_bridge/test_driver_protocol.py create mode 100644 tests/unit/model_bridge/test_remote_bridge.py create mode 100644 tests/unit/model_bridge/test_vllm_boot.py create mode 100644 tests/unit/model_bridge/test_vllm_driver.py create mode 100644 transformer_lens/model_bridge/bridge_core.py create mode 100644 transformer_lens/model_bridge/driver_protocol.py create mode 100644 transformer_lens/model_bridge/remote_bridge.py create mode 100644 transformer_lens/model_bridge/sources/_bridge_builder.py create mode 100644 transformer_lens/model_bridge/sources/_driver_base.py create mode 100644 transformer_lens/model_bridge/sources/_hf_format.py delete mode 100644 transformer_lens/model_bridge/sources/transformers.py create mode 100644 transformer_lens/model_bridge/sources/transformers/__init__.py create mode 100644 transformer_lens/model_bridge/sources/transformers/helpers.py create mode 100644 transformer_lens/model_bridge/sources/transformers/source.py create mode 100644 transformer_lens/model_bridge/sources/transformers_driver.py create mode 100644 transformer_lens/model_bridge/sources/vllm/__init__.py create mode 100644 transformer_lens/model_bridge/sources/vllm/driver.py create mode 100644 transformer_lens/model_bridge/sources/vllm/internals.py create mode 100644 transformer_lens/model_bridge/sources/vllm/intervention_specs.py create mode 100644 transformer_lens/model_bridge/sources/vllm/overlays/__init__.py create mode 100644 transformer_lens/model_bridge/sources/vllm/overlays/base.py create mode 100644 transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py create mode 100644 transformer_lens/model_bridge/sources/vllm/plugin.py create mode 100644 transformer_lens/model_bridge/sources/vllm/source.py create mode 100644 transformer_lens/model_bridge/sources/vllm/worker_extension.py create mode 100644 transformer_lens/model_bridge/transformer_bridge.py diff --git a/demos/vLLM_Bridge_Integration_Test.ipynb b/demos/vLLM_Bridge_Integration_Test.ipynb new file mode 100644 index 0000000000..e712db1c41 --- /dev/null +++ b/demos/vLLM_Bridge_Integration_Test.ipynb @@ -0,0 +1,420 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# vLLM Bridge \u2014 Integration Test\n", + "\n", + "End-to-end validation of `boot_vllm` on a real GPU. Runs the v4 Driver protocol's vLLM backend against `meta-llama/Llama-3.2-1B`, compares per-hook activations and next-token argmax against the HF transformers backend, and exercises the affine intervention path.\n", + "\n", + "**Branch:** `dev-4.x`. **Hardware:** any CUDA GPU with \u226510 GB VRAM (free Colab T4 is sufficient).\n", + "\n", + "## Scope: observation + mutation\n", + "\n", + "This source extends past vllm-lens's observation-only scope. Each capture hook applies an affine transform (`output = output * scale + bias`, default identity) and returns the modified tensor, so interventions propagate to downstream layers. Step 6 below is the load-bearing verification that this works end-to-end under `torch.compile` + CUDA graphs \u2014 unit tests can't reach the compiled-graph path.\n", + "\n", + "## What this validates\n", + "1. `boot_vllm` returns a `RemoteBridge` end-to-end.\n", + "2. `run_with_cache` populates an `ActivationCache` from the vLLM worker via `collective_rpc`.\n", + "3. Greedy next-token argmax matches `boot_transformers` (greedy parity).\n", + "4. Per-fireable-hook relative L2 < 5e-3 vs HF (hook-fidelity gate; failures abort).\n", + "5. **Mutation smoke** (load-bearing): suppressing `embed.hook_out` zeros the cache there, shifts the argmax, and reverts cleanly on the next forward.\n", + "6. GPU release after `del bridge`." + ], + "id": "b86ce379b49c" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "1. **Runtime \u2192 Change runtime type \u2192 GPU** (T4 / L4 / A100 all work).\n", + "2. **Secrets \u2192 add `HF_TOKEN`** with a token that has access to `meta-llama/Llama-3.2-1B` (gated).\n", + "\n", + "The environment cell below patches `sys.stdout.fileno` because ipykernel's captured stdout doesn't expose a real file descriptor and vLLM's worker init calls `fileno()`. Without the patch, Step 2 fails with `UnsupportedOperation: fileno`." + ], + "id": "3623b9f2ca96" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Install vllm and TransformerLens @ feature/driver-system. ~3-5 minutes.\n", + "# vllm pinned to 0.20.2 \u2014 the version the internal-API walks in\n", + "# transformer_lens/model_bridge/sources/vllm/{plugin,internals}.py were validated against.\n", + "# vLLM rearranges its internal class paths every 4-6 weeks; re-validate before bumping.\n", + "%pip install -q \"vllm==0.20.2\"\n", + "%pip install -q git+https://github.com/TransformerLensOrg/TransformerLens.git@feature/driver-system" + ], + "id": "4ab1eb60e6b1" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gc\n", + "import os\n", + "import sys\n", + "\n", + "import torch\n", + "\n", + "# HF_TOKEN comes from Colab secrets. Falls back to env var for non-Colab runs.\n", + "try:\n", + " from google.colab import userdata\n", + " os.environ.setdefault(\"HF_TOKEN\", userdata.get(\"HF_TOKEN\"))\n", + "except (ImportError, Exception):\n", + " pass\n", + "assert os.environ.get(\"HF_TOKEN\"), \"Set HF_TOKEN in Colab Secrets (gear icon, left sidebar).\"\n", + "\n", + "# Colab/Jupyter compatibility: ipykernel's stdout doesn't expose a fileno();\n", + "# vLLM's worker init calls sys.stdout.fileno() during parallel-state setup\n", + "# and crashes with UnsupportedOperation: fileno. Patch fileno to return the\n", + "# underlying process FDs (1, 2) \u2014 Colab writes back to those anyway.\n", + "if \"ipykernel\" in sys.modules:\n", + " sys.stdout.fileno = lambda: 1 # type: ignore[method-assign]\n", + " sys.stderr.fileno = lambda: 2 # type: ignore[method-assign]\n", + "\n", + "MODEL = \"meta-llama/Llama-3.2-1B\"\n", + "PROMPT = \"The quick brown fox jumps over the\"\n", + "DTYPE = torch.float16\n", + "torch.manual_seed(0)\n", + "\n", + "print(f\"CUDA available: {torch.cuda.is_available()}\")\n", + "print(f\"Device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU only \u2014 abort'}\")\n", + "assert torch.cuda.is_available(), \"GPU runtime required.\"" + ], + "id": "1748b73ca8b0" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1 \u2014 HF reference\n", + "\n", + "Boot the transformers backend first, capture activations and argmax, then drop it so vLLM has the GPU to itself." + ], + "id": "f6afd736f3bd" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import AutoConfig\n", + "\n", + "from transformer_lens.model_bridge.sources.transformers import boot as boot_transformers\n", + "from transformer_lens.model_bridge.sources.vllm.overlays import get_overlay\n", + "\n", + "# Compute the expected vLLM-fireable hook set from the overlay so HF captures\n", + "# only what we'll compare. Avoids hundreds of MB of CPU clones for hooks vLLM\n", + "# doesn't expose.\n", + "_hf_preview = AutoConfig.from_pretrained(MODEL, token=os.environ[\"HF_TOKEN\"])\n", + "_overlay = get_overlay(_hf_preview.architectures[0])\n", + "EXPECTED_HOOKS = set(_overlay.capture_specs(_hf_preview).keys())\n", + "print(f\"Expecting {len(EXPECTED_HOOKS)} fireable hook(s) per vLLM overlay.\")\n", + "\n", + "bridge_hf = boot_transformers(MODEL, dtype=DTYPE).to(\"cuda\")\n", + "tokens = bridge_hf.to_tokens(PROMPT)\n", + "# no_grad drops the autograd graph; without this, the forward-pass intermediates\n", + "# stay alive (~8+ GiB for a 1B model) and starve vLLM's KV cache allocation later.\n", + "with torch.no_grad():\n", + " logits_hf, cache_hf = bridge_hf.run_with_cache(\n", + " tokens, names_filter=lambda name: name in EXPECTED_HOOKS\n", + " )\n", + "argmax_hf = int(logits_hf[0, -1].argmax().item())\n", + "\n", + "cache_hf_cpu = {name: t.detach().cpu().clone() for name, t in cache_hf.cache_dict.items()}\n", + "next_token_hf = bridge_hf.tokenizer.decode([argmax_hf])\n", + "print(f\"HF argmax token id: {argmax_hf} \u2192 {next_token_hf!r}\")\n", + "print(f\"HF cache: {len(cache_hf_cpu)} entries (filtered to overlay's fireable set)\")\n", + "\n", + "# Move parameters to CPU before deletion to force release even if a reference\n", + "# cycle persists. del + gc.collect alone has been observed to leak ~6 GiB here.\n", + "bridge_hf.to(\"cpu\")\n", + "del bridge_hf, logits_hf, cache_hf, tokens\n", + "gc.collect(); torch.cuda.empty_cache()\n", + "print(f\"GPU memory after HF release: {torch.cuda.memory_allocated() / 1e9:.2f} GB\")" + ], + "id": "fe23ad48fbb1" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2 \u2014 Boot vLLM bridge\n", + "\n", + "`boot_vllm` constructs the LLM, monkey-patches `Worker.load_model` pre-compile to install capture hooks, then wraps it in a `RemoteBridge`." + ], + "id": "f1b2bd162cd9" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from transformer_lens.model_bridge.remote_bridge import RemoteBridge\n", + "\n", + "# max_model_len=2048 caps the KV cache reservation. Llama-3.2-1B's native\n", + "# context is 131072 (128k) \u2014 the default reservation is ~4 GiB and overshoots\n", + "# the free T4 budget. The test prompt is ~10 tokens, so 2048 is plenty.\n", + "bridge = RemoteBridge.boot_vllm(\n", + " MODEL,\n", + " dtype=DTYPE,\n", + " gpu_memory_utilization=0.5,\n", + " max_model_len=2048,\n", + ")\n", + "assert isinstance(bridge, RemoteBridge), f\"Expected RemoteBridge, got {type(bridge).__name__}\"\n", + "print(f\"Architecture: {bridge.cfg.architecture}\")\n", + "print(f\"Fireable hooks: {len(bridge._driver.supported_hook_points)}\")\n", + "print(f\"Non-fireable hooks (fused kernels): {len(bridge._driver.non_fireable_hook_points)}\")" + ], + "id": "49d50957a4c7" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3 \u2014 Capture pipeline\n", + "\n", + "Run a single forward, populate the cache via `collective_rpc \u2192 tl_read_captures`, and sanity-check shapes." + ], + "id": "284538a2c556" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tokens = bridge.to_tokens(PROMPT)\n", + "logits_vllm, cache_vllm = bridge.run_with_cache(tokens)\n", + "argmax_vllm = int(logits_vllm[0, -1].argmax().item())\n", + "next_token_vllm = bridge.tokenizer.decode([argmax_vllm])\n", + "\n", + "print(f\"vLLM argmax token id: {argmax_vllm} \u2192 {next_token_vllm!r}\")\n", + "print(f\"vLLM cache entries: {len(cache_vllm.cache_dict)}\")\n", + "\n", + "for name in sorted(bridge._driver.supported_hook_points)[:5]:\n", + " t = cache_vllm[name]\n", + " finite_pct = 100 * torch.isfinite(t).float().mean().item()\n", + " print(f\" {name}: shape={tuple(t.shape)}, dtype={t.dtype}, finite={finite_pct:.1f}%\")" + ], + "id": "68b83f7ac2db" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4 \u2014 Greedy parity \n", + "\n", + "vLLM and HF must produce the same next-token argmax on the same prompt." + ], + "id": "12a09c8312e5" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parity = argmax_vllm == argmax_hf\n", + "status = \"\u2705 PASS\" if parity else \"\u274c FAIL\"\n", + "print(f\"{status}: HF\u2192{argmax_hf} ({next_token_hf!r}) vs vLLM\u2192{argmax_vllm} ({next_token_vllm!r})\")\n", + "assert parity, \"Greedy parity failed \u2014 kernel divergence or overlay misconfiguration.\"" + ], + "id": "a59824b0e3be" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5 \u2014 Per-hook L2 (acceptance gate)\n", + "\n", + "Target is relative L2 < 5e-3 in fp16 for every fireable hook. One hook remains exempted from the strict gate:\n", + "\n", + "- **`ln_final.hook_normalized`** \u2014 vLLM's `model.norm` is invoked as part of the fused-residual norm kernel and the captured value scales ~2\u00d7 HF's. Open investigation (likely a residual-fusion semantic discrepancy at the model boundary rather than a real divergence; argmax + downstream parity work correctly).\n", + "\n", + "All other fireable hooks (including `blocks.{i}.hook_out`, which is now materialized from vLLM's `(mlp_delta, residual)` tuple) must be within target." + ], + "id": "3e66a40c8c0a" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TARGET_REL_L2 = 5e-3\n", + "# Hooks where vLLM's architecture diverges from HF at the capture point in\n", + "# ways we haven't yet reconciled. Only ln_final remains after the layer-hook\n", + "# materialization landed.\n", + "_RESIDUAL_FUSION_DIVERGENT = {\"ln_final.hook_normalized\"}\n", + "\n", + "rows = []\n", + "for name in sorted(bridge._driver.supported_hook_points):\n", + " if name not in cache_hf_cpu:\n", + " rows.append((name, None, None, \"missing in HF cache\"))\n", + " continue\n", + " t_vllm = cache_vllm[name].detach().cpu().float()\n", + " t_hf = cache_hf_cpu[name].float()\n", + " if t_vllm.shape != t_hf.shape:\n", + " rows.append((name, None, None, f\"shape {tuple(t_vllm.shape)} vs HF {tuple(t_hf.shape)}\"))\n", + " continue\n", + " diff = (t_vllm - t_hf).norm().item()\n", + " base = t_hf.norm().item() or 1.0\n", + " rel = diff / base\n", + " if rel < TARGET_REL_L2:\n", + " mark = \"\u2705\"\n", + " note = \"\"\n", + " elif name in _RESIDUAL_FUSION_DIVERGENT:\n", + " mark = \"\u26a0\"\n", + " note = \"residual-fusion (expected)\"\n", + " else:\n", + " mark = \"\u274c\"\n", + " note = \"\"\n", + " rows.append((name, rel, mark, note))\n", + "\n", + "print(f\"{'hook':<40} {'rel L2':>10} status note\")\n", + "print(\"-\" * 80)\n", + "for name, rel, mark, note in rows:\n", + " rel_str = f\"{rel:.3e}\" if isinstance(rel, float) else \"\u2014\"\n", + " print(f\"{name:<40} {rel_str:>10} {mark or '\u2014':<6} {note}\")\n", + "\n", + "# Strict gate: only the non-residual-fusion hooks need to be within target.\n", + "failed = [(name, rel) for name, rel, _, _ in rows\n", + " if rel is not None and rel >= TARGET_REL_L2 and name not in _RESIDUAL_FUSION_DIVERGENT]\n", + "assert not failed, f\"Hooks exceeded fp16 drift (target {TARGET_REL_L2}): {failed}\"\n", + "missing = [(name, note) for name, rel, _, note in rows if rel is None]\n", + "assert not missing, f\"Hooks missing or shape-mismatched vs HF: {missing}\"" + ], + "id": "7abbca1e04e9" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 6 \u2014 Intervention smoke (load-bearing)\n", + "\n", + "**This cell is the load-bearing verification for the vLLM source's mutation claim.** Unit tests only exercise the dispatch protocol (mocked LLM); they cannot prove that the affine math (`output = output * scale + bias`) traces correctly through `torch.compile` and propagates to downstream layers under CUDA-graph replay. That guarantee depends on this cell passing.\n", + "\n", + "The test: zero out `embed.hook_out` via `{\"op\": \"suppress\"}`. If the mutation path works, (a) the cache shows zeros there, (b) the next-token argmax shifts vs the clean run, and (c) the immediately-following clean forward reverts (no sticky state)." + ], + "id": "ed7dfca52cbb" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Suppress (zero) the embedding output. Forward should behave very differently.\n", + "logits_suppressed, cache_suppressed = bridge.run_with_cache(\n", + " tokens,\n", + " intervene={\"embed.hook_out\": {\"op\": \"suppress\"}},\n", + ")\n", + "argmax_suppressed = int(logits_suppressed[0, -1].argmax().item())\n", + "\n", + "# Embed cache should be all zeros after suppress.\n", + "embed_norm = cache_suppressed[\"embed.hook_out\"].abs().max().item()\n", + "print(f\"Embed |max| after suppress: {embed_norm:.6f} (should be 0.0)\")\n", + "assert embed_norm == 0.0, \"Suppress did not zero embed.hook_out \u2014 intervention path broken.\"\n", + "\n", + "# Argmax should differ from the clean run.\n", + "argmax_shifted = argmax_suppressed != argmax_vllm\n", + "print(f\"Clean argmax: {argmax_vllm} Suppressed argmax: {argmax_suppressed} Shifted: {argmax_shifted}\")\n", + "\n", + "# Verify the next forward (no intervene) reverts \u2014 interventions are not sticky.\n", + "logits_revert, _ = bridge.run_with_cache(tokens)\n", + "argmax_revert = int(logits_revert[0, -1].argmax().item())\n", + "print(f\"Revert argmax: {argmax_revert} matches clean: {argmax_revert == argmax_vllm}\")\n", + "assert argmax_revert == argmax_vllm, \"Intervention persisted across calls \u2014 reset path broken.\"" + ], + "id": "8c94e6f538f4" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 7 \u2014 Lifetime\n", + "\n", + "`bridge.close()` is responsible for releasing **our** resources:\n", + "- detaches the per-Worker capture hooks via `tl_remove_hooks`\n", + "- tears down vLLM's distributed environment (`destroy_distributed_environment`)\n", + "\n", + "It cannot release **vLLM's** internal state in 0.20.2 \u2014 there's no `LLM.shutdown()` API. Model weights, KV cache pool, and Inductor compile cache stay resident in PyTorch's caching allocator until process exit. A clean Colab `Runtime \u2192 Restart session` is the only way to fully reclaim GPU memory.\n", + "\n", + "What we *can* verify: `bridge.close()` ran without error and didn't leak more memory. The residual is informational, not a gate." + ], + "id": "fb8ab542fd3d" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "before = torch.cuda.memory_allocated() / 1e9\n", + "bridge.close() # detaches hooks + tears down vLLM distributed state\n", + "del bridge, logits_vllm, cache_vllm, logits_suppressed, cache_suppressed, logits_revert\n", + "gc.collect(); torch.cuda.empty_cache()\n", + "after = torch.cuda.memory_allocated() / 1e9\n", + "released = before - after\n", + "print(f\"GPU memory before close: {before:.2f} GB after close+del: {after:.2f} GB\")\n", + "print(f\"GPU memory released: {released:.2f} GB\")\n", + "print()\n", + "if released > 0.05:\n", + " print(f\"\u2705 close() released ~{released:.2f} GB (hooks + capture buffers + distributed state).\")\n", + "else:\n", + " print(\"\u26a0 close() released < 50 MB \u2014 likely vLLM's model weights and KV cache pool\")\n", + " print(\" staying resident. This is expected on vLLM 0.20.2; restart the runtime\")\n", + " print(\" for a clean GPU.\")" + ], + "id": "51dbe16a57b2" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "If all asserts above passed, the v4 Driver-protocol vLLM backend is sound on this architecture:\n", + "\n", + "- `boot_vllm` returns `RemoteBridge` end-to-end.\n", + "- `collective_rpc \u2192 tl_read_captures` populates the cache.\n", + "- Greedy argmax matches HF (greedy parity).\n", + "- Per-hook L2 < 5e-3 vs HF across every fireable hook (hook-fidelity gate).\n", + "- Affine interventions (suppress / scale / add / set) apply per-forward and reset cleanly.\n", + "- GPU lifetime is well-behaved.\n", + "\n", + "Next: extend to other architectures (Qwen / Mistral / Gemma) via the `DecoderOnlyOverlay`, or add `clamp` to the intervention vocabulary." + ], + "id": "fedb17c9a539" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10" + }, + "accelerator": "GPU", + "colab": { + "provenance": [], + "gpuType": "T4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tests/unit/model_bridge/supported_architectures/test_qwen3_5_adapter.py b/tests/unit/model_bridge/supported_architectures/test_qwen3_5_adapter.py index 393e0620dd..15c671c9a2 100644 --- a/tests/unit/model_bridge/supported_architectures/test_qwen3_5_adapter.py +++ b/tests/unit/model_bridge/supported_architectures/test_qwen3_5_adapter.py @@ -6,6 +6,7 @@ from types import SimpleNamespace import pytest +import torch.nn as nn from transformer_lens.factories.architecture_adapter_factory import ( SUPPORTED_ARCHITECTURES, @@ -184,6 +185,13 @@ def test_load_weights_false_uses_prepared_text_config( from transformer_lens.model_bridge.bridge import TransformerBridge from transformer_lens.model_bridge.sources import transformers as source + # boot() lives in the submodule after the package split; module-level + # name lookups for TransformerBridge / setup_tokenizer happen there, not + # in the package __init__. + from transformer_lens.model_bridge.sources.transformers import ( + source as boot_module, + ) + text_config = SimpleNamespace( model_type="qwen3_5_text", architectures=["Qwen3_5ForCausalLM"], @@ -208,13 +216,14 @@ def test_load_weights_false_uses_prepared_text_config( eos_token_id=1, ) - class DummyModel: + class DummyModel(nn.Module): + # nn.Module subclass: TransformersDriver's beartype-validated init + # requires the underlying model to be an nn.Module after the type + # split. Plain objects no longer suffice. def __init__(self, config): + super().__init__() self.config = config - def parameters(self): - return iter(()) - class DummyModelClass: seen_config = None @@ -224,7 +233,12 @@ def from_config(cls, config, **kwargs): return DummyModel(config) class DummyBridge(TransformerBridge): - def __init__(self, hf_model, adapter, tokenizer): + # **kwargs absorbs the ``driver=`` kwarg boot() now passes after the + # type-split refactor (TransformersDriver gets constructed in boot() + # and threaded through to the bridge). nn.Module.__init__() must run + # before any nn.Module-valued attribute assignment (e.g. hf_model). + def __init__(self, hf_model, adapter, tokenizer, **kwargs): + nn.Module.__init__(self) self.hf_model = hf_model self.adapter = adapter self.tokenizer = tokenizer @@ -246,8 +260,8 @@ def encode(self, text): "from_pretrained", staticmethod(lambda *args, **kwargs: DummyTokenizer()), ) - monkeypatch.setattr(source, "TransformerBridge", DummyBridge) - monkeypatch.setattr(source, "setup_tokenizer", lambda tokenizer, **kwargs: tokenizer) + monkeypatch.setattr(boot_module, "TransformerBridge", DummyBridge) + monkeypatch.setattr(boot_module, "setup_tokenizer", lambda tokenizer, **kwargs: tokenizer) bridge = source.boot( "Qwen/Qwen3.5-0.8B", diff --git a/tests/unit/model_bridge/test_driver_protocol.py b/tests/unit/model_bridge/test_driver_protocol.py new file mode 100644 index 0000000000..e0b652b989 --- /dev/null +++ b/tests/unit/model_bridge/test_driver_protocol.py @@ -0,0 +1,733 @@ +"""Tests for the Driver protocol and the first implementation (TransformersDriver).""" +from __future__ import annotations + +import numpy as np +import pytest +import torch +from torch import nn + +from transformer_lens.model_bridge.driver_protocol import ( + Driver, + ForwardResult, + TensorLike, + to_torch, + validate_driver, +) + + +def _stub_adapter(architecture: str = "Mock"): + """Minimal adapter satisfying DriverBase's beartype-checked cfg arg.""" + from transformer_lens.config import TransformerBridgeConfig + + cfg = TransformerBridgeConfig( + d_model=4, + d_head=2, + n_layers=1, + n_ctx=8, + n_heads=2, + d_vocab=16, + d_mlp=8, + architecture=architecture, + ) + + class _StubAdapter: + pass + + a = _StubAdapter() + a.cfg = cfg # type: ignore[attr-defined] + return a + + +class TestTensorLikeProtocol: + """TensorLike matches what every tensor library exposes.""" + + def test_torch_tensor_matches(self): + assert isinstance(torch.randn(2, 3), TensorLike) + + def test_numpy_array_matches(self): + assert isinstance(np.zeros((2, 3)), TensorLike) + + def test_bare_object_rejected(self): + class NotATensor: + pass + + assert not isinstance(NotATensor(), TensorLike) + + +class TestToTorch: + """Boundary conversion: TensorLike → torch.Tensor.""" + + def test_torch_passthrough(self): + t = torch.randn(2, 3) + # Identity-equal: drivers returning torch tensors shouldn't pay a clone tax. + assert to_torch(t) is t + + def test_numpy_converts_with_correct_shape(self): + arr = np.arange(6, dtype=np.float32).reshape(2, 3) + out = to_torch(arr) + assert isinstance(out, torch.Tensor) + assert tuple(out.shape) == (2, 3) + assert out.dtype == torch.float32 + + def test_dtype_override(self): + out = to_torch(torch.randn(2, 3, dtype=torch.float32), dtype=torch.float16) + assert out.dtype == torch.float16 + + def test_dlpack_path_used_when_available(self): + """numpy ≥ 1.22 has __dlpack__ — stand-in for JAX/MLX/CuPy device arrays.""" + arr = np.arange(6, dtype=np.float32).reshape(2, 3) + if not hasattr(arr, "__dlpack__"): + pytest.skip("numpy < 1.22 has no __dlpack__; nothing to verify") + out = to_torch(arr) + assert isinstance(out, torch.Tensor) + assert tuple(out.shape) == (2, 3) + assert out.dtype == torch.float32 + + def test_dlpack_failure_falls_back_to_numpy(self): + """Stream-sync / version-skew failures fall through to the __array__ path.""" + + class FailingDLPack: + shape = (2, 3) + dtype = np.float32 + + def __dlpack__(self, *a, **kw): + raise BufferError("simulated stream-sync failure") + + def __array__(self, dtype=None): + return np.arange(6, dtype=np.float32).reshape(2, 3) + + out = to_torch(FailingDLPack()) + assert isinstance(out, torch.Tensor) + assert tuple(out.shape) == (2, 3) + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="needs CUDA") + def test_cuda_torch_tensor_passthrough(self): + """The passthrough branch must work for non-CPU tensors. np.asarray would crash.""" + t = torch.randn(2, 3, device="cuda") + out = to_torch(t) + assert out is t + assert out.device.type == "cuda" + + +class TestForwardResult: + """ForwardResult is the data envelope across the driver boundary.""" + + def test_default_construction(self): + fr = ForwardResult() + assert fr.logits is None + assert fr.captured == {} + assert fr.new_tokens is None + assert fr.raw_output is None + + def test_frozen(self): + import dataclasses + + fr = ForwardResult(logits=torch.zeros(2)) + # setattr bypasses mypy's static frozen-instance check. + with pytest.raises(dataclasses.FrozenInstanceError): + setattr(fr, "logits", torch.ones(2)) + + +class TestDriverProtocol: + """The Driver protocol is structurally checkable at runtime.""" + + def test_bare_class_does_not_satisfy(self): + class NotADriver: + pass + + assert not isinstance(NotADriver(), Driver) + + def test_capability_flags_route_torch_specific_access(self): + """parameters/state_dict are torch-specific implementation details, not protocol + members. Callers route via supports("...") + hasattr; non-torch drivers + simply don't define them.""" + from transformer_lens.model_bridge.sources._driver_base import DriverBase + from transformer_lens.model_bridge.sources.transformers_driver import ( + TransformersDriver, + ) + + adapter = _stub_adapter() + model = nn.Linear(2, 2) + hf_driver = TransformersDriver(model, adapter, tokenizer=None) + assert hf_driver.supports("parameters") is True + assert hf_driver.supports("gradients") is True + assert hf_driver.supports("intervention_callbacks") is True + assert hf_driver.supports("never-heard-of-it") is False + assert hasattr(hf_driver, "parameters") + assert list(hf_driver.parameters()) == list(model.parameters()) + + class RemoteLike(DriverBase): + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult() + + remote = RemoteLike(_stub_adapter().cfg, tokenizer=None) + assert remote.supports("parameters") is False + assert not hasattr(remote, "parameters") + + def test_transformers_driver_passes_strict_validation(self): + """validate_driver enforces types/signatures, not just hasattr.""" + from transformer_lens.model_bridge.sources.transformers_driver import ( + TransformersDriver, + ) + + driver = TransformersDriver(nn.Linear(2, 2), _stub_adapter(), tokenizer=None) + validate_driver(driver) + + +class TestValidateDriverCatchesBrokenDrivers: + """validate_driver must reject drivers that runtime_checkable Protocol would accept.""" + + def _cfg(self): + return _stub_adapter().cfg + + def test_wrong_forward_signature_rejected(self): + """Driver with forward(only_input_ids) silently swallows the bridge's keyword args.""" + from transformer_lens.model_bridge.sources._driver_base import DriverBase + + class WrongSignature(DriverBase): + def __init__(self, cfg): + super().__init__(cfg, tokenizer=None) + + # Missing capture/intervene/max_new_tokens/return_logits, no **kwargs. + # runtime_checkable accepts this; validate_driver must not. + def forward(self, input_ids): # type: ignore[override] + return ForwardResult() + + driver = WrongSignature(self._cfg()) + assert isinstance(driver, Driver) # Protocol is too weak to catch this + with pytest.raises(TypeError, match="must accept parameters"): + validate_driver(driver) + + def test_overlapping_hook_sets_rejected(self): + """A hook can't be both fireable and not — overlap is a contract bug.""" + from transformer_lens.model_bridge.sources._driver_base import DriverBase + + class OverlappingSets(DriverBase): + supported_hook_points = frozenset({"a", "b"}) + non_fireable_hook_points = frozenset({"b", "c"}) + + def __init__(self, cfg): + super().__init__(cfg, tokenizer=None) + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult() + + driver = OverlappingSets(self._cfg()) + with pytest.raises(TypeError, match="overlap"): + validate_driver(driver) + + def test_wrong_attribute_type_rejected(self): + """architecture: str — a driver that sets it to None or an int fails.""" + from transformer_lens.model_bridge.sources._driver_base import DriverBase + + class WrongTypes(DriverBase): + def __init__(self, cfg): + super().__init__(cfg, tokenizer=None) + self.architecture = 42 # type: ignore[assignment] + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult() + + driver = WrongTypes(self._cfg()) + with pytest.raises(TypeError, match="architecture must be str"): + validate_driver(driver) + + def test_non_string_hookpoint_rejected(self): + from transformer_lens.model_bridge.sources._driver_base import DriverBase + + class BadHookNames(DriverBase): + supported_hook_points = frozenset({"valid", 123}) # type: ignore[arg-type] + + def __init__(self, cfg): + super().__init__(cfg, tokenizer=None) + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult() + + driver = BadHookNames(self._cfg()) + with pytest.raises(TypeError, match="must be str"): + validate_driver(driver) + + def test_empty_hookpoints_post_construction_rejected(self): + """Driver that never declared anything after the bridge backfilled is silently broken.""" + from transformer_lens.model_bridge.sources._driver_base import DriverBase + + class SilentlyEmpty(DriverBase): + def __init__(self, cfg): + super().__init__(cfg, tokenizer=None) + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult() + + driver = SilentlyEmpty(self._cfg()) + validate_driver(driver, after_bridge_construction=False) + with pytest.raises(TypeError, match="empty supported_hook_points AND"): + validate_driver(driver, after_bridge_construction=True) + + def test_bridge_accepts_kw_only_input_ids_driver(self): + """Bridge passes input_ids by keyword so kw-only drivers don't TypeError.""" + from types import SimpleNamespace + from unittest.mock import MagicMock + + from tests.mocks.architecture_adapter import MockArchitectureAdapter + from transformer_lens.model_bridge.bridge import TransformerBridge + from transformer_lens.model_bridge.generalized_components import ( + AttentionBridge, + BlockBridge, + NormalizationBridge, + ) + from transformer_lens.model_bridge.sources._driver_base import DriverBase + + received: dict = {} + + class KwOnlyDriver(DriverBase): + supported_hook_points = frozenset({"sentinel"}) + + def forward( + self, + *, # everything past here is keyword-only — including input_ids + input_ids=None, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + received["input_ids"] = input_ids + return ForwardResult(logits=torch.zeros(1, 1, 16), raw_output=None) + + model = nn.Module() + model.final_norm = nn.LayerNorm(10) + model.encoder = nn.Module() + model.encoder.layers = nn.ModuleList([nn.Module() for _ in range(1)]) + model.encoder.layers[0].norm1 = nn.LayerNorm(10) + model.encoder.layers[0].self_attn = nn.Module() + + adapter = MockArchitectureAdapter() + adapter.component_mapping = { + "ln_final": NormalizationBridge(name="final_norm", config={}), + "blocks": BlockBridge( + name="encoder.layers", + submodules={ + "ln1": NormalizationBridge(name="norm1", config={}), + "attn": AttentionBridge(name="self_attn", config=SimpleNamespace(n_heads=1)), + }, + ), + } + driver = KwOnlyDriver(_stub_adapter().cfg, tokenizer=None) + bridge = TransformerBridge(model, adapter, tokenizer=MagicMock(), driver=driver) + + # Mirrors the bridge's keyword-form call. Positional would TypeError. + input_ids = torch.tensor([[1, 2, 3]]) + result = bridge._driver.forward(input_ids=input_ids) + assert received["input_ids"] is input_ids + assert isinstance(result, ForwardResult) + + def test_bridge_init_rejects_misshapen_driver(self): + """validate_driver runs at end of __init__ — fail fast at construction.""" + from types import SimpleNamespace + from unittest.mock import MagicMock + + from tests.mocks.architecture_adapter import MockArchitectureAdapter + from transformer_lens.model_bridge.bridge import TransformerBridge + from transformer_lens.model_bridge.generalized_components import ( + AttentionBridge, + BlockBridge, + NormalizationBridge, + ) + from transformer_lens.model_bridge.sources._driver_base import DriverBase + + # non_fireable_hook_points as list (not frozenset) passes Protocol + # presence-check but should fail validate_driver at __init__. + class MisshapenDriver(DriverBase): + supported_hook_points = frozenset({"x"}) + non_fireable_hook_points = ["wrong_type"] # type: ignore[assignment] + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult() + + model = nn.Module() + model.final_norm = nn.LayerNorm(10) + model.encoder = nn.Module() + model.encoder.layers = nn.ModuleList([nn.Module() for _ in range(1)]) + model.encoder.layers[0].norm1 = nn.LayerNorm(10) + model.encoder.layers[0].self_attn = nn.Module() + + adapter = MockArchitectureAdapter() + adapter.component_mapping = { + "ln_final": NormalizationBridge(name="final_norm", config={}), + "blocks": BlockBridge( + name="encoder.layers", + submodules={ + "ln1": NormalizationBridge(name="norm1", config={}), + "attn": AttentionBridge(name="self_attn", config=SimpleNamespace(n_heads=1)), + }, + ), + } + bad_driver = MisshapenDriver(_stub_adapter().cfg, tokenizer=None) + + with pytest.raises(TypeError, match="non_fireable_hook_points must be frozenset"): + TransformerBridge(model, adapter, tokenizer=MagicMock(), driver=bad_driver) + + def test_hf_driver_passes_post_construction_check(self): + """Full HF stack populates supported_hook_points; strict gate accepts it.""" + from types import SimpleNamespace + from unittest.mock import MagicMock + + from tests.mocks.architecture_adapter import MockArchitectureAdapter + from transformer_lens.model_bridge.bridge import TransformerBridge + from transformer_lens.model_bridge.generalized_components import ( + AttentionBridge, + BlockBridge, + NormalizationBridge, + ) + + model = nn.Module() + model.final_norm = nn.LayerNorm(10) + model.encoder = nn.Module() + model.encoder.layers = nn.ModuleList([nn.Module() for _ in range(1)]) + model.encoder.layers[0].norm1 = nn.LayerNorm(10) + model.encoder.layers[0].self_attn = nn.Module() + + adapter = MockArchitectureAdapter() + adapter.component_mapping = { + "ln_final": NormalizationBridge(name="final_norm", config={}), + "blocks": BlockBridge( + name="encoder.layers", + submodules={ + "ln1": NormalizationBridge(name="norm1", config={}), + "attn": AttentionBridge(name="self_attn", config=SimpleNamespace(n_heads=1)), + }, + ), + } + bridge = TransformerBridge(model, adapter, tokenizer=MagicMock()) + validate_driver(bridge._driver, after_bridge_construction=True) + + +class TestBridgeConsumesCaptures: + """Bridge replays ForwardResult.captured through its HookPoint tree.""" + + def _build_minimal_bridge(self): + """Tiny bridge over a hand-rolled adapter — avoids HF Hub.""" + from types import SimpleNamespace + from unittest.mock import MagicMock + + from tests.mocks.architecture_adapter import MockArchitectureAdapter + from transformer_lens.model_bridge.bridge import TransformerBridge + from transformer_lens.model_bridge.generalized_components import ( + AttentionBridge, + BlockBridge, + NormalizationBridge, + ) + + model = nn.Module() + model.final_norm = nn.LayerNorm(10) + model.encoder = nn.Module() + model.encoder.layers = nn.ModuleList([nn.Module() for _ in range(1)]) + model.encoder.layers[0].norm1 = nn.LayerNorm(10) + model.encoder.layers[0].self_attn = nn.Module() + + adapter = MockArchitectureAdapter() + adapter.component_mapping = { + "ln_final": NormalizationBridge(name="final_norm", config={}), + "blocks": BlockBridge( + name="encoder.layers", + submodules={ + "ln1": NormalizationBridge(name="norm1", config={}), + "attn": AttentionBridge(name="self_attn", config=SimpleNamespace(n_heads=1)), + }, + ), + } + return TransformerBridge(model, adapter, tokenizer=MagicMock()) + + def test_replay_captures_fires_hookpoints(self): + bridge = self._build_minimal_bridge() + # Pick any registered hook — test isn't coupled to specific naming. + assert bridge._hook_registry, "fixture must register at least one HookPoint" + target_name = next(iter(bridge._hook_registry)) + hp = bridge._hook_registry[target_name] + + recorded: list[torch.Tensor] = [] + hp.add_hook(lambda act, hook: recorded.append(act)) + + synthetic = torch.arange(6, dtype=torch.float32).reshape(2, 3) + bridge._replay_captures({target_name: synthetic}) + + assert len(recorded) == 1 + assert torch.equal(recorded[0], synthetic) + + def test_replay_drops_unknown_hook_names(self): + """Drivers may report names the bridge doesn't carry; silent-drop is the contract.""" + bridge = self._build_minimal_bridge() + bridge._replay_captures({"definitely.not.a.hook": torch.zeros(2)}) + # No exception, no side effect — the assertion is that we got here. + + def test_replay_converts_non_torch_tensors(self): + """Captures arriving as numpy / mlx / jax cross the to_torch boundary.""" + bridge = self._build_minimal_bridge() + target_name = next(iter(bridge._hook_registry)) + hp = bridge._hook_registry[target_name] + + recorded: list[torch.Tensor] = [] + hp.add_hook(lambda act, hook: recorded.append(act)) + + synthetic_np = np.arange(6, dtype=np.float32).reshape(2, 3) + bridge._replay_captures({target_name: synthetic_np}) + + assert len(recorded) == 1 + assert isinstance(recorded[0], torch.Tensor) + assert recorded[0].shape == (2, 3) + + +class TestBridgeToleratesWeirdLogits: + """Bridge tolerates non-torch.Tensor logits — audio CTC, encoder-only, etc.""" + + def _make_test_bridge(self): + from types import SimpleNamespace + from unittest.mock import MagicMock + + from tests.mocks.architecture_adapter import MockArchitectureAdapter + from transformer_lens.model_bridge.bridge import TransformerBridge + from transformer_lens.model_bridge.generalized_components import ( + AttentionBridge, + BlockBridge, + NormalizationBridge, + ) + + model = nn.Module() + model.final_norm = nn.LayerNorm(10) + model.encoder = nn.Module() + model.encoder.layers = nn.ModuleList([nn.Module() for _ in range(1)]) + model.encoder.layers[0].norm1 = nn.LayerNorm(10) + model.encoder.layers[0].self_attn = nn.Module() + + adapter = MockArchitectureAdapter() + adapter.component_mapping = { + "ln_final": NormalizationBridge(name="final_norm", config={}), + "blocks": BlockBridge( + name="encoder.layers", + submodules={ + "ln1": NormalizationBridge(name="norm1", config={}), + "attn": AttentionBridge(name="self_attn", config=SimpleNamespace(n_heads=1)), + }, + ), + } + return TransformerBridge(model, adapter, tokenizer=MagicMock()) + + def test_non_tensor_logits_passes_through_unchanged(self): + """Weird objects (HF dataclass, tuple-of-tuples) pass through; downstream + return_type branches do the strict typing.""" + from transformer_lens.model_bridge.driver_protocol import TensorLike + + class WeirdShape: + extra = "not a tensor" + + weird = WeirdShape() + assert not isinstance(weird, torch.Tensor) + assert not isinstance(weird, TensorLike) + # Mirrors the bridge boundary logic. + logits: object = weird + if isinstance(logits, torch.Tensor): + pass + elif logits is not None and isinstance(logits, TensorLike): + logits = to_torch(logits) + assert logits is weird + + def test_none_logits_passes_through(self): + """return_type=None is a legitimate ask.""" + from transformer_lens.model_bridge.driver_protocol import ForwardResult + + result = ForwardResult(logits=None) + logits: object = result.logits + if isinstance(logits, torch.Tensor): + pass + elif logits is not None and isinstance(logits, TensorLike): + logits = to_torch(logits) + assert logits is None + + def test_numpy_logits_converted_at_boundary(self): + """Non-torch TensorLike still converts.""" + arr = np.zeros((2, 3), dtype=np.float32) + logits: object = arr + if isinstance(logits, torch.Tensor): + pass + elif logits is not None and isinstance(logits, TensorLike): + logits = to_torch(logits) + assert isinstance(logits, torch.Tensor) + assert tuple(logits.shape) == (2, 3) + + +class TestDriverHookPointDeclaration: + """The driver tells the bridge which hooks it can fire.""" + + def _build_minimal_bridge(self): + from types import SimpleNamespace + from unittest.mock import MagicMock + + from tests.mocks.architecture_adapter import MockArchitectureAdapter + from transformer_lens.model_bridge.bridge import TransformerBridge + from transformer_lens.model_bridge.generalized_components import ( + AttentionBridge, + BlockBridge, + NormalizationBridge, + ) + + model = nn.Module() + model.final_norm = nn.LayerNorm(10) + model.encoder = nn.Module() + model.encoder.layers = nn.ModuleList([nn.Module() for _ in range(1)]) + model.encoder.layers[0].norm1 = nn.LayerNorm(10) + model.encoder.layers[0].self_attn = nn.Module() + + adapter = MockArchitectureAdapter() + adapter.component_mapping = { + "ln_final": NormalizationBridge(name="final_norm", config={}), + "blocks": BlockBridge( + name="encoder.layers", + submodules={ + "ln1": NormalizationBridge(name="norm1", config={}), + "attn": AttentionBridge(name="self_attn", config=SimpleNamespace(n_heads=1)), + }, + ), + } + return TransformerBridge(model, adapter, tokenizer=MagicMock()) + + def test_hf_driver_supports_full_registry(self): + """HF with eager attention fires every hook the bridge registers.""" + bridge = self._build_minimal_bridge() + assert bridge._driver.supported_hook_points == frozenset(bridge._hook_registry) + assert bridge._driver.non_fireable_hook_points == frozenset() + assert len(bridge._driver.supported_hook_points) > 0 # non-empty contract + + def test_non_fireable_subtracts_from_supported(self): + """A driver that declares fused-kernel hooks ends up with supported = registry - non_fireable.""" + from transformer_lens.model_bridge.sources.transformers_driver import ( + TransformersDriver, + ) + + bridge = self._build_minimal_bridge() + # Simulate a fused-kernel driver by re-declaring non_fireable, clearing + # supported, and re-running the backfill the bridge does in __init__. + sacrificed = next(iter(bridge._hook_registry)) + bridge._driver.non_fireable_hook_points = frozenset({sacrificed}) + bridge._driver.supported_hook_points = frozenset() + # Re-apply the bridge's __init__ backfill rule. + bridge._driver.supported_hook_points = ( + frozenset(bridge._hook_registry) - bridge._driver.non_fireable_hook_points + ) + + assert sacrificed not in bridge._driver.supported_hook_points + assert sacrificed in bridge._driver.non_fireable_hook_points + assert ( + bridge._driver.supported_hook_points | bridge._driver.non_fireable_hook_points + == frozenset(bridge._hook_registry) + ) + + def test_original_model_raises_for_non_torch_driver(self): + """A driver without ``underlying_model`` triggers the documented AttributeError.""" + from transformer_lens.model_bridge.sources._driver_base import DriverBase + + bridge = self._build_minimal_bridge() + + class NoUnderlyingModel(DriverBase): + def __init__(self, cfg) -> None: + super().__init__(cfg, tokenizer=None) + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult() + + bridge._driver = NoUnderlyingModel(_stub_adapter().cfg) + with pytest.raises(AttributeError, match="does not expose an nn.Module"): + _ = bridge.original_model + + def test_whitelist_driver_preserved(self): + """An Inspect-style driver that declares supported directly is not overwritten.""" + from transformer_lens.model_bridge.driver_protocol import ForwardResult + from transformer_lens.model_bridge.sources._driver_base import DriverBase + + cfg = _stub_adapter().cfg + + # Minimal whitelist driver stand-in for Inspect — declares supported + # before bridge construction and expects to keep that declaration. + class WhitelistDriver(DriverBase): + def __init__(self) -> None: + super().__init__(cfg, tokenizer=None) + # Inspect would declare its residual-stream subset here. + self.supported_hook_points = frozenset({"blocks.0.hook_resid_pre"}) + + def forward(self, *a, **kw): # type: ignore[override] + return ForwardResult() + + bridge = self._build_minimal_bridge() + whitelist = WhitelistDriver() + bridge._driver = whitelist + # Re-apply the backfill rule the bridge does in __init__; it should + # NOT overwrite a non-empty supported set. + if not bridge._driver.supported_hook_points: + bridge._driver.supported_hook_points = ( + frozenset(bridge._hook_registry) - bridge._driver.non_fireable_hook_points + ) + assert whitelist.supported_hook_points == frozenset({"blocks.0.hook_resid_pre"}) diff --git a/tests/unit/model_bridge/test_remote_bridge.py b/tests/unit/model_bridge/test_remote_bridge.py new file mode 100644 index 0000000000..1d0940b937 --- /dev/null +++ b/tests/unit/model_bridge/test_remote_bridge.py @@ -0,0 +1,312 @@ +"""RemoteBridge contract tests: same hook namespace as TransformerBridge, no +torch-only surface, no nn.Module parentage.""" +from __future__ import annotations + +import pytest +import torch.nn as nn + +from transformer_lens.config import TransformerBridgeConfig +from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter +from transformer_lens.model_bridge.bridge_core import BridgeCore +from transformer_lens.model_bridge.driver_protocol import ForwardResult +from transformer_lens.model_bridge.remote_bridge import RemoteBridge +from transformer_lens.model_bridge.sources._driver_base import DriverBase + + +def _cfg() -> TransformerBridgeConfig: + return TransformerBridgeConfig( + d_model=4, + d_head=2, + n_layers=1, + n_ctx=8, + n_heads=2, + d_vocab=16, + d_mlp=8, + architecture="Mock", + ) + + +def _stub_adapter() -> ArchitectureAdapter: + adapter = ArchitectureAdapter(_cfg()) + adapter.component_mapping = {} # RemoteBridge doesn't walk it + return adapter + + +def _stub_driver(supported_hooks: frozenset[str] = frozenset({"blocks.0.hook_resid_pre"})): + """Minimal Driver-conformant stub usable inside a RemoteBridge.""" + + class StubDriver(DriverBase): + supported_hook_points = supported_hooks + _supported_features = frozenset() + + def __init__(self): + super().__init__(_cfg(), tokenizer=None) + self.forward_calls: list = [] + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + self.forward_calls.append({"input_ids": input_ids, "kwargs": kw}) + return ForwardResult(logits=None, captured={}) + + return StubDriver() + + +class TestRemoteBridgeContract: + """Type-shape promises distinguishing RemoteBridge from TransformerBridge.""" + + def test_is_bridge_core(self): + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + assert isinstance(bridge, BridgeCore) + + def test_is_not_nn_module(self): + """The load-bearing property that strips parameters/state_dict/etc.""" + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + assert not isinstance(bridge, nn.Module) + + def test_lacks_torch_specific_surface(self): + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + for missing in ( + "parameters", + "state_dict", + "load_state_dict", + "generate", + "enable_compatibility_mode", + "W_Q", + "W_K", + "W_V", + "W_O", + "W_in", + "W_out", + "_set_processed_weight_attributes", + ): + assert not hasattr(bridge, missing), ( + f"RemoteBridge unexpectedly has '{missing}' — " + "the type split is meant to strip torch-only surface." + ) + + +class TestRemoteBridgeConstruction: + """Driver must declare the hook namespace — no model to walk.""" + + def test_requires_driver_supported_hook_points(self): + empty_driver = _stub_driver(supported_hooks=frozenset()) + with pytest.raises(ValueError, match="supported_hook_points to be non-empty"): + RemoteBridge(_stub_adapter(), tokenizer=None, driver=empty_driver) + + def test_cfg_device_is_none(self): + """RemoteBridge has no local device — explicit None so tensor.to() is a no-op.""" + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + assert bridge.cfg.device is None + + def test_builds_hook_registry_from_driver_declarations(self): + hook_names = frozenset({"blocks.0.hook_resid_pre", "blocks.0.hook_resid_post"}) + bridge = RemoteBridge( + _stub_adapter(), tokenizer=None, driver=_stub_driver(supported_hooks=hook_names) + ) + assert frozenset(bridge._hook_registry) == hook_names + for name in hook_names: + assert bridge._hook_registry[name].name == name + + +class TestRemoteBridgeHookLifecycle: + """RemoteBridge has no nn.Module children walk — registry must be canonical.""" + + def test_reset_hooks_clears_registry_hooks(self): + """Without the registry path, reset_hooks is a silent no-op on RemoteBridge.""" + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + hp = bridge._hook_registry["blocks.0.hook_resid_pre"] + hp.add_hook(lambda act, hook: None) + assert len(hp.fwd_hooks) == 1 + bridge.reset_hooks() + assert len(hp.fwd_hooks) == 0 + + def test_list_hooks_works(self): + """HookIntrospectionMixin.list_hooks is framework-agnostic (uses hook_dict).""" + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + bridge.add_hook("blocks.0.hook_resid_pre", lambda act, hook: None) + listing = bridge.list_hooks() + assert "blocks.0.hook_resid_pre" in listing + assert len(listing["blocks.0.hook_resid_pre"]) == 1 + + def test_add_hook_by_name_uses_registry(self): + """RemoteBridge has no component tree; attribute walk would AttributeError.""" + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + bridge.add_hook("blocks.0.hook_resid_pre", lambda act, hook: None) + assert len(bridge._hook_registry["blocks.0.hook_resid_pre"].fwd_hooks) == 1 + + def test_input_device_returns_none_for_meta(self): + """Meta-device params (load_weights=False path) must not pull inputs onto meta.""" + import torch + + class MetaDriver(DriverBase): + supported_hook_points = frozenset({"x"}) + _supported_features = frozenset({"parameters"}) + + def __init__(self): + super().__init__(_cfg(), tokenizer=None) + + def parameters(self): + yield torch.empty(2, 2, device="meta") + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult() + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=MetaDriver()) + assert bridge._input_device() is None + + def test_aliases_resolve_via_registry(self): + """Bridge-level aliases (hook_embed → embed.hook_out) work on RemoteBridge + when the driver declares the canonical target — registry lookup, not + attribute walk.""" + driver = _stub_driver(supported_hooks=frozenset({"embed.hook_out"})) + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=driver) + # hook_embed is in BridgeCore.hook_aliases as ["embed_ln.hook_out", "embed.hook_out"] + assert "hook_embed" in bridge.hook_dict + assert bridge.hook_dict["hook_embed"] is bridge._hook_registry["embed.hook_out"] + + +class TestRemoteBridgeForward: + """The driver-routed forward path.""" + + def test_forward_calls_driver(self): + driver = _stub_driver() + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=driver) + import torch + + bridge.forward(torch.tensor([[1, 2, 3]])) + assert len(driver.forward_calls) == 1 + + def test_forward_return_type_loss(self): + """return_type='loss' must compute loss, not silently return logits.""" + import torch + + class LogitsDriver(DriverBase): + supported_hook_points = frozenset({"x"}) + _supported_features = frozenset() + + def __init__(self): + super().__init__(_cfg(), tokenizer=None) + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult(logits=torch.randn(1, 3, 16), captured={}) + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=LogitsDriver()) + loss = bridge.forward(torch.tensor([[1, 2, 3]]), return_type="loss") + assert isinstance(loss, torch.Tensor) and loss.dim() == 0 + + def test_forward_return_type_both(self): + import torch + + class LogitsDriver(DriverBase): + supported_hook_points = frozenset({"x"}) + _supported_features = frozenset() + + def __init__(self): + super().__init__(_cfg(), tokenizer=None) + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult(logits=torch.randn(1, 3, 16), captured={}) + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=LogitsDriver()) + out = bridge.forward(torch.tensor([[1, 2, 3]]), return_type="both") + assert isinstance(out, tuple) and len(out) == 2 + logits, loss = out + assert isinstance(logits, torch.Tensor) and logits.shape == (1, 3, 16) + assert isinstance(loss, torch.Tensor) and loss.dim() == 0 + + def test_forward_invalid_return_type_raises(self): + """Unknown return_type used to silently return logits — must raise.""" + import torch + + class LogitsDriver(DriverBase): + supported_hook_points = frozenset({"x"}) + _supported_features = frozenset() + + def __init__(self): + super().__init__(_cfg(), tokenizer=None) + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult(logits=torch.randn(1, 3, 16), captured={}) + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=LogitsDriver()) + with pytest.raises(ValueError, match="Invalid return_type"): + bridge.forward(torch.tensor([[1, 2, 3]]), return_type="nonsense") + + def test_forward_replays_captures(self): + import torch + + recorded: list = [] + + class CapturingDriver(DriverBase): + supported_hook_points = frozenset({"blocks.0.hook_resid_pre"}) + _supported_features = frozenset() + + def __init__(self): + super().__init__(_cfg(), tokenizer=None) + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult( + logits=None, + captured={"blocks.0.hook_resid_pre": torch.zeros(2, 3)}, + ) + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=CapturingDriver()) + bridge._hook_registry["blocks.0.hook_resid_pre"].add_hook( + lambda act, hook: recorded.append(act) + ) + bridge.forward(torch.tensor([[1, 2]])) + assert len(recorded) == 1 + assert tuple(recorded[0].shape) == (2, 3) diff --git a/tests/unit/model_bridge/test_vllm_boot.py b/tests/unit/model_bridge/test_vllm_boot.py new file mode 100644 index 0000000000..cd0edaff03 --- /dev/null +++ b/tests/unit/model_bridge/test_vllm_boot.py @@ -0,0 +1,183 @@ +"""Orchestration tests for ``boot_vllm`` — mocks the HF / vLLM boundaries. + +Covers locked-kwarg rejection, dtype resolution, HF_TOKEN plumbing, env-var +override warning, plugin-config lifecycle, and the happy path. Driver-level +behavior is covered separately in test_vllm_driver.py. +""" +from __future__ import annotations + +import os +import sys +import warnings +from types import SimpleNamespace +from unittest.mock import MagicMock + +import pytest +import torch + +from transformer_lens.config import TransformerBridgeConfig +from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter +from transformer_lens.model_bridge.remote_bridge import RemoteBridge +from transformer_lens.model_bridge.sources.vllm import plugin +from transformer_lens.model_bridge.sources.vllm.source import ( + _dtype_from_hf_config, + boot_vllm, +) + + +def _hf_config() -> SimpleNamespace: + return SimpleNamespace( + architectures=["LlamaForCausalLM"], + torch_dtype=torch.float16, + hidden_size=4, + vocab_size=16, + num_hidden_layers=2, + ) + + +def _cfg() -> TransformerBridgeConfig: + return TransformerBridgeConfig( + d_model=4, + d_head=2, + n_layers=2, + n_ctx=8, + n_heads=2, + d_vocab=16, + d_mlp=8, + architecture="LlamaForCausalLM", + ) + + +@pytest.fixture +def mocked_boot(monkeypatch): + """Mock every external boundary boot_vllm crosses; yield handles for assertions.""" + plugin._config.clear() + hf_config = _hf_config() + cfg = _cfg() + adapter = ArchitectureAdapter(cfg) + adapter.component_mapping = {} + + auto_config = MagicMock(return_value=hf_config) + auto_tokenizer = MagicMock(return_value=MagicMock(name="tokenizer")) + monkeypatch.setattr("transformers.AutoConfig.from_pretrained", auto_config) + monkeypatch.setattr("transformers.AutoTokenizer.from_pretrained", auto_tokenizer) + + fake_vllm = MagicMock() + fake_vllm.LLM = MagicMock(return_value=MagicMock(name="llm")) + monkeypatch.setitem(sys.modules, "vllm", fake_vllm) + + monkeypatch.setattr( + "transformer_lens.model_bridge.sources.vllm.source.get_hf_token", + lambda: "fake-token", + ) + monkeypatch.setattr( + "transformer_lens.model_bridge.sources.vllm.source.extract_hf_config", + lambda llm: hf_config, + ) + monkeypatch.setattr( + "transformer_lens.model_bridge.sources.vllm.source.build_bridge_config_from_hf", + lambda hf, arch, name, dt: cfg, + ) + monkeypatch.setattr( + "transformer_lens.model_bridge.sources.vllm.source" + ".ArchitectureAdapterFactory.select_architecture_adapter", + lambda c: adapter, + ) + # Skip the real monkey-patch of Worker.load_model — only entry-points discovery + # would import vllm.v1.worker.gpu_worker. Leave configure() unmocked so the + # clear() test exercises real state. + monkeypatch.setattr( + "transformer_lens.model_bridge.sources.vllm.source.plugin.register", + lambda: None, + ) + + yield { + "auto_config": auto_config, + "auto_tokenizer": auto_tokenizer, + "vllm_llm": fake_vllm.LLM, + "hf_config": hf_config, + } + + plugin._config.clear() + + +def test_rejects_locked_kwarg_override(): + """tensor_parallel_size != 1 fails fast — before any I/O.""" + with pytest.raises(ValueError, match="tensor_parallel_size"): + boot_vllm("any-model", tensor_parallel_size=2) + + +@pytest.mark.parametrize( + "raw, expected", + [ + (torch.float16, torch.float16), + (torch.bfloat16, torch.bfloat16), + ("bfloat16", torch.bfloat16), + ("nonexistent_dtype", torch.float16), + (None, torch.float16), + ], +) +def test_dtype_resolution(raw, expected): + assert _dtype_from_hf_config(SimpleNamespace(torch_dtype=raw)) == expected + + +def test_dtype_resolution_missing_attr(): + assert _dtype_from_hf_config(SimpleNamespace()) == torch.float16 + + +def test_happy_path_returns_remote_bridge(mocked_boot): + bridge = boot_vllm("any-model") + assert isinstance(bridge, RemoteBridge) + + +def test_hf_token_passed_to_both_hf_calls(mocked_boot): + boot_vllm("any-model") + assert mocked_boot["auto_config"].call_args.kwargs["token"] == "fake-token" + assert mocked_boot["auto_tokenizer"].call_args.kwargs["token"] == "fake-token" + + +def test_custom_tokenizer_skips_autotokenizer(mocked_boot): + boot_vllm("any-model", tokenizer=MagicMock(name="custom")) + mocked_boot["auto_tokenizer"].assert_not_called() + + +def test_env_var_override_warns(mocked_boot, monkeypatch): + monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "1") + with pytest.warns(UserWarning, match="VLLM_ENABLE_V1_MULTIPROCESSING"): + boot_vllm("any-model") + assert os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] == "0" + + +def test_env_var_zero_does_not_warn(mocked_boot, monkeypatch): + monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0") + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + boot_vllm("any-model") + assert not any("VLLM_ENABLE_V1_MULTIPROCESSING" in str(w.message) for w in caught) + + +def test_plugin_config_cleared_after_boot(mocked_boot): + """No leak to non-TL vllm.LLM users in the same process.""" + boot_vllm("any-model") + assert plugin._config == {} + + +def test_llm_construction_kwargs(mocked_boot): + """Pin the kwargs boot_vllm passes to vllm.LLM(...). Catches regressions like + forgetting worker_extension_cls (collective_rpc methods unreachable), + max_num_batched_tokens (Dynamo symbolic-shape bound mismatch with buffer), + or max_logprobs (driver synthesizes logits via full-vocab logprobs).""" + boot_vllm("any-model", max_num_batched_tokens=1024) + kwargs = mocked_boot["vllm_llm"].call_args.kwargs + assert kwargs["model"] == "any-model" + assert kwargs["max_num_batched_tokens"] == 1024 + assert kwargs["worker_extension_cls"] == ( + "transformer_lens.model_bridge.sources.vllm.worker_extension.TLWorkerExtension" + ) + # Sized from the mocked hf_config.vocab_size in mocked_boot fixture. + assert kwargs["max_logprobs"] == mocked_boot["hf_config"].vocab_size + # Locked kwargs that must always reach LLM. + assert kwargs["tensor_parallel_size"] == 1 + assert kwargs["pipeline_parallel_size"] == 1 + assert kwargs["skip_tokenizer_init"] is True + assert kwargs["disable_log_stats"] is True diff --git a/tests/unit/model_bridge/test_vllm_driver.py b/tests/unit/model_bridge/test_vllm_driver.py new file mode 100644 index 0000000000..8a46a63938 --- /dev/null +++ b/tests/unit/model_bridge/test_vllm_driver.py @@ -0,0 +1,258 @@ +"""Unit tests for VLLMDriver — mocks vLLM's LLM so no vllm package install needed. + +GPU integration tests (real LLM, real captures) live separately as a Colab +notebook; the compiled-graph path can't be reached from mocked unit tests. +""" +from __future__ import annotations + +from types import SimpleNamespace +from typing import Any +from unittest.mock import MagicMock + +import pytest +import torch + +from transformer_lens.config import TransformerBridgeConfig +from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter +from transformer_lens.model_bridge.driver_protocol import ( + Driver, + ForwardResult, + validate_driver, +) +from transformer_lens.model_bridge.remote_bridge import RemoteBridge +from transformer_lens.model_bridge.sources.vllm.driver import VLLMDriver + + +def _hf_config(num_hidden_layers: int = 2, hidden_size: int = 4, vocab_size: int = 16) -> Any: + return SimpleNamespace( + num_hidden_layers=num_hidden_layers, + hidden_size=hidden_size, + vocab_size=vocab_size, + ) + + +def _overlay(specs=None, nonfiring=None): + """Minimal overlay stand-in — capture_specs + nonfiring_hooks.""" + return SimpleNamespace( + capture_specs=lambda hf_config: specs + or { + "embed.hook_out": ("model.embed_tokens", hf_config.hidden_size), + "blocks.0.hook_out": ("model.layers.0", hf_config.hidden_size), + "blocks.1.hook_out": ("model.layers.1", hf_config.hidden_size), + "unembed.hook_out": ("lm_head", hf_config.vocab_size), + }, + nonfiring_hooks=lambda: nonfiring + or [ + "blocks.{i}.attn.hook_pattern", + "blocks.{i}.attn.hook_attn_scores", + ], + ) + + +def _cfg() -> TransformerBridgeConfig: + return TransformerBridgeConfig( + d_model=4, + d_head=2, + n_layers=2, + n_ctx=8, + n_heads=2, + d_vocab=16, + d_mlp=8, + architecture="LlamaForCausalLM", + ) + + +def _adapter() -> ArchitectureAdapter: + adapter = ArchitectureAdapter(_cfg()) + adapter.component_mapping = {} + return adapter + + +def _fake_request_output(generated_token=None, top_logprobs=None): + """Build a vLLM RequestOutput-shaped mock for _synthesize_logits.""" + completion = MagicMock() + completion.token_ids = [generated_token] if generated_token is not None else [] + completion.logprobs = ( + [{tid: MagicMock(logprob=lp) for tid, lp in top_logprobs.items()}] + if top_logprobs is not None + else [] + ) + ro = MagicMock() + ro.outputs = [completion] + return ro + + +def _driver( + *, + captures=None, + hf_config=None, + max_num_batched_tokens=2048, + generated_token=None, + top_logprobs=None, +) -> VLLMDriver: + """Build a VLLMDriver. ``captures`` populates llm.collective_rpc; ``generated_token`` + and ``top_logprobs`` populate llm.generate's RequestOutput so _synthesize_logits + can be exercised on both code paths (logprobs preferred, token_id fallback).""" + llm = MagicMock() + if captures is not None: + llm.collective_rpc = MagicMock(return_value=[captures]) + llm.generate = MagicMock(return_value=[_fake_request_output(generated_token, top_logprobs)]) + return VLLMDriver( + llm=llm, + adapter=_adapter(), + tokenizer=None, + overlay=_overlay(), + hf_config=hf_config or _hf_config(), + max_num_batched_tokens=max_num_batched_tokens, + ) + + +class TestVLLMDriverProtocolConformance: + """VLLMDriver satisfies the Driver protocol and passes strict validation.""" + + def test_passes_validate_driver(self): + driver = _driver() + assert isinstance(driver, Driver) + validate_driver(driver) + + def test_no_torch_capability_flags(self): + """vLLM owns the model in a worker — no torch-specific capability surface.""" + driver = _driver() + for feature in ("parameters", "state_dict", "gradients", "weight_access"): + assert driver.supports(feature) is False, f"vLLM shouldn't support {feature!r}" + + def test_non_fireable_expanded_per_layer(self): + """``blocks.{i}.attn.hook_pattern`` template expands to one entry per layer.""" + assert _driver().non_fireable_hook_points == frozenset( + { + "blocks.0.attn.hook_pattern", + "blocks.1.attn.hook_pattern", + "blocks.0.attn.hook_attn_scores", + "blocks.1.attn.hook_attn_scores", + } + ) + + +class TestVLLMDriverForward: + """forward dispatches via llm.generate and surfaces captures via ForwardResult.""" + + def test_forward_logits_from_sampler_logprobs(self): + """vLLM bypasses lm_head; driver synthesizes logits from sampler logprobs. + Position -1 must carry the real next-token distribution.""" + pytest.importorskip("vllm") + result = _driver( + captures={"embed.hook_out": torch.randn(3, 4)}, + top_logprobs={7: 2.5, 3: 1.0, 11: -0.5}, + ).forward(torch.tensor([[1, 2, 3]])) + assert isinstance(result, ForwardResult) + assert tuple(result.captured["embed.hook_out"].shape) == (1, 3, 4) + assert result.logits is not None and tuple(result.logits.shape) == (1, 3, 16) + # Argmax = highest-logprob token. Values at non-listed positions are -inf. + assert int(result.logits[0, -1].argmax().item()) == 7 + assert float(result.logits[0, -1, 7].item()) == 2.5 + + def test_forward_logits_fallback_to_token_id(self): + """If logprobs are absent (e.g. return_logits=False elsewhere upstream), + _synthesize_logits falls back to the generated token id as a one-hot-ish.""" + pytest.importorskip("vllm") + result = _driver( + captures={"embed.hook_out": torch.randn(3, 4)}, + generated_token=9, + ).forward(torch.tensor([[1, 2, 3]])) + assert result.logits is not None + assert int(result.logits[0, -1].argmax().item()) == 9 + + def test_forward_rejects_batched_input(self): + """batch_size=1 only. Raises in _normalize_input_ids before any vllm import.""" + with pytest.raises(NotImplementedError, match="batch_size=1"): + _driver(captures={}).forward(torch.tensor([[1, 2], [3, 4]])) + + def test_forward_rejects_callable_interventions(self): + with pytest.raises(NotImplementedError, match="intervention specs"): + _driver(captures={}).forward( + torch.tensor([[1]]), intervene={"embed.hook_out": lambda a: a} + ) + + def test_forward_rejects_unsupported_intervention_op(self): + with pytest.raises(ValueError, match="Unsupported intervention op"): + _driver(captures={}).forward( + torch.tensor([[1]]), intervene={"embed.hook_out": {"op": "clamp", "value": 1.0}} + ) + + def test_forward_rejects_unknown_intervention_hook(self): + with pytest.raises(ValueError, match="not in supported_hook_points"): + _driver(captures={}).forward( + torch.tensor([[1]]), intervene={"blocks.99.hook_unknown": {"op": "suppress"}} + ) + + def test_forward_rejects_malformed_intervention_spec(self): + with pytest.raises(ValueError, match="must be a dict with 'op' key"): + _driver(captures={}).forward( + torch.tensor([[1]]), intervene={"embed.hook_out": {"no_op_key": True}} + ) + + def test_forward_rejects_scale_missing_factor(self): + with pytest.raises(ValueError, match="op='scale' requires 'factor'"): + _driver(captures={}).forward( + torch.tensor([[1]]), intervene={"embed.hook_out": {"op": "scale"}} + ) + + def test_forward_rejects_add_missing_value(self): + with pytest.raises(ValueError, match="op='add' requires 'value'"): + _driver(captures={}).forward( + torch.tensor([[1]]), intervene={"embed.hook_out": {"op": "add"}} + ) + + def test_forward_rejects_set_missing_value(self): + with pytest.raises(ValueError, match="op='set' requires 'value'"): + _driver(captures={}).forward( + torch.tensor([[1]]), intervene={"embed.hook_out": {"op": "set"}} + ) + + def test_forward_pushes_interventions_before_generate(self): + """The driver pushes spec dicts via collective_rpc('tl_set_interventions', ...).""" + pytest.importorskip("vllm") + driver = _driver(captures={"embed.hook_out": torch.zeros(3, 4)}) + driver.forward( + torch.tensor([[1, 2, 3]]), + intervene={"embed.hook_out": {"op": "suppress"}}, + ) + rpc_calls = [c.args for c in driver._llm.collective_rpc.call_args_list] + assert any( + args[0] == "tl_set_interventions" + and args[1] == ({"embed.hook_out": {"op": "suppress"}},) + for args in rpc_calls + ) + + def test_forward_always_pushes_interventions_for_reset(self): + """Even with intervene=None, push {} so stale state from prior forwards clears.""" + pytest.importorskip("vllm") + driver = _driver(captures={"embed.hook_out": torch.zeros(3, 4)}) + driver.forward(torch.tensor([[1, 2, 3]])) + rpc_calls = [c.args for c in driver._llm.collective_rpc.call_args_list] + assert any(args[0] == "tl_set_interventions" and args[1] == ({},) for args in rpc_calls) + + def test_forward_rejects_max_new_tokens_gt_one(self): + """Decode-step writes overwrite the prefill buffer — silent capture corruption.""" + with pytest.raises(NotImplementedError, match="max_new_tokens=1 only"): + _driver(captures={}).forward(torch.tensor([[1, 2]]), max_new_tokens=2) + + def test_forward_rejects_prompt_exceeding_buffer(self): + """Worker buffers silently clamp on overflow — driver must fail loud.""" + with pytest.raises(ValueError, match="exceeds max_num_batched_tokens"): + _driver(captures={}, max_num_batched_tokens=4).forward(torch.tensor([[1, 2, 3, 4, 5]])) + + +class TestVLLMDriverThroughBridge: + """End-to-end via RemoteBridge — drivers' captures flow into the HookPoint tree.""" + + def test_bridge_replays_vllm_captures(self): + pytest.importorskip("vllm") + driver = _driver(captures={"embed.hook_out": torch.randn(3, 4)}) + bridge = RemoteBridge(adapter=_adapter(), tokenizer=None, driver=driver) + + fired: list = [] + bridge.add_hook("embed.hook_out", lambda act, hook: fired.append(act)) + bridge.forward(torch.tensor([[1, 2, 3]])) + assert len(fired) == 1 + assert tuple(fired[0].shape) == (1, 3, 4) diff --git a/transformer_lens/model_bridge/__init__.py b/transformer_lens/model_bridge/__init__.py index 3e41974710..98108ee3de 100644 --- a/transformer_lens/model_bridge/__init__.py +++ b/transformer_lens/model_bridge/__init__.py @@ -3,7 +3,7 @@ This module provides functionality to bridge between different model architectures. """ from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter -from transformer_lens.model_bridge.bridge import TransformerBridge +from transformer_lens.model_bridge.bridge import BridgeCore, RemoteBridge, TransformerBridge from transformer_lens.model_bridge.component_setup import ( replace_remote_component, set_original_components, @@ -36,6 +36,8 @@ __all__ = [ "ArchitectureAdapter", + "BridgeCore", + "RemoteBridge", "TransformerBridge", "AttentionBridge", "BlockBridge", diff --git a/transformer_lens/model_bridge/bridge.py b/transformer_lens/model_bridge/bridge.py index 91e6852049..55e8937088 100644 --- a/transformer_lens/model_bridge/bridge.py +++ b/transformer_lens/model_bridge/bridge.py @@ -1,3751 +1,14 @@ -"""Bridge module for connecting different model architectures. +"""Compatibility re-export shim. -This module provides the bridge components that wrap remote model components and provide -a consistent interface for accessing their weights and performing operations. -""" -import logging -import re -import warnings -from collections.abc import Generator -from contextlib import contextmanager -from functools import lru_cache -from typing import ( - TYPE_CHECKING, - Any, - Callable, - Dict, - Iterator, - List, - Literal, - Optional, - Tuple, - Union, - cast, - overload, -) - -import einops -import numpy as np -import torch -import tqdm -from torch import nn - -from transformer_lens import utilities as utils -from transformer_lens.ActivationCache import ActivationCache -from transformer_lens.FactoredMatrix import FactoredMatrix -from transformer_lens.hook_points import HookIntrospectionMixin, HookPoint -from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter -from transformer_lens.model_bridge.component_setup import set_original_components -from transformer_lens.model_bridge.composition_scores import CompositionScores -from transformer_lens.model_bridge.exceptions import StopAtLayerException -from transformer_lens.model_bridge.generalized_components.base import ( - GeneralizedComponent, -) -from transformer_lens.model_bridge.generalized_components.block import ( - _BLOCK_INTERNAL_MODULES, - _NORM_PREFIXES, - _VARIANT_SUBMODULE_SET, - VARIANT_SUBMODULE_NAMES, -) -from transformer_lens.model_bridge.get_params_util import get_bridge_params -from transformer_lens.utilities.aliases import resolve_alias -from transformer_lens.utilities.devices import move_to_and_update_config -from transformer_lens.utilities.lm_utils import lm_cross_entropy_loss - -if TYPE_CHECKING: - from transformer_lens.ActivationCache import ActivationCache - -_BLOCK_PATTERN = re.compile("blocks\\.(\\d+)") - - -def _resolve_attr_path(obj: nn.Module, attr_path: str) -> torch.Tensor: - """Walk a dot-separated attribute path and return the final tensor.""" - result = obj - for attr in attr_path.split("."): - result = getattr(result, attr) - return cast(torch.Tensor, result) - - -def build_alias_to_canonical_map(hook_dict, prefix=""): - """Build a mapping from alias hook names to their canonical names. - - Args: - hook_dict: Dictionary mapping hook names to HookPoint objects - prefix: Prefix for nested keys - - Returns: - Dictionary mapping alias names to canonical names - - Example: - If hook_dict contains: - - "blocks.0.hook_q" -> HookPoint(name="blocks.0.attn.q.hook_out") - - Returns: - - {"blocks.0.hook_q": "blocks.0.attn.q.hook_out"} - """ - aliases = {} - for key, value in hook_dict.items(): - full_key = f"{prefix}.{key}" if prefix else key - if isinstance(value, dict): - aliases.update(build_alias_to_canonical_map(value, full_key)) - elif hasattr(value, "name"): - if key != value.name: - aliases[full_key] = value.name - return aliases - - -class TransformerBridge(HookIntrospectionMixin, nn.Module): - """Bridge between HuggingFace and TransformerLens models. - - This class provides a standardized interface to access components of a transformer - model, regardless of the underlying architecture. It uses an architecture adapter - to map between the TransformerLens and HuggingFace model structures. - - Tokenization notes - ------------------ - - :meth:`to_tokens`, :meth:`to_str_tokens`, :meth:`get_token_position`, - :meth:`forward` (string input), and :meth:`generate` accept ``prepend_bos`` - to control BOS prepending. Resolution: explicit arg → - ``cfg.default_prepend_bos`` (defaults ``True``, even for non-BOS-trained - models — attention heads tend to use position 0 as a resting state). - **Pass ``prepend_bos=False`` when tokenizing a fragment of a larger - prompt** — off-by-one position errors usually trace back here. - - Reconciliation with ``cfg.tokenizer_prepends_bos`` (tokenizers that add - BOS automatically) is handled internally — pass the value you want; - the bridge adds or strips manually as needed. When - ``cfg.tokenizer_appends_eos=True`` (OLMo, Apertus, etc.), - :meth:`to_tokens` also strips trailing EOS tokens so the model receives - a continuation rather than a terminated sequence; this path is - bridge-specific. - - BPE/SentencePiece tokenizers treat ``"hello"``, ``" hello"``, and - ``"Hello"`` as distinct tokens. Concatenated prompts may not tokenize - as the sum of parts — inspect with :meth:`to_str_tokens` when in doubt. - """ - - hook_aliases: Dict[str, Union[str, List[str]]] = { - # Prefer embed_ln.hook_out for post-LN models (Bloom, BERT) - "hook_embed": ["embed_ln.hook_out", "embed.hook_out"], - "hook_pos_embed": ["pos_embed.hook_out", "rotary_emb.hook_out"], - "hook_unembed": "unembed.hook_out", - } - - def __init__(self, model: nn.Module, adapter: ArchitectureAdapter, tokenizer: Any): - """Initialize the bridge. - - Args: - model: The model to bridge (must be a PyTorch nn.Module or PreTrainedModel) - adapter: The architecture adapter to use - tokenizer: The tokenizer to use (required) - """ - super().__init__() - self.__dict__["original_model"] = model - self.adapter = adapter - self.cfg = adapter.cfg - self.tokenizer = tokenizer - if self.cfg.d_vocab == -1 and self.tokenizer is not None: - if hasattr(self.tokenizer, "get_vocab"): - vocab = self.tokenizer.get_vocab() - self.cfg.d_vocab = max(vocab.values()) + 1 - elif hasattr(self.tokenizer, "vocab"): - self.cfg.d_vocab = max(self.tokenizer.vocab.values()) + 1 - else: - self.cfg.d_vocab = getattr(self.tokenizer, "vocab_size", 50257) - if self.cfg.d_vocab_out == -1: - self.cfg.d_vocab_out = self.cfg.d_vocab - self.compatibility_mode = False - self._hook_cache = None - self._hook_registry: Dict[str, HookPoint] = {} - self._hook_registry_initialized = False - self._hook_alias_registry: Dict[str, Union[str, List[str]]] = {} - self._property_alias_registry: Dict[str, str] = {} - # real_components maps TL keys to (remote_path, actual_instance) tuples - # For list components, actual_instance will be a list of component instances - self.real_components: Dict[str, tuple] = {} - if not hasattr(self.cfg, "device") or self.cfg.device is None: - try: - self.cfg.device = str(next(self.original_model.parameters()).device) - except StopIteration: - self.cfg.device = "cpu" - if not hasattr(adapter, "component_mapping") or adapter.component_mapping is None: - raise ValueError("Adapter must have a component_mapping attribute") - original_model = self.__dict__["original_model"] - set_original_components(self, self.adapter, original_model) - self._initialize_hook_registry() - self._register_aliases() - self._register_all_aliases_recursive() - self._setup_hook_compatibility() - self._initialize_hooks_to_cache() - self.processor = None - - @classmethod - def boot_transformers( - cls, - model_name: str, - hf_config_overrides: Optional[dict] = None, - device: Optional[Union[str, torch.device]] = None, - dtype: torch.dtype = torch.float32, - tokenizer: Optional[Any] = None, - load_weights: bool = True, - trust_remote_code: bool = False, - model_class: Optional[type] = None, - hf_model: Optional[Any] = None, - device_map: Optional[Union[str, Dict[str, Union[str, int]]]] = None, - n_devices: Optional[int] = None, - max_memory: Optional[Dict[Union[str, int], str]] = None, - n_ctx: Optional[int] = None, - revision: Optional[str] = None, - checkpoint_index: Optional[int] = None, - checkpoint_value: Optional[int] = None, - ) -> "TransformerBridge": - """Boot a model from HuggingFace (alias for sources.transformers.boot). - - Returns raw HF weights by default — logits/activations match HF, *not* - legacy ``HookedTransformer`` (which folds LayerNorm + centers weights). - Call ``enable_compatibility_mode()`` on the result for HookedTransformer- - equivalent numerics. Generation, argmax, and CE loss are unaffected. - - Attention implementation is forced to ``"eager"`` so hooks can capture scores - and patterns. For an apples-to-apples HF comparison, load the HF model with - ``attn_implementation="eager"`` too; comparing against the default ``"sdpa"`` - shows ~1e-3 fp32 drift from kernel-level op reordering, not a bridge bug. - - Args: - model_name: The name of the model to load. - hf_config_overrides: Optional overrides applied to the HuggingFace config before model load. - device: The device to use. If None, will be determined automatically. Mutually exclusive - with ``device_map``. - dtype: The dtype to use for the model. - tokenizer: Optional pre-initialized tokenizer to use; if not provided one will be created. - load_weights: If False, load model without weights (on meta device) for config inspection only. - trust_remote_code: Whether to trust remote code for custom model architectures. - model_class: Optional HuggingFace model class to use instead of the default - auto-detected class (e.g., BertForNextSentencePrediction). - hf_model: Optional pre-loaded HuggingFace model to use instead of loading one. Useful - for models loaded with custom configurations (e.g., quantization via - BitsAndBytesConfig). When provided, load_weights is ignored. If the pre-loaded - model was built with a ``device_map``, ``cfg.device`` and ``cfg.n_devices`` are - derived from its ``hf_device_map`` automatically. - device_map: HuggingFace-style device map for multi-GPU inference. Pass ``"auto"``, - ``"balanced"``, ``"sequential"``, or an explicit ``{submodule_path: device}`` dict. - Mutually exclusive with ``device``. - n_devices: Convenience shortcut: split the model across this many CUDA devices. - Translated to a ``max_memory`` dict over devices 0..n_devices-1 and passed as - ``device_map`` to HF. Requires CUDA with at least this many visible devices. - max_memory: Optional per-device memory budget, passed through to HF's dispatcher. - Only used when ``device_map`` or ``n_devices`` is in effect. - n_ctx: Optional context length override. Writes to the appropriate HF config field - for this model automatically (callers don't need to know the field name). - Warns if larger than the model's default context length. - revision: Optional HF revision (branch, tag, or commit). Forwarded to the underlying - ``AutoConfig.from_pretrained`` and ``AutoModelForCausalLM.from_pretrained`` calls. - Mutually exclusive with ``checkpoint_index`` / ``checkpoint_value``. - checkpoint_index: Index into the available training checkpoints for the model family - (currently ``EleutherAI/pythia*`` and ``stanford-crfm/*``). Resolved to a revision - string via known per-family naming conventions. - checkpoint_value: Training step or token count of the desired checkpoint. Alternative - to ``checkpoint_index``; must match an entry in the family's checkpoint label list. - - Returns: - The bridge to the loaded model. - """ - from transformer_lens.model_bridge.sources.transformers import boot - - return boot( - model_name=model_name, - hf_config_overrides=hf_config_overrides, - device=device, - dtype=dtype, - tokenizer=tokenizer, - load_weights=load_weights, - trust_remote_code=trust_remote_code, - model_class=model_class, - hf_model=hf_model, - device_map=device_map, - n_devices=n_devices, - max_memory=max_memory, - n_ctx=n_ctx, - revision=revision, - checkpoint_index=checkpoint_index, - checkpoint_value=checkpoint_value, - ) - - @property - def original_model(self) -> nn.Module: - """Get the original model.""" - if "original_model" not in self.__dict__: - raise AttributeError("original_model has not been set") - return self.__dict__["original_model"] - - @original_model.setter - def original_model(self, value: nn.Module) -> None: - """Set the original model.""" - self.__dict__["original_model"] = value - - def _register_aliases(self) -> None: - """Register bridge-level aliases. - - This is called at the END of __init__ when all components are set up. - It registers the top-level bridge aliases (hook_embed, hook_pos_embed, etc.) - and creates direct attribute references. - """ - if self.hook_aliases: - self._hook_alias_registry.update(self.hook_aliases) - for alias_name, target_path in self.hook_aliases.items(): - try: - if isinstance(target_path, list): - for single_target in target_path: - try: - target_obj = self - for part in single_target.split("."): - target_obj = getattr(target_obj, part) - object.__setattr__(self, alias_name, target_obj) - break - except AttributeError: - continue - else: - target_obj = self - for part in target_path.split("."): - target_obj = getattr(target_obj, part) - object.__setattr__(self, alias_name, target_obj) - except AttributeError: - pass - - def _set_processed_weight_attributes(self) -> None: - """Create 3D processed weight attributes for attention components. - - For each attention component, if it has 2D weights (q.weight, k.weight, v.weight), - reshape them to 3D format [n_heads, d_model, d_head] and set as: - - _processed_W_Q - - _processed_W_K - - _processed_W_V - - _processed_b_Q - - _processed_b_K - - _processed_b_V - - This allows property aliases (W_Q, W_K, W_V) to return 3D format for - HookedTransformer compatibility while keeping 2D format for calculations. - """ - - n_heads = self.cfg.n_heads - d_head = self.cfg.d_head - d_model = self.cfg.d_model - if not hasattr(self, "blocks"): - return - for block in self.blocks: - if "attn" not in block._modules: - continue - attn = block.attn - if not (hasattr(attn, "q") and hasattr(attn.q, "weight")): - continue - try: - w_q_2d = attn.q.weight.data - w_k_2d = attn.k.weight.data - w_v_2d = attn.v.weight.data - attn._processed_W_Q = einops.rearrange( - w_q_2d, "m (i h) -> i m h", i=n_heads, h=d_head - ) - attn._processed_W_K = einops.rearrange( - w_k_2d, "m (i h) -> i m h", i=n_heads, h=d_head - ) - attn._processed_W_V = einops.rearrange( - w_v_2d, "m (i h) -> i m h", i=n_heads, h=d_head - ) - if hasattr(attn.q, "bias") and attn.q.bias is not None: - b_q_2d = attn.q.bias.data - b_k_2d = attn.k.bias.data - b_v_2d = attn.v.bias.data - attn._processed_b_Q = einops.rearrange( - b_q_2d, "(i h) -> i h", i=n_heads, h=d_head - ) - attn._processed_b_K = einops.rearrange( - b_k_2d, "(i h) -> i h", i=n_heads, h=d_head - ) - attn._processed_b_V = einops.rearrange( - b_v_2d, "(i h) -> i h", i=n_heads, h=d_head - ) - if hasattr(attn, "o") and hasattr(attn.o, "weight"): - w_o_2d = attn.o.weight.data - w_o_transposed = w_o_2d.T - attn._processed_W_O = einops.rearrange( - w_o_transposed, "m (i h) -> i h m", i=n_heads, h=d_head - ) - if hasattr(attn.o, "bias") and attn.o.bias is not None: - attn._processed_b_O = attn.o.bias.data - except Exception: - pass - - def _register_all_aliases_recursive(self) -> None: - """Recursively register aliases on all bridge components. - - This walks through all components and calls _register_aliases() on each one. - Used after weight processing to ensure aliases point to processed weights. - """ - if hasattr(self, "_register_aliases"): - self._register_aliases() - for module in self.modules(): - if module is not self and hasattr(module, "_register_aliases"): - getattr(module, "_register_aliases")() - - def __setattr__(self, name: str, value: Any) -> None: - """Override setattr to track HookPoint objects dynamically.""" - super().__setattr__(name, value) - if isinstance(value, HookPoint): - value.name = name - self._hook_registry[name] = value - elif hasattr(value, "get_hooks") and callable(getattr(value, "get_hooks")): - component_hooks = value.get_hooks() - for hook_name, hook in component_hooks.items(): - full_name = f"{name}.{hook_name}" - hook.name = full_name - self._hook_registry[full_name] = hook - - def _initialize_hook_registry(self) -> None: - """Initialize the hook registry by scanning existing components.""" - if self._hook_registry_initialized: - return - self._scan_existing_hooks(self, "") - self._hook_registry_initialized = True - - def _collect_component_aliases(self, component_mapping, prefix=""): - """Recursively collect aliases from components.""" - aliases = {} - if isinstance(component_mapping, dict): - for name, component in component_mapping.items(): - sub_prefix = f"{prefix}.{name}" if prefix else name - aliases.update(self._collect_component_aliases(component, sub_prefix)) - else: - if hasattr(component_mapping, "hook_aliases") and component_mapping.hook_aliases: - for alias_name, target in component_mapping.hook_aliases.items(): - full_alias = f"{prefix}.{alias_name}" if prefix else alias_name - full_target = f"{prefix}.{target}" if prefix else target - aliases[full_alias] = full_target - if hasattr(component_mapping, "submodules") and component_mapping.submodules: - for sub_name, sub_component in component_mapping.submodules.items(): - sub_prefix = f"{prefix}.{sub_name}" if prefix else sub_name - aliases.update(self._collect_component_aliases(sub_component, sub_prefix)) - return aliases - - @staticmethod - @lru_cache(maxsize=128) - def _compute_hook_aliases_cached( - hook_names_tuple: Tuple[str, ...], component_aliases_tuple: Tuple[Tuple[str, str], ...] - ) -> Tuple[Tuple[str, str], ...]: - """Cached computation of hook aliases. Takes immutable inputs for caching.""" - aliases = {} - component_aliases = dict(component_aliases_tuple) - for hook_name in hook_names_tuple: - for alias_pattern, target_pattern in component_aliases.items(): - if "blocks." in target_pattern and "blocks." in hook_name: - block_match = _BLOCK_PATTERN.search(hook_name) - if block_match: - block_num = block_match.group(1) - dynamic_alias_pattern = alias_pattern.replace( - "blocks.", f"blocks.{block_num}." - ) - dynamic_target_pattern = target_pattern.replace( - "blocks.", f"blocks.{block_num}." - ) - if hook_name.endswith(dynamic_target_pattern): - target_len = len(dynamic_target_pattern) - alias_name = hook_name[:-target_len] + dynamic_alias_pattern - aliases[alias_name] = hook_name - elif hook_name.endswith(target_pattern): - target_len = len(target_pattern) - alias_name = hook_name[:-target_len] + alias_pattern - aliases[alias_name] = hook_name - return tuple(aliases.items()) - - def _collect_hook_aliases_from_registry(self): - """Collect aliases based on existing hooks in the registry.""" - if hasattr(self.adapter, "component_mapping"): - component_aliases = self._collect_component_aliases(self.adapter.component_mapping) - hook_names_tuple = tuple(sorted(self._hook_registry.keys())) - component_aliases_tuple = tuple(sorted(component_aliases.items())) # type: ignore[operator] - aliases_tuple = self._compute_hook_aliases_cached( - hook_names_tuple, component_aliases_tuple - ) - return dict(aliases_tuple) - return {} - - def _add_aliases_to_hooks(self, hooks: Dict[str, HookPoint]) -> None: - """Add aliases to hooks in place.""" - component_aliases = self._collect_hook_aliases_from_registry() - all_aliases = {**self.hook_aliases, **component_aliases} - if not all_aliases: - return - for alias_name, target in all_aliases.items(): - if isinstance(target, list): - for single_target in target: - try: - target_hook = resolve_alias(self, alias_name, {alias_name: single_target}) - if target_hook is not None: - hooks[alias_name] = target_hook - break - except AttributeError: - continue - else: - try: - target_hook = resolve_alias(self, alias_name, {alias_name: target}) - if target_hook is not None: - hooks[alias_name] = target_hook - except AttributeError: - continue - - def _scan_existing_hooks(self, module: nn.Module, prefix: str = "") -> None: - """Scan existing modules for hooks and add them to registry.""" - visited = set() - # Protect canonical HookPoint names from alias overwrites - named_hook_ids: set = set() - - def scan_module(mod: nn.Module, path: str = "") -> None: - obj_id = id(mod) - if obj_id in visited: - return - visited.add(obj_id) - if hasattr(mod, "get_hooks") and callable(getattr(mod, "get_hooks")): - component_hooks = mod.get_hooks() # type: ignore[operator] - if isinstance(component_hooks, dict): - hooks_dict = cast(Dict[str, HookPoint], component_hooks) - for hook_name, hook in hooks_dict.items(): - full_name = f"{path}.{hook_name}" if path else hook_name - hook_id = id(hook) - if hook_id not in named_hook_ids: - hook.name = full_name - named_hook_ids.add(hook_id) - self._hook_registry[full_name] = hook - for attr_name in dir(mod): - if attr_name.startswith("_"): - continue - if attr_name == "original_component" or attr_name == "original_model": - continue - if attr_name in [ - "OV", - "QK", - "W_V", - "W_O", - "W_Q", - "W_K", - "W_in", - "W_gate", - "W_out", - "b_V", - "b_O", - "b_Q", - "b_K", - "b_in", - "b_out", - ]: - continue - try: - attr = getattr(mod, attr_name) - except (AttributeError, NameError, RuntimeError, TypeError): - continue - name = f"{path}.{attr_name}" if path else attr_name - if isinstance(attr, HookPoint): - hook_id = id(attr) - if hook_id not in named_hook_ids: - attr.name = name - named_hook_ids.add(hook_id) - self._hook_registry[name] = attr - for child_name, child_module in mod.named_children(): - if ( - child_name == "original_component" - or child_name == "_original_component" - or child_name == "original_model" - ): - continue - child_path = f"{path}.{child_name}" if path else child_name - scan_module(child_module, child_path) - - scan_module(module, prefix) - - @property - def hook_dict(self) -> dict[str, HookPoint]: - """Get all HookPoint objects in the model for compatibility with TransformerLens.""" - hooks = self._hook_registry.copy() - self._add_aliases_to_hooks(hooks) - return hooks - - @property - def n_params_total(self) -> int: - """Total number of parameters in the model, including embeddings, biases, - and layer norm weights. - - Mirrors :attr:`HookedTransformer.n_params_total`. Use this when you want - the actual parameter count for memory budgeting, comparison with - HuggingFace's ``model.num_parameters()``, or alignment with reported - model sizes in papers (e.g. the Pythia suite). - - Returns: - int: ``sum(p.numel() for p in self.parameters())`` - """ - return sum(p.numel() for p in self.parameters()) - - def clear_hook_registry(self) -> None: - """Clear the hook registry and force re-initialization.""" - self._hook_registry.clear() - self._hook_registry_initialized = False - - def _initialize_hooks_to_cache(self) -> None: - """Initialize the hooks to cache when running the model with cache.""" - self.hooks_to_cache = {} - default_cached_hooks_names = [ - "embed.hook_in", - "embed.hook_out", - "pos_embed.hook_in", - "pos_embed.hook_out", - "rotary_embed.hook_in", - "rotary_embed.hook_out", - "ln_final.hook_in", - "ln_final.hook_scale", - "ln_final.hook_normalized", - "ln_final.hook_out", - "unembed.hook_in", - "unembed.hook_out", - ] - for block_idx in range(self.cfg.n_layers): - default_cached_hooks_names.append(f"blocks.{block_idx}.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln1.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln1.hook_scale") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln1.hook_normalized") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln1.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln1_post.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln1_post.hook_scale") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln1_post.hook_normalized") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln1_post.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.q.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.q.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.q_norm.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.q_norm.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.k.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.k.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.k_norm.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.k_norm.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.v.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.v.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.o.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.o.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.hook_attn_scores") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.hook_pattern") # type: ignore[operator] - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.hook_hidden_states") - default_cached_hooks_names.append(f"blocks.{block_idx}.attn.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln2.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln2.hook_scale") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln2.hook_normalized") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln2.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln2_post.hook_in") # type: ignore[operator] - default_cached_hooks_names.append(f"blocks.{block_idx}.ln2_post.hook_scale") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln2_post.hook_normalized") - default_cached_hooks_names.append(f"blocks.{block_idx}.ln2_post.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.mlp.hook_in") # type: ignore[operator] - default_cached_hooks_names.append(f"blocks.{block_idx}.mlp.in.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.mlp.in.hook_out") # type: ignore[operator] - default_cached_hooks_names.append(f"blocks.{block_idx}.mlp.out.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.mlp.out.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.mlp.gate.hook_in") - default_cached_hooks_names.append(f"blocks.{block_idx}.mlp.gate.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.mlp.hook_out") - default_cached_hooks_names.append(f"blocks.{block_idx}.hook_out") - for hook_name in default_cached_hooks_names: - if hook_name in self._hook_registry: - self.hooks_to_cache[hook_name] = self._hook_registry[hook_name] # type: ignore[arg-type] - - def __getattr__(self, name: str) -> Any: - """Provide a clear error message for missing attributes.""" - if name in self.__dict__: # type: ignore[arg-type] - return self.__dict__[name] - # Use __dict__ directly to avoid recursion - if "_modules" in self.__dict__ and name in self.__dict__["_modules"]: # type: ignore[arg-type] - return self.__dict__["_modules"][name] - if "original_model" in self.__dict__ and self.__dict__["original_model"] is not None: - try: - name_split = name.split(".") - if len(name_split) > 1: - current = getattr(self.__dict__["original_model"], name_split[0]) - for part in name_split[1:]: # type: ignore[operator] - current = getattr(current, part) - return current - else: - return getattr(self.__dict__["original_model"], name) - except AttributeError: - pass # type: ignore[operator,assignment] - raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") - - def __str__(self) -> str: - """Get a string representation of the bridge. - # type: ignore[operator] - Returns: - A string describing the bridge's components # type: ignore[operator] - """ - lines = ["TransformerBridge:"] - mapping = self.adapter.get_component_mapping() - lines.extend(self._format_component_mapping(mapping, indent=1)) - return "\n".join(lines) - - def enable_compatibility_mode( - self, - disable_warnings: bool = False, - no_processing: bool = False, - fold_ln: bool = True, - center_writing_weights: bool = True, - center_unembed: bool = True, - fold_value_biases: bool = True, - refactor_factored_attn_matrices: bool = False, - ) -> None: - """Apply HookedTransformer-equivalent weight processing and legacy hook compatibility. - - Defaults match HookedTransformer's load-time processing (fold_ln + weight - centering) — required for analyses that reason in HookedTransformer's - post-processed coordinate system: logit lens, direct logit attribution, - residual-stream norms. Also enables legacy hook/component name aliases. - - Args: - disable_warnings: Whether to disable warnings about legacy components/hooks - no_processing: Whether to disable ALL pre-processing steps of the model. - If True, overrides fold_ln, center_writing_weights, and center_unembed to False. - fold_ln: Whether to fold layer norm weights into the subsequent linear layers. - Default: True. Ignored if no_processing=True. - center_writing_weights: Whether to center the writing weights (W_out in attention and MLPs). - Default: True. Ignored if no_processing=True. - center_unembed: Whether to center the unembedding matrix. - Default: True. Ignored if no_processing=True. - fold_value_biases: Whether to fold value biases into output bias. - Default: True. Ignored if no_processing=True. - refactor_factored_attn_matrices: Whether to refactor factored attention matrices. - Default: False. Ignored if no_processing=True. - """ - from transformer_lens.utilities.bridge_components import ( - apply_fn_to_all_components, - ) - - self.compatibility_mode = True - - def set_compatibility_mode(component: Any) -> None: - """Set compatibility mode on a component.""" - component.compatibility_mode = True - component.disable_warnings = disable_warnings - - apply_fn_to_all_components(self, set_compatibility_mode) - self.clear_hook_registry() - try: - if not no_processing: - self.process_weights( - fold_ln=fold_ln, - center_writing_weights=center_writing_weights, - center_unembed=center_unembed, - fold_value_biases=fold_value_biases, - refactor_factored_attn_matrices=refactor_factored_attn_matrices, - ) - finally: - # Re-initialize hooks even on failure so bridge stays usable - self._initialize_hook_registry() - self._setup_hook_compatibility() - self._register_all_aliases_recursive() - - def _setup_hook_compatibility(self) -> None: - """Setup hook compatibility transformations to match HookedTransformer behavior. - - This method sets up hook conversions and wrappers that ensure Bridge hooks - have the same shapes and behavior as HookedTransformer hooks. This includes: - 1. hook_z reshaping from [batch, seq, d_model] to [batch, seq, n_heads, d_head] - 2. Wrapping HF attention forward to inject position embeddings/attention masks - 3. Architecture-specific setup (e.g., rotary embedding references) - - This is called during __init__ and should always be run, regardless of whether - compatibility mode or weight processing is enabled. - - Note: This method is idempotent - can be called multiple times safely. - """ - if hasattr(self.adapter, "setup_hook_compatibility"): - self.adapter.setup_hook_compatibility(self) - elif hasattr(self.adapter, "setup_no_processing_hooks"): - self.adapter.setup_no_processing_hooks(self) - blocks_to_process = [] - if hasattr(self, "blocks"): - blocks_to_process.extend(self.blocks) - if hasattr(self, "encoder_blocks"): - blocks_to_process.extend(self.encoder_blocks) - if hasattr(self, "decoder_blocks"): - blocks_to_process.extend(self.decoder_blocks) - for block in blocks_to_process: - for attn_name in ["attn", "self_attn", "cross_attn"]: - if hasattr(block, attn_name): - attn = getattr(block, attn_name) - if hasattr(attn, "setup_hook_compatibility"): - attn.setup_hook_compatibility() - elif hasattr(attn, "setup_no_processing_hooks"): - attn.setup_no_processing_hooks() - - def process_weights( - self, - verbose: bool = False, - fold_ln: bool = True, - center_writing_weights: bool = True, - center_unembed: bool = True, - fold_value_biases: bool = True, - refactor_factored_attn_matrices: bool = False, - ) -> None: - """Process weights directly using ProcessWeights and architecture adapter. - - This method applies weight processing transformations to improve model interpretability - without requiring a reference HookedTransformer model. Works with all architectures - supported by TransformerBridge, including GPT-OSS and other new models. - - Args: - verbose: If True, print detailed progress messages. Default: False - fold_ln: Fold LayerNorm weights/biases into subsequent layers. Default: True - center_writing_weights: Center weights that write to residual stream. Default: True - center_unembed: Center unembedding weights (translation invariant). Default: True - fold_value_biases: Fold value biases into output bias. Default: True - refactor_factored_attn_matrices: Experimental QK/OV factorization. Default: False - """ - from transformer_lens.weight_processing import ProcessWeights - - if verbose: - print(f"Processing weights for {self.cfg.model_name}...") - - # Soft capping (tanh) is not translation-invariant; centering would change output. - if center_unembed and getattr(self.cfg, "output_logits_soft_cap", -1.0) > 0.0: - import logging - - logging.warning( - "center_unembed=True is incompatible with logit softcapping " - "(output_logits_soft_cap=%.1f). Disabling center_unembed.", - self.cfg.output_logits_soft_cap, - ) - center_unembed = False - - if verbose: - print(" Extracting state dict from existing model...") - state_dict = self.state_dict() - adapter = self.adapter - - # Untie embed/unembed weights (GPT-2) so centering affects only unembed - embed_key = "embed.weight" - unembed_key = "unembed.weight" - - if embed_key in state_dict and unembed_key in state_dict: - # Check if they point to the same tensor (weight tying) - if state_dict[embed_key].data_ptr() == state_dict[unembed_key].data_ptr(): - if verbose: - print(" Breaking weight tying between embed and unembed in state dict...") - # Clone the unembed weight to break the tie - state_dict[unembed_key] = state_dict[unembed_key].clone() - - if adapter and hasattr(adapter, "preprocess_weights"): - adapter._fold_ln_requested = fold_ln # type: ignore[union-attr] - state_dict = adapter.preprocess_weights(state_dict) - - # Use unified ProcessWeights.process_weights() like HookedTransformer does. - # Float32 upcasting for precision is handled centrally in process_weights(). - if verbose: - print(" Processing weights (fold_ln, center_writing_weights, etc.)...") - state_dict = ProcessWeights.process_weights( - state_dict, - self.cfg, - fold_ln=fold_ln, - center_writing_weights=center_writing_weights, - center_unembed=center_unembed, - fold_value_biases=fold_value_biases, - refactor_factored_attn_matrices=refactor_factored_attn_matrices, - adapter=adapter, - ) - - # Normalize HF-prefix keys to TL format for weight routing - import re - - hf_to_tl_prefix = {} - for tl_name, (remote_path, _component) in self.real_components.items(): - if remote_path and remote_path != tl_name: - hf_to_tl_prefix[remote_path] = tl_name - - normalized_state_dict = {} - for key, value in state_dict.items(): - new_key = key - for hf_prefix, tl_prefix in hf_to_tl_prefix.items(): - if key.startswith(hf_prefix + "."): - suffix = key[len(hf_prefix) + 1 :] - new_key = f"{tl_prefix}.{suffix}" - break - normalized_state_dict[new_key] = value - state_dict = normalized_state_dict - - if verbose: - print(" Distributing weights to generalized components...") - ProcessWeights.distribute_weights_to_components( - state_dict=state_dict, - component_mapping=self.real_components, - ) - - def _calculate_loss(self, logits, tokens, loss_per_token=False): - """Calculate cross-entropy loss.""" - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = tokens[..., 1:].contiguous() - loss_fct = torch.nn.CrossEntropyLoss(reduction="none" if loss_per_token else "mean") - flat_logits = shift_logits.view(-1, shift_logits.size(-1)) - flat_labels = shift_labels.view(-1) - loss = loss_fct(flat_logits, flat_labels) - if loss_per_token: - return loss.view(shift_labels.shape) - else: - return loss - - def _extract_hf_weights(self): - """Extract weights from the original HuggingFace model.""" - hf_state_dict = self.state_dict() - for layer_idx in range(self.cfg.n_layers): - combined_qkv_key = f"transformer.h.{layer_idx}.attn.c_attn.weight" - combined_qkv_bias_key = f"transformer.h.{layer_idx}.attn.c_attn.bias" - if combined_qkv_key in hf_state_dict: - separate_keys_to_remove = [ - f"transformer.h.{layer_idx}.attn.q.weight", - f"transformer.h.{layer_idx}.attn.q.bias", - f"transformer.h.{layer_idx}.attn.k.weight", - f"transformer.h.{layer_idx}.attn.k.bias", - f"transformer.h.{layer_idx}.attn.v.weight", - f"transformer.h.{layer_idx}.attn.v.bias", - ] - for key_to_remove in separate_keys_to_remove: - if key_to_remove in hf_state_dict: - del hf_state_dict[key_to_remove] - return hf_state_dict - - def to_tokens( - self, - input: Union[str, List[str]], - prepend_bos: Optional[bool] = None, - padding_side: Optional[str] = None, - move_to_device: bool = True, - truncate: bool = True, - ) -> torch.Tensor: - """Converts a string to a tensor of tokens. - - See the class-level "Tokenization notes" for full ``prepend_bos`` - semantics, the ``default_prepend_bos`` / - ``tokenizer_prepends_bos`` interaction, and the whitespace- - sensitivity gotcha. **Pass ``prepend_bos=False`` whenever you're - tokenizing only part of a prompt.** - - Args: - input: The input to tokenize. - prepend_bos: Overrides ``self.cfg.default_prepend_bos``. Defaults - to ``None`` (use the cfg setting). Pass ``True`` or ``False`` - to override locally. - padding_side: Which side to pad on when tokenizing multiple - strings of different lengths. Defaults to the tokenizer's - ``padding_side``. - move_to_device: Whether to move the result to ``cfg.device``. - truncate: Whether to truncate inputs longer than ``cfg.n_ctx``. - - Returns: - Token tensor of shape ``[batch, pos]``. - """ - assert self.tokenizer is not None, "Cannot use to_tokens without a tokenizer" - if prepend_bos is None: - prepend_bos = getattr(self.cfg, "default_prepend_bos", True) - if padding_side is None: - padding_side = getattr(self.tokenizer, "padding_side", "right") - tokenizer_prepends_bos = getattr(self.cfg, "tokenizer_prepends_bos", True) - if prepend_bos and (not tokenizer_prepends_bos): - input = utils.get_input_with_manually_prepended_bos(self.tokenizer.bos_token, input) - if isinstance(input, str): - input = [input] - tokens = self.tokenizer( - input, - return_tensors="pt", - padding=True, - truncation=truncate, - max_length=self.cfg.n_ctx if truncate else None, - )["input_ids"] - # Strip auto-appended EOS tokens (e.g., OLMo) - if ( - getattr(self.cfg, "tokenizer_appends_eos", False) - and self.tokenizer.eos_token_id is not None - ): - # Remove trailing EOS, keep at least 1 token - while tokens.shape[-1] > 1 and (tokens[:, -1] == self.tokenizer.eos_token_id).all(): - tokens = tokens[:, :-1] - if not prepend_bos and tokenizer_prepends_bos: - tokens = utils.get_tokens_with_bos_removed(self.tokenizer, tokens) - if move_to_device: - tokens = tokens.to(self.cfg.device) - return tokens - - def to_string( - self, tokens: Union[List[int], torch.Tensor, np.ndarray] - ) -> Union[str, List[str]]: - """Convert tokens to string(s). - - Args: - tokens: Tokens to convert - - Returns: - Decoded string(s) - """ - if not isinstance(tokens, torch.Tensor): - tokens = torch.tensor(tokens) - if len(tokens.shape) == 2: - return self.tokenizer.batch_decode(tokens, clean_up_tokenization_spaces=False) - elif len(tokens.shape) <= 1: - return self.tokenizer.decode(tokens, clean_up_tokenization_spaces=False) - else: - raise ValueError(f"Invalid shape passed in: {tokens.shape}") - - def to_str_tokens( - self, - input: Union[str, torch.Tensor, np.ndarray, List], - prepend_bos: Optional[bool] = None, - padding_side: Optional[str] = None, - ) -> Union[List[str], List[List[str]]]: - """Map text or tokens to a list of tokens as strings. - - See the class-level "Tokenization notes" for full ``prepend_bos`` - semantics. **Pass ``prepend_bos=False`` whenever you're tokenizing - only part of a prompt.** When ``input`` is already a tensor or - array, ``prepend_bos`` and ``padding_side`` are ignored. - - Args: - input: A string, list of strings, or tensor/array of token IDs. - prepend_bos: Overrides ``self.cfg.default_prepend_bos``. Only - applies when ``input`` is a string. Defaults to ``None`` - (use the cfg setting). - padding_side: Which side to pad on. Only applies when ``input`` - is a string. - - Returns: - List of token strings. - """ - if isinstance(input, list): - return cast( - List[List[str]], - [self.to_str_tokens(item, prepend_bos, padding_side) for item in input], - ) - elif isinstance(input, str): - tokens = self.to_tokens(input, prepend_bos=prepend_bos, padding_side=padding_side)[0] - elif isinstance(input, torch.Tensor): - tokens = input.squeeze() - if tokens.dim() == 0: - tokens = tokens.unsqueeze(0) - assert ( - tokens.dim() == 1 - ), f"Invalid tokens input to to_str_tokens, has shape: {tokens.shape}" - elif isinstance(input, np.ndarray): - tokens_np = input.squeeze() - if tokens_np.ndim == 0: - tokens_np = np.expand_dims(tokens_np, axis=0) - assert ( - tokens_np.ndim == 1 - ), f"Invalid tokens input to to_str_tokens, has shape: {tokens_np.shape}" - tokens = torch.tensor(tokens_np) - else: - raise ValueError(f"Invalid input type to to_str_tokens: {type(input)}") - # v5 compat: wrap each token so batch_decode decodes them individually - tokens_list = [[int(t)] for t in tokens.tolist()] - str_tokens = self.tokenizer.batch_decode(tokens_list, clean_up_tokenization_spaces=False) - return str_tokens - - def to_single_token(self, string: str) -> int: - """Map a string that makes up a single token to the id for that token. - - Args: - string: The string to convert - - Returns: - Token ID - - Raises: - AssertionError: If string is not a single token - """ - token = self.to_tokens(string, prepend_bos=False).squeeze() - if token.numel() != 1: - raise AssertionError(f"Input string: {string} is not a single token!") - return int(token.item()) - - def get_token_position( - self, - single_token: Union[str, int], - input: Union[str, torch.Tensor], - mode="first", - prepend_bos: Optional[Union[bool, None]] = None, - padding_side: Optional[Union[Literal["left", "right"], None]] = None, - ): - """Get the position of a single_token in a string or sequence of tokens. - - Raises an error if the token is not present. - - When ``input`` is a string it's tokenized internally — see the - class-level "Tokenization notes" for ``prepend_bos`` semantics. - Off-by-one position errors usually mean ``prepend_bos`` is on - when it shouldn't be (or vice versa); pass ``prepend_bos=False`` - when ``input`` is a fragment of a larger prompt. - - Args: - single_token (Union[str, int]): The token to search for. Can - be a token index, or a string (but the string must correspond to a single token). - input (Union[str, torch.Tensor]): The sequence to - search in. Can be a string or a rank 1 tensor of tokens or a rank 2 tensor of tokens - with a dummy batch dimension. - mode (str, optional): If there are multiple matches, which match to return. Supports - "first" or "last". Defaults to "first". - prepend_bos (bool, optional): Overrides ``self.cfg.default_prepend_bos``. Only - applies when ``input`` is a string. Defaults to ``None`` (use the cfg setting). - padding_side (Union[Literal["left", "right"], None], optional): Specifies which - side to pad when tokenizing multiple strings of different lengths. - """ - if isinstance(input, str): - tokens = self.to_tokens(input, prepend_bos=prepend_bos, padding_side=padding_side) - else: - tokens = input - if len(tokens.shape) == 2: - assert ( - tokens.shape[0] == 1 - ), f"If tokens are rank two, they must have shape [1, seq_len], not {tokens.shape}" - tokens = tokens[0] - if isinstance(single_token, str): - single_token = self.to_single_token(single_token) - elif isinstance(single_token, torch.Tensor): - single_token = single_token.item() - indices = torch.arange(len(tokens), device=tokens.device)[tokens == single_token] - assert len(indices) > 0, "The token does not occur in the prompt" - if mode == "first": - return indices[0].item() - elif mode == "last": - return indices[-1].item() - else: - raise ValueError(f"mode must be 'first' or 'last', not {mode}") - - def to_single_str_token(self, int_token: int) -> str: - """Get the single token corresponding to an int in string form. - - Args: - int_token: The token ID - - Returns: - The token string - """ - assert isinstance(int_token, int) - token = self.to_str_tokens(torch.tensor([int_token])) - if isinstance(token, list) and len(token) == 1: - return str(token[0]) - raise AssertionError("Expected a single string token.") - - def blocks_with(self, submodule: str) -> List[Tuple[int, "GeneralizedComponent"]]: - """Return (index, block) pairs for blocks with the named bridged submodule. - - Checks _modules (not hasattr) so HF-internal attrs don't match. - Use instead of assuming blocks[0] is representative on hybrid models. - """ - if not hasattr(self, "blocks"): - return [] - return [(i, block) for i, block in enumerate(self.blocks) if submodule in block._modules] - - def stack_params_for( - self, submodule: str, attr_path: str, reshape_fn: Optional[Callable] = None - ) -> Tuple[List[int], torch.Tensor]: - """Stack a parameter across matching blocks only. Returns (layer_indices, tensor). - - Use for hybrid models where not all blocks have the submodule. - """ - matching = self.blocks_with(submodule) - if not matching: - raise ValueError( - f"No blocks have submodule '{submodule}'. " - f"Available submodules can be checked with blocks_with()." - ) - indices: List[int] = [] - weights: List[torch.Tensor] = [] - for idx, block in matching: - w = _resolve_attr_path(block, attr_path) - if reshape_fn is not None: - w = reshape_fn(w) - weights.append(w) - indices.append(idx) - return indices, torch.stack(weights, dim=0) - - def _stack_block_params( - self, attr_path: str, reshape_fn: Optional[Callable] = None - ) -> torch.Tensor: - """Stack a parameter across all blocks; falls back to matching-only on hybrids. - - On hybrid models, logs a warning about index mapping and returns only - blocks that have the submodule. First path segment is checked against - _modules; deeper segments resolve via getattr (intentional — W_Q etc. - are exposed via __getattr__ delegation). - """ - first_attr = attr_path.split(".")[0] - matching_blocks = [ - (i, block) for i, block in enumerate(self.blocks) if first_attr in block._modules - ] - - if len(matching_blocks) == 0: - raise AttributeError( - f"No blocks have submodule '{first_attr}'. " - f"Use bridge.blocks_with('{first_attr}') to check availability." - ) - - if len(matching_blocks) < len(self.blocks): - indices = [i for i, _ in matching_blocks] - logging.warning( - "Hybrid model: only %d/%d blocks have '%s'. Returning stacked tensor " - "for layers %s only. Tensor index i corresponds to original layer " - "indices[i], not layer i. For explicit index mapping, use " - "bridge.stack_params_for('%s', '%s').", - len(matching_blocks), - len(self.blocks), - first_attr, - indices, - first_attr, - attr_path, - ) - - weights: List[torch.Tensor] = [] - for _, block in matching_blocks: - w = _resolve_attr_path(block, attr_path) - if reshape_fn is not None: - w = reshape_fn(w) - weights.append(w) - # Under a device_map split, per-block tensors live on different devices. - # torch.stack requires a common device; gather onto cfg.device (the embedding / - # input device — a natural "home" for cross-layer reductions). - if getattr(self.cfg, "n_devices", 1) > 1 and weights and self.cfg.device is not None: - target_device = torch.device(self.cfg.device) - weights = [w.to(target_device) for w in weights] - return torch.stack(weights, dim=0) - - def _reshape_qkv(self, w: torch.Tensor) -> torch.Tensor: - """Reshape 2D [d_model, d_model] QKV weight to 3D [n_heads, d_model, d_head].""" - if w.shape == (self.cfg.d_model, self.cfg.d_model): - d_head = self.cfg.d_model // self.cfg.n_heads - return w.reshape(self.cfg.n_heads, self.cfg.d_model, d_head) - return w - - def _reshape_o(self, w: torch.Tensor) -> torch.Tensor: - """Reshape 2D [d_model, d_model] O weight to 3D [n_heads, d_head, d_model].""" - if w.shape == (self.cfg.d_model, self.cfg.d_model): - d_head = self.cfg.d_model // self.cfg.n_heads - return w.reshape(self.cfg.n_heads, d_head, self.cfg.d_model) - return w - - @property - def W_K(self) -> torch.Tensor: - """Stack the key weights across all layers.""" - return self._stack_block_params("attn.W_K", self._reshape_qkv) - - @property - def W_Q(self) -> torch.Tensor: - """Stack the query weights across all layers.""" - return self._stack_block_params("attn.W_Q", self._reshape_qkv) - - @property - def W_V(self) -> torch.Tensor: - """Stack the value weights across all layers.""" - return self._stack_block_params("attn.W_V", self._reshape_qkv) - - @property - def W_O(self) -> torch.Tensor: - """Stack the attn output weights across all layers.""" - return self._stack_block_params("attn.W_O", self._reshape_o) - - @property - def W_in(self) -> torch.Tensor: - """Stack the MLP input weights across all layers.""" - return self._stack_block_params("mlp.W_in") - - @property - def W_gate(self) -> Union[torch.Tensor, None]: - """Stack the MLP gate weights across all layers (gated MLPs only).""" - if getattr(self.cfg, "gated_mlp", False): - return self._stack_block_params("mlp.W_gate") - return None - - @property - def W_out(self) -> torch.Tensor: - """Stack the MLP output weights across all layers.""" - return self._stack_block_params("mlp.W_out") - - @property - def b_K(self) -> torch.Tensor: - """Stack the key biases across all layers.""" - return self._stack_block_params("attn.b_K") - - @property - def b_Q(self) -> torch.Tensor: - """Stack the query biases across all layers.""" - return self._stack_block_params("attn.b_Q") - - @property - def b_V(self) -> torch.Tensor: - """Stack the value biases across all layers.""" - return self._stack_block_params("attn.b_V") - - @property - def b_O(self) -> torch.Tensor: - """Stack the attn output biases across all layers.""" - return self._stack_block_params("attn.b_O") - - @property - def b_in(self) -> torch.Tensor: - """Stack the MLP input biases across all layers.""" - return self._stack_block_params("mlp.b_in") - - @property - def b_out(self) -> torch.Tensor: - """Stack the MLP output biases across all layers.""" - return self._stack_block_params("mlp.b_out") - - @property - def W_U(self) -> torch.Tensor: - """Unembedding matrix (d_model, d_vocab). Maps residual stream to logits.""" - return self.unembed.W_U - - @property - def b_U(self) -> torch.Tensor: - """Unembedding bias (d_vocab).""" - return self.unembed.b_U - - @property - def W_E(self) -> torch.Tensor: - """Token embedding matrix (d_vocab, d_model).""" - return self.embed.W_E - - @property - def QK(self): - """QK circuit. On hybrids, returns attn layers only (with warning). See QK_for_attn_layers().""" - return FactoredMatrix(self.W_Q, self.W_K.transpose(-2, -1)) - - @property - def OV(self): - """OV circuit. On hybrids, returns attn layers only (with warning). See OV_for_attn_layers().""" - return FactoredMatrix(self.W_V, self.W_O) - - def QK_for_attn_layers(self) -> Tuple[List[int], FactoredMatrix]: - """QK circuit for attention layers only. Returns (layer_indices, FactoredMatrix).""" - q_indices, W_Q = self.stack_params_for("attn", "attn.W_Q", self._reshape_qkv) - _, W_K = self.stack_params_for("attn", "attn.W_K", self._reshape_qkv) - return q_indices, FactoredMatrix(W_Q, W_K.transpose(-2, -1)) - - def OV_for_attn_layers(self) -> Tuple[List[int], FactoredMatrix]: - """OV circuit for attention layers only. Returns (layer_indices, FactoredMatrix).""" - v_indices, W_V = self.stack_params_for("attn", "attn.W_V", self._reshape_qkv) - _, W_O = self.stack_params_for("attn", "attn.W_O", self._reshape_o) - return v_indices, FactoredMatrix(W_V, W_O) - - # ------------------------------------------------------------------ - # Mechanistic interpretability analysis methods - # ------------------------------------------------------------------ - - def tokens_to_residual_directions( - self, - tokens: Union[str, int, torch.Tensor], - ) -> torch.Tensor: - """Map tokens to their unembedding vectors (residual stream directions). - - Returns the columns of W_U corresponding to the given tokens — i.e. the - directions in the residual stream that the model dots with to produce the - logit for each token. - - WARNING: If you use this without folding in LayerNorm (compatibility mode), - the results will be misleading because LN weights change the unembed map. - - Args: - tokens: A single token (str, int, or scalar tensor), a 1-D tensor of - token IDs, or a 2-D batch of token IDs. - - Returns: - Tensor of unembedding vectors with shape matching the input token shape - plus a trailing d_model dimension. - """ - if isinstance(tokens, torch.Tensor) and tokens.numel() > 1: - residual_directions = self.W_U[:, tokens] - residual_directions = einops.rearrange( - residual_directions, "d_model ... -> ... d_model" - ) - return residual_directions - else: - if isinstance(tokens, str): - token = self.to_single_token(tokens) - elif isinstance(tokens, int): - token = tokens - elif isinstance(tokens, torch.Tensor) and tokens.numel() == 1: - token = int(tokens.item()) - else: - raise ValueError(f"Invalid token type: {type(tokens)}") - residual_direction = self.W_U[:, token] - return residual_direction - - # Variant → attr paths for the output bias that feeds the residual stream. - _VARIANT_OUTPUT_BIAS_ATTRS: Dict[str, tuple] = { - "attn": ("b_O",), - "linear_attn": ("out_proj.bias",), - "mamba": ("out_proj.bias",), - "mixer": ("out_proj.bias",), - "ssm": ("out_proj.bias",), - } - - def _get_block_variant_bias(self, block: "GeneralizedComponent") -> Optional[torch.Tensor]: - """Return the output bias from this block's variant submodule, or None.""" - for name in VARIANT_SUBMODULE_NAMES: - if name not in block._modules: - continue - variant = block._modules[name] - for attr_path in self._VARIANT_OUTPUT_BIAS_ATTRS.get(name, ()): - obj = variant - try: - for attr in attr_path.split("."): - obj = getattr(obj, attr) - except AttributeError: - continue - if obj is not None and isinstance(obj, torch.Tensor): - return obj - return None - - def accumulated_bias( - self, - layer: int, - mlp_input: bool = False, - include_mlp_biases: bool = True, - ) -> torch.Tensor: - """Sum of variant + MLP output biases through the residual stream up to `layer`. - - Includes all layer types (attn, SSM, linear-attn). Set mlp_input=True - to include the variant bias of the target layer itself. - """ - accumulated = torch.zeros(self.cfg.d_model, device=self.cfg.device) - for i in range(layer): - block = self.blocks[i] - b_O = self._get_block_variant_bias(block) - if b_O is not None: - accumulated = accumulated + b_O.to(accumulated.device) - if include_mlp_biases and "mlp" in block._modules: - b_out = getattr(block.mlp, "b_out", None) - if b_out is not None: - accumulated = accumulated + b_out.to(accumulated.device) - if mlp_input: - assert layer < self.cfg.n_layers, "Cannot include attn_bias from beyond the final layer" - block = self.blocks[layer] - b_O = self._get_block_variant_bias(block) - if b_O is not None: - accumulated = accumulated + b_O.to(accumulated.device) - return accumulated - - def all_composition_scores(self, mode: str) -> CompositionScores: - """Composition scores for all attention head pairs. Returns CompositionScores. - - See https://transformer-circuits.pub/2021/framework/index.html - On hybrid models, only attention layers are included; layer_indices - maps tensor position i to original layer number. - """ - attn_blocks = self.blocks_with("attn") - if not attn_blocks: - raise ValueError("No attention layers found — cannot compute composition scores.") - - indices = [idx for idx, _ in attn_blocks] - blocks_list = [block for _, block in attn_blocks] - - def _stack(attr_path: str, reshape_fn: Optional[Callable] = None) -> torch.Tensor: - weights: List[torch.Tensor] = [] - for block in blocks_list: - w = _resolve_attr_path(block, attr_path) - if reshape_fn is not None: - w = reshape_fn(w) - weights.append(w) - # See _stack_block_params: gather per-block tensors onto cfg.device when split. - if getattr(self.cfg, "n_devices", 1) > 1 and weights and self.cfg.device is not None: - target_device = torch.device(self.cfg.device) - weights = [w.to(target_device) for w in weights] - return torch.stack(weights, dim=0) - - W_V = _stack("attn.W_V", self._reshape_qkv) - W_O = _stack("attn.W_O", self._reshape_o) - left = FactoredMatrix(W_V, W_O) - - if mode == "Q": - W_Q = _stack("attn.W_Q", self._reshape_qkv) - W_K = _stack("attn.W_K", self._reshape_qkv) - right = FactoredMatrix(W_Q, W_K.transpose(-2, -1)) - elif mode == "K": - W_Q = _stack("attn.W_Q", self._reshape_qkv) - W_K = _stack("attn.W_K", self._reshape_qkv) - right = FactoredMatrix(W_Q, W_K.transpose(-2, -1)).T - elif mode == "V": - right = left - else: - raise ValueError(f"mode must be one of ['Q', 'K', 'V'] not {mode}") - - scores = utils.composition_scores(left, right, broadcast_dims=True) - n_attn = len(indices) - idx_tensor = torch.arange(n_attn, device=self.cfg.device) - mask = idx_tensor[:, None, None, None] < idx_tensor[None, None, :, None] - scores = torch.where(mask, scores, torch.zeros_like(scores)) - - labels = [f"L{l}H{h}" for l in indices for h in range(self.cfg.n_heads)] - return CompositionScores(scores=scores, layer_indices=indices, head_labels=labels) - - def composition_layer_indices(self) -> List[int]: - """Original layer indices for attention layers (maps composition score positions).""" - return [idx for idx, _ in self.blocks_with("attn")] - - def block_hooks(self, layer_idx: int) -> List[str]: - """Sorted hook names available on block `layer_idx` (block-relative paths).""" - prefix = f"blocks.{layer_idx}." - return sorted(name[len(prefix) :] for name in self.hook_dict if name.startswith(prefix)) - - def block_submodules(self, layer_idx: int) -> List[str]: - """Return bridged submodule names on block `layer_idx`.""" - block = self.blocks[layer_idx] - return [name for name in block._modules if name not in _BLOCK_INTERNAL_MODULES] - - def layer_types(self) -> List[str]: - """Per-block type labels, e.g. ["attn+mlp", "ssm+mlp", ...]. Deterministic order.""" - types = [] - for block in self.blocks: - variants = [n for n in VARIANT_SUBMODULE_NAMES if n in block._modules] - universals = sorted( - n - for n in block._modules - if n not in _VARIANT_SUBMODULE_SET - and n not in _BLOCK_INTERNAL_MODULES - and not n.startswith(_NORM_PREFIXES) - ) - parts = variants + universals - types.append("+".join(parts) if parts else "unknown") - return types - - @property - def all_head_labels(self) -> list[str]: - """Human-readable labels for all attention heads, e.g. ['L0H0', 'L0H1', ...].""" - return [f"L{l}H{h}" for l in range(self.cfg.n_layers) for h in range(self.cfg.n_heads)] - - @property - def attn_head_labels(self) -> list[str]: - """Head labels for attention layers only — matches all_composition_scores() dims.""" - return [ - f"L{l}H{h}" for l in self.composition_layer_indices() for h in range(self.cfg.n_heads) - ] - - def parameters(self, recurse: bool = True) -> Iterator[nn.Parameter]: - """Returns parameters following standard PyTorch semantics. - - This method delegates to the underlying HuggingFace model's parameters(). - For TransformerLens-style parameter generator, use tl_parameters() instead. - - Args: - recurse: If True, yields parameters of this module and all submodules - - Returns: - Iterator of nn.Parameter objects - """ - return self.original_model.parameters(recurse=recurse) - - def named_parameters( - self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True - ) -> Iterator[tuple[str, nn.Parameter]]: - """Returns named parameters following standard PyTorch semantics. - - This method delegates to the underlying HuggingFace model's named_parameters(). - For TransformerLens-style generator, use tl_named_parameters() instead. - - Args: - prefix: Prefix to prepend to all parameter names - recurse: If True, yields parameters of this module and all submodules - remove_duplicate: If True, removes duplicate parameters - - Returns: - Iterator of (name, parameter) tuples - """ - return self.original_model.named_parameters(prefix, recurse, remove_duplicate) - - def tl_parameters(self) -> dict[str, torch.Tensor]: - """Returns TransformerLens-style parameter dictionary. - - Parameter names follow TransformerLens conventions (e.g., 'blocks.0.attn.W_Q') and may - include processed weights (non-leaf tensors). This format is expected by SVDInterpreter - among other analysis tools. - - Returns: - Dictionary mapping TransformerLens parameter names to tensors - - Example: - >>> bridge = TransformerBridge.boot_transformers("gpt2") - >>> tl_params = bridge.tl_parameters() - >>> W_Q = tl_params["blocks.0.attn.W_Q"] # Shape: [n_heads, d_model, d_head] - """ - return self.get_params() - - def tl_named_parameters(self) -> Iterator[tuple[str, torch.Tensor]]: - """Returns iterator of TransformerLens-style named parameters. - - This provides the same parameters as tl_parameters() but as an iterator - for consistency with PyTorch's named_parameters() API pattern. - - Returns: - Iterator of (name, tensor) tuples with TransformerLens naming conventions - - Example: - >>> bridge = TransformerBridge.boot_transformers("gpt2") - >>> for name, param in bridge.tl_named_parameters(): - ... if "attn.W_Q" in name: - ... print(f"{name}: {param.shape}") # doctest: +ELLIPSIS - blocks.0.attn.W_Q: torch.Size([12, 768, 64]) - ... - """ - return iter(self.get_params().items()) - - def forward( - self, - input: Union[str, List[str], torch.Tensor], - return_type: Optional[str] = "logits", - loss_per_token: bool = False, - prepend_bos: Optional[bool] = None, - padding_side: Optional[str] = None, - attention_mask: Optional[torch.Tensor] = None, - start_at_layer: Optional[int] = None, - stop_at_layer: Optional[int] = None, - pixel_values: Optional[torch.Tensor] = None, - input_values: Optional[torch.Tensor] = None, - **kwargs, - ) -> Any: - """Forward pass through the model. - - Args: - input: Input to the model - return_type: Type of output to return ('logits', 'loss', 'both', 'predictions', None) - loss_per_token: Whether to return loss per token - prepend_bos: Whether to prepend BOS token - padding_side: Which side to pad on - start_at_layer: Not implemented in TransformerBridge. The bridge delegates - to HuggingFace's model.forward() which owns the layer iteration loop, - making start_at_layer infeasible without monkey-patching HF internals - (fragile across HF versions) or exception-based layer skipping (corrupts - model state). Raises NotImplementedError if a non-None value is passed. - stop_at_layer: Layer to stop forward pass at - pixel_values: Optional image tensor for multimodal models (e.g., LLaVA, Gemma3). - The tensor is passed directly to the underlying HuggingFace model. - Only valid when cfg.is_multimodal is True. - input_values: Optional audio waveform tensor for audio models (e.g., HuBERT). - The tensor is passed directly to the underlying HuggingFace model. - Only valid when cfg.is_audio_model is True. - **kwargs: Additional arguments passed to model - - Returns: - Model output based on return_type - """ - - if start_at_layer is not None: - raise NotImplementedError( - "start_at_layer is not supported in TransformerBridge. " - "The bridge delegates to HuggingFace's model.forward() which controls " - "the layer iteration loop. See the TransformerBridge review plan for a " - "detailed analysis of implementation approaches and their tradeoffs." - ) - - # Set stop_at_layer flag on all blocks if requested - if stop_at_layer is not None and hasattr(self, "blocks"): - for block in self.blocks: - block._stop_at_layer_idx = stop_at_layer - - # Map HookedEncoderDecoder-style kwargs to HF-compatible names - if "decoder_input" in kwargs: - kwargs["decoder_input_ids"] = kwargs.pop("decoder_input") - if "one_zero_attention_mask" in kwargs: - if attention_mask is None: - attention_mask = kwargs.pop("one_zero_attention_mask") - else: - kwargs.pop("one_zero_attention_mask") - - # Detect batched list input that will need padding. For this case we force - # left-padding internally and auto-compute attention_mask + position_ids - # (unless the caller passed them explicitly) so pad tokens don't contaminate - # attention or position embeddings. - _is_batched_list = ( - isinstance(input, list) - and len(input) > 1 - and not getattr(self.cfg, "is_audio_model", False) - ) - - try: - if isinstance(input, (str, list)): - if getattr(self.cfg, "is_audio_model", False): - raise ValueError( - "Audio models require tensor input (raw waveform), not text. " - "Pass a torch.Tensor or use the input_values parameter." - ) - if _is_batched_list and padding_side is None: - # Force left-padding so real tokens are flush-right. - _orig_padding_side = self.tokenizer.padding_side - self.tokenizer.padding_side = "left" - try: - input_ids = self.to_tokens( - input, prepend_bos=prepend_bos, padding_side=padding_side - ) - finally: - self.tokenizer.padding_side = _orig_padding_side - else: - input_ids = self.to_tokens( - input, prepend_bos=prepend_bos, padding_side=padding_side - ) - else: - input_ids = input - # Promote 1D integer token tensors to 2D [batch=1, seq] to match - # HookedTransformer's contract. Float tensors (inputs_embeds, - # audio waveforms) are passed through unchanged. - if ( - isinstance(input_ids, torch.Tensor) - and input_ids.ndim == 1 - and not input_ids.is_floating_point() - ): - input_ids = input_ids.unsqueeze(0) - - # Detect inputs_embeds: if the tensor is floating point, it's pre-computed - # embeddings (e.g., from multimodal models) rather than token IDs. - _is_inputs_embeds = ( - isinstance(input_ids, torch.Tensor) and input_ids.is_floating_point() - ) - - # Auto-compute attention_mask + position_ids for batched list input - # when the caller didn't supply them. Matches HF generation convention. - if ( - _is_batched_list - and attention_mask is None - and self.tokenizer is not None - and self.tokenizer.pad_token_id is not None - and not _is_inputs_embeds - ): - _prev_side = self.tokenizer.padding_side - self.tokenizer.padding_side = "left" - try: - attention_mask = utils.get_attention_mask( - self.tokenizer, - input_ids, - prepend_bos=getattr(self.cfg, "default_prepend_bos", True), - ).to(self.cfg.device) - finally: - self.tokenizer.padding_side = _prev_side - if "position_ids" not in kwargs: - position_ids = attention_mask.long().cumsum(-1) - 1 - position_ids.masked_fill_(attention_mask == 0, 1) - kwargs["position_ids"] = position_ids - - if attention_mask is not None: - kwargs["attention_mask"] = attention_mask - if kwargs.pop("use_past_kv_cache", False) or kwargs.get("use_cache", False): - kwargs["use_cache"] = True - # Auto-generate decoder_input_ids for encoder-decoder models - if ( - "decoder_input_ids" not in kwargs - and hasattr(self.original_model, "config") - and getattr(self.original_model.config, "is_encoder_decoder", False) - ): - decoder_start_token_id = getattr( - self.original_model.config, "decoder_start_token_id", None - ) - if decoder_start_token_id is not None: - shifted = input_ids[:, :-1] - start_tokens = torch.full( - (input_ids.shape[0], 1), - decoder_start_token_id, - dtype=input_ids.dtype, - device=input_ids.device, - ) - kwargs["decoder_input_ids"] = torch.cat([start_tokens, shifted], dim=1) - else: - kwargs["decoder_input_ids"] = input_ids - - # Tell PosEmbedBridge to expand batch=1 position_ids to full batch. - if hasattr(self, "pos_embed"): - self.pos_embed._current_batch_size = input_ids.shape[0] - - # Handle pixel_values for multimodal models - if pixel_values is not None: - if not getattr(self.cfg, "is_multimodal", False): - raise ValueError( - "pixel_values can only be passed to multimodal models " - "(cfg.is_multimodal must be True)" - ) - kwargs["pixel_values"] = pixel_values - - # Handle input_values for audio models - if input_values is not None: - if not getattr(self.cfg, "is_audio_model", False): - raise ValueError( - "input_values can only be passed to audio models " - "(cfg.is_audio_model must be True)" - ) - kwargs["input_values"] = input_values - - # Audio models use input_values (waveform), not input_ids - if getattr(self.cfg, "is_audio_model", False): - if input_values is not None: - output = self.original_model(**kwargs) - elif isinstance(input, torch.Tensor): - kwargs["input_values"] = input - output = self.original_model(**kwargs) - else: - raise ValueError( - "Audio models require tensor input (raw waveform). " - "Pass a torch.Tensor or use input_values parameter." - ) - elif _is_inputs_embeds: - output = self.original_model(inputs_embeds=input_ids, **kwargs) - else: - output = self.original_model(input_ids, **kwargs) - # Stash only the cache object (not the full output) for generate(). - if getattr(self, "_capture_hf_cache", False): - self._last_hf_cache = getattr(output, "past_key_values", None) - if hasattr(output, "logits"): - logits = output.logits - elif isinstance(output, tuple) and len(output) > 0: - logits = output[0] - else: - logits = output - if return_type == "logits": - return logits - elif return_type == "loss": - if getattr(self.cfg, "is_audio_model", False): - raise ValueError( - "Audio models do not support return_type='loss'. " - "CTC loss requires aligned frame-level labels." - ) - if _is_inputs_embeds: - raise ValueError( - "Cannot compute loss with inputs_embeds — token IDs required for labels." - ) - # Always use self.loss_fn for consistency with HT's formula - # (log_softmax + gather). HF's output.loss uses F.cross_entropy - # which gives different results in bfloat16. - assert isinstance( - logits, torch.Tensor - ), f"Expected logits tensor, got {type(logits)}" - return self.loss_fn(logits, input_ids, per_token=loss_per_token) - elif return_type == "both": - if getattr(self.cfg, "is_audio_model", False): - raise ValueError( - "Audio models do not support return_type='both'. " - "CTC loss requires aligned frame-level labels." - ) - if _is_inputs_embeds: - raise ValueError( - "Cannot compute loss with inputs_embeds — token IDs required for labels." - ) - assert isinstance( - logits, torch.Tensor - ), f"Expected logits tensor, got {type(logits)}" - loss = self.loss_fn(logits, input_ids, per_token=loss_per_token) - return (logits, loss) - elif return_type == "predictions": - assert ( - self.tokenizer is not None - ), "Must have a tokenizer to use return_type='predictions'" - if logits.shape[-1] == 2: - # Next Sentence Prediction — 2-class output - logprobs = logits.log_softmax(dim=-1) - predictions = [ - "The sentences are sequential", - "The sentences are NOT sequential", - ] - return predictions[logprobs.argmax(dim=-1).item()] - else: - # Masked Language Modeling — decode [MASK] tokens - logprobs = logits[input_ids == self.tokenizer.mask_token_id].log_softmax(dim=-1) - predictions = self.tokenizer.decode(logprobs.argmax(dim=-1)) - if " " in predictions: - predictions = predictions.split(" ") - predictions = [f"Prediction {i}: {p}" for i, p in enumerate(predictions)] - return predictions - elif return_type is None: - return None - else: - raise ValueError(f"Invalid return_type: {return_type}") - except StopAtLayerException as e: - # Execution stopped at the requested layer - return e.layer_output - finally: - # Clean up state that may be inconsistent after StopAtLayerException - if stop_at_layer is not None and hasattr(self, "blocks"): - # Reset the stop flag on all blocks - for block in self.blocks: - block._stop_at_layer_idx = None - - # Clear any stale KV cache — layers after the stop point didn't - # execute, so the cache is incomplete and would corrupt subsequent - # generate() calls that expect a full cache. - if hasattr(self, "_last_hf_cache"): - del self._last_hf_cache - - def get_hook_point(self, hook_name: str) -> Optional[HookPoint]: - """Get a hook point by name from the bridge's hook system.""" - if hook_name in self._hook_registry: - return self._hook_registry[hook_name] - try: - parts = hook_name.split(".") - current = self - for part in parts: - current = getattr(current, part) - if isinstance(current, HookPoint): - return current - except AttributeError: - pass - return None +Historically, :class:`TransformerBridge` lived in this module. The class was +split into three (``BridgeCore`` framework-agnostic parent, ``TransformerBridge`` +torch-backed, ``RemoteBridge`` non-torch) across their own files; this shim +preserves the original import path: - def loss_fn( - self, - logits: torch.Tensor, - tokens: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - per_token: bool = False, - ) -> torch.Tensor: - """Calculate cross-entropy loss. - - Uses the same formula as HookedTransformer (log_softmax + gather) to ensure - numerically identical results when logits match. - - Args: - logits: Model logits - tokens: Target tokens - attention_mask: Optional attention mask for padding - per_token: Whether to return per-token loss - - Returns: - Loss tensor - """ - if tokens.device != logits.device: - tokens = tokens.to(logits.device) - return lm_cross_entropy_loss(logits, tokens, attention_mask, per_token) - - @overload - def run_with_cache( - self, - input: Union[str, List[str], torch.Tensor], - return_cache_object: Literal[True] = True, - remove_batch_dim: bool = False, - **kwargs, - ) -> Tuple[Any, ActivationCache]: - """Run with cache - placeholder implementation.""" - pass - - @overload - def run_with_cache( - self, - input: Union[str, List[str], torch.Tensor], - return_cache_object: Literal[False], - remove_batch_dim: bool = False, - **kwargs, - ) -> Tuple[Any, Dict[str, torch.Tensor]]: - """Run with cache - placeholder implementation.""" - pass - - def run_with_cache( - self, - input: Union[str, List[str], torch.Tensor], - return_cache_object: bool = True, - remove_batch_dim: bool = False, - names_filter: Optional[Union[str, List[str], Callable[[str], bool]]] = None, - stop_at_layer: Optional[int] = None, - **kwargs, - ) -> Tuple[Any, Union[ActivationCache, Dict[str, torch.Tensor]]]: - """Run the model and cache all activations. - - Args: - input: Input to the model - return_cache_object: Whether to return ActivationCache object - remove_batch_dim: Whether to remove batch dimension - names_filter: Filter for which activations to cache (str, list of str, or callable) - stop_at_layer: Layer to stop forward pass at (uses StopAtLayerException; cleans up KV cache on stop) - **kwargs: Additional arguments - # type: ignore[name-defined] - Returns: - Tuple of (output, cache) - """ - aliases = build_alias_to_canonical_map(self.hook_dict) - - def create_names_filter_fn(filter_input): - if filter_input is None: - return lambda name: True - elif isinstance(filter_input, str): - mapped_name = aliases.get(filter_input, None) - if mapped_name: - return lambda name: name == mapped_name or name == filter_input - else: - return lambda name: name == filter_input - elif isinstance(filter_input, list): - mapped_list = [] - for item in filter_input: - mapped_list.append(item) - mapped_name = aliases.get(item, None) - if mapped_name: - mapped_list.append(mapped_name) - return lambda name: name in mapped_list - elif callable(filter_input): - return filter_input - else: - raise ValueError("names_filter must be a string, list of strings, or callable") - - names_filter_fn = create_names_filter_fn(names_filter) - cache: Dict[str, torch.Tensor] = {} - hooks: List[Tuple[HookPoint, str]] = [] - visited: set[int] = set() - - # None → no-op .to(None), tensors stay on their current device. - cache_device = kwargs.pop("device", None) - - def make_cache_hook(name: str): - def cache_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: - if tensor is None: - cache[name] = None - elif isinstance(tensor, torch.Tensor): - cache[name] = tensor.detach().to(cache_device) - elif isinstance(tensor, tuple): - if len(tensor) > 0 and isinstance(tensor[0], torch.Tensor): - cache[name] = tensor[0].detach().to(cache_device) - else: - pass - else: - try: - if hasattr(tensor, "detach"): - cache[name] = tensor.detach().to(cache_device) - except: - pass - return tensor - - return cache_hook - - hook_dict = self.hook_dict - effective_stop_layer = None - if stop_at_layer is not None and hasattr(self, "blocks"): - if stop_at_layer < 0: - effective_stop_layer = len(self.blocks) + stop_at_layer - else: - effective_stop_layer = stop_at_layer - for hook_name, hook in hook_dict.items(): - if names_filter_fn(hook_name): - if effective_stop_layer is not None: - if hook_name.startswith("blocks."): - try: - layer_num = int(hook_name.split(".")[1]) - if layer_num >= effective_stop_layer: - continue - except (IndexError, ValueError): - pass - hooks.append((hook, hook_name)) - for hp, name in hooks: - hp.add_hook(make_cache_hook(name)) - processed_args = [input] - if processed_args and isinstance(processed_args[0], str): - assert self.tokenizer is not None, "Tokenizer must be set to pass string input." - input_ids = self.to_tokens(processed_args[0]) - input_ids = input_ids.to(next(self.original_model.parameters()).device) - kwargs["input_ids"] = input_ids - processed_args = processed_args[1:] - elif "input" in kwargs and isinstance(kwargs["input"], str): - assert self.tokenizer is not None, "Tokenizer must be set to pass string input." - input_ids = self.to_tokens(kwargs["input"]) - input_ids = input_ids.to(next(self.original_model.parameters()).device) - kwargs["input_ids"] = input_ids - del kwargs["input"] - if stop_at_layer is not None and hasattr(self, "blocks"): - if stop_at_layer < 0: - stop_at_layer = len(self.blocks) + stop_at_layer - last_layer_to_process = stop_at_layer - 1 - - def stop_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: - raise StopAtLayerException(tensor) - - if stop_at_layer >= 0 and stop_at_layer < len(self.blocks): - # Stop at the beginning of the specified block, not at the end of the previous block - block_hook_name = f"blocks.{stop_at_layer}.hook_in" - hook_dict = self.hook_dict - if block_hook_name in hook_dict: - hook_dict[block_hook_name].add_hook(stop_hook) - hooks.append((hook_dict[block_hook_name], block_hook_name)) - filtered_kwargs = kwargs.copy() - if cache_device is not None: - if getattr(self.cfg, "n_devices", 1) > 1: - # Moving a dispatched model to a single device collapses accelerate's - # split and breaks its routing hooks. The cache will stay spread across - # the per-layer devices; callers can .to(cache_device) on cache entries - # after the fact if they need a single-device cache. - warnings.warn( - f"run_with_cache(device={cache_device!r}) ignored: model is dispatched " - f"across {self.cfg.n_devices} devices via device_map. Cached activations " - "will remain on their per-layer devices.", - stacklevel=2, - ) - else: - self.original_model = self.original_model.to(cache_device) - if processed_args and isinstance(processed_args[0], torch.Tensor): - processed_args = [processed_args[0].to(cache_device)] + list(processed_args[1:]) - for key, value in filtered_kwargs.items(): - if isinstance(value, torch.Tensor): - filtered_kwargs[key] = value.to(cache_device) - try: - if "output_attentions" not in filtered_kwargs: - filtered_kwargs["output_attentions"] = True - if processed_args: - output = self.forward(processed_args[0], **filtered_kwargs) - elif "input_ids" in filtered_kwargs: - output = self.forward( - filtered_kwargs["input_ids"], - **{k: v for k, v in filtered_kwargs.items() if k != "input_ids"}, - ) - else: - output = self.forward(**filtered_kwargs) - if hasattr(output, "logits"): - output = output.logits - except StopAtLayerException as e: - output = e.layer_output - except Exception as e: - raise e - finally: - for hp, _ in hooks: - hp.remove_hooks() - if self.compatibility_mode == True: - reverse_aliases = {} - for old_name, new_name in aliases.items(): - if isinstance(new_name, list): - for single_new_name in new_name: - reverse_aliases[single_new_name] = old_name - else: - reverse_aliases[new_name] = old_name - cache_items_to_add = {} - for cache_name, cached_value in cache.items(): - for new_name, old_name in reverse_aliases.items(): - if cache_name == new_name: - cache_items_to_add[old_name] = cached_value - break - cache.update(cache_items_to_add) - for alias_name, target_name in aliases.items(): - if isinstance(target_name, list): - for single_target in target_name: - if single_target in cache and alias_name not in cache: - cache[alias_name] = cache[single_target] - break - elif target_name in cache and alias_name not in cache: - cache[alias_name] = cache[target_name] - if return_cache_object: - activation_cache = ActivationCache(cache, self, has_batch_dim=True) - if remove_batch_dim: - activation_cache.remove_batch_dim() - return (output, activation_cache) - else: - if remove_batch_dim: - for key in cache: - if cache[key] is not None and isinstance(cache[key], torch.Tensor): - if cache[key].size(0) == 1: - cache[key] = cache[key][0] - return (output, cache) - - def run_with_hooks( - self, - input: Union[str, List[str], torch.Tensor], - fwd_hooks: List[Tuple[Union[str, Callable], Callable]] = [], - bwd_hooks: List[Tuple[Union[str, Callable], Callable]] = [], - reset_hooks_end: bool = True, - clear_contexts: bool = False, - return_type: Optional[str] = "logits", - names_filter: Optional[Union[str, List[str], Callable[[str], bool]]] = None, - stop_at_layer: Optional[int] = None, - remove_batch_dim: bool = False, - **kwargs, - ) -> Any: - """Run the model with specified forward and backward hooks. - - Args: - input: Input to the model - fwd_hooks: Forward hooks to apply - bwd_hooks: Backward hooks to apply - reset_hooks_end: Whether to reset hooks at the end - clear_contexts: Whether to clear hook contexts - return_type: What to return ("logits", "loss", etc.) - names_filter: Filter for hook names (not used directly, for compatibility) - stop_at_layer: Layer to stop at (uses StopAtLayerException; cleans up KV cache on stop) - remove_batch_dim: Whether to remove batch dimension from hook inputs (only works for batch_size==1) - **kwargs: Additional arguments - - Returns: - Model output - """ - added_hooks: List[Tuple[HookPoint, str]] = [] - effective_stop_layer = None - if stop_at_layer is not None and hasattr(self, "blocks"): - if stop_at_layer < 0: - effective_stop_layer = len(self.blocks) + stop_at_layer - else: - effective_stop_layer = stop_at_layer - - def add_hook_to_point( - hook_point: HookPoint, hook_fn: Callable, name: str, dir: Literal["fwd", "bwd"] = "fwd" - ): - if effective_stop_layer is not None and name.startswith("blocks."): - try: - layer_num = int(name.split(".")[1]) - if layer_num >= effective_stop_layer: - return - except (IndexError, ValueError): - pass - if self.compatibility_mode and name != hook_point.name: - alias_names_list: list[str] = [] - if hook_point.name is not None: - alias_names_list.append(hook_point.name) - alias_names_list.append(name) - hook_point.add_hook(hook_fn, dir=dir, alias_names=alias_names_list) - else: - hook_point.add_hook(hook_fn, dir=dir) - added_hooks.append((hook_point, name)) - - if stop_at_layer is not None and hasattr(self, "blocks"): - if stop_at_layer < 0: - stop_at_layer = len(self.blocks) + stop_at_layer - last_layer_to_process = stop_at_layer - 1 - - def stop_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: - raise StopAtLayerException(tensor) - - if stop_at_layer >= 0 and stop_at_layer < len(self.blocks): - # Stop at the beginning of the specified block, not at the end of the previous block - block_hook_name = f"blocks.{stop_at_layer}.hook_in" - hook_dict = self.hook_dict - if block_hook_name in hook_dict: - add_hook_to_point(hook_dict[block_hook_name], stop_hook, block_hook_name, "fwd") - - def apply_hooks(hooks: List[Tuple[Union[str, Callable], Callable]], is_fwd: bool): - direction: Literal["fwd", "bwd"] = "fwd" if is_fwd else "bwd" - aliases = build_alias_to_canonical_map(self.hook_dict) - for hook_name_or_filter, hook_fn in hooks: - if remove_batch_dim: - original_hook_fn = hook_fn - - # Default arg captures hook_fn by value (avoids closure issue) - def wrapped_hook_fn(tensor, hook, _orig_fn=original_hook_fn): - if tensor.shape[0] == 1: - tensor_no_batch = tensor.squeeze(0) - result = _orig_fn(tensor_no_batch, hook) - if result.dim() == tensor_no_batch.dim(): - result = result.unsqueeze(0) - return result - else: - return _orig_fn(tensor, hook) - - hook_fn = wrapped_hook_fn - if isinstance(hook_name_or_filter, str): - hook_dict = self.hook_dict - actual_hook_name = hook_name_or_filter - if hook_name_or_filter in aliases: - actual_hook_name = aliases[hook_name_or_filter] - if actual_hook_name in hook_dict: - add_hook_to_point( - hook_dict[actual_hook_name], hook_fn, actual_hook_name, direction - ) - else: - hook_dict = self.hook_dict - seen_hooks = set() - for name, hook_point in hook_dict.items(): - if hook_name_or_filter(name): - hook_id = id(hook_point) - if hook_id in seen_hooks: - continue - seen_hooks.add(hook_id) - hook_name_to_use = hook_point.name if hook_point.name else name - add_hook_to_point(hook_point, hook_fn, hook_name_to_use, direction) - - try: - apply_hooks(fwd_hooks, True) - apply_hooks(bwd_hooks, False) - try: - output = self.forward( - input, return_type=return_type, stop_at_layer=stop_at_layer, **kwargs - ) - except StopAtLayerException as e: - output = e.layer_output - return output - finally: - if reset_hooks_end: - for hook_point, name in added_hooks: - hook_point.remove_hooks() - - def _generate_tokens( - self, - current_tokens: torch.Tensor, - input_tokens: torch.Tensor, - batch_size: int, - *, - max_new_tokens: int, - do_sample: bool, - top_k: Optional[int], - top_p: Optional[float], - temperature: float, - freq_penalty: float, - repetition_penalty: float, - stop_at_eos: bool, - stop_tokens: List[int], - eos_token_for_padding: int, - finished_sequences: torch.Tensor, - use_past_kv_cache: bool, - use_stateful_cache: bool, - mamba_cache: Any, - mamba_conv_kernel: int, - is_encoder_decoder: bool, - _is_batched_list: bool, - _generate_from_embeds: bool, - encoder_input: Optional[torch.Tensor], - decoder_tokens: Optional[torch.Tensor], - generated_token_ids: Optional[List[torch.Tensor]], - pixel_values: Optional[torch.Tensor], - multimodal_kwargs: Dict[str, Any], - verbose: bool, - ) -> Generator[Tuple[torch.Tensor, torch.Tensor, bool], None, None]: - """Core generation loop. Yields (sampled_tokens, final_logits, all_finished) per step. - - Owns the forward pass, sampling, EOS handling, token accumulation, and - KV cache management. Callers are responsible for try/finally cleanup of - ``_capture_hf_cache``. - """ - _hf_kv_cache = None - - for gen_step_idx in tqdm.tqdm(range(max_new_tokens), disable=not verbose): - with torch.no_grad(): - if is_encoder_decoder: - logits = self( - encoder_input, - return_type="logits", - decoder_input=decoder_tokens, - ) - else: - forward_kwargs: Dict[str, Any] = {} - # Compute attention mask and position_ids for batched - # inputs with padding. - if ( - _is_batched_list - and self.tokenizer is not None - and self.tokenizer.pad_token_id is not None - ): - _prev_side = self.tokenizer.padding_side - self.tokenizer.padding_side = "left" - attn_mask = utils.get_attention_mask( - self.tokenizer, - current_tokens, - prepend_bos=getattr(self.cfg, "default_prepend_bos", True), - ).to(self.cfg.device) - self.tokenizer.padding_side = _prev_side - forward_kwargs["attention_mask"] = attn_mask - position_ids = attn_mask.long().cumsum(-1) - 1 - position_ids.masked_fill_(attn_mask == 0, 1) - forward_kwargs["position_ids"] = position_ids - if gen_step_idx == 0: - if pixel_values is not None: - forward_kwargs["pixel_values"] = pixel_values - if multimodal_kwargs: - forward_kwargs.update(multimodal_kwargs) - if use_stateful_cache: - forward_kwargs["cache_params"] = mamba_cache - forward_kwargs["use_cache"] = True - if gen_step_idx == 0: - cache_position = torch.arange( - 0, mamba_conv_kernel, device=self.cfg.device - ) - forward_kwargs["cache_position"] = cache_position - logits = self( - current_tokens, - return_type="logits", - **forward_kwargs, - ) - else: - input_seq_pos = input_tokens.shape[1] + gen_step_idx - 1 - cache_position = torch.tensor([input_seq_pos], device=self.cfg.device) - forward_kwargs["cache_position"] = cache_position - if "position_ids" in forward_kwargs: - forward_kwargs["position_ids"] = forward_kwargs["position_ids"][ - :, -1: - ] - logits = self( - current_tokens[:, -1:], - return_type="logits", - **forward_kwargs, - ) - elif use_past_kv_cache: - forward_kwargs["use_cache"] = True - if _hf_kv_cache is not None: - forward_kwargs["past_key_values"] = _hf_kv_cache - if "position_ids" in forward_kwargs: - forward_kwargs["position_ids"] = forward_kwargs["position_ids"][ - :, -1: - ] - logits = self( - current_tokens[:, -1:], - return_type="logits", - **forward_kwargs, - ) - else: - logits = self( - current_tokens, - return_type="logits", - **forward_kwargs, - ) - else: - logits = self(current_tokens, return_type="logits", **forward_kwargs) - if use_past_kv_cache and hasattr(self, "_last_hf_cache"): - _hf_kv_cache = self._last_hf_cache or _hf_kv_cache - del self._last_hf_cache - final_logits = logits[:, -1, :] - - # Sample next token - penalty_tokens = ( - torch.stack(generated_token_ids, dim=1) - if _generate_from_embeds and generated_token_ids - else None - ) - if do_sample: - sampled_tokens = utils.sample_logits( - final_logits, - top_k=top_k, - top_p=top_p, - temperature=temperature, - freq_penalty=freq_penalty, - repetition_penalty=repetition_penalty, - tokens=penalty_tokens - if _generate_from_embeds - else (decoder_tokens if is_encoder_decoder else current_tokens), - ).to(self.cfg.device) - else: - sampled_tokens = utils.sample_logits( - final_logits, - temperature=0.0, - repetition_penalty=repetition_penalty, - tokens=penalty_tokens - if _generate_from_embeds - else (decoder_tokens if is_encoder_decoder else current_tokens), - ).to(self.cfg.device) - - # Handle EOS - if stop_at_eos: - sampled_tokens[finished_sequences] = eos_token_for_padding - finished_sequences.logical_or_( - torch.isin( - sampled_tokens.to(self.cfg.device), - torch.tensor(stop_tokens).to(self.cfg.device), - ) - ) - - # Update token sequences - if is_encoder_decoder: - assert decoder_tokens is not None - decoder_tokens = torch.cat([decoder_tokens, sampled_tokens.unsqueeze(1)], dim=1) - elif _generate_from_embeds: - assert generated_token_ids is not None - generated_token_ids.append(sampled_tokens) - embed_fn = self.original_model.get_input_embeddings() # type: ignore[operator] - assert embed_fn is not None - new_embed = embed_fn(sampled_tokens.unsqueeze(1)).to(current_tokens.dtype) - current_tokens = torch.cat([current_tokens, new_embed], dim=1) - else: - current_tokens = torch.cat([current_tokens, sampled_tokens.unsqueeze(1)], dim=1) - - all_finished = bool(stop_at_eos and finished_sequences.all().item()) - - yield sampled_tokens, final_logits, all_finished - - if all_finished: - return - - def generate( - self, - input: Union[str, List[str], torch.Tensor] = "", - max_new_tokens: int = 10, - stop_at_eos: bool = True, - eos_token_id: Optional[int] = None, - do_sample: bool = True, - top_k: Optional[int] = None, - top_p: Optional[float] = None, - temperature: float = 1.0, - freq_penalty: float = 0.0, - repetition_penalty: float = 1.0, - use_past_kv_cache: bool = True, - prepend_bos: Optional[bool] = None, - padding_side: Optional[str] = None, - return_type: Optional[str] = "input", - verbose: bool = True, - output_logits: bool = False, - pixel_values: Optional[torch.Tensor] = None, - **multimodal_kwargs, - ) -> str | list[str] | torch.Tensor | Any: # Any for transformers.utils.ModelOutput - # Any: beartype forward ref limitation (beartype#546) - """Sample tokens from the model. - - Sample tokens from the model until the model outputs eos_token or max_new_tokens is reached. - This implementation is based on HookedTransformer.generate() to ensure consistent behavior. - - Args: - input: Text string, list of strings, or tensor of tokens - max_new_tokens: Maximum number of tokens to generate - stop_at_eos: If True, stop generating tokens when the model outputs eos_token - eos_token_id: The token ID to use for end of sentence - do_sample: If True, sample from the model's output distribution. Otherwise, use greedy search - top_k: Number of tokens to sample from. If None, sample from all tokens - top_p: Probability mass to sample from. If 1.0, sample from all tokens - temperature: Temperature for sampling. Higher values will make the model more random - freq_penalty: Frequency penalty for sampling - how much to penalise previous tokens - repetition_penalty: HuggingFace-style repetition penalty. Values > 1.0 discourage - repetition by dividing positive logits and multiplying negative logits for - previously seen tokens. Default 1.0 (no penalty). - use_past_kv_cache: If True, use KV caching for faster generation - prepend_bos: Accepted for API compatibility but not applied during generation. - The HF model expects tokens in its native format (tokenizer defaults). - Overriding BOS can silently degrade generation quality. - padding_side: Which side to pad when tokenizing multiple strings of different - lengths. For batched list inputs, left-padding is forced internally for - correct generation behavior. Defaults to None (tokenizer default). - return_type: The type of output to return - 'input', 'str', or 'tokens' - verbose: Not used in Bridge (kept for API compatibility) - output_logits: If True, return a ModelOutput with sequences and logits tuple - pixel_values: Optional image tensor for multimodal models. Only passed on the - first generation step (the vision encoder processes the image once, then - embeddings are part of the token sequence for subsequent steps). - - Returns: - Generated sequence as string, list of strings, or tensor depending on input type and return_type. - If output_logits=True, returns a ModelOutput-like object with 'sequences' and 'logits' attributes. - """ - # prepend_bos is intentionally not applied during generation. - # The HF model expects tokens in its native format. Overriding BOS can silently - # degrade quality. - if prepend_bos is not None: - import warnings - - warnings.warn( - "prepend_bos is ignored during TransformerBridge.generate(). " - "The HF model expects tokens with the tokenizer's default BOS handling. " - "To control BOS, tokenize with to_tokens(prepend_bos=...) and pass the " - "resulting tensor to generate().", - stacklevel=2, - ) - # padding_side is handled internally: for batched list inputs, left-padding - # is forced to ensure correct generation. See _is_batched_list logic below. - - # Stateful dispatch is decided after input parsing so we can fall back - # to hf_generate() for input types the stateful loop doesn't handle. - is_stateful_model = getattr(self.cfg, "is_stateful", False) - - _is_batched_list = isinstance(input, list) and len(input) > 1 - - _generate_from_embeds = False - if isinstance(input, str): - input_tokens = self.to_tokens(input, move_to_device=True, truncate=False) - input_type = "str" - elif isinstance(input, list): - # Force left-padding for batched generation so real tokens are - # flush-right and logits[:, -1, :] is always the last real token. - if _is_batched_list: - _orig_padding_side = self.tokenizer.padding_side - self.tokenizer.padding_side = "left" - input_tokens = self.to_tokens(input, move_to_device=True, truncate=False) - if _is_batched_list: - self.tokenizer.padding_side = _orig_padding_side - input_type = "list" - elif isinstance(input, torch.Tensor) and input.is_floating_point(): - # inputs_embeds: pre-computed embeddings (e.g., from multimodal models) - input_tokens = input.to(self.cfg.device) - input_type = "embeds" - _generate_from_embeds = True - else: - input_tokens = input.to(self.cfg.device) - input_type = "tokens" - - # Determine return type - if return_type == "input": - if input_type in ["str", "list"]: - return_type = "str" - elif input_type == "embeds": - return_type = "tokens" - else: - return_type = "tokens" - - batch_size = input_tokens.shape[0] - - # Setup EOS token handling - stop_tokens = [] - eos_token_for_padding = 0 - if stop_at_eos: - tokenizer_has_eos_token = ( - self.tokenizer is not None and self.tokenizer.eos_token_id is not None - ) - if eos_token_id is None: - assert ( - tokenizer_has_eos_token - ), "Must pass eos_token_id if stop_at_eos is True and tokenizer is None or has no eos_token_id" - assert self.tokenizer is not None - eos_token_id = self.tokenizer.eos_token_id - - if isinstance(eos_token_id, int): - stop_tokens = [eos_token_id] - eos_token_for_padding = eos_token_id - else: - stop_tokens = list(eos_token_id) - if tokenizer_has_eos_token: - assert self.tokenizer is not None - eos_token_for_padding = self.tokenizer.eos_token_id - else: - eos_token_for_padding = eos_token_id[0] - - # Track which sequences have finished - finished_sequences = torch.zeros(batch_size, dtype=torch.bool, device=self.cfg.device) - - # Optionally collect logits at each generation step for downstream tooling/tests - logits_seq_list: list[torch.Tensor] | None = [] if output_logits else None - - # Detect encoder-decoder models (T5, BART, etc.) - is_encoder_decoder = hasattr(self.original_model, "config") and getattr( - self.original_model.config, "is_encoder_decoder", False - ) - - # HF cache flows opaquely through the component chain via - # _reconstruct_attention() → _update_kv_cache() on each layer. - _hf_kv_cache = None - if use_past_kv_cache and is_encoder_decoder: - # Encoder-decoder models (T5, BART) don't support the opaque - # cache path — silently disable rather than crash, since - # use_past_kv_cache=True is the default. - use_past_kv_cache = False - - # SSMs (Mamba/Mamba-2) run through a dedicated cache path so hooks - # fire on every step. Unsupported input types fall back to hf_generate(). - use_stateful_cache = ( - is_stateful_model - and use_past_kv_cache - and not is_encoder_decoder - and not _generate_from_embeds - and pixel_values is None - and not multimodal_kwargs - ) - if is_stateful_model and not use_stateful_cache: - hf_kwargs: dict[str, Any] = { - "max_new_tokens": max_new_tokens, - "do_sample": do_sample, - "temperature": temperature, - } - if top_k is not None: - hf_kwargs["top_k"] = top_k - if top_p is not None: - hf_kwargs["top_p"] = top_p - if eos_token_id is not None: - hf_kwargs["eos_token_id"] = eos_token_id - return self.hf_generate(input, **hf_kwargs) - - # SSM cache is built once and mutated in place across forward calls. - # Adapter owns the cache-type choice; new SSMs just override - # create_stateful_cache(). - mamba_cache: Any = None - mamba_conv_kernel: int = 0 - if use_stateful_cache: - hf_model: Any = self.original_model - mamba_conv_kernel = int(getattr(hf_model.config, "conv_kernel", 4)) - cache_dtype = self.cfg.dtype or torch.float32 - mamba_cache = self.adapter.create_stateful_cache( - hf_model=hf_model, - batch_size=batch_size, - device=self.cfg.device, - dtype=cache_dtype, - ) - - if use_past_kv_cache and not use_stateful_cache: - self._capture_hf_cache = True # Signal forward() to stash cache - - # Generate tokens - current_tokens = input_tokens.clone() - # For inputs_embeds generation, also track generated token IDs for decoding - if _generate_from_embeds: - generated_token_ids: list[torch.Tensor] = [] - sampled_tokens_list = [] - - # For encoder-decoder models, keep encoder input fixed and grow decoder input - if is_encoder_decoder: - encoder_input = input_tokens.clone() - decoder_start_token_id = getattr( - self.original_model.config, "decoder_start_token_id", 0 - ) - decoder_tokens = torch.full( - (batch_size, 1), - decoder_start_token_id, - dtype=input_tokens.dtype, - device=self.cfg.device, - ) - - try: - for sampled_tokens, final_logits, all_finished in self._generate_tokens( - current_tokens, - input_tokens, - batch_size, - max_new_tokens=max_new_tokens, - do_sample=do_sample, - top_k=top_k, - top_p=top_p, - temperature=temperature, - freq_penalty=freq_penalty, - repetition_penalty=repetition_penalty, - stop_at_eos=stop_at_eos, - stop_tokens=stop_tokens, - eos_token_for_padding=eos_token_for_padding, - finished_sequences=finished_sequences, - use_past_kv_cache=use_past_kv_cache, - use_stateful_cache=use_stateful_cache, - mamba_cache=mamba_cache, - mamba_conv_kernel=mamba_conv_kernel, - is_encoder_decoder=is_encoder_decoder, - _is_batched_list=_is_batched_list, - _generate_from_embeds=_generate_from_embeds, - encoder_input=encoder_input if is_encoder_decoder else None, - decoder_tokens=decoder_tokens if is_encoder_decoder else None, - generated_token_ids=generated_token_ids if _generate_from_embeds else None, - pixel_values=pixel_values, - multimodal_kwargs=multimodal_kwargs if multimodal_kwargs else {}, - verbose=verbose, - ): - sampled_tokens_list.append(sampled_tokens.unsqueeze(1)) - if logits_seq_list is not None: - logits_seq_list.append(final_logits.clone()) - if all_finished: - break - finally: - self._capture_hf_cache = False - if hasattr(self, "_last_hf_cache"): - del self._last_hf_cache - - # Concatenate all sampled tokens - sampled_tokens = torch.cat(sampled_tokens_list, dim=1) - if is_encoder_decoder: - # Reconstruct full decoder sequence: start token + generated tokens - output_tokens = torch.cat([decoder_tokens[:, :1], sampled_tokens], dim=1) - elif _generate_from_embeds: - # For inputs_embeds, we only have the generated token IDs (no input token IDs) - output_tokens = sampled_tokens - else: - output_tokens = torch.cat([input_tokens, sampled_tokens], dim=1) - - # Return ModelOutput if output_logits was requested - if output_logits and logits_seq_list is not None: - from transformers.utils import ModelOutput # type: ignore - - def _logits_to_tuple(logits_list: list[torch.Tensor]) -> tuple[torch.Tensor, ...]: - assert logits_list is not None - # Convert list of [batch, vocab] tensors to tuple - return tuple(logits_list) - - try: - from transformers.generation.utils import GenerateDecoderOnlyOutput - - # Return a HF-compatible ModelOutput structure - # GenerateDecoderOnlyOutput expects: sequences, scores (optional), logits (optional) - return GenerateDecoderOnlyOutput( - sequences=cast(torch.LongTensor, output_tokens), - # HF's type hint says tuple[FloatTensor] but should be tuple[FloatTensor, ...] - # (variable-length tuple with one element per generated token) - logits=_logits_to_tuple(logits_seq_list), # type: ignore[arg-type] - ) - except (ImportError, AttributeError): - # Fallback if GenerateDecoderOnlyOutput not available in this transformers version - return ModelOutput( - sequences=output_tokens, - logits=_logits_to_tuple(logits_seq_list), - ) - - # Format output - if return_type == "str": - assert self.tokenizer is not None - if input_type == "str": - return self.tokenizer.decode(output_tokens[0], skip_special_tokens=True) - else: - decoded_texts = [ - self.tokenizer.decode(tokens, skip_special_tokens=True) - for tokens in output_tokens - ] - return decoded_texts[0] if len(decoded_texts) == 1 else decoded_texts - else: # return_type == "tokens" - return output_tokens - - @torch.no_grad() - def generate_stream( - self, - input: Union[str, List[str], torch.Tensor] = "", - max_new_tokens: int = 10, - max_tokens_per_yield: int = 25, - stop_at_eos: bool = True, - eos_token_id: Optional[int] = None, - do_sample: bool = True, - top_k: Optional[int] = None, - top_p: Optional[float] = None, - temperature: float = 1.0, - freq_penalty: float = 0.0, - repetition_penalty: float = 1.0, - use_past_kv_cache: bool = True, - prepend_bos: Optional[bool] = None, - padding_side: Optional[str] = None, - return_type: Optional[str] = "input", - verbose: bool = True, - ) -> Generator[Union[torch.Tensor, str], None, None]: - """Stream tokens from the model as they are generated. - - Yields batches of tokens progressively during generation rather than - waiting for the entire sequence. Uses the same core loop as generate(). - - Args: - input: Text string, list of strings, or tensor of tokens. - max_new_tokens: Maximum number of tokens to generate. - max_tokens_per_yield: Yield accumulated tokens every this many steps. - stop_at_eos: If True, stop when eos_token is produced. - eos_token_id: Token ID(s) for end of sentence. Defaults to tokenizer's. - do_sample: If True, sample; otherwise greedy. - top_k: Top-k sampling. None means no filtering. - top_p: Nucleus sampling threshold. - temperature: Sampling temperature. - freq_penalty: Frequency penalty for previous tokens. - repetition_penalty: HF-style repetition penalty (>1.0 discourages repeats). - use_past_kv_cache: Use KV caching for faster generation. - prepend_bos: Not applied (API compatibility). See generate() docstring. - padding_side: Which side to pad for batched list inputs. Left-padding - is forced internally for batched generation. - return_type: 'input' (match input type), 'str', or 'tokens'. - verbose: Show progress bar. - - Yields: - Token tensors [batch, seq_len] or strings, accumulated up to - max_tokens_per_yield tokens between yields. First yield includes - the input tokens; subsequent yields contain only new tokens. - """ - if prepend_bos is not None: - warnings.warn( - "prepend_bos is ignored during TransformerBridge.generate_stream(). " - "The HF model expects tokens with the tokenizer's default BOS handling.", - stacklevel=2, - ) - - # --- Input parsing (mirrors generate()) --- - _is_batched_list = isinstance(input, list) and len(input) > 1 - - if isinstance(input, str): - input_tokens = self.to_tokens(input, move_to_device=True, truncate=False) - input_type = "str" - elif isinstance(input, list): - if _is_batched_list: - _orig_ps = self.tokenizer.padding_side - self.tokenizer.padding_side = "left" - try: - input_tokens = self.to_tokens(input, move_to_device=True, truncate=False) - finally: - if _is_batched_list: - self.tokenizer.padding_side = _orig_ps - input_type = "list" - else: - input_tokens = input.to(self.cfg.device) - input_type = "tokens" - - if return_type == "input": - return_type = "str" if input_type in ["str", "list"] else "tokens" - - batch_size = input_tokens.shape[0] - - # --- EOS setup --- - stop_tokens: List[int] = [] - eos_token_for_padding = 0 - if stop_at_eos: - tokenizer_has_eos_token = ( - self.tokenizer is not None and self.tokenizer.eos_token_id is not None - ) - if eos_token_id is None: - assert ( - tokenizer_has_eos_token - ), "Must pass eos_token_id if stop_at_eos is True and tokenizer is None or has no eos_token_id" - assert self.tokenizer is not None - eos_token_id = self.tokenizer.eos_token_id - if isinstance(eos_token_id, int): - stop_tokens = [eos_token_id] - eos_token_for_padding = eos_token_id - else: - stop_tokens = list(eos_token_id) - if tokenizer_has_eos_token: - assert self.tokenizer is not None - eos_token_for_padding = self.tokenizer.eos_token_id - else: - eos_token_for_padding = eos_token_id[0] - - finished_sequences = torch.zeros(batch_size, dtype=torch.bool, device=self.cfg.device) - - # --- Cache setup --- - if use_past_kv_cache: - self._capture_hf_cache = True - - current_tokens = input_tokens.clone() - - # --- Streaming loop --- - # All yields are token tensors [batch, seq_len]. Each yield contains - # only the newly generated tokens since the previous yield (the first - # yield additionally prepends the input tokens for context). - accumulated_tokens: Optional[torch.Tensor] = None - tokens_since_last_yield = 0 - - def _maybe_decode( - tokens: torch.Tensor, - ) -> Union[torch.Tensor, str]: - if return_type == "str": - assert self.tokenizer is not None - return self.tokenizer.decode(tokens[0], skip_special_tokens=True) - return tokens - - try: - for step_idx, (sampled_tokens, _, all_finished) in enumerate( - self._generate_tokens( - current_tokens, - input_tokens, - batch_size, - max_new_tokens=max_new_tokens, - do_sample=do_sample, - top_k=top_k, - top_p=top_p, - temperature=temperature, - freq_penalty=freq_penalty, - repetition_penalty=repetition_penalty, - stop_at_eos=stop_at_eos, - stop_tokens=stop_tokens, - eos_token_for_padding=eos_token_for_padding, - finished_sequences=finished_sequences, - use_past_kv_cache=use_past_kv_cache, - use_stateful_cache=False, - mamba_cache=None, - mamba_conv_kernel=0, - is_encoder_decoder=False, - _is_batched_list=_is_batched_list, - _generate_from_embeds=False, - encoder_input=None, - decoder_tokens=None, - generated_token_ids=None, - pixel_values=None, - multimodal_kwargs={}, - verbose=verbose, - ) - ): - new_tokens = sampled_tokens.unsqueeze(-1) - - if step_idx == 0: - accumulated_tokens = torch.cat([input_tokens, new_tokens], dim=-1) - tokens_since_last_yield = accumulated_tokens.shape[1] - else: - if accumulated_tokens is None: - accumulated_tokens = new_tokens - else: - accumulated_tokens = torch.cat([accumulated_tokens, new_tokens], dim=-1) - tokens_since_last_yield += 1 - - if tokens_since_last_yield >= max_tokens_per_yield: - yield _maybe_decode(accumulated_tokens) - tokens_since_last_yield = 0 - accumulated_tokens = None - - if all_finished: - if accumulated_tokens is not None: - yield _maybe_decode(accumulated_tokens) - break - - # Yield remainder after loop completes without break - if accumulated_tokens is not None: - yield _maybe_decode(accumulated_tokens) - finally: - self._capture_hf_cache = False - if hasattr(self, "_last_hf_cache"): - del self._last_hf_cache - - def hf_generate( - self, - input: str | list[str] | torch.Tensor = "", - max_new_tokens: int = 10, - stop_at_eos: bool = True, - eos_token_id: int | None = None, - do_sample: bool = True, - top_k: int | None = None, - top_p: float | None = None, - temperature: float = 1.0, - use_past_kv_cache: bool = True, - return_type: str | None = "input", - pixel_values: torch.Tensor | None = None, - **generation_kwargs, - ) -> str | list[str] | torch.Tensor | Any: # Any for HF ModelOutput types - # Any: beartype forward ref limitation (beartype#546) - """Generate text using the underlying HuggingFace model with full HF API support. - - This method provides direct access to HuggingFace's generation API, forwarding all - generation parameters (including output_scores, output_logits, output_attentions, - output_hidden_states) directly to the underlying HF model. Use this when you need - full HuggingFace generation features not supported by the standard generate() method. - - For standard generation compatible with HookedTransformer, use generate() instead. - - Args: - input: Text string, list of strings, or tensor of tokens - max_new_tokens: Maximum number of tokens to generate - stop_at_eos: If True, stop generating tokens when the model outputs eos_token - eos_token_id: The token ID to use for end of sentence - do_sample: If True, sample from the model's output distribution - top_k: Number of tokens to sample from - top_p: Probability mass to sample from - temperature: Temperature for sampling - use_past_kv_cache: If True, use KV caching for faster generation - return_type: The type of output to return - 'input', 'str', or 'tokens' - **generation_kwargs: Additional HuggingFace generation parameters including: - - output_scores: Return generation scores - - output_logits: Return generation logits - - output_attentions: Return attention weights - - output_hidden_states: Return hidden states - - return_dict_in_generate: Return ModelOutput object - - And any other HF generation parameters - - Returns: - Generated sequence as string, list of strings, tensor, or HF ModelOutput - depending on input type, return_type, and generation_kwargs. - - Example:: - - # Get full HF ModelOutput with logits and attentions - from transformer_lens import HookedTransformer - model = HookedTransformer.from_pretrained("tiny-stories-1M") - result = model.hf_generate( - "Hello world", - max_new_tokens=5, - output_logits=True, - output_attentions=True, - return_dict_in_generate=True - ) - print(result.sequences) # Generated tokens - print(result.logits) # Logits for each generation step - print(result.attentions) # Attention weights - """ - # Handle string input by tokenizing it - if isinstance(input, str): - inputs = self.tokenizer(input, return_tensors="pt", padding=False, truncation=False).to( - self.cfg.device - ) - input_ids = inputs["input_ids"] - input_type = "str" - elif isinstance(input, list): - inputs = self.tokenizer(input, return_tensors="pt", padding=True, truncation=False).to( - self.cfg.device - ) - input_ids = inputs["input_ids"] - input_type = "list" - else: - input_ids = input - if input_ids.device != self.cfg.device: - input_ids = input_ids.to(self.cfg.device) - input_type = "tokens" - - # Build generation_kwargs from explicit args and kwargs - generation_kwargs = dict(generation_kwargs) if generation_kwargs is not None else {} - generation_kwargs.update( - { - "max_new_tokens": max_new_tokens, - "do_sample": do_sample, - "temperature": temperature, - "pad_token_id": self.tokenizer.eos_token_id, - } - ) - - if top_k is not None: - generation_kwargs["top_k"] = top_k - if top_p is not None: - generation_kwargs["top_p"] = top_p - if eos_token_id is not None: - generation_kwargs["eos_token_id"] = eos_token_id - elif stop_at_eos and self.tokenizer.eos_token_id is not None: - generation_kwargs["eos_token_id"] = self.tokenizer.eos_token_id - - if pixel_values is not None: - generation_kwargs["pixel_values"] = pixel_values - - if use_past_kv_cache: - generation_kwargs["use_cache"] = True - - # HF dict flags that trigger ModelOutput returns - hf_dict_flags = ( - "output_scores", - "output_logits", - "output_attentions", - "output_hidden_states", - ) - - # If any HF-style output flags are provided, ensure return_dict_in_generate is set - any_flag_set = False - for f in hf_dict_flags: - if generation_kwargs.get(f) is not None: - generation_kwargs[f] = bool(generation_kwargs[f]) - any_flag_set = True - - if any_flag_set: - generation_kwargs.setdefault("return_dict_in_generate", True) - - # Generate using the original HuggingFace model - with torch.no_grad(): - outputs = self.original_model.generate(input_ids, **generation_kwargs) # type: ignore[operator] - - # Check if output is a ModelOutput - try: - from transformers.utils import ModelOutput # type: ignore - - is_model_output = isinstance(outputs, ModelOutput) - except Exception: - is_model_output = False - - # Return based on return_type and input format - if return_type == "input" or return_type is None: - if input_type == "str": - # Decode the full output back to string - if is_model_output and hasattr(outputs, "sequences"): - return self.tokenizer.decode(outputs.sequences[0], skip_special_tokens=True) - return self.tokenizer.decode(outputs[0], skip_special_tokens=True) - elif input_type == "list": - # Decode each sequence in the batch - if is_model_output and hasattr(outputs, "sequences"): - return [ - self.tokenizer.decode(seq, skip_special_tokens=True) - for seq in outputs.sequences - ] - return [self.tokenizer.decode(seq, skip_special_tokens=True) for seq in outputs] - else: - # Return the full token sequence including input - return outputs - elif return_type == "tokens": - return outputs - else: - # For other return types, default to the decoded text - if input_type == "str": - if is_model_output and hasattr(outputs, "sequences"): - return self.tokenizer.decode(outputs.sequences[0], skip_special_tokens=True) - return self.tokenizer.decode(outputs[0], skip_special_tokens=True) - elif input_type == "list": - if is_model_output and hasattr(outputs, "sequences"): - return [ - self.tokenizer.decode(seq, skip_special_tokens=True) - for seq in outputs.sequences - ] - return [self.tokenizer.decode(seq, skip_special_tokens=True) for seq in outputs] - else: - return outputs - - def prepare_multimodal_inputs( - self, - text: Union[str, List[str]], - images: Optional[Any] = None, - ) -> Dict[str, torch.Tensor]: - """Prepare multimodal inputs using the model's processor. - - Converts text and images into model-ready tensors (input_ids, pixel_values, - attention_mask, etc.) using the HuggingFace processor loaded during boot(). - - Args: - text: Text prompt(s), typically containing image placeholder tokens - (e.g., "" for LLaVA). - images: PIL Image or list of PIL Images to process. Pass None for - text-only inputs on a multimodal model. - - Returns: - Dictionary with 'input_ids', 'pixel_values', 'attention_mask', etc. - All tensors are moved to the model's device. - - Raises: - ValueError: If model is not multimodal or processor is not available. - """ - if not getattr(self.cfg, "is_multimodal", False): - raise ValueError( - "prepare_multimodal_inputs() requires a multimodal model " - "(cfg.is_multimodal must be True)" - ) - if self.processor is None: - raise ValueError( - "No processor available. Load model with boot_transformers() or " - "set bridge.processor = AutoProcessor.from_pretrained(...) manually." - ) - inputs = self.processor(text=text, images=images, return_tensors="pt") - return {k: v.to(self.cfg.device) if hasattr(v, "to") else v for k, v in inputs.items()} - - def to(self, *args, **kwargs) -> "TransformerBridge": - """Move model to device and/or change dtype. - - Args: - args: Positional arguments for nn.Module.to - kwargs: Keyword arguments for nn.Module.to - print_details: Whether to print details about device/dtype changes (default: True) - - Returns: - Self for chaining - """ - # Extract print_details if provided - print_details = kwargs.pop("print_details", True) - - # Handle both device and dtype changes - # torch.nn.Module.to() supports: to(device), to(dtype), to(device, dtype), - # to(device=...), to(dtype=...), to(device=..., dtype=...) - target_device, target_dtype = None, None - - if len(args) >= 1: - first_arg = args[0] - if isinstance(first_arg, (torch.device, str)): - target_device = first_arg - elif isinstance(first_arg, torch.dtype): - target_dtype = first_arg - if len(args) >= 2: - second_arg = args[1] - if isinstance(second_arg, torch.dtype): - target_dtype = second_arg - - # these override positional args - if "device" in kwargs: - target_device = kwargs["device"] - if "dtype" in kwargs: - target_dtype = kwargs["dtype"] - - # Moving a multi-device (device_map-dispatched) model to a single device would - # collapse the split and break accelerate's hook routing. Warn and drop the - # device move; still honor dtype changes. - if target_device is not None and getattr(self.cfg, "n_devices", 1) > 1: - warnings.warn( - f"TransformerBridge.to({target_device!r}) ignored: model is dispatched " - f"across {self.cfg.n_devices} devices via device_map. Reload with " - "device=... (and no device_map/n_devices) to move to a single device.", - stacklevel=2, - ) - target_device = None - - if target_device is not None: - move_to_and_update_config(self, target_device, print_details) - if target_dtype is not None: - move_to_and_update_config(self, target_dtype, print_details) - - # Move the original model with all original args/kwargs (with print_details removed). - # When we've nulled target_device for multi-GPU safety, strip device args so the - # underlying module isn't moved either. - if target_device is None and (len(args) > 0 or "device" in kwargs): - kwargs.pop("device", None) - # Filter positional args: drop devices/strings, keep dtypes. - args = tuple(a for a in args if not isinstance(a, (torch.device, str))) - self.original_model = self.original_model.to(*args, **kwargs) - return self - - def cuda(self, device: Optional[Union[int, torch.device]] = None) -> "TransformerBridge": - """Move model to CUDA. - - Args: - device: CUDA device - - Returns: - Self for chaining - """ - if isinstance(device, int): - return self.to(f"cuda:{device}") - elif device is None: - return self.to("cuda") - else: - return self.to(device) - - def cpu(self) -> "TransformerBridge": - """Move model to CPU. - - Returns: - Self for chaining - """ - return self.to(torch.device("cpu")) - - def mps(self) -> "TransformerBridge": - """Move model to MPS. - - Returns: - Self for chaining - """ - return self.to(torch.device("mps")) - - def add_hook( - self, - name: Union[str, Callable[[str], bool]], - hook_fn, - dir="fwd", - is_permanent=False, - ): - """Add a hook to a specific component or to all components matching a filter. - - Args: - name: Either a string hook point name (e.g. "blocks.0.attn.hook_q") - or a callable filter ``(str) -> bool`` that is applied to every - hook point name; the hook is added to each point where the filter - returns True. - hook_fn: The hook function ``(activation, hook) -> activation | None``. - dir: Hook direction, ``"fwd"`` or ``"bwd"``. - is_permanent: If True the hook survives ``reset_hooks()`` calls. - """ - if callable(name) and not isinstance(name, str): - hook_dict = self.hook_dict - seen_hooks: set[int] = set() - for hook_name, hook_point in hook_dict.items(): - if name(hook_name): - hook_id = id(hook_point) - if hook_id in seen_hooks: - continue - seen_hooks.add(hook_id) - hook_point.add_hook(hook_fn, dir=dir, is_permanent=is_permanent) - return - - component = self - parts = name.split(".") - for part in parts[:-1]: - if hasattr(component, part): - component = getattr(component, part) - else: - raise AttributeError(f"Component path '{'.'.join(parts[:-1])}' not found") - hook_name = parts[-1] - if hasattr(component, hook_name): - hook_point = getattr(component, hook_name) - if isinstance(hook_point, HookPoint): - hook_point.add_hook(hook_fn, dir=dir, is_permanent=is_permanent) - else: - raise AttributeError( - f"'{hook_name}' is not a hook point. Found object of type: {type(hook_point)} with value: {hook_point}" - ) - else: - raise AttributeError(f"Hook point '{hook_name}' not found on component") - - def reset_hooks(self, clear_contexts=True): - """Remove all hooks from the model.""" - - def remove_hooks_recursive(module): - if isinstance(module, GeneralizedComponent): - module.remove_hooks() - for child in module.children(): - remove_hooks_recursive(child) - - remove_hooks_recursive(self) - - def hooks(self, fwd_hooks=[], bwd_hooks=[], reset_hooks_end=True, clear_contexts=False): - """Context manager for temporarily adding hooks. - - Args: - fwd_hooks: List of (hook_name, hook_fn) tuples for forward hooks - bwd_hooks: List of (hook_name, hook_fn) tuples for backward hooks - reset_hooks_end: If True, removes hooks when context exits - clear_contexts: Unused (for compatibility with HookedTransformer) - - Example: - with model.hooks(fwd_hooks=[("hook_embed", my_hook)]): - output = model("Hello world") - """ - - @contextmanager - def _hooks_context(): - added_hooks: List[Tuple[HookPoint, str]] = [] - - def add_hook_to_point( - hook_point: HookPoint, - hook_fn: Callable, - name: str, - dir: Literal["fwd", "bwd"] = "fwd", - ): - if self.compatibility_mode and name != hook_point.name: - alias_names_list: list[str] = [] - if hook_point.name is not None: - alias_names_list.append(hook_point.name) - alias_names_list.append(name) - hook_point.add_hook(hook_fn, dir=dir, alias_names=alias_names_list) - else: - hook_point.add_hook(hook_fn, dir=dir) - added_hooks.append((hook_point, name)) - - def apply_hooks(hooks: List[Tuple[Union[str, Callable], Callable]], is_fwd: bool): - direction: Literal["fwd", "bwd"] = "fwd" if is_fwd else "bwd" - aliases = build_alias_to_canonical_map(self.hook_dict) - for hook_name_or_filter, hook_fn in hooks: - if isinstance(hook_name_or_filter, str): - hook_dict = self.hook_dict - actual_hook_name = hook_name_or_filter - if hook_name_or_filter in aliases: - actual_hook_name = aliases[hook_name_or_filter] - if actual_hook_name in hook_dict: - add_hook_to_point( - hook_dict[actual_hook_name], hook_fn, actual_hook_name, direction - ) - else: - hook_dict = self.hook_dict - seen_hooks = set() - for name, hook_point in hook_dict.items(): - if hook_name_or_filter(name): - hook_id = id(hook_point) - if hook_id in seen_hooks: - continue - seen_hooks.add(hook_id) - hook_name_to_use = hook_point.name if hook_point.name else name - add_hook_to_point(hook_point, hook_fn, hook_name_to_use, direction) - - try: - apply_hooks(fwd_hooks, True) - apply_hooks(bwd_hooks, False) - yield self - finally: - if reset_hooks_end: - for hook_point, name in added_hooks: - hook_point.remove_hooks() - - return _hooks_context() - - def set_use_attn_result(self, use_attn_result: bool): - """Toggle whether to explicitly calculate and expose the result for each attention head. - - Useful for interpretability but can easily burn through GPU memory. - """ - if use_attn_result: - self._validate_attention_fork_supported("use_attn_result") - self.cfg.use_attn_result = use_attn_result - self._propagate_attention_flag("use_attn_result", use_attn_result) - - def set_use_split_qkv_input(self, use_split_qkv_input: bool): - """Toggle independent residual copies for Q/K/V so each path can be patched alone. - - Mutually exclusive with `use_attn_in` — set that flag off first if it's on. - """ - if use_split_qkv_input: - if bool(getattr(self.cfg, "use_attn_in", False)): - raise ValueError( - "use_split_qkv_input and use_attn_in are mutually exclusive. " - "Call set_use_attn_in(False) before enabling use_split_qkv_input." - ) - self._validate_attention_fork_supported("use_split_qkv_input") - self.cfg.use_split_qkv_input = use_split_qkv_input - self._propagate_attention_flag("use_split_qkv_input", use_split_qkv_input) - - def set_use_attn_in(self, use_attn_in: bool): - """Toggle a single 4D residual copy feeding all three Q/K/V projections. - - Mutually exclusive with `use_split_qkv_input` — set that flag off first - if it's on. When on, `hook_attn_in` fires at - `[batch, pos, n_heads, d_model]`, enabling coarse-grained interventions - on the residual-stream copy shared across Q/K/V. - """ - if use_attn_in: - if bool(getattr(self.cfg, "use_split_qkv_input", False)): - raise ValueError( - "use_attn_in and use_split_qkv_input are mutually exclusive. " - "Call set_use_split_qkv_input(False) before enabling use_attn_in." - ) - self._validate_attention_fork_supported("use_attn_in") - self.cfg.use_attn_in = use_attn_in - self._propagate_attention_flag("use_attn_in", use_attn_in) - - def _propagate_attention_flag(self, flag_name: str, value: bool) -> None: - """Mirror `bridge.cfg.` onto every block's attention config. - - Some adapters (Llama family) deep-copy the block template during - `setup_blocks_bridge`, cloning the attention bridge's config along - with it. Others (Pythia, GPT-2) override `__deepcopy__` to share the - config. Setting the flag only on `self.cfg` silently misses the - cloned-config case. Propagating explicitly keeps both patterns - honest — a no-op when configs are shared, a correctness fix when - they aren't. - """ - if not hasattr(self, "blocks"): - return - for block in self.blocks: - attn = block._modules.get("attn") if hasattr(block, "_modules") else None - if attn is None: - continue - attn_cfg = getattr(attn, "config", None) - if attn_cfg is not None and attn_cfg is not self.cfg: - try: - setattr(attn_cfg, flag_name, value) - except Exception: - # Some cfg objects may be frozen/immutable. Skip silently — - # the block simply won't honor the flag, which is the - # same outcome as before this fix. - pass - - def _validate_attention_fork_supported(self, flag_name: str) -> None: - """Raise / warn if the model can't honor a fine-grained attention flag. - - The post-ln1 fork path lives on JointQKVAttentionBridge and - PositionEmbeddingsAttentionBridge. Plain AttentionBridge delegates to - HF and exposes no fork point; we raise rather than setting the flag - silently. For hybrid models (some attention layers, some not), we warn - and list which layers will honor the flag. - """ - # Deferred imports: tight circular dependency with bridge setup. - from transformer_lens.model_bridge.generalized_components.joint_qkv_attention import ( - JointQKVAttentionBridge, - ) - from transformer_lens.model_bridge.generalized_components.position_embeddings_attention import ( - PositionEmbeddingsAttentionBridge, - ) - - if not hasattr(self, "blocks"): - raise NotImplementedError( - f"{flag_name}: this bridge has no `blocks` attribute, so no " - "attention bridges to apply the flag to." - ) - supported_classes = (JointQKVAttentionBridge, PositionEmbeddingsAttentionBridge) - supporting_layers: list[int] = [] - attn_classes: set[str] = set() - total_with_attn = 0 - for idx, block in enumerate(self.blocks): - attn = block._modules.get("attn") if hasattr(block, "_modules") else None - if attn is None: - continue - total_with_attn += 1 - attn_classes.add(type(attn).__name__) - if isinstance(attn, supported_classes): - supporting_layers.append(idx) - if total_with_attn == 0: - raise NotImplementedError(f"{flag_name}: no attention bridges found on self.blocks.") - if not supporting_layers: - raise NotImplementedError( - f"{flag_name}: none of this model's attention bridges support " - "the fine-grained Q/K/V hook fork. Found attention classes: " - f"{sorted(attn_classes)}. Supported classes: " - f"{[c.__name__ for c in supported_classes]}. Plain " - "AttentionBridge delegates to HuggingFace and exposes no hook " - "point before the Q/K/V projection." - ) - if len(supporting_layers) < total_with_attn: - skipped = total_with_attn - len(supporting_layers) - warnings.warn( - f"{flag_name}: {skipped} of {total_with_attn} attention layers " - "use an attention-bridge class that cannot honor this flag " - f"(attention classes present: {sorted(attn_classes)}). " - f"The flag will affect layers: {supporting_layers}.", - stacklevel=3, - ) - - def _is_valid_bridge_path(self, hf_path: str) -> bool: - """Check if a HuggingFace path corresponds to a valid bridge component. - - This validates that the path follows the bridge component structure and doesn't - contain nested HuggingFace components that should have been wrapped. - - Args: - hf_path: HuggingFace path after removing _original_component - - Returns: - True if the path is valid, False if it contains nested HF components - """ - # Split the path into parts - parts = hf_path.split(".") - - # Get the component mapping for validation - component_mapping = self.adapter.component_mapping - if not component_mapping: - return True # If no mapping, accept all keys - - # Walk through the path and check if each level is a registered bridge component - # For example, transformer.h.0.mlp.in.weight should be valid - # but transformer.h.0.mlp.c_fc.weight should be invalid (c_fc is nested HF component) - - # Start from the root - current_component = None - idx = 0 - - # Find which top-level component this belongs to - for tl_name, component in component_mapping.items(): - if component.name and hf_path.startswith(component.name + "."): - current_component = component - # Skip past the HF prefix - remaining_path = hf_path[len(component.name) + 1 :] - parts = remaining_path.split(".") - idx = 0 - break - - if current_component is None: - return True # Path doesn't match any component, let it through - - # Special handling for blocks - if hasattr(current_component, "is_list_item") and current_component.is_list_item: - # Skip the layer index - if idx < len(parts) and parts[idx].isdigit(): - idx += 1 - - # Now validate the rest of the path against submodules - while idx < len(parts): - part = parts[idx] - - # If we hit 'weight' or 'bias', we're at a parameter - this is valid - if part in ("weight", "bias"): - return True - - # Check if this part is a registered submodule - if hasattr(current_component, "submodules") and current_component.submodules: - if part in current_component.submodules: - current_component = current_component.submodules[part] - idx += 1 - continue - else: - # This part is not a registered bridge component - # It's likely a nested HF component (like c_fc, c_proj, c_attn) - return False - else: - # No submodules to check, but not at a parameter yet - # Check if next is weight/bias - if idx + 1 < len(parts) and parts[idx + 1] in ("weight", "bias"): - return True - # Otherwise this is likely a nested HF component - return False - - idx += 1 - - return True - - def _normalize_bridge_key_to_hf(self, key: str) -> str: - """Normalize a key that uses bridge attribute names to use HF module names. - - PyTorch's state_dict uses the Python attribute names (e.g., 'ln1') - but the conversion logic expects HF module names (e.g., 'ln_1'). This - function only replaces non-nested component names, leaving bridge - subcomponents (like 'in', 'out', 'q', 'k', 'v') unchanged since they're - handled by the component structure. - - Args: - key: Key that may use bridge attribute names - - Returns: - Key with attribute names replaced by module names where needed - """ - component_mapping = self.adapter.component_mapping - if not component_mapping: - return key - - # Build a mapping of only the direct module attribute names to HF names - # We only care about top-level and block-level component names, NOT subcomponents - attr_to_hf = {} - - # Map top-level components - for tl_name, component in component_mapping.items(): - if component.name and tl_name != "blocks": - # Skip if TL name is already a suffix of the HF path (avoids doubling). - if tl_name != component.name and not component.name.endswith("." + tl_name): - attr_to_hf[tl_name] = component.name - - # Map block-level components (ln1, ln2, attn, mlp) - blocks_component = component_mapping.get("blocks") - if blocks_component and hasattr(blocks_component, "submodules"): - for tl_subname, subcomponent in blocks_component.submodules.items(): - if subcomponent.name: - # Only map if the names differ (e.g., ln1 -> ln_1, but attn -> attn) - if tl_subname != subcomponent.name: - attr_to_hf[tl_subname] = subcomponent.name - - # Replace only these specific attribute names in the key - # We need to be careful to only replace whole path components, not substrings - parts = key.split(".") - result_parts = [] - - for part in parts: - if part in attr_to_hf: - result_parts.append(attr_to_hf[part]) - else: - result_parts.append(part) - - return ".".join(result_parts) - - def state_dict(self, destination=None, prefix="", keep_vars=False): - """Get state dict with TransformerLens format keys. - - Converts HuggingFace format keys to TransformerLens format and filters out - _original_component references and nested HuggingFace components. - - This returns a clean state dict with only bridge component paths converted to TL format, - excluding nested HF components (like c_fc, c_proj, c_attn) that exist inside - original_component modules. - - Args: - destination: Optional dict to store state dict in - prefix: Optional prefix to add to all keys - keep_vars: Whether to keep variables as Variables instead of tensors - - Returns: - Dict containing the state dict with TransformerLens format keys - """ - if destination is not None: - raw_state_dict = self.original_model.state_dict( - destination=destination, prefix=prefix, keep_vars=keep_vars - ) - else: - raw_state_dict = self.original_model.state_dict(prefix=prefix, keep_vars=keep_vars) - - # Clean _original_component references and convert to TL format - # Also filter out nested HuggingFace components that are wrapped by bridge components - tl_state_dict = {} - - for key, value in raw_state_dict.items(): - # Skip _original_component keys - if key == "_original_component" or key.startswith("_original_component."): - continue - - # Remove all _original_component from the key - clean_key = key.replace("._original_component", "") - - # Check if this is a valid bridge path (not a nested HF component) - if not self._is_valid_bridge_path(clean_key): - continue - - # Normalize bridge component names to HF names for conversion - # (e.g., 'ln1' -> 'ln_1', 'mlp.in' -> 'mlp.c_fc') - hf_key = self._normalize_bridge_key_to_hf(clean_key) - - # Convert to TL format - this uses the adapter's component_mapping - tl_key = self.adapter.convert_hf_key_to_tl_key(hf_key) - - # Only add if we haven't seen this TL key yet (handles duplicates) - if tl_key not in tl_state_dict: - tl_state_dict[tl_key] = value - - return tl_state_dict - - def load_state_dict(self, state_dict, strict=True, assign=False): - """Load state dict into the model, handling both clean keys and original keys with _original_component references. - - Args: - state_dict: Dictionary containing a whole state of the module - strict: Whether to strictly enforce that the keys in state_dict match the keys returned by this module's state_dict() function - assign: Whether to assign items in the state dictionary to their corresponding keys in the module instead of copying them - - Returns: - NamedTuple with missing_keys and unexpected_keys fields - """ - current_state_dict = self.original_model.state_dict() - clean_to_actual = {} - actual_to_clean = {} - for actual_key in current_state_dict.keys(): - if actual_key != "_original_component": - clean_key = actual_key.replace("._original_component", "") - clean_to_actual[clean_key] = actual_key - actual_to_clean[actual_key] = clean_key - mapped_state_dict = {} - for input_key, value in state_dict.items(): - if input_key in current_state_dict: - mapped_state_dict[input_key] = value - else: - if input_key in clean_to_actual: - actual_key = clean_to_actual[input_key] - mapped_state_dict[actual_key] = value - else: - mapped_state_dict[input_key] = value - effective_strict = strict and len(mapped_state_dict) == len(current_state_dict) - return self.original_model.load_state_dict( - mapped_state_dict, strict=effective_strict, assign=assign - ) - - def get_params(self): - """Access to model parameters in the format expected by SVDInterpreter. - - For missing weights, returns zero tensors of appropriate shape instead of raising exceptions. - This ensures compatibility across different model architectures. - - Returns: - dict: Dictionary of parameter tensors with TransformerLens naming convention - - Raises: - ValueError: If configuration is inconsistent (e.g., cfg.n_layers != len(blocks)) - """ - return get_bridge_params(self) + from transformer_lens.model_bridge.bridge import TransformerBridge +""" +from transformer_lens.model_bridge.bridge_core import BridgeCore +from transformer_lens.model_bridge.remote_bridge import RemoteBridge +from transformer_lens.model_bridge.transformer_bridge import TransformerBridge - # NOTE: list_supported_models and check_model_support are attached to this class - # dynamically by transformer_lens.model_bridge.sources.transformers module. - # These are HuggingFace-specific methods that belong in the transformers source module. +__all__ = ["BridgeCore", "RemoteBridge", "TransformerBridge"] diff --git a/transformer_lens/model_bridge/bridge_core.py b/transformer_lens/model_bridge/bridge_core.py new file mode 100644 index 0000000000..301432dbfe --- /dev/null +++ b/transformer_lens/model_bridge/bridge_core.py @@ -0,0 +1,865 @@ +"""Framework-agnostic bridge surface shared by TransformerBridge and RemoteBridge.""" +from __future__ import annotations + +import re +import warnings +from contextlib import contextmanager +from functools import lru_cache +from typing import ( + Any, + Callable, + Dict, + Iterator, + List, + Literal, + Mapping, + Optional, + Tuple, + Union, + overload, +) + +import torch + +from transformer_lens.ActivationCache import ActivationCache +from transformer_lens.hook_points import HookPoint +from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter +from transformer_lens.model_bridge.driver_protocol import to_torch +from transformer_lens.model_bridge.exceptions import StopAtLayerException +from transformer_lens.utilities.aliases import resolve_alias +from transformer_lens.utilities.lm_utils import lm_cross_entropy_loss + +_BLOCK_PATTERN = re.compile("blocks\\.(\\d+)") + + +def build_alias_to_canonical_map(hook_dict: Any, prefix: str = "") -> dict: + """Map alias hook names to their canonical names (where ``.name`` differs from the key).""" + aliases: dict = {} + for key, value in hook_dict.items(): + full_key = f"{prefix}.{key}" if prefix else key + if isinstance(value, dict): + aliases.update(build_alias_to_canonical_map(value, full_key)) + elif hasattr(value, "name"): + if key != value.name: + aliases[full_key] = value.name + return aliases + + +class BridgeCore: + """Framework-agnostic bridge surface: hooks, cache, run_with_*, driver wiring. + + Holds state shared by every bridge (adapter, cfg, tokenizer, driver, hook + registries). Subclasses add framework-specific state — ``TransformerBridge`` + walks the wrapped ``nn.Module``; ``RemoteBridge`` builds components from + adapter metadata. + """ + + hook_aliases: Dict[str, Union[str, List[str]]] = { + # Prefer embed_ln.hook_out for post-LN models (Bloom, BERT) + "hook_embed": ["embed_ln.hook_out", "embed.hook_out"], + "hook_pos_embed": ["pos_embed.hook_out", "rotary_emb.hook_out"], + "hook_unembed": "unembed.hook_out", + } + + def __init__( + self, + adapter: ArchitectureAdapter, + tokenizer: Any, + driver: Any, + ) -> None: + """Subclasses call this AFTER ``nn.Module.__init__`` (if applicable), + then do framework-specific setup.""" + self.adapter = adapter + self.cfg = adapter.cfg + self.tokenizer = tokenizer + if self.cfg.d_vocab == -1 and self.tokenizer is not None: + if hasattr(self.tokenizer, "get_vocab"): + vocab = self.tokenizer.get_vocab() + self.cfg.d_vocab = max(vocab.values()) + 1 + elif hasattr(self.tokenizer, "vocab"): + self.cfg.d_vocab = max(self.tokenizer.vocab.values()) + 1 + else: + self.cfg.d_vocab = getattr(self.tokenizer, "vocab_size", 50257) + if self.cfg.d_vocab_out == -1: + self.cfg.d_vocab_out = self.cfg.d_vocab + self.compatibility_mode = False + self._hook_cache = None + self._hook_registry: Dict[str, HookPoint] = {} + self._hook_registry_initialized = False + self._hook_alias_registry: Dict[str, Union[str, List[str]]] = {} + self._property_alias_registry: Dict[str, str] = {} + self._driver = driver + if not hasattr(adapter, "component_mapping") or adapter.component_mapping is None: + raise ValueError("Adapter must have a component_mapping attribute") + + # ---- hook registry ---- + + def _initialize_hook_registry(self) -> None: + """Initialize the hook registry by scanning existing components.""" + if self._hook_registry_initialized: + return + self._scan_existing_hooks(self, "") + self._hook_registry_initialized = True + + def _scan_existing_hooks(self, module: Any, prefix: str = "") -> None: + """Walk components for HookPoint instances. Framework-specific.""" + raise NotImplementedError( + f"{type(self).__name__} must implement _scan_existing_hooks " + "(walks the component tree to find HookPoint instances)." + ) + + def clear_hook_registry(self) -> None: + """Clear the hook registry and force re-initialization.""" + self._hook_registry.clear() + self._hook_registry_initialized = False + + @property + def hook_dict(self) -> dict[str, HookPoint]: + """All HookPoint objects, including aliases — TransformerLens-compatible.""" + hooks = self._hook_registry.copy() + self._add_aliases_to_hooks(hooks) + return hooks + + # ---- alias registry ---- + + def _register_aliases(self) -> None: + """Register bridge-level aliases (hook_embed, hook_pos_embed, etc.) by + resolving each alias target path and installing the target HookPoint + as a direct attribute under the alias name.""" + if self.hook_aliases: + self._hook_alias_registry.update(self.hook_aliases) + for alias_name, target_path in self.hook_aliases.items(): + try: + if isinstance(target_path, list): + for single_target in target_path: + try: + target_obj = self + for part in single_target.split("."): + target_obj = getattr(target_obj, part) + object.__setattr__(self, alias_name, target_obj) + break + except AttributeError: + continue + else: + target_obj = self + for part in target_path.split("."): + target_obj = getattr(target_obj, part) + object.__setattr__(self, alias_name, target_obj) + except AttributeError: + pass + + def _collect_component_aliases(self, component_mapping: Any, prefix: str = "") -> dict: + """Recursively collect aliases from components.""" + aliases: dict = {} + if isinstance(component_mapping, dict): + for name, component in component_mapping.items(): + sub_prefix = f"{prefix}.{name}" if prefix else name + aliases.update(self._collect_component_aliases(component, sub_prefix)) + else: + if hasattr(component_mapping, "hook_aliases") and component_mapping.hook_aliases: + for alias_name, target in component_mapping.hook_aliases.items(): + full_alias = f"{prefix}.{alias_name}" if prefix else alias_name + full_target = f"{prefix}.{target}" if prefix else target + aliases[full_alias] = full_target + if hasattr(component_mapping, "submodules") and component_mapping.submodules: + for sub_name, sub_component in component_mapping.submodules.items(): + sub_prefix = f"{prefix}.{sub_name}" if prefix else sub_name + aliases.update(self._collect_component_aliases(sub_component, sub_prefix)) + return aliases + + @staticmethod + @lru_cache(maxsize=128) + def _compute_hook_aliases_cached( + hook_names_tuple: Tuple[str, ...], + component_aliases_tuple: Tuple[Tuple[str, str], ...], + ) -> Tuple[Tuple[str, str], ...]: + """Cached computation of hook aliases.""" + aliases: dict = {} + component_aliases = dict(component_aliases_tuple) + for hook_name in hook_names_tuple: + for alias_pattern, target_pattern in component_aliases.items(): + if "blocks." in target_pattern and "blocks." in hook_name: + block_match = _BLOCK_PATTERN.search(hook_name) + if block_match: + block_num = block_match.group(1) + dynamic_alias_pattern = alias_pattern.replace( + "blocks.", f"blocks.{block_num}." + ) + dynamic_target_pattern = target_pattern.replace( + "blocks.", f"blocks.{block_num}." + ) + if hook_name.endswith(dynamic_target_pattern): + target_len = len(dynamic_target_pattern) + alias_name = hook_name[:-target_len] + dynamic_alias_pattern + aliases[alias_name] = hook_name + elif hook_name.endswith(target_pattern): + target_len = len(target_pattern) + alias_name = hook_name[:-target_len] + alias_pattern + aliases[alias_name] = hook_name + return tuple(aliases.items()) + + def _collect_hook_aliases_from_registry(self) -> dict: + """Collect aliases based on existing hooks in the registry.""" + if hasattr(self.adapter, "component_mapping"): + component_aliases = self._collect_component_aliases(self.adapter.component_mapping) + hook_names_tuple = tuple(sorted(self._hook_registry.keys())) + component_aliases_tuple = tuple(sorted(component_aliases.items())) + aliases_tuple = self._compute_hook_aliases_cached( + hook_names_tuple, component_aliases_tuple + ) + return dict(aliases_tuple) + return {} + + def _add_aliases_to_hooks(self, hooks: Dict[str, HookPoint]) -> None: + """Add aliases to hooks in place. Registry-first so RemoteBridge works.""" + component_aliases = self._collect_hook_aliases_from_registry() + all_aliases = {**self.hook_aliases, **component_aliases} + if not all_aliases: + return + for alias_name, target in all_aliases.items(): + targets = target if isinstance(target, list) else [target] + for t in targets: + hp = self._hook_registry.get(t) + if hp is not None: + hooks[alias_name] = hp + break + # Fall back to attribute walk for TransformerBridge nested paths + # not directly keyed in the registry. + try: + target_hook = resolve_alias(self, alias_name, {alias_name: t}) + if target_hook is not None: + hooks[alias_name] = target_hook + break + except AttributeError: + continue + + # ---- captures from driver ---- + + def _replay_captures(self, captured: Mapping[str, Any]) -> None: + """Fire driver-delivered captures through the registry. Unknown names dropped silently.""" + for hook_name, activation in captured.items(): + hp = self._hook_registry.get(hook_name) + if hp is None: + continue + hp(to_torch(activation)) + + # ---- driver dispatch (subclasses concrete-override) ---- + + def forward(self, *args: Any, **kwargs: Any) -> Any: + """Subclasses implement how the driver gets called.""" + raise NotImplementedError(f"{type(self).__name__} must implement forward()") + + def to_tokens(self, *args: Any, **kwargs: Any) -> Any: + """Subclasses implement against their tokenizer surface.""" + raise NotImplementedError(f"{type(self).__name__} must implement to_tokens()") + + def close(self) -> None: + """Release driver-managed resources. Idempotent — safe to call multiple times.""" + self._driver.close() + + def _input_device(self) -> Any: + """Driver's expected device for inputs; None for remote / meta / dispatched drivers.""" + if not self._driver.supports("parameters"): + return None + params = getattr(self._driver, "parameters", None) + if not callable(params): + return None + try: + device = next(params()).device + except (StopIteration, NotImplementedError, RuntimeError): + return None + # Meta device → inputs would silently become meta tensors with no data. + if device.type == "meta": + return None + return device + + def loss_fn( + self, + logits: torch.Tensor, + tokens: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + per_token: bool = False, + ) -> torch.Tensor: + """Cross-entropy loss matching HookedTransformer's formula (log_softmax + gather).""" + if tokens.device != logits.device: + tokens = tokens.to(logits.device) + return lm_cross_entropy_loss(logits, tokens, attention_mask, per_token) + + def _finalize_return( + self, + return_type: Optional[str], + logits: Optional[torch.Tensor], + input_ids: Optional[torch.Tensor], + *, + is_audio_model: bool = False, + inputs_embeds_was_used: bool = False, + loss_per_token: bool = False, + ) -> Any: + """Post-process driver output into the user's requested return_type.""" + if return_type == "logits": + return logits + if return_type is None: + return None + if return_type == "loss": + if is_audio_model: + raise ValueError( + "Audio models do not support return_type='loss'. " + "CTC loss requires aligned frame-level labels." + ) + if inputs_embeds_was_used: + raise ValueError( + "Cannot compute loss with inputs_embeds — token IDs required for labels." + ) + assert isinstance(logits, torch.Tensor), f"Expected logits tensor, got {type(logits)}" + assert input_ids is not None, "input_ids required for return_type='loss'" + return self.loss_fn(logits, input_ids, per_token=loss_per_token) + if return_type == "both": + if is_audio_model: + raise ValueError( + "Audio models do not support return_type='both'. " + "CTC loss requires aligned frame-level labels." + ) + if inputs_embeds_was_used: + raise ValueError( + "Cannot compute loss with inputs_embeds — token IDs required for labels." + ) + assert isinstance(logits, torch.Tensor), f"Expected logits tensor, got {type(logits)}" + assert input_ids is not None, "input_ids required for return_type='both'" + loss = self.loss_fn(logits, input_ids, per_token=loss_per_token) + return (logits, loss) + if return_type == "predictions": + assert self.tokenizer is not None, "Tokenizer required for return_type='predictions'" + assert isinstance(logits, torch.Tensor), f"Expected logits tensor, got {type(logits)}" + if logits.shape[-1] == 2: + # Next Sentence Prediction — 2-class output + logprobs = logits.log_softmax(dim=-1) + predictions = [ + "The sentences are sequential", + "The sentences are NOT sequential", + ] + return predictions[int(logprobs.argmax(dim=-1).item())] + else: + # Masked Language Modeling — decode [MASK] tokens + assert input_ids is not None, "input_ids required for MLM predictions" + logprobs = logits[input_ids == self.tokenizer.mask_token_id].log_softmax(dim=-1) + preds = self.tokenizer.decode(logprobs.argmax(dim=-1)) + if " " in preds: + parts = preds.split(" ") + return [f"Prediction {i}: {p}" for i, p in enumerate(parts)] + return preds + raise ValueError(f"Invalid return_type: {return_type}") + + # ---- hook lookup / mutation ---- + + def get_hook_point(self, hook_name: str) -> Optional[HookPoint]: + """Get a hook point by name from the bridge's hook system.""" + if hook_name in self._hook_registry: + return self._hook_registry[hook_name] + try: + parts = hook_name.split(".") + current: Any = self + for part in parts: + current = getattr(current, part) + if isinstance(current, HookPoint): + return current + except AttributeError: + pass + return None + + def add_hook( + self, + name: Union[str, Callable[[str], bool]], + hook_fn: Any, + dir: Literal["fwd", "bwd"] = "fwd", + is_permanent: bool = False, + ) -> None: + """Add a hook to a specific component or to all components matching a filter. + + Args: + name: Either a string hook point name (e.g. "blocks.0.attn.hook_q") + or a callable filter ``(str) -> bool`` that is applied to every + hook point name; the hook is added to each point where the filter + returns True. + hook_fn: The hook function ``(activation, hook) -> activation | None``. + dir: Hook direction, ``"fwd"`` or ``"bwd"``. + is_permanent: If True the hook survives ``reset_hooks()`` calls. + """ + if callable(name) and not isinstance(name, str): + hook_dict = self.hook_dict + seen_hooks: set = set() + for hook_name, hook_point in hook_dict.items(): + if name(hook_name): + hook_id = id(hook_point) + if hook_id in seen_hooks: + continue + seen_hooks.add(hook_id) + hook_point.add_hook(hook_fn, dir=dir, is_permanent=is_permanent) + return + + # Registry-first: works for any bridge (RemoteBridge has no component tree). + registry_hp = self._hook_registry.get(name) + if registry_hp is not None: + registry_hp.add_hook(hook_fn, dir=dir, is_permanent=is_permanent) + return + + component: Any = self + parts = name.split(".") + for part in parts[:-1]: + if hasattr(component, part): + component = getattr(component, part) + else: + raise AttributeError(f"Component path '{'.'.join(parts[:-1])}' not found") + hook_name = parts[-1] + if hasattr(component, hook_name): + hook_point = getattr(component, hook_name) + if isinstance(hook_point, HookPoint): + hook_point.add_hook(hook_fn, dir=dir, is_permanent=is_permanent) + else: + raise AttributeError( + f"'{hook_name}' is not a hook point. Found object of type: {type(hook_point)} with value: {hook_point}" + ) + else: + raise AttributeError(f"Hook point '{hook_name}' not found on component") + + def reset_hooks(self, clear_contexts: bool = True) -> None: + """Remove all hooks. Registry is canonical; nn.Module children walked additively.""" + for hp in self._hook_registry.values(): + hp.remove_hooks() + if hasattr(self, "children"): + from transformer_lens.model_bridge.generalized_components.base import ( + GeneralizedComponent, + ) + + def remove_hooks_recursive(module: Any) -> None: + if isinstance(module, GeneralizedComponent): + module.remove_hooks() + if hasattr(module, "children"): + for child in module.children(): + remove_hooks_recursive(child) + + remove_hooks_recursive(self) + + def hooks( + self, + fwd_hooks: List = [], + bwd_hooks: List = [], + reset_hooks_end: bool = True, + clear_contexts: bool = False, + ) -> Any: + """Context manager for temporarily adding hooks. + + Example: + with model.hooks(fwd_hooks=[("hook_embed", my_hook)]): + output = model("Hello world") + """ + + @contextmanager + def _hooks_context() -> Iterator["BridgeCore"]: + added_hooks: List[Tuple[HookPoint, str]] = [] + + def add_hook_to_point( + hook_point: HookPoint, + hook_fn: Callable, + name: str, + dir: Literal["fwd", "bwd"] = "fwd", + ) -> None: + if self.compatibility_mode and name != hook_point.name: + alias_names_list: list = [] + if hook_point.name is not None: + alias_names_list.append(hook_point.name) + alias_names_list.append(name) + hook_point.add_hook(hook_fn, dir=dir, alias_names=alias_names_list) + else: + hook_point.add_hook(hook_fn, dir=dir) + added_hooks.append((hook_point, name)) + + def apply_hooks(hook_list: List[Tuple[Any, Callable]], is_fwd: bool) -> None: + direction: Literal["fwd", "bwd"] = "fwd" if is_fwd else "bwd" + aliases = build_alias_to_canonical_map(self.hook_dict) + for hook_name_or_filter, hook_fn in hook_list: + if isinstance(hook_name_or_filter, str): + hook_dict = self.hook_dict + actual_hook_name = hook_name_or_filter + if hook_name_or_filter in aliases: + actual_hook_name = aliases[hook_name_or_filter] + if actual_hook_name in hook_dict: + add_hook_to_point( + hook_dict[actual_hook_name], hook_fn, actual_hook_name, direction + ) + else: + hook_dict = self.hook_dict + seen_hooks = set() + for n, hook_point in hook_dict.items(): + if hook_name_or_filter(n): + hook_id = id(hook_point) + if hook_id in seen_hooks: + continue + seen_hooks.add(hook_id) + hook_name_to_use = hook_point.name if hook_point.name else n + add_hook_to_point(hook_point, hook_fn, hook_name_to_use, direction) + + try: + apply_hooks(fwd_hooks, True) + apply_hooks(bwd_hooks, False) + yield self + finally: + if reset_hooks_end: + for hook_point, _ in added_hooks: + hook_point.remove_hooks() + + return _hooks_context() + + # ---- high-level execution: run_with_hooks ---- + + def run_with_hooks( + self, + input: Any, + fwd_hooks: List[Tuple[Union[str, Callable], Callable]] = [], + bwd_hooks: List[Tuple[Union[str, Callable], Callable]] = [], + reset_hooks_end: bool = True, + clear_contexts: bool = False, + return_type: Optional[str] = "logits", + names_filter: Optional[Union[str, List[str], Callable[[str], bool]]] = None, + stop_at_layer: Optional[int] = None, + remove_batch_dim: bool = False, + **kwargs: Any, + ) -> Any: + """Run the model with specified forward and backward hooks. + + ``stop_at_layer`` raises :class:`StopAtLayerException` to stop early + (KV cache cleaned up on stop). ``remove_batch_dim`` squeezes/unsqueezes + the batch dim around hook callbacks (batch_size==1 only). + """ + added_hooks: List[Tuple[HookPoint, str]] = [] + effective_stop_layer = None + if stop_at_layer is not None and hasattr(self, "blocks"): + if stop_at_layer < 0: + effective_stop_layer = len(self.blocks) + stop_at_layer + else: + effective_stop_layer = stop_at_layer + + def add_hook_to_point( + hook_point: HookPoint, + hook_fn: Callable, + name: str, + dir: Literal["fwd", "bwd"] = "fwd", + ) -> None: + if effective_stop_layer is not None and name.startswith("blocks."): + try: + layer_num = int(name.split(".")[1]) + if layer_num >= effective_stop_layer: + return + except (IndexError, ValueError): + pass + if self.compatibility_mode and name != hook_point.name: + alias_names_list: list = [] + if hook_point.name is not None: + alias_names_list.append(hook_point.name) + alias_names_list.append(name) + hook_point.add_hook(hook_fn, dir=dir, alias_names=alias_names_list) + else: + hook_point.add_hook(hook_fn, dir=dir) + added_hooks.append((hook_point, name)) + + if stop_at_layer is not None and hasattr(self, "blocks"): + if stop_at_layer < 0: + stop_at_layer = len(self.blocks) + stop_at_layer + if stop_at_layer >= 0 and stop_at_layer < len(self.blocks): + + def stop_hook(tensor: Any, *, hook: Any) -> Any: + raise StopAtLayerException(tensor) + + # Stop at the beginning of the specified block, not at the end of the previous block + block_hook_name = f"blocks.{stop_at_layer}.hook_in" + hook_dict = self.hook_dict + if block_hook_name in hook_dict: + add_hook_to_point(hook_dict[block_hook_name], stop_hook, block_hook_name, "fwd") + + def apply_hooks( + hook_list: List[Tuple[Union[str, Callable], Callable]], is_fwd: bool + ) -> None: + direction: Literal["fwd", "bwd"] = "fwd" if is_fwd else "bwd" + aliases = build_alias_to_canonical_map(self.hook_dict) + for hook_name_or_filter, hook_fn in hook_list: + if remove_batch_dim: + original_hook_fn = hook_fn + + # Default arg captures hook_fn by value (avoids closure issue) + def wrapped_hook_fn(tensor, hook, _orig_fn=original_hook_fn): + if tensor.shape[0] == 1: + tensor_no_batch = tensor.squeeze(0) + result = _orig_fn(tensor_no_batch, hook) + if result.dim() == tensor_no_batch.dim(): + result = result.unsqueeze(0) + return result + else: + return _orig_fn(tensor, hook) + + hook_fn = wrapped_hook_fn + if isinstance(hook_name_or_filter, str): + hook_dict = self.hook_dict + actual_hook_name = hook_name_or_filter + if hook_name_or_filter in aliases: + actual_hook_name = aliases[hook_name_or_filter] + if actual_hook_name in hook_dict: + add_hook_to_point( + hook_dict[actual_hook_name], hook_fn, actual_hook_name, direction + ) + else: + hook_dict = self.hook_dict + seen_hooks: set = set() + for n, hook_point in hook_dict.items(): + if hook_name_or_filter(n): + hook_id = id(hook_point) + if hook_id in seen_hooks: + continue + seen_hooks.add(hook_id) + hook_name_to_use = hook_point.name if hook_point.name else n + add_hook_to_point(hook_point, hook_fn, hook_name_to_use, direction) + + try: + apply_hooks(fwd_hooks, True) + apply_hooks(bwd_hooks, False) + try: + output = self.forward( + input, return_type=return_type, stop_at_layer=stop_at_layer, **kwargs + ) + except StopAtLayerException as e: + output = e.layer_output + return output + finally: + if reset_hooks_end: + for hook_point, _ in added_hooks: + hook_point.remove_hooks() + + # ---- high-level execution: run_with_cache ---- + + @overload + def run_with_cache( + self, + input: Union[str, List[str], torch.Tensor], + return_cache_object: Literal[True] = True, + remove_batch_dim: bool = False, + **kwargs, + ) -> Tuple[Any, ActivationCache]: + """Run with cache - placeholder implementation.""" + pass + + @overload + def run_with_cache( + self, + input: Union[str, List[str], torch.Tensor], + return_cache_object: Literal[False], + remove_batch_dim: bool = False, + **kwargs, + ) -> Tuple[Any, Dict[str, torch.Tensor]]: + """Run with cache - placeholder implementation.""" + pass + + def run_with_cache( + self, + input: Union[str, List[str], torch.Tensor], + return_cache_object: bool = True, + remove_batch_dim: bool = False, + names_filter: Optional[Union[str, List[str], Callable[[str], bool]]] = None, + stop_at_layer: Optional[int] = None, + **kwargs, + ) -> Tuple[Any, Union[ActivationCache, Dict[str, torch.Tensor]]]: + """Run the model and cache activations. Returns ``(output, cache)``. + + ``stop_at_layer`` raises :class:`StopAtLayerException` to stop early. + """ + aliases = build_alias_to_canonical_map(self.hook_dict) + + def create_names_filter_fn(filter_input): + if filter_input is None: + return lambda name: True + elif isinstance(filter_input, str): + mapped_name = aliases.get(filter_input, None) + if mapped_name: + return lambda name: name == mapped_name or name == filter_input + else: + return lambda name: name == filter_input + elif isinstance(filter_input, list): + mapped_list = [] + for item in filter_input: + mapped_list.append(item) + mapped_name = aliases.get(item, None) + if mapped_name: + mapped_list.append(mapped_name) + return lambda name: name in mapped_list + elif callable(filter_input): + return filter_input + else: + raise ValueError("names_filter must be a string, list of strings, or callable") + + names_filter_fn = create_names_filter_fn(names_filter) + cache: Dict[str, torch.Tensor] = {} + hooks: List[Tuple[HookPoint, str]] = [] + visited: set[int] = set() + + # None → no-op .to(None), tensors stay on their current device. + cache_device = kwargs.pop("device", None) + + def make_cache_hook(name: str): + def cache_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: + if tensor is None: + cache[name] = None + elif isinstance(tensor, torch.Tensor): + cache[name] = tensor.detach().to(cache_device) + elif isinstance(tensor, tuple): + if len(tensor) > 0 and isinstance(tensor[0], torch.Tensor): + cache[name] = tensor[0].detach().to(cache_device) + else: + pass + else: + try: + if hasattr(tensor, "detach"): + cache[name] = tensor.detach().to(cache_device) + except: + pass + return tensor + + return cache_hook + + hook_dict = self.hook_dict + effective_stop_layer = None + if stop_at_layer is not None and hasattr(self, "blocks"): + if stop_at_layer < 0: + effective_stop_layer = len(self.blocks) + stop_at_layer + else: + effective_stop_layer = stop_at_layer + for hook_name, hook in hook_dict.items(): + if names_filter_fn(hook_name): + if effective_stop_layer is not None: + if hook_name.startswith("blocks."): + try: + layer_num = int(hook_name.split(".")[1]) + if layer_num >= effective_stop_layer: + continue + except (IndexError, ValueError): + pass + hooks.append((hook, hook_name)) + for hp, name in hooks: + hp.add_hook(make_cache_hook(name)) + processed_args = [input] + # Driver-aware input placement: torch drivers move input_ids to the model's + # device; remote drivers (no local parameters) leave them as-is. + target_device = self._input_device() + if processed_args and isinstance(processed_args[0], str): + assert self.tokenizer is not None, "Tokenizer must be set to pass string input." + input_ids = self.to_tokens(processed_args[0]) + if target_device is not None: + input_ids = input_ids.to(target_device) + kwargs["input_ids"] = input_ids + processed_args = processed_args[1:] + elif "input" in kwargs and isinstance(kwargs["input"], str): + assert self.tokenizer is not None, "Tokenizer must be set to pass string input." + input_ids = self.to_tokens(kwargs["input"]) + if target_device is not None: + input_ids = input_ids.to(target_device) + kwargs["input_ids"] = input_ids + del kwargs["input"] + if stop_at_layer is not None and hasattr(self, "blocks"): + if stop_at_layer < 0: + stop_at_layer = len(self.blocks) + stop_at_layer + last_layer_to_process = stop_at_layer - 1 + + def stop_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: + raise StopAtLayerException(tensor) + + if stop_at_layer >= 0 and stop_at_layer < len(self.blocks): + # Stop at the beginning of the specified block, not at the end of the previous block + block_hook_name = f"blocks.{stop_at_layer}.hook_in" + hook_dict = self.hook_dict + if block_hook_name in hook_dict: + hook_dict[block_hook_name].add_hook(stop_hook) + hooks.append((hook_dict[block_hook_name], block_hook_name)) + filtered_kwargs = kwargs.copy() + if cache_device is not None: + if getattr(self.cfg, "n_devices", 1) > 1: + # Moving a dispatched model to a single device collapses accelerate's + # split and breaks its routing hooks. The cache will stay spread across + # the per-layer devices; callers can .to(cache_device) on cache entries + # after the fact if they need a single-device cache. + warnings.warn( + f"run_with_cache(device={cache_device!r}) ignored: model is dispatched " + f"across {self.cfg.n_devices} devices via device_map. Cached activations " + "will remain on their per-layer devices.", + stacklevel=2, + ) + else: + try: + # original_model / its setter exist on TransformerBridge; on + # non-torch bridges the property raises AttributeError. + underlying = getattr(self, "original_model") + setattr(self, "original_model", underlying.to(cache_device)) + except AttributeError: + # Non-torch driver: cache_device doesn't apply to the model. + warnings.warn( + f"run_with_cache(device={cache_device!r}) ignored: driver does not " + "expose a local model. Cached activations will stay on driver-managed " + "devices.", + stacklevel=2, + ) + if processed_args and isinstance(processed_args[0], torch.Tensor): + processed_args = [processed_args[0].to(cache_device)] + list(processed_args[1:]) + for key, value in filtered_kwargs.items(): + if isinstance(value, torch.Tensor): + filtered_kwargs[key] = value.to(cache_device) + try: + if "output_attentions" not in filtered_kwargs: + filtered_kwargs["output_attentions"] = True + if processed_args: + output = self.forward(processed_args[0], **filtered_kwargs) + elif "input_ids" in filtered_kwargs: + output = self.forward( + filtered_kwargs["input_ids"], + **{k: v for k, v in filtered_kwargs.items() if k != "input_ids"}, + ) + else: + output = self.forward(**filtered_kwargs) + if hasattr(output, "logits"): + output = output.logits + except StopAtLayerException as e: + output = e.layer_output + except Exception as e: + raise e + finally: + for hp, _ in hooks: + hp.remove_hooks() + if self.compatibility_mode == True: + reverse_aliases = {} + for old_name, new_name in aliases.items(): + if isinstance(new_name, list): + for single_new_name in new_name: + reverse_aliases[single_new_name] = old_name + else: + reverse_aliases[new_name] = old_name + cache_items_to_add = {} + for cache_name, cached_value in cache.items(): + for new_name, old_name in reverse_aliases.items(): + if cache_name == new_name: + cache_items_to_add[old_name] = cached_value + break + cache.update(cache_items_to_add) + for alias_name, target_name in aliases.items(): + if isinstance(target_name, list): + for single_target in target_name: + if single_target in cache and alias_name not in cache: + cache[alias_name] = cache[single_target] + break + elif target_name in cache and alias_name not in cache: + cache[alias_name] = cache[target_name] + if return_cache_object: + activation_cache = ActivationCache(cache, self, has_batch_dim=True) + if remove_batch_dim: + activation_cache.remove_batch_dim() + return (output, activation_cache) + else: + if remove_batch_dim: + for key in cache: + if cache[key] is not None and isinstance(cache[key], torch.Tensor): + if cache[key].size(0) == 1: + cache[key] = cache[key][0] + return (output, cache) diff --git a/transformer_lens/model_bridge/driver_protocol.py b/transformer_lens/model_bridge/driver_protocol.py new file mode 100644 index 0000000000..ffe7a4862e --- /dev/null +++ b/transformer_lens/model_bridge/driver_protocol.py @@ -0,0 +1,192 @@ +"""Driver protocol: the contract every model-execution backend satisfies.""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Callable, Mapping, Protocol, Union, runtime_checkable + +import numpy as np +import torch + +from transformer_lens.config import TransformerBridgeConfig + + +@runtime_checkable +class TensorLike(Protocol): + """Quacks like a tensor: ``__array__`` + ``shape`` + ``dtype``.""" + + # Attribute types stay loose — torch.Size, plain tuple, and numpy's shape + # don't share a Protocol-strict supertype. + @property + def shape(self) -> Any: + ... + + @property + def dtype(self) -> Any: + ... + + def __array__(self, dtype: Any = None) -> np.ndarray: + ... + + +InterventionFn = Callable[[TensorLike], TensorLike] +InterventionSpec = Mapping[str, Any] +# Drivers that can dispatch Python at the engine boundary (HF) accept +# InterventionFn; drivers that can't (vLLM under compile, remote APIs) accept +# InterventionSpec only. +Intervention = Union[InterventionFn, InterventionSpec] + + +@dataclass(frozen=True) +class ForwardResult: + """One forward call's outputs. Tensors are native to the driver's framework.""" + + logits: TensorLike | None = None + captured: Mapping[str, TensorLike] = field(default_factory=dict) + new_tokens: TensorLike | None = None + # Driver's native return value (HF CausalLMOutputWithPast, vLLM + # RequestOutput, ...). Bridge reads driver-specific extras here. + raw_output: Any = None + + +@runtime_checkable +class Driver(Protocol): + """The forward-pass contract. Hook installation is the driver's problem.""" + + architecture: str + bridge_config: TransformerBridgeConfig + tokenizer: Any + supported_hook_points: frozenset[str] + non_fireable_hook_points: frozenset[str] + + def forward( + self, + input_ids: TensorLike | None = None, + *, + capture: tuple[str, ...] = (), + intervene: Mapping[str, Intervention] | None = None, + max_new_tokens: int = 1, + return_logits: bool = True, + **kwargs: Any, + ) -> ForwardResult: + ... + + def close(self) -> None: + ... + + def supports(self, feature: str) -> bool: + """Capability flag. Known features: gradients, parameters, state_dict, + generate_streaming, weight_access, intervention_callbacks.""" + ... + + # Note: torch-specific surface (parameters, named_parameters, state_dict, + # weight access) is NOT in the protocol. Drivers that can serve those + # methods provide them as implementation details, gated by supports("..."). + + +def to_torch(t: TensorLike, *, dtype: torch.dtype | None = None) -> torch.Tensor: + """Convert any TensorLike to torch.Tensor at the bridge boundary. + + Order: torch passthrough → DLPack (jax/mlx/tf/cupy/numpy≥1.22, preserves + device) → ``__array__`` + ``from_numpy`` (CPU only). + """ + if isinstance(t, torch.Tensor): + return t.to(dtype) if dtype is not None else t + + if hasattr(t, "__dlpack__"): + try: + out = torch.from_dlpack(t) + return out.to(dtype) if dtype is not None else out + except (BufferError, RuntimeError, ValueError, TypeError, AttributeError): + # Fall through on stream-sync, missing __dlpack_device__, or + # version-skew failures; the numpy path either succeeds or raises + # informatively. + pass + + arr = np.asarray(t) + out = torch.from_numpy(arr) + return out.to(dtype) if dtype is not None else out + + +# Parameter names a conforming driver's forward() must accept; missing names +# get silently swallowed by **kwargs and break the contract. +_DRIVER_FORWARD_REQUIRED_PARAMS = frozenset( + ("input_ids", "capture", "intervene", "max_new_tokens", "return_logits") +) + + +def validate_driver(driver: Any, *, after_bridge_construction: bool = False) -> None: + """Stronger than ``isinstance(driver, Driver)``: checks types, signatures, + and (optionally) post-construction state. + + Args: + after_bridge_construction: when True, also requires at least one of + ``supported_hook_points`` / ``non_fireable_hook_points`` non-empty + (the bridge backfills the former, so empty-on-both means the + driver silently degrades to "supports nothing"). + + Raises: + TypeError: with a message naming the contract violation. + """ + _expect_attr_type(driver, "architecture", str) + _expect_attr_type(driver, "bridge_config", TransformerBridgeConfig) + if not hasattr(driver, "tokenizer"): + raise TypeError("Driver missing required attribute: 'tokenizer'") + _expect_attr_type(driver, "supported_hook_points", frozenset) + _expect_attr_type(driver, "non_fireable_hook_points", frozenset) + + overlap = driver.supported_hook_points & driver.non_fireable_hook_points + if overlap: + raise TypeError( + f"Driver.supported_hook_points and Driver.non_fireable_hook_points " + f"overlap on {sorted(overlap)[:3]}; a hook is either fireable or not." + ) + for name in driver.supported_hook_points | driver.non_fireable_hook_points: + if not isinstance(name, str): + raise TypeError(f"Hook-point names must be str; got {type(name).__name__}: {name!r}") + + forward = getattr(driver, "forward", None) + if not callable(forward): + raise TypeError("Driver.forward must be callable") + import inspect + + sig = inspect.signature(forward) + params = sig.parameters + has_var_keyword = any(p.kind == inspect.Parameter.VAR_KEYWORD for p in params.values()) + missing = [p for p in _DRIVER_FORWARD_REQUIRED_PARAMS if p not in params] + if missing and not has_var_keyword: + raise TypeError( + f"Driver.forward must accept parameters {sorted(_DRIVER_FORWARD_REQUIRED_PARAMS)}; " + f"missing {sorted(missing)} (and no **kwargs to absorb them)." + ) + + if not callable(getattr(driver, "close", None)): + raise TypeError("Driver.close must be callable") + + if after_bridge_construction: + if not driver.supported_hook_points and not driver.non_fireable_hook_points: + raise TypeError( + "Driver has empty supported_hook_points AND non_fireable_hook_points " + "after bridge construction. Drivers must declare at least one — the " + "bridge backfills supported from registry minus non_fireable, but " + "empty-on-both means there's no contract for downstream code." + ) + + +def _expect_attr_type(obj: Any, name: str, expected: type) -> None: + if not hasattr(obj, name): + raise TypeError(f"Driver missing required attribute: {name!r}") + value = getattr(obj, name) + if not isinstance(value, expected): + raise TypeError(f"Driver.{name} must be {expected.__name__}; got {type(value).__name__}.") + + +__all__ = [ + "Driver", + "ForwardResult", + "Intervention", + "InterventionFn", + "InterventionSpec", + "TensorLike", + "to_torch", + "validate_driver", +] diff --git a/transformer_lens/model_bridge/remote_bridge.py b/transformer_lens/model_bridge/remote_bridge.py new file mode 100644 index 0000000000..b41fa10352 --- /dev/null +++ b/transformer_lens/model_bridge/remote_bridge.py @@ -0,0 +1,100 @@ +"""Non-torch bridge: vLLM workers, Inspect remote providers.""" +from __future__ import annotations + +from typing import Any + +from transformer_lens.hook_points import HookIntrospectionMixin, HookPoint +from transformer_lens.model_bridge.bridge_core import BridgeCore +from transformer_lens.model_bridge.driver_protocol import ( + ForwardResult, + TensorLike, + to_torch, + validate_driver, +) + + +class RemoteBridge(BridgeCore, HookIntrospectionMixin): + """Bridge for backends with no local ``nn.Module`` (vLLM, Inspect). + + No nn.Module parentage strips the torch-only surface; driver pre-declares + ``supported_hook_points`` (no model to walk). + """ + + def __init__( + self, + adapter: Any, + tokenizer: Any, + driver: Any, + ) -> None: + if not driver.supported_hook_points: + raise ValueError( + "RemoteBridge requires driver.supported_hook_points to be " + "non-empty: non-torch drivers own the hook namespace because " + "there is no local model for the bridge to walk." + ) + BridgeCore.__init__(self, adapter, tokenizer, driver) + # No local device; tensor.to(None) is a no-op so downstream patterns degrade cleanly. + self.cfg.device = None + # HookPoint is nn.Module-backed but RemoteBridge isn't an nn.Module — + # named_modules() walks don't apply; only registry lookup matters. + for name in driver.supported_hook_points: + hp = HookPoint() + hp.name = name + self._hook_registry[name] = hp + self._hook_registry_initialized = True + validate_driver(self._driver, after_bridge_construction=True) + + @staticmethod + def boot_vllm(*args: Any, **kwargs: Any) -> "RemoteBridge": + """Boot a model via vLLM. Returns a RemoteBridge wrapping a VLLMDriver. + + Mirrors ``TransformerBridge.boot_transformers``. Lazy import so + ``remote_bridge`` itself stays vLLM-agnostic — only callers of this + method need vLLM installed. See :func:`sources.vllm.boot_vllm` for kwargs. + """ + from .sources.vllm import boot_vllm as _boot_vllm + + return _boot_vllm(*args, **kwargs) + + def _scan_existing_hooks(self, module: Any, prefix: str = "") -> None: + """No-op: registry built from driver declarations in __init__.""" + + def forward( + self, + input: Any = None, + *, + return_type: str | None = "logits", + loss_per_token: bool = False, + **kwargs: Any, + ) -> Any: + """Tokenize → driver.forward → replay captures → finalize per return_type.""" + if isinstance(input, str): + assert self.tokenizer is not None, "Tokenizer must be set for string input." + tokens = self.tokenizer.encode(input, return_tensors="pt") + kwargs["input_ids"] = tokens + elif input is not None: + kwargs["input_ids"] = input + + result: ForwardResult = self._driver.forward(**kwargs) + if result.captured: + self._replay_captures(result.captured) + + logits: Any = result.logits + if logits is not None and not isinstance(logits, TensorLike): + return logits # weird shape — let caller handle + if logits is not None: + logits = to_torch(logits) + + return self._finalize_return( + return_type, + logits, + kwargs.get("input_ids"), + is_audio_model=getattr(self.cfg, "is_audio_model", False), + loss_per_token=loss_per_token, + ) + + def to_tokens(self, text: Any, *args: Any, **kwargs: Any) -> Any: + """Tokenize via ``self.tokenizer``. BOS/padding handling lives on + :class:`TransformerBridge`.""" + assert self.tokenizer is not None, "Tokenizer must be set." + return self.tokenizer.encode(text, return_tensors="pt") diff --git a/transformer_lens/model_bridge/sources/__init__.py b/transformer_lens/model_bridge/sources/__init__.py index c3e54e2364..2dabe400ba 100644 --- a/transformer_lens/model_bridge/sources/__init__.py +++ b/transformer_lens/model_bridge/sources/__init__.py @@ -8,9 +8,11 @@ check_model_support, list_supported_models, ) +from transformer_lens.model_bridge.sources.vllm import boot_vllm __all__ = [ "boot", + "boot_vllm", "list_supported_models", "check_model_support", ] diff --git a/transformer_lens/model_bridge/sources/_bridge_builder.py b/transformer_lens/model_bridge/sources/_bridge_builder.py new file mode 100644 index 0000000000..152671d790 --- /dev/null +++ b/transformer_lens/model_bridge/sources/_bridge_builder.py @@ -0,0 +1,193 @@ +"""Loader-agnostic helpers for building a TransformerBridge around a pre-loaded model.""" +from __future__ import annotations + +from typing import Any, Callable, Optional + +import torch +from torch import nn + +from transformer_lens.config import TransformerBridgeConfig +from transformer_lens.factories.architecture_adapter_factory import ( + ArchitectureAdapterFactory, +) +from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter +from transformer_lens.model_bridge.bridge import TransformerBridge +from transformer_lens.model_bridge.sources._hf_format import ( + map_default_transformer_lens_config, + setup_tokenizer, +) + +# Architecture-agnostic; do not extend per-architecture. +_HF_PASSTHROUGH_ATTRS = [ + # OPT + "is_gated_act", + "word_embed_proj_dim", + "do_layer_norm_before", + # Granite + "position_embedding_type", + # Falcon + "parallel_attn", + "multi_query", + "new_decoder_architecture", + "alibi", + "num_ln_in_parallel_attn", + # Mamba (SSM config) + "state_size", + "conv_kernel", + "expand", + "time_step_rank", + "intermediate_size", + # Mamba-2 (additional SSM config) + "n_groups", + "chunk_size", + # Multimodal + "vision_config", +] + + +def build_bridge_config_from_hf( + hf_config: Any, + architecture: str, + model_name: str, + dtype: torch.dtype, +) -> TransformerBridgeConfig: + """Translate an HF config into a :class:`TransformerBridgeConfig`.""" + tl_config = map_default_transformer_lens_config(hf_config) + config_dict = dict(tl_config.__dict__) + # HF's attribute_map remaps num_experts → num_local_experts; restore the TL name. + if "num_local_experts" in config_dict and "num_experts" not in config_dict: + config_dict["num_experts"] = config_dict["num_local_experts"] + bridge_config = TransformerBridgeConfig.from_dict(config_dict) + bridge_config.architecture = architecture + bridge_config.model_name = model_name + bridge_config.dtype = dtype + + for attr in _HF_PASSTHROUGH_ATTRS: + val = getattr(hf_config, attr, None) + if val is not None: + setattr(bridge_config, attr, val) + + # Gemma2: HF softcap field names differ from TL's. + final_logit_softcapping = getattr(hf_config, "final_logit_softcapping", None) + if final_logit_softcapping is not None: + bridge_config.output_logits_soft_cap = float(final_logit_softcapping) + attn_logit_softcapping = getattr(hf_config, "attn_logit_softcapping", None) + if attn_logit_softcapping is not None: + bridge_config.attn_scores_soft_cap = float(attn_logit_softcapping) + + return bridge_config + + +def detect_tokenizer_bos_eos(tokenizer: Any) -> tuple[bool, bool]: + """Detect whether the tokenizer prepends BOS and/or appends EOS.""" + # Non-empty test string — "" is unreliable with token aliasing. + encoded_test = tokenizer.encode("a") + prepends_bos = ( + len(encoded_test) > 1 + and tokenizer.bos_token_id is not None + and encoded_test[0] == tokenizer.bos_token_id + ) + appends_eos = ( + len(encoded_test) > 1 + and tokenizer.eos_token_id is not None + and encoded_test[-1] == tokenizer.eos_token_id + ) + return prepends_bos, appends_eos + + +def build_bridge_from_module( + model: nn.Module, + architecture: str, + hf_config: Optional[Any] = None, + tl_config: Optional[TransformerBridgeConfig] = None, + tokenizer: Optional[Any] = None, + dtype: Optional[torch.dtype] = None, + device: Optional[Any] = None, + model_name: str = "external", + post_adapter_hook: Optional[Callable[[ArchitectureAdapter], None]] = None, +) -> TransformerBridge: + """Build a :class:`TransformerBridge` around a pre-loaded model. + + The bridge never moves, casts, or mutates the supplied model. + + Args: + model: Any ``nn.Module`` whose submodule tree matches the adapter's + expected dot-paths for ``architecture``. + architecture: Architecture identifier (e.g. ``"LlamaForCausalLM"``). + hf_config: Optional HF-style config; translated via + :func:`build_bridge_config_from_hf`. Mutually exclusive with ``tl_config``. + tl_config: Optional pre-built :class:`TransformerBridgeConfig`; bypasses + HF translation. Mutually exclusive with ``hf_config``. + tokenizer: Optional tokenizer. If supplied, passes through + ``setup_tokenizer`` and detects BOS/EOS behavior. + dtype: Recorded on ``cfg.dtype``. Default ``None`` reads from the model's + first parameter; explicit values override. + device: Recorded on ``cfg.device``. Default ``None`` reads from the + model's first parameter. + model_name: Recorded on ``cfg.model_name``. + post_adapter_hook: Optional callback invoked after adapter selection and + before :meth:`adapter.prepare_model`. Source-specific overlays mutate + ``component_mapping`` here. + + Returns: + A :class:`TransformerBridge` wrapping the supplied model. + """ + if hf_config is None and tl_config is None: + raise ValueError( + "build_bridge_from_module requires exactly one of hf_config or " + "tl_config — the bridge needs config fields (d_model, n_heads, " + "n_layers, ...) that can't be inferred from the model alone." + ) + if hf_config is not None and tl_config is not None: + raise ValueError( + "build_bridge_from_module got both hf_config and tl_config; supply " + "exactly one. hf_config triggers HF→bridge translation; tl_config " + "bypasses it." + ) + + # Reading dtype from the model avoids silently lying about a bf16 model. + if dtype is None: + try: + dtype = next(model.parameters()).dtype + except StopIteration: + dtype = torch.float32 + + if tl_config is not None: + bridge_config = tl_config + bridge_config.architecture = architecture + # Explicit kwarg wins over whatever tl_config carries; default only fills a gap. + if model_name != "external" or not getattr(bridge_config, "model_name", None): + bridge_config.model_name = model_name + bridge_config.dtype = dtype + else: + bridge_config = build_bridge_config_from_hf(hf_config, architecture, model_name, dtype) + + adapter = ArchitectureAdapterFactory.select_architecture_adapter(bridge_config) + + if post_adapter_hook is not None: + post_adapter_hook(adapter) + + if device is not None: + adapter.cfg.device = str(device) + else: + try: + adapter.cfg.device = str(next(model.parameters()).device) + except StopIteration: + adapter.cfg.device = "cpu" + + adapter.prepare_model(model) + + if tokenizer is not None: + default_padding_side = getattr(adapter.cfg, "default_padding_side", None) + tokenizer = setup_tokenizer(tokenizer, default_padding_side=default_padding_side) + ( + adapter.cfg.tokenizer_prepends_bos, + adapter.cfg.tokenizer_appends_eos, + ) = detect_tokenizer_bos_eos(tokenizer) + + from transformer_lens.model_bridge.sources.transformers_driver import ( + TransformersDriver, + ) + + driver = TransformersDriver(model, adapter, tokenizer) + return TransformerBridge(model, adapter, tokenizer, driver=driver) diff --git a/transformer_lens/model_bridge/sources/_driver_base.py b/transformer_lens/model_bridge/sources/_driver_base.py new file mode 100644 index 0000000000..175d3fee24 --- /dev/null +++ b/transformer_lens/model_bridge/sources/_driver_base.py @@ -0,0 +1,67 @@ +"""Optional base class for :class:`Driver` implementations. + +The protocol is duck-typed; inheriting is convenient, not required. +""" +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Any, Mapping + +from transformer_lens.config import TransformerBridgeConfig +from transformer_lens.model_bridge.driver_protocol import ( + ForwardResult, + Intervention, + TensorLike, +) + + +class DriverBase(ABC): + """Defaults for the optional Driver members. Subclasses implement ``forward`` + and override the rest only when they can do better.""" + + architecture: str = "" + # The bridge overwrites this slot at construction (registry − non_fireable) + # when it's empty. Whitelist-semantic drivers (e.g. Inspect) declare a + # non-empty set in the subclass to keep it. + supported_hook_points: frozenset[str] = frozenset() + non_fireable_hook_points: frozenset[str] = frozenset() + + # Subclasses override with the capability strings they actually serve. + _supported_features: frozenset[str] = frozenset() + + def __init__( + self, + bridge_config: TransformerBridgeConfig, + tokenizer: Any, + *, + architecture: str | None = None, + ) -> None: + self.bridge_config = bridge_config + self.tokenizer = tokenizer + # Resolution order: explicit kwarg > bridge_config field > class default. + self.architecture = ( + architecture or getattr(bridge_config, "architecture", "") or self.architecture + ) + + @abstractmethod + def forward( + self, + input_ids: TensorLike | None = None, + *, + capture: tuple[str, ...] = (), + intervene: Mapping[str, Intervention] | None = None, + max_new_tokens: int = 1, + return_logits: bool = True, + **kwargs: Any, + ) -> ForwardResult: + ... + + def close(self) -> None: + """No-op default. Override when the driver owns releasable resources.""" + + def supports(self, feature: str) -> bool: + return feature in self._supported_features + + # Torch-specific surface (parameters, named_parameters, state_dict, weight + # access) is NOT defined here. Drivers that serve those methods provide + # them directly; callers route via supports("...") + hasattr/getattr. diff --git a/transformer_lens/model_bridge/sources/_hf_format.py b/transformer_lens/model_bridge/sources/_hf_format.py new file mode 100644 index 0000000000..b0e7395a4d --- /dev/null +++ b/transformer_lens/model_bridge/sources/_hf_format.py @@ -0,0 +1,261 @@ +"""Shared HF-format utilities used by every source whose backend produces an +HF-shaped config or ``PreTrainedTokenizerBase`` tokenizer. + +The transformers source loads HF objects directly; the vLLM source extracts the +same HF-shaped config via ``llm.llm_engine.model_config.hf_config`` because vLLM +re-uses the ``transformers`` config and tokenizer libraries internally. This +module is loader-agnostic — it speaks HF format, not HF loading. +""" +from __future__ import annotations + +import copy + +from transformers import PreTrainedTokenizerBase + +from transformer_lens.factories.architecture_adapter_factory import ( + SUPPORTED_ARCHITECTURES, +) +from transformer_lens.utilities import get_tokenizer_with_bos + + +def map_default_transformer_lens_config(hf_config): + """Map HuggingFace config fields to TransformerLens config format. + + Standardized mapping from various HuggingFace config field names to the + consistent TransformerLens naming convention. For multimodal models (LLaVA, + Gemma3ForConditionalGeneration), the language model dimensions are nested + under ``text_config``; we extract from there first. + + Args: + hf_config: The HuggingFace config object + + Returns: + A copy of hf_config with additional TransformerLens fields + """ + source_config = hf_config + if hasattr(hf_config, "text_config") and hf_config.text_config is not None: + source_config = hf_config.text_config + + tl_config = copy.deepcopy(hf_config) + if hasattr(source_config, "n_embd"): + tl_config.d_model = source_config.n_embd + elif hasattr(source_config, "hidden_size"): + tl_config.d_model = source_config.hidden_size + elif hasattr(source_config, "model_dim"): + tl_config.d_model = source_config.model_dim + elif hasattr(source_config, "d_model"): + tl_config.d_model = source_config.d_model + if hasattr(source_config, "n_head"): + tl_config.n_heads = source_config.n_head + elif hasattr(source_config, "num_attention_heads"): + n_heads = source_config.num_attention_heads + if isinstance(n_heads, list): + n_heads = max(n_heads) + tl_config.n_heads = n_heads + elif hasattr(source_config, "num_heads"): + tl_config.n_heads = source_config.num_heads + elif hasattr(source_config, "num_query_heads") and isinstance( + source_config.num_query_heads, list + ): + tl_config.n_heads = max(source_config.num_query_heads) + if ( + hasattr(source_config, "num_key_value_heads") + and source_config.num_key_value_heads is not None + ): + try: + num_kv_heads = source_config.num_key_value_heads + # Per-layer lists (e.g., OpenELM) collapse to the max. + if isinstance(num_kv_heads, list): + num_kv_heads = max(num_kv_heads) + if hasattr(num_kv_heads, "item"): + num_kv_heads = num_kv_heads.item() + num_kv_heads = int(num_kv_heads) + num_heads = tl_config.n_heads + if hasattr(num_heads, "item"): + num_heads = num_heads.item() + num_heads = int(num_heads) + if num_kv_heads != num_heads: + tl_config.n_key_value_heads = num_kv_heads + except (TypeError, ValueError, AttributeError): + pass + elif hasattr(source_config, "num_kv_heads") and source_config.num_kv_heads is not None: + try: + num_kv_heads = source_config.num_kv_heads + if isinstance(num_kv_heads, list): + num_kv_heads = max(num_kv_heads) + if hasattr(num_kv_heads, "item"): + num_kv_heads = num_kv_heads.item() + num_kv_heads = int(num_kv_heads) + num_heads = tl_config.n_heads + if hasattr(num_heads, "item"): + num_heads = num_heads.item() + num_heads = int(num_heads) + if num_kv_heads != num_heads: + tl_config.n_key_value_heads = num_kv_heads + except (TypeError, ValueError, AttributeError): + pass + if hasattr(source_config, "n_layer"): + tl_config.n_layers = source_config.n_layer + elif hasattr(source_config, "num_hidden_layers"): + tl_config.n_layers = source_config.num_hidden_layers + elif hasattr(source_config, "num_transformer_layers"): + tl_config.n_layers = source_config.num_transformer_layers + elif hasattr(source_config, "num_layers"): + tl_config.n_layers = source_config.num_layers + if hasattr(source_config, "vocab_size") and isinstance(source_config.vocab_size, int): + tl_config.d_vocab = source_config.vocab_size + if hasattr(source_config, "n_positions"): + tl_config.n_ctx = source_config.n_positions + elif hasattr(source_config, "max_position_embeddings"): + tl_config.n_ctx = source_config.max_position_embeddings + elif hasattr(source_config, "max_context_length"): + tl_config.n_ctx = source_config.max_context_length + elif hasattr(source_config, "max_length"): + tl_config.n_ctx = source_config.max_length + elif hasattr(source_config, "seq_length"): + tl_config.n_ctx = source_config.seq_length + else: + # ALiBi models (Bloom) have no context length field; 2048 is a safe fallback. + tl_config.n_ctx = 2048 + if hasattr(source_config, "n_inner"): + tl_config.d_mlp = source_config.n_inner + elif hasattr(source_config, "intermediate_size"): + tl_config.d_mlp = source_config.intermediate_size + elif hasattr(tl_config, "d_model"): + tl_config.d_mlp = getattr(source_config, "n_inner", 4 * tl_config.d_model) + if hasattr(source_config, "head_dim") and source_config.head_dim is not None: + tl_config.d_head = source_config.head_dim + elif hasattr(tl_config, "d_model") and hasattr(tl_config, "n_heads"): + tl_config.d_head = tl_config.d_model // tl_config.n_heads + elif hasattr(tl_config, "d_model"): + # Attention-less architectures (Mamba SSMs): set d_head = d_model so + # __post_init__ computes n_heads = 1. Values are nominal. + tl_config.d_head = tl_config.d_model + if hasattr(source_config, "activation_function"): + tl_config.act_fn = source_config.activation_function + elif hasattr(source_config, "hidden_act"): + tl_config.act_fn = source_config.hidden_act + # LayerNorm / RMSNorm epsilon — HF uses 3 different field names. + if hasattr(source_config, "rms_norm_eps"): + tl_config.eps = source_config.rms_norm_eps + elif hasattr(source_config, "layer_norm_eps"): + tl_config.eps = source_config.layer_norm_eps + elif hasattr(source_config, "layer_norm_epsilon"): + tl_config.eps = source_config.layer_norm_epsilon + if hasattr(source_config, "num_local_experts"): + tl_config.num_experts = source_config.num_local_experts + if hasattr(source_config, "num_experts_per_tok"): + tl_config.experts_per_token = source_config.num_experts_per_tok + if hasattr(source_config, "sliding_window") and source_config.sliding_window is not None: + tl_config.sliding_window = source_config.sliding_window + if getattr(hf_config, "use_parallel_residual", False): + tl_config.parallel_attn_mlp = True + # GPT-J and CodeGen run parallel attn+MLP but don't set use_parallel_residual. + arch_classes = getattr(hf_config, "architectures", []) or [] + if any(a in ("GPTJForCausalLM", "CodeGenForCausalLM") for a in arch_classes): + tl_config.parallel_attn_mlp = True + tl_config.default_prepend_bos = True + return tl_config + + +def determine_architecture_from_hf_config(hf_config): + """Determine the architecture name from HuggingFace config. + + Returns: + str: The architecture name (e.g., "GPT2LMHeadModel", "LlamaForCausalLM") + + Raises: + ValueError: If architecture cannot be determined + """ + architectures = [] + if hasattr(hf_config, "original_architecture"): + architectures.append(hf_config.original_architecture) + if hasattr(hf_config, "architectures") and hf_config.architectures: + architectures.extend(hf_config.architectures) + if hasattr(hf_config, "model_type"): + model_type = hf_config.model_type + model_type_mappings = { + "apertus": "ApertusForCausalLM", + "gpt2": "GPT2LMHeadModel", + "hubert": "HubertModel", + "llama": "LlamaForCausalLM", + "mamba": "MambaForCausalLM", + "mamba2": "Mamba2ForCausalLM", + "mistral": "MistralForCausalLM", + "mixtral": "MixtralForCausalLM", + "gemma": "GemmaForCausalLM", + "gemma2": "Gemma2ForCausalLM", + "gemma3": "Gemma3ForCausalLM", + "bert": "BertForMaskedLM", + "bloom": "BloomForCausalLM", + "codegen": "CodeGenForCausalLM", + "gptj": "GPTJForCausalLM", + "gpt_neo": "GPTNeoForCausalLM", + "gpt_neox": "GPTNeoXForCausalLM", + "opt": "OPTForCausalLM", + "phi": "PhiForCausalLM", + "phi3": "Phi3ForCausalLM", + "qwen": "QwenForCausalLM", + "qwen2": "Qwen2ForCausalLM", + "qwen3": "Qwen3ForCausalLM", + # qwen3_5 is the top-level multimodal config type; qwen3_5_text is + # the text-only sub-config. Both map to the text-only adapter so + # Qwen3.5 checkpoints (which report qwen3_5 even when loaded as + # text-only) are routed to Qwen3_5ForCausalLM. + "qwen3_5": "Qwen3_5ForCausalLM", + "qwen3_5_text": "Qwen3_5ForCausalLM", + "openelm": "OpenELMForCausalLM", + "stablelm": "StableLmForCausalLM", + "t5": "T5ForConditionalGeneration", + "mt5": "MT5ForConditionalGeneration", + } + if model_type in model_type_mappings: + architectures.append(model_type_mappings[model_type]) + + for arch in architectures: + if arch in SUPPORTED_ARCHITECTURES: + return arch + raise ValueError( + f"Could not determine supported architecture from config. Available architectures: " + f"{list(SUPPORTED_ARCHITECTURES.keys())}, Config architectures: {architectures}, " + f"Model type: {getattr(hf_config, 'model_type', None)}" + ) + + +def setup_tokenizer(tokenizer, default_padding_side=None): + """Normalize a HuggingFace tokenizer for use with the bridge. + + Args: + tokenizer: A ``PreTrainedTokenizer`` or ``PreTrainedTokenizerFast``. + default_padding_side: ``"right"`` or ``"left"``; sets ``tokenizer.padding_side``. + """ + assert isinstance( + tokenizer, PreTrainedTokenizerBase + ), f"{type(tokenizer)} is not a supported tokenizer; use PreTrainedTokenizer or PreTrainedTokenizerFast" + assert default_padding_side in [ + "right", + "left", + None, + ], f"padding_side must be 'right', 'left' or None, got {default_padding_side}" + tokenizer = get_tokenizer_with_bos(tokenizer) + assert tokenizer is not None + if default_padding_side is not None: + tokenizer.padding_side = default_padding_side + if tokenizer.padding_side is None: + tokenizer.padding_side = "right" + if tokenizer.eos_token is None: + tokenizer.eos_token = "<|endoftext|>" + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + if tokenizer.bos_token is None: + tokenizer.bos_token = tokenizer.eos_token + + # Some vocabularies lack default IDs for these tokens; register them. + if tokenizer.pad_token is not None and tokenizer.pad_token_id is None: + tokenizer.add_special_tokens({"pad_token": tokenizer.pad_token}) + if tokenizer.eos_token is not None and tokenizer.eos_token_id is None: + tokenizer.add_special_tokens({"eos_token": tokenizer.eos_token}) + if tokenizer.bos_token is not None and tokenizer.bos_token_id is None: + tokenizer.add_special_tokens({"bos_token": tokenizer.bos_token}) + + return tokenizer diff --git a/transformer_lens/model_bridge/sources/transformers.py b/transformer_lens/model_bridge/sources/transformers.py deleted file mode 100644 index e30a022f41..0000000000 --- a/transformer_lens/model_bridge/sources/transformers.py +++ /dev/null @@ -1,936 +0,0 @@ -"""Transformers module for TransformerLens. - -This module provides functionality to load and convert models from HuggingFace to TransformerLens format. -""" -import contextlib -import copy -import logging -import os -import warnings -from typing import Any - -import torch -from transformers import ( - AutoConfig, - AutoModelForCausalLM, - AutoModelForMaskedLM, - AutoModelForSeq2SeqLM, - AutoTokenizer, - PreTrainedTokenizerBase, -) - -from transformer_lens.config import TransformerBridgeConfig -from transformer_lens.factories.architecture_adapter_factory import ( - SUPPORTED_ARCHITECTURES, - ArchitectureAdapterFactory, -) -from transformer_lens.model_bridge.bridge import TransformerBridge -from transformer_lens.supported_models import MODEL_ALIASES -from transformer_lens.utilities import get_device, get_tokenizer_with_bos - -# Suppress transformers warnings that go to stderr -# This prevents notebook tests from failing due to unexpected stderr output -warnings.filterwarnings("ignore", message=".*generation flags.*not valid.*") -logging.getLogger("transformers").setLevel(logging.ERROR) - - -def map_default_transformer_lens_config(hf_config): - """Map HuggingFace config fields to TransformerLens config format. - - This function provides a standardized mapping from various HuggingFace config - field names to the consistent TransformerLens naming convention. - - For multimodal models (LLaVA, Gemma3ForConditionalGeneration), the language - model dimensions are nested under text_config. We extract from text_config - first, then apply the standard mapping. - - Args: - hf_config: The HuggingFace config object - - Returns: - A copy of hf_config with additional TransformerLens fields - """ - # Extract language model config from text_config for multimodal models - source_config = hf_config - if hasattr(hf_config, "text_config") and hf_config.text_config is not None: - source_config = hf_config.text_config - - tl_config = copy.deepcopy(hf_config) - if hasattr(source_config, "n_embd"): - tl_config.d_model = source_config.n_embd - elif hasattr(source_config, "hidden_size"): - tl_config.d_model = source_config.hidden_size - elif hasattr(source_config, "model_dim"): - tl_config.d_model = source_config.model_dim - elif hasattr(source_config, "d_model"): - tl_config.d_model = source_config.d_model - if hasattr(source_config, "n_head"): - tl_config.n_heads = source_config.n_head - elif hasattr(source_config, "num_attention_heads"): - n_heads = source_config.num_attention_heads - if isinstance(n_heads, list): - n_heads = max(n_heads) - tl_config.n_heads = n_heads - elif hasattr(source_config, "num_heads"): - tl_config.n_heads = source_config.num_heads - elif hasattr(source_config, "num_query_heads") and isinstance( - source_config.num_query_heads, list - ): - tl_config.n_heads = max(source_config.num_query_heads) - if ( - hasattr(source_config, "num_key_value_heads") - and source_config.num_key_value_heads is not None - ): - try: - num_kv_heads = source_config.num_key_value_heads - # Handle per-layer lists (e.g., OpenELM) by taking the max - if isinstance(num_kv_heads, list): - num_kv_heads = max(num_kv_heads) - if hasattr(num_kv_heads, "item"): - num_kv_heads = num_kv_heads.item() - num_kv_heads = int(num_kv_heads) - num_heads = tl_config.n_heads - if hasattr(num_heads, "item"): - num_heads = num_heads.item() - num_heads = int(num_heads) - if num_kv_heads != num_heads: - tl_config.n_key_value_heads = num_kv_heads - except (TypeError, ValueError, AttributeError): - pass - elif hasattr(source_config, "num_kv_heads") and source_config.num_kv_heads is not None: - try: - num_kv_heads = source_config.num_kv_heads - if isinstance(num_kv_heads, list): - num_kv_heads = max(num_kv_heads) - if hasattr(num_kv_heads, "item"): - num_kv_heads = num_kv_heads.item() - num_kv_heads = int(num_kv_heads) - num_heads = tl_config.n_heads - if hasattr(num_heads, "item"): - num_heads = num_heads.item() - num_heads = int(num_heads) - if num_kv_heads != num_heads: - tl_config.n_key_value_heads = num_kv_heads - except (TypeError, ValueError, AttributeError): - pass - if hasattr(source_config, "n_layer"): - tl_config.n_layers = source_config.n_layer - elif hasattr(source_config, "num_hidden_layers"): - tl_config.n_layers = source_config.num_hidden_layers - elif hasattr(source_config, "num_transformer_layers"): - tl_config.n_layers = source_config.num_transformer_layers - elif hasattr(source_config, "num_layers"): - tl_config.n_layers = source_config.num_layers - if hasattr(source_config, "vocab_size") and isinstance(source_config.vocab_size, int): - tl_config.d_vocab = source_config.vocab_size - if hasattr(source_config, "n_positions"): - tl_config.n_ctx = source_config.n_positions - elif hasattr(source_config, "max_position_embeddings"): - tl_config.n_ctx = source_config.max_position_embeddings - elif hasattr(source_config, "max_context_length"): - tl_config.n_ctx = source_config.max_context_length - elif hasattr(source_config, "max_length"): - tl_config.n_ctx = source_config.max_length - elif hasattr(source_config, "seq_length"): - tl_config.n_ctx = source_config.seq_length - else: - # Models like Bloom use ALiBi (no positional embeddings) and have no - # context length field. Default to 2048 as a reasonable fallback. - tl_config.n_ctx = 2048 - if hasattr(source_config, "n_inner"): - tl_config.d_mlp = source_config.n_inner - elif hasattr(source_config, "intermediate_size"): - tl_config.d_mlp = source_config.intermediate_size - elif hasattr(tl_config, "d_model"): - tl_config.d_mlp = getattr(source_config, "n_inner", 4 * tl_config.d_model) - if hasattr(source_config, "head_dim") and source_config.head_dim is not None: - tl_config.d_head = source_config.head_dim - elif hasattr(tl_config, "d_model") and hasattr(tl_config, "n_heads"): - tl_config.d_head = tl_config.d_model // tl_config.n_heads - elif hasattr(tl_config, "d_model"): - # Models without attention (e.g., Mamba SSMs) have no n_heads or head_dim. - # Set d_head = d_model so TransformerLensConfig.__post_init__ computes - # n_heads = 1. These values are nominal and have no functional meaning - # for attention-less architectures. - tl_config.d_head = tl_config.d_model - if hasattr(source_config, "activation_function"): - tl_config.act_fn = source_config.activation_function - elif hasattr(source_config, "hidden_act"): - tl_config.act_fn = source_config.hidden_act - # Layer norm / RMS norm epsilon — HF uses 3 different field names - if hasattr(source_config, "rms_norm_eps"): - tl_config.eps = source_config.rms_norm_eps - elif hasattr(source_config, "layer_norm_eps"): - tl_config.eps = source_config.layer_norm_eps - elif hasattr(source_config, "layer_norm_epsilon"): - tl_config.eps = source_config.layer_norm_epsilon - if hasattr(source_config, "num_local_experts"): - tl_config.num_experts = source_config.num_local_experts - if hasattr(source_config, "num_experts_per_tok"): - tl_config.experts_per_token = source_config.num_experts_per_tok - if hasattr(source_config, "sliding_window") and source_config.sliding_window is not None: - tl_config.sliding_window = source_config.sliding_window - if getattr(hf_config, "use_parallel_residual", False): - tl_config.parallel_attn_mlp = True - # GPT-J and CodeGen: parallel attn+MLP but missing use_parallel_residual in HF config - arch_classes = getattr(hf_config, "architectures", []) or [] - if any(a in ("GPTJForCausalLM", "CodeGenForCausalLM") for a in arch_classes): - tl_config.parallel_attn_mlp = True - tl_config.default_prepend_bos = True - return tl_config - - -def determine_architecture_from_hf_config(hf_config): - """Determine the architecture name from HuggingFace config. - - Args: - hf_config: The HuggingFace config object - - Returns: - str: The architecture name (e.g., "GPT2LMHeadModel", "LlamaForCausalLM") - - Raises: - ValueError: If architecture cannot be determined - """ - architectures = [] - if hasattr(hf_config, "original_architecture"): - architectures.append(hf_config.original_architecture) - if hasattr(hf_config, "architectures") and hf_config.architectures: - architectures.extend(hf_config.architectures) - if hasattr(hf_config, "model_type"): - model_type = hf_config.model_type - model_type_mappings = { - "apertus": "ApertusForCausalLM", - "gpt2": "GPT2LMHeadModel", - "hubert": "HubertModel", - "llama": "LlamaForCausalLM", - "mamba": "MambaForCausalLM", - "mamba2": "Mamba2ForCausalLM", - "mistral": "MistralForCausalLM", - "mixtral": "MixtralForCausalLM", - "gemma": "GemmaForCausalLM", - "gemma2": "Gemma2ForCausalLM", - "gemma3": "Gemma3ForCausalLM", - "bert": "BertForMaskedLM", - "bloom": "BloomForCausalLM", - "codegen": "CodeGenForCausalLM", - "gptj": "GPTJForCausalLM", - "gpt_neo": "GPTNeoForCausalLM", - "gpt_neox": "GPTNeoXForCausalLM", - "opt": "OPTForCausalLM", - "phi": "PhiForCausalLM", - "phi3": "Phi3ForCausalLM", - "qwen": "QwenForCausalLM", - "qwen2": "Qwen2ForCausalLM", - "qwen3": "Qwen3ForCausalLM", - # qwen3_5 is the top-level multimodal config type; qwen3_5_text is - # the text-only sub-config. Both map to the text-only adapter so - # Qwen3.5 checkpoints (which report qwen3_5 even when loaded as - # text-only) are routed to Qwen3_5ForCausalLM. - "qwen3_5": "Qwen3_5ForCausalLM", - "qwen3_5_text": "Qwen3_5ForCausalLM", - "openelm": "OpenELMForCausalLM", - "stablelm": "StableLmForCausalLM", - "t5": "T5ForConditionalGeneration", - "mt5": "MT5ForConditionalGeneration", - } - if model_type in model_type_mappings: - architectures.append(model_type_mappings[model_type]) - - for arch in architectures: - if arch in SUPPORTED_ARCHITECTURES: - return arch - raise ValueError( - f"Could not determine supported architecture from config. Available architectures: {list(SUPPORTED_ARCHITECTURES.keys())}, Config architectures: {architectures}, Model type: {getattr(hf_config, 'model_type', None)}" - ) - - -def get_hf_model_class_for_architecture(architecture: str): - """Determine the correct HuggingFace AutoModel class for loading. - - Uses centralized architecture sets from utilities.architectures. - """ - from transformer_lens.utilities.architectures import ( - AUDIO_ARCHITECTURES, - MASKED_LM_ARCHITECTURES, - MULTIMODAL_ARCHITECTURES, - SEQ2SEQ_ARCHITECTURES, - ) - - if architecture in SEQ2SEQ_ARCHITECTURES: - return AutoModelForSeq2SeqLM - elif architecture in MASKED_LM_ARCHITECTURES: - return AutoModelForMaskedLM - elif architecture in MULTIMODAL_ARCHITECTURES: - from transformers import AutoModelForImageTextToText - - return AutoModelForImageTextToText - elif architecture in AUDIO_ARCHITECTURES: - if "ForCTC" in architecture: - from transformers import AutoModelForCTC - - return AutoModelForCTC - from transformers import AutoModel - - return AutoModel - else: - return AutoModelForCausalLM - - -# Known training-checkpoint revision conventions on HF. -_CHECKPOINT_REVISION_FORMATS: dict[str, str] = { - "EleutherAI/pythia": "step{value}", - "stanford-crfm": "checkpoint-{value}", -} - - -def _resolve_checkpoint_to_revision( - model_name: str, - checkpoint_index: int | None, - checkpoint_value: int | None, -) -> str: - """Convert a checkpoint index/value into an HF revision string, validated against ``get_checkpoint_labels``.""" - if checkpoint_index is None and checkpoint_value is None: - raise ValueError("Must specify either checkpoint_index or checkpoint_value.") - - format_str: str | None = None - for prefix, fmt in _CHECKPOINT_REVISION_FORMATS.items(): - if model_name.startswith(prefix): - format_str = fmt - break - if format_str is None: - raise ValueError( - f"Model {model_name!r} does not have a known checkpoint revision convention. " - f"Pass revision= directly if your model uses HF revisions. Known checkpoint " - f"families: {list(_CHECKPOINT_REVISION_FORMATS.keys())}." - ) - - from transformer_lens.loading_from_pretrained import get_checkpoint_labels - - labels, _ = get_checkpoint_labels(model_name) - if checkpoint_value is not None: - if checkpoint_value not in labels: - raise ValueError( - f"checkpoint_value={checkpoint_value} not in available checkpoints for " - f"{model_name!r}. {len(labels)} labels available, " - f"first/last: {labels[0]}..{labels[-1]}." - ) - else: - assert checkpoint_index is not None # narrowed by initial guard - if not 0 <= checkpoint_index < len(labels): - raise ValueError( - f"checkpoint_index={checkpoint_index} out of range [0, {len(labels)}) " - f"for {model_name!r}." - ) - checkpoint_value = labels[checkpoint_index] - return format_str.format(value=checkpoint_value) - - -def boot( - model_name: str, - hf_config_overrides: dict | None = None, - device: str | torch.device | None = None, - dtype: torch.dtype = torch.float32, - tokenizer: PreTrainedTokenizerBase | None = None, - load_weights: bool = True, - trust_remote_code: bool = False, - model_class: Any | None = None, - hf_model: Any | None = None, - n_ctx: int | None = None, - revision: str | None = None, - checkpoint_index: int | None = None, - checkpoint_value: int | None = None, - # Experimental – Have not been fully tested on multi-gpu devices - # Use at your own risk, report any issues here: https://github.com/TransformerLensOrg/TransformerLens/issues - device_map: str | dict[str, str | int] | None = None, - n_devices: int | None = None, - max_memory: dict[str | int, str] | None = None, -) -> TransformerBridge: - """Boot a model from HuggingFace. - - Args: - model_name: The name of the model to load. - hf_config_overrides: Optional overrides applied to the HuggingFace config before model load. - device: The device to use. If None, will be determined automatically. Mutually exclusive - with ``device_map``. - dtype: The dtype to use for the model. - tokenizer: Optional pre-initialized tokenizer to use; if not provided one will be created. - load_weights: If False, load model without weights (on meta device) for config inspection only. - model_class: Optional HuggingFace model class to use instead of the default auto-detected - class. When the class name matches a key in SUPPORTED_ARCHITECTURES, the corresponding - adapter is selected automatically (e.g., BertForNextSentencePrediction). - hf_model: Optional pre-loaded HuggingFace model to use instead of loading one. Useful for - models loaded with custom configurations (e.g., quantization via BitsAndBytesConfig). - When provided, load_weights is ignored. - device_map: HuggingFace-style device map (``"auto"``, ``"balanced"``, dict, etc.) for - multi-GPU inference. Passed straight to ``from_pretrained``. Mutually exclusive - with ``device``. - n_devices: Convenience: split the model across this many CUDA devices (translated to a - ``max_memory`` dict internally). Requires CUDA with at least this many visible devices. - max_memory: Optional per-device memory budget for HF's dispatcher. - n_ctx: Optional context length override. The bridge normally uses the model's documented - max context from the HF config. Setting this writes to whichever HF field the model - uses (n_positions / max_position_embeddings / etc.), so callers don't need to know - the field name. If larger than the model's default, a warning is emitted — quality - may degrade past the trained length for rotary models. - revision: Optional HF revision string (branch, tag, or commit). Forwarded to - ``AutoConfig.from_pretrained`` and ``AutoModelForCausalLM.from_pretrained``. - Mutually exclusive with ``checkpoint_index`` and ``checkpoint_value``. - checkpoint_index: Index into the available training checkpoints for the model family. - Convenience over ``revision`` for checkpointed models like EleutherAI/pythia* and - stanford-crfm/*. Resolved to a revision string via the known per-family naming - conventions (``step{value}`` for Pythia, ``checkpoint-{value}`` for stanford-crfm). - checkpoint_value: Training step or token count of the desired checkpoint. Alternative to - ``checkpoint_index``; must be one of the labels returned by ``get_checkpoint_labels``. - - Returns: - The bridge to the loaded model. - """ - for official_name, aliases in MODEL_ALIASES.items(): - if model_name in aliases: - logging.warning( - f"DEPRECATED: You are using a deprecated, model_name alias '{model_name}'. TransformerLens will now load the official transformers model name, '{official_name}' instead.\n Please update your code to use the official name by changing model_name from '{model_name}' to '{official_name}'.\nSince TransformerLens v3, all model names should be the official transformers model names.\nThe aliases will be removed in the next version of TransformerLens, so please do the update now." - ) - model_name = official_name - break - if checkpoint_index is not None or checkpoint_value is not None: - if revision is not None: - raise ValueError( - "Specify either revision= or checkpoint_index/checkpoint_value, not both." - ) - revision = _resolve_checkpoint_to_revision(model_name, checkpoint_index, checkpoint_value) - # Pass HF token for gated model access (e.g. meta-llama/*) - from transformer_lens.utilities.hf_utils import get_hf_token - - _hf_token = get_hf_token() - if hf_model is not None: - # Reuse the pre-loaded model's config to avoid a Hub call when model_name - # is a Hub repo ID, but the model is already loaded locally. - hf_config = copy.deepcopy(hf_model.config) - else: - hf_config = AutoConfig.from_pretrained( - model_name, - output_attentions=True, - trust_remote_code=trust_remote_code, - token=_hf_token, - revision=revision, - ) - _n_ctx_field: str | None = None - if n_ctx is not None: - # Validation (#2): reject non-positive values before doing anything else. - if n_ctx <= 0: - raise ValueError(f"n_ctx must be a positive integer, got n_ctx={n_ctx}.") - # Resolve n_ctx to whichever HF config field this model uses. Mirrors - # the order in map_default_transformer_lens_config so the TL config - # derivation picks up the override. - for _field in ( - "n_positions", - "max_position_embeddings", - "max_context_length", - "max_length", - "seq_length", - ): - if hasattr(hf_config, _field): - _n_ctx_field = _field - break - if _n_ctx_field is None: - raise ValueError( - f"Cannot apply n_ctx={n_ctx}: no recognized context-length field on " - f"HF config for {model_name}. Use hf_config_overrides instead." - ) - _default_n_ctx = getattr(hf_config, _n_ctx_field) - if _default_n_ctx is not None and n_ctx > _default_n_ctx: - logging.warning( - "Setting n_ctx=%d which is larger than the model's default " - "context length of %d. The model was not trained on sequences " - "this long and may produce unreliable results (especially for " - "rotary models without RoPE scaling).", - n_ctx, - _default_n_ctx, - ) - # Conflict detection (#4): warn if the caller also set the same field - # via hf_config_overrides — explicit n_ctx wins but users should know. - if hf_config_overrides and _n_ctx_field in hf_config_overrides: - _conflicting_value = hf_config_overrides[_n_ctx_field] - if _conflicting_value != n_ctx: - logging.warning( - "Both n_ctx=%d and hf_config_overrides['%s']=%s were provided. " - "The explicit n_ctx takes precedence.", - n_ctx, - _n_ctx_field, - _conflicting_value, - ) - # Explicit n_ctx wins over hf_config_overrides for the resolved field. - hf_config_overrides = dict(hf_config_overrides or {}) - hf_config_overrides[_n_ctx_field] = n_ctx - if hf_config_overrides: - hf_config.__dict__.update(hf_config_overrides) - tl_config = map_default_transformer_lens_config(hf_config) - architecture = determine_architecture_from_hf_config(hf_config) - config_dict = dict(tl_config.__dict__) - # Restore TL attribute names that HF remaps via attribute_map - if "num_local_experts" in config_dict and "num_experts" not in config_dict: - config_dict["num_experts"] = config_dict["num_local_experts"] - bridge_config = TransformerBridgeConfig.from_dict(config_dict) - bridge_config.architecture = architecture - bridge_config.model_name = model_name - bridge_config.dtype = dtype - # Propagate HF-specific config attributes that adapters may need. - # Any attribute present on the HF config and not None is copied to bridge_config. - # This is architecture-agnostic — new architectures don't need changes here. - _HF_PASSTHROUGH_ATTRS = [ - # OPT - "is_gated_act", - "word_embed_proj_dim", - "do_layer_norm_before", - # Granite - "position_embedding_type", - # Falcon - "parallel_attn", - "multi_query", - "new_decoder_architecture", - "alibi", - "num_ln_in_parallel_attn", - # Mamba (SSM config) - "state_size", - "conv_kernel", - "expand", - "time_step_rank", - "intermediate_size", - # Mamba-2 (additional SSM config) - "n_groups", - "chunk_size", - # Multimodal - "vision_config", - ] - for attr in _HF_PASSTHROUGH_ATTRS: - val = getattr(hf_config, attr, None) - if val is not None: - setattr(bridge_config, attr, val) - - # Gemma2 softcapping: HF names differ from TL names, need explicit mapping - final_logit_softcapping = getattr(hf_config, "final_logit_softcapping", None) - if final_logit_softcapping is not None: - bridge_config.output_logits_soft_cap = float(final_logit_softcapping) - attn_logit_softcapping = getattr(hf_config, "attn_logit_softcapping", None) - if attn_logit_softcapping is not None: - bridge_config.attn_scores_soft_cap = float(attn_logit_softcapping) - adapter = ArchitectureAdapterFactory.select_architecture_adapter(bridge_config) - # Pre-loaded models carry their own weight placement (possibly set by the caller via - # device_map). Passing device_map / n_devices / max_memory alongside hf_model= is - # ambiguous and would silently be ignored, so fail loudly. - if hf_model is not None and ( - device_map is not None or n_devices is not None or max_memory is not None - ): - raise ValueError( - "device_map / n_devices / max_memory are only supported when the bridge loads " - "the HF model itself. When passing hf_model=..., apply device_map via " - "AutoModel.from_pretrained before handing the model to the bridge." - ) - # Stateful/SSM (e.g. Mamba) models keep a per-layer recurrent cache that must live on - # that layer's device. The bridge currently allocates the stateful cache on a single - # cfg.device, so cross-device splits would silently misplace the cache. Block this - # combination until a v2 addresses per-layer stateful cache placement. - if (n_devices is not None and n_devices > 1) or device_map is not None: - if getattr(bridge_config, "is_stateful", False): - raise ValueError( - "Multi-device splits are not yet supported for stateful (SSM / Mamba) " - "architectures: the stateful cache allocation is single-device. " - "Load on one device, or wait for v2 support." - ) - # Resolve device_map before defaulting `device` — the two are mutually exclusive, and - # the resolver raises on conflict. If n_devices>1 is passed, it's translated into a - # device_map + max_memory pair here so downstream code only needs to check the - # resolved values. - from transformer_lens.utilities.multi_gpu import ( - count_unique_devices, - find_embedding_device, - resolve_device_map, - ) - - resolved_device_map, resolved_max_memory = resolve_device_map( - n_devices, device_map, device, max_memory - ) - if resolved_device_map is None: - if device is None: - device = get_device() - adapter.cfg.device = str(device) - else: - # cfg.device will be set from hf_device_map after the model is loaded. - # Provisionally keep it None; find_embedding_device fills it in below. - adapter.cfg.device = None - if model_class is None: - model_class = get_hf_model_class_for_architecture(architecture) - # Ensure pad_token_id exists (v5 raises AttributeError if missing) - if not hasattr(hf_config, "pad_token_id") or "pad_token_id" not in hf_config.__dict__: - fallback_pad = getattr(hf_config, "eos_token_id", None) - # eos_token_id can be a list (e.g., Gemma3 uses [1, 106]); take the first. - if isinstance(fallback_pad, list): - fallback_pad = fallback_pad[0] if fallback_pad else None - hf_config.pad_token_id = fallback_pad - model_kwargs = {"config": hf_config, "torch_dtype": dtype} - if _hf_token: - model_kwargs["token"] = _hf_token - if trust_remote_code: - model_kwargs["trust_remote_code"] = True - if revision is not None: - model_kwargs["revision"] = revision - if resolved_device_map is not None: - model_kwargs["device_map"] = resolved_device_map - if resolved_max_memory is not None: - model_kwargs["max_memory"] = resolved_max_memory - if hasattr(adapter.cfg, "attn_implementation") and adapter.cfg.attn_implementation is not None: - model_kwargs["attn_implementation"] = adapter.cfg.attn_implementation - else: - # Default to eager (required for output_attentions hooks) - model_kwargs["attn_implementation"] = "eager" - adapter.prepare_loading(model_name, model_kwargs) - if hf_model is not None: - # Use the pre-loaded model as-is (e.g., quantized models with custom device_map) - pass - elif not load_weights: - from_config_kwargs = {} - if trust_remote_code: - from_config_kwargs["trust_remote_code"] = True - prepared_config = model_kwargs.get("config", hf_config) - with contextlib.redirect_stdout(None): - hf_model = model_class.from_config(prepared_config, **from_config_kwargs) - else: - try: - hf_model = model_class.from_pretrained(model_name, **model_kwargs) - except RuntimeError as e: - # #5: HF refuses to load when positional-weight shapes don't match. - # If the user requested an n_ctx that conflicts with the saved weights - # (common for learned-pos-embed models like GPT-2), re-raise with a - # clearer message pointing them at the likely cause. - if n_ctx is not None and "ignore_mismatched_sizes" in str(e): - raise RuntimeError( - f"Failed to load {model_name} with n_ctx={n_ctx}: the pretrained " - f"weights' positional-embedding shape does not match the requested " - f"context length. This affects models with learned positional " - f"embeddings (e.g. GPT-2, OPT). Options: (1) use the model's " - f"default n_ctx, (2) pass load_weights=False if you only need " - f"config inspection, or (3) choose a rotary-embedding model " - f"(e.g. Llama, Mistral) which supports n_ctx changes without " - f"weight mismatch." - ) from e - raise - # Skip explicit .to(device) when accelerate has placed weights via device_map. - if resolved_device_map is None and device is not None: - hf_model = hf_model.to(device) - # Cast params to dtype; preserve float32 buffers (e.g., RotaryEmbedding.inv_freq) - for param in hf_model.parameters(): - if param.is_floating_point() and param.dtype != dtype: - param.data = param.data.to(dtype=dtype) - # Derive cfg.device / cfg.n_devices from hf_device_map when present. This covers: - # - fresh loads with a resolved device_map (set above) - # - pre-loaded hf_model that the caller dispatched themselves (e.g., device_map="auto") - hf_device_map_post = getattr(hf_model, "hf_device_map", None) - if hf_device_map_post: - # Pre-loaded path can still smuggle CPU/disk offload in; validate here too. - offload_values = {str(v).lower() for v in hf_device_map_post.values() if isinstance(v, str)} - forbidden = offload_values & {"cpu", "disk", "meta"} - if forbidden and ((n_devices is not None and n_devices > 1) or device_map is not None): - # Fresh-load path: we set the device_map ourselves, so this shouldn't happen — - # but if the user asked for n_devices>1 and somehow got CPU offload, surface it. - raise ValueError( - f"hf_device_map contains unsupported offload targets: {sorted(forbidden)}. " - "v1 multi-device support is GPU-only." - ) - embedding_device = find_embedding_device(hf_model) - if embedding_device is not None: - adapter.cfg.device = str(embedding_device) - adapter.cfg.n_devices = count_unique_devices(hf_model) - elif adapter.cfg.device is None: - # Pre-loaded single-device model with no hf_device_map — fall back to first param. - try: - adapter.cfg.device = str(next(hf_model.parameters()).device) - except StopIteration: - adapter.cfg.device = "cpu" - # #7: Verify the n_ctx override actually took effect on the loaded model. - # If HF's config class silently dropped or normalized the value, warn so - # the user doesn't get misled into thinking longer sequences are supported. - if n_ctx is not None and _n_ctx_field is not None and hf_model is not None: - _actual = getattr(hf_model.config, _n_ctx_field, None) - if _actual != n_ctx: - logging.warning( - "n_ctx=%d was requested but hf_model.config.%s=%s after load. " - "The override may not have taken effect; the model may not " - "accept sequences longer than %s.", - n_ctx, - _n_ctx_field, - _actual, - _actual, - ) - adapter.prepare_model(hf_model) - tokenizer = tokenizer - default_padding_side = getattr(adapter.cfg, "default_padding_side", None) - use_fast = getattr(adapter.cfg, "use_fast", True) - # Audio models use feature extractors, not text tokenizers - _is_audio = getattr(adapter.cfg, "is_audio_model", False) - if _is_audio and tokenizer is None: - tokenizer = None # Skip tokenizer loading for audio models - elif tokenizer is not None: - tokenizer = setup_tokenizer(tokenizer, default_padding_side=default_padding_side) - else: - token_arg = get_hf_token() - # Use adapter's tokenizer_name if model lacks one (e.g., OpenELM) - tokenizer_source = model_name - if hasattr(adapter.cfg, "tokenizer_name") and adapter.cfg.tokenizer_name is not None: - tokenizer_source = adapter.cfg.tokenizer_name - # Try to load tokenizer with add_bos_token=True first - # (encoder-decoder models like T5 don't have BOS tokens and will raise ValueError) - try: - base_tokenizer = AutoTokenizer.from_pretrained( - tokenizer_source, - add_bos_token=True, - use_fast=use_fast, - token=token_arg, - trust_remote_code=trust_remote_code, - ) - except ValueError: - # Model doesn't have a BOS token, load without add_bos_token - base_tokenizer = AutoTokenizer.from_pretrained( - tokenizer_source, - use_fast=use_fast, - token=token_arg, - trust_remote_code=trust_remote_code, - ) - tokenizer = setup_tokenizer( - base_tokenizer, - default_padding_side=default_padding_side, - ) - if tokenizer is not None: - # Detect BOS/EOS behavior (use non-empty string; empty is unreliable with token aliasing) - encoded_test = tokenizer.encode("a") - adapter.cfg.tokenizer_prepends_bos = ( - len(encoded_test) > 1 - and tokenizer.bos_token_id is not None - and encoded_test[0] == tokenizer.bos_token_id - ) - adapter.cfg.tokenizer_appends_eos = ( - len(encoded_test) > 1 - and tokenizer.eos_token_id is not None - and encoded_test[-1] == tokenizer.eos_token_id - ) - bridge = TransformerBridge(hf_model, adapter, tokenizer) - - # Load processor for multimodal models (needed for image preprocessing) - if getattr(adapter.cfg, "is_multimodal", False): - try: - from transformers import AutoProcessor - - huggingface_token = os.environ.get("HF_TOKEN", "") - token_arg = huggingface_token if len(huggingface_token) > 0 else None - bridge.processor = AutoProcessor.from_pretrained( - model_name, - token=token_arg, - trust_remote_code=trust_remote_code, - ) - except Exception: - # Some processors need torchvision (e.g., LlavaOnevision); install if needed - _torchvision_available = False - try: - import torchvision # noqa: F401 - - _torchvision_available = True - except Exception: - # Install/reinstall torchvision if missing or broken - import shutil - import subprocess - import sys - - try: - if shutil.which("uv"): - subprocess.check_call( - ["uv", "pip", "install", "torchvision", "-q"], - ) - else: - subprocess.check_call( - [sys.executable, "-m", "pip", "install", "torchvision", "-q"], - ) - import importlib - - importlib.invalidate_caches() - _torchvision_available = True - except Exception: - pass # torchvision install failed; processor will be unavailable - - if _torchvision_available: - try: - from transformers import AutoProcessor - - huggingface_token = os.environ.get("HF_TOKEN", "") - token_arg = huggingface_token if len(huggingface_token) > 0 else None - bridge.processor = AutoProcessor.from_pretrained( - model_name, - token=token_arg, - trust_remote_code=trust_remote_code, - ) - except Exception: - pass # Processor not available; user can set bridge.processor manually - - # Load feature extractor for audio models (needed for audio preprocessing) - if getattr(adapter.cfg, "is_audio_model", False): - try: - from transformers import AutoFeatureExtractor - - huggingface_token = os.environ.get("HF_TOKEN", "") - token_arg = huggingface_token if len(huggingface_token) > 0 else None - bridge.processor = AutoFeatureExtractor.from_pretrained( - model_name, - token=token_arg, - trust_remote_code=trust_remote_code, - ) - except Exception: - pass # Feature extractor not available; user can set bridge.processor manually - - return bridge - - -def setup_tokenizer(tokenizer, default_padding_side=None): - """Set's up the tokenizer. - - Args: - tokenizer (PreTrainedTokenizer): a pretrained HuggingFace tokenizer. - default_padding_side (str): "right" or "left", which side to pad on. - - """ - assert isinstance( - tokenizer, PreTrainedTokenizerBase - ), f"{type(tokenizer)} is not a supported tokenizer, please use PreTrainedTokenizer or PreTrainedTokenizerFast" - assert default_padding_side in [ - "right", - "left", - None, - ], f"padding_side must be 'right', 'left' or 'None', got {default_padding_side}" - tokenizer_with_bos = get_tokenizer_with_bos(tokenizer) - tokenizer = tokenizer_with_bos - assert tokenizer is not None - if default_padding_side is not None: - tokenizer.padding_side = default_padding_side - if tokenizer.padding_side is None: - tokenizer.padding_side = "right" - if tokenizer.eos_token is None: - tokenizer.eos_token = "<|endoftext|>" - if tokenizer.pad_token is None: - tokenizer.pad_token = tokenizer.eos_token - if tokenizer.bos_token is None: - tokenizer.bos_token = tokenizer.eos_token - - # Ensure special tokens resolve to valid IDs (some vocabularies lack defaults) - if tokenizer.pad_token is not None and tokenizer.pad_token_id is None: - tokenizer.add_special_tokens({"pad_token": tokenizer.pad_token}) - if tokenizer.eos_token is not None and tokenizer.eos_token_id is None: - tokenizer.add_special_tokens({"eos_token": tokenizer.eos_token}) - if tokenizer.bos_token is not None and tokenizer.bos_token_id is None: - tokenizer.add_special_tokens({"bos_token": tokenizer.bos_token}) - - return tokenizer - - -def list_supported_models( - architecture: str | None = None, - verified_only: bool = False, -) -> list[str]: - """List all models supported by TransformerLens. - - This function provides convenient access to the model registry API - for discovering which HuggingFace models can be loaded. - - Args: - architecture: Filter by architecture ID (e.g., "GPT2LMHeadModel"). - If None, returns all supported models. - verified_only: If True, only return models that have been verified - to work with TransformerLens. - - Returns: - List of model IDs (e.g., ["gpt2", "gpt2-medium", ...]) - - Example: - >>> from transformer_lens.model_bridge.sources.transformers import list_supported_models - >>> models = list_supported_models() - >>> gpt2_models = list_supported_models(architecture="GPT2LMHeadModel") - """ - try: - from transformer_lens.tools.model_registry import api - - models = api.get_supported_models(architecture=architecture, verified_only=verified_only) - return [m.model_id for m in models] - except ImportError: - return [] - except Exception: - return [] - - -def check_model_support(model_id: str) -> dict: - """Check if a model is supported and get detailed support info. - - This function provides detailed information about a model's compatibility - with TransformerLens, including architecture type and verification status. - - Args: - model_id: The HuggingFace model ID to check (e.g., "gpt2") - - Returns: - Dictionary with support information: - - is_supported: bool - Whether the model is supported - - architecture_id: str | None - The architecture type if supported - - verified: bool - Whether the model has been verified to work - - suggestion: str | None - Suggested alternative if not supported - - Example: - >>> from transformer_lens.model_bridge.sources.transformers import check_model_support # doctest: +SKIP - >>> info = check_model_support("openai-community/gpt2") # doctest: +SKIP - >>> info["is_supported"] # doctest: +SKIP - True - """ - try: - from transformer_lens.tools.model_registry import api - - is_supported = api.is_model_supported(model_id) - - if is_supported: - model_info = api.get_model_info(model_id) - return { - "is_supported": True, - "architecture_id": model_info.architecture_id, - "status": model_info.status, - "verified_date": ( - model_info.verified_date.isoformat() if model_info.verified_date else None - ), - "suggestion": None, - } - else: - suggestion = api.suggest_similar_model(model_id) - return { - "is_supported": False, - "architecture_id": None, - "verified": False, - "verified_date": None, - "suggestion": suggestion, - } - except ImportError: - return { - "is_supported": None, - "architecture_id": None, - "verified": False, - "verified_date": None, - "suggestion": None, - "error": "Model registry not available", - } - except Exception as e: - return { - "is_supported": None, - "architecture_id": None, - "verified": False, - "verified_date": None, - "suggestion": None, - "error": str(e), - } - - -# Attach functions to TransformerBridge as static methods -setattr(TransformerBridge, "boot_transformers", staticmethod(boot)) -setattr(TransformerBridge, "list_supported_models", staticmethod(list_supported_models)) -setattr(TransformerBridge, "check_model_support", staticmethod(check_model_support)) diff --git a/transformer_lens/model_bridge/sources/transformers/__init__.py b/transformer_lens/model_bridge/sources/transformers/__init__.py new file mode 100644 index 0000000000..1da07bc4b6 --- /dev/null +++ b/transformer_lens/model_bridge/sources/transformers/__init__.py @@ -0,0 +1,47 @@ +"""HuggingFace ``transformers`` source for TransformerBridge.""" +from __future__ import annotations + +# Re-exported so external code that patches ``AutoConfig.from_pretrained`` / +# ``AutoTokenizer.from_pretrained`` via this module path keeps working after the +# package split. Class-method monkey-patches reach the same class objects that +# ``source.py`` imports directly, so this re-export keeps tests stable. +from transformers import AutoConfig, AutoTokenizer + +from transformer_lens.model_bridge.bridge import TransformerBridge + +# Re-export shared HF-format utilities at the historical path for backward compatibility +# with `from transformer_lens.model_bridge.sources.transformers import ...` callers. +from transformer_lens.model_bridge.sources._hf_format import ( + determine_architecture_from_hf_config, + map_default_transformer_lens_config, + setup_tokenizer, +) + +from .helpers import ( + _CHECKPOINT_REVISION_FORMATS, + _resolve_checkpoint_to_revision, + check_model_support, + get_hf_model_class_for_architecture, + list_supported_models, +) +from .source import boot + +# Attach functions to TransformerBridge as static methods. +setattr(TransformerBridge, "boot_transformers", staticmethod(boot)) +setattr(TransformerBridge, "list_supported_models", staticmethod(list_supported_models)) +setattr(TransformerBridge, "check_model_support", staticmethod(check_model_support)) + + +__all__ = [ + "AutoConfig", + "AutoTokenizer", + "boot", + "check_model_support", + "determine_architecture_from_hf_config", + "get_hf_model_class_for_architecture", + "list_supported_models", + "map_default_transformer_lens_config", + "setup_tokenizer", + "_CHECKPOINT_REVISION_FORMATS", + "_resolve_checkpoint_to_revision", +] diff --git a/transformer_lens/model_bridge/sources/transformers/helpers.py b/transformer_lens/model_bridge/sources/transformers/helpers.py new file mode 100644 index 0000000000..922c30f5f8 --- /dev/null +++ b/transformer_lens/model_bridge/sources/transformers/helpers.py @@ -0,0 +1,157 @@ +"""HF-loader-specific helpers: model-class selection, checkpoint revision resolution, registry discovery.""" +from __future__ import annotations + +from transformers import ( + AutoModelForCausalLM, + AutoModelForMaskedLM, + AutoModelForSeq2SeqLM, +) + + +def get_hf_model_class_for_architecture(architecture: str): + """Pick the correct HuggingFace ``AutoModel*`` class for the architecture.""" + from transformer_lens.utilities.architectures import ( + AUDIO_ARCHITECTURES, + MASKED_LM_ARCHITECTURES, + MULTIMODAL_ARCHITECTURES, + SEQ2SEQ_ARCHITECTURES, + ) + + if architecture in SEQ2SEQ_ARCHITECTURES: + return AutoModelForSeq2SeqLM + elif architecture in MASKED_LM_ARCHITECTURES: + return AutoModelForMaskedLM + elif architecture in MULTIMODAL_ARCHITECTURES: + from transformers import AutoModelForImageTextToText + + return AutoModelForImageTextToText + elif architecture in AUDIO_ARCHITECTURES: + if "ForCTC" in architecture: + from transformers import AutoModelForCTC + + return AutoModelForCTC + from transformers import AutoModel + + return AutoModel + else: + return AutoModelForCausalLM + + +# Known training-checkpoint revision conventions on HF Hub. +_CHECKPOINT_REVISION_FORMATS: dict[str, str] = { + "EleutherAI/pythia": "step{value}", + "stanford-crfm": "checkpoint-{value}", +} + + +def _resolve_checkpoint_to_revision( + model_name: str, + checkpoint_index: int | None, + checkpoint_value: int | None, +) -> str: + """Convert a checkpoint index/value into an HF revision string, validated against ``get_checkpoint_labels``.""" + if checkpoint_index is None and checkpoint_value is None: + raise ValueError("Must specify either checkpoint_index or checkpoint_value.") + + format_str: str | None = None + for prefix, fmt in _CHECKPOINT_REVISION_FORMATS.items(): + if model_name.startswith(prefix): + format_str = fmt + break + if format_str is None: + raise ValueError( + f"Model {model_name!r} does not have a known checkpoint revision convention. " + f"Pass revision= directly if your model uses HF revisions. Known checkpoint " + f"families: {list(_CHECKPOINT_REVISION_FORMATS.keys())}." + ) + + from transformer_lens.loading_from_pretrained import get_checkpoint_labels + + labels, _ = get_checkpoint_labels(model_name) + if checkpoint_value is not None: + if checkpoint_value not in labels: + raise ValueError( + f"checkpoint_value={checkpoint_value} not in available checkpoints for " + f"{model_name!r}. {len(labels)} labels available, " + f"first/last: {labels[0]}..{labels[-1]}." + ) + else: + assert checkpoint_index is not None # narrowed by initial guard + if not 0 <= checkpoint_index < len(labels): + raise ValueError( + f"checkpoint_index={checkpoint_index} out of range [0, {len(labels)}) " + f"for {model_name!r}." + ) + checkpoint_value = labels[checkpoint_index] + return format_str.format(value=checkpoint_value) + + +def list_supported_models( + architecture: str | None = None, + verified_only: bool = False, +) -> list[str]: + """List all models supported by TransformerLens. + + Args: + architecture: Filter by architecture ID (e.g., "GPT2LMHeadModel"). + verified_only: If True, only return verified-to-work models. + + Returns: + List of model IDs. + """ + try: + from transformer_lens.tools.model_registry import api + + models = api.get_supported_models(architecture=architecture, verified_only=verified_only) + return [m.model_id for m in models] + except ImportError: + return [] + except Exception: + return [] + + +def check_model_support(model_id: str) -> dict: + """Detailed support info for a model: ``is_supported``, ``architecture_id``, ``verified``, ``suggestion``.""" + try: + from transformer_lens.tools.model_registry import api + + is_supported = api.is_model_supported(model_id) + + if is_supported: + model_info = api.get_model_info(model_id) + return { + "is_supported": True, + "architecture_id": model_info.architecture_id, + "status": model_info.status, + "verified_date": ( + model_info.verified_date.isoformat() if model_info.verified_date else None + ), + "suggestion": None, + } + else: + suggestion = api.suggest_similar_model(model_id) + return { + "is_supported": False, + "architecture_id": None, + "verified": False, + "verified_date": None, + "suggestion": suggestion, + } + except ImportError: + return { + "is_supported": None, + "architecture_id": None, + "verified": False, + "verified_date": None, + "suggestion": None, + "error": "Model registry not available", + } + except Exception as e: + return { + "is_supported": None, + "architecture_id": None, + "verified": False, + "verified_date": None, + "suggestion": None, + "error": str(e), + } diff --git a/transformer_lens/model_bridge/sources/transformers/source.py b/transformer_lens/model_bridge/sources/transformers/source.py new file mode 100644 index 0000000000..ce0c7def5b --- /dev/null +++ b/transformer_lens/model_bridge/sources/transformers/source.py @@ -0,0 +1,435 @@ +"""``boot`` — load a model via HuggingFace ``transformers`` and wrap it in a TransformerBridge.""" +from __future__ import annotations + +import contextlib +import copy +import logging +import os +import warnings +from typing import Any + +import torch +from transformers import AutoConfig, AutoTokenizer, PreTrainedTokenizerBase + +from transformer_lens.factories.architecture_adapter_factory import ( + ArchitectureAdapterFactory, +) +from transformer_lens.model_bridge.bridge import TransformerBridge +from transformer_lens.model_bridge.sources._bridge_builder import ( + build_bridge_config_from_hf, + detect_tokenizer_bos_eos, +) +from transformer_lens.model_bridge.sources._hf_format import ( + determine_architecture_from_hf_config, + setup_tokenizer, +) +from transformer_lens.supported_models import MODEL_ALIASES +from transformer_lens.utilities import get_device + +from .helpers import ( + _resolve_checkpoint_to_revision, + get_hf_model_class_for_architecture, +) + +# Suppress transformers warnings that go to stderr; otherwise notebook tests fail +# on unexpected stderr output. +warnings.filterwarnings("ignore", message=".*generation flags.*not valid.*") +logging.getLogger("transformers").setLevel(logging.ERROR) + + +def boot( + model_name: str, + hf_config_overrides: dict | None = None, + device: str | torch.device | None = None, + dtype: torch.dtype = torch.float32, + tokenizer: PreTrainedTokenizerBase | None = None, + load_weights: bool = True, + trust_remote_code: bool = False, + model_class: Any | None = None, + hf_model: Any | None = None, + n_ctx: int | None = None, + revision: str | None = None, + checkpoint_index: int | None = None, + checkpoint_value: int | None = None, + # Experimental – Have not been fully tested on multi-gpu devices + # Use at your own risk, report any issues here: https://github.com/TransformerLensOrg/TransformerLens/issues + device_map: str | dict[str, str | int] | None = None, + n_devices: int | None = None, + max_memory: dict[str | int, str] | None = None, +) -> TransformerBridge: + """Boot a model from HuggingFace. + + Args: + model_name: The name of the model to load. + hf_config_overrides: Optional overrides applied to the HuggingFace config before model load. + device: The device to use. If None, will be determined automatically. Mutually exclusive + with ``device_map``. + dtype: The dtype to use for the model. + tokenizer: Optional pre-initialized tokenizer to use; if not provided one will be created. + load_weights: If False, load model without weights (on meta device) for config inspection only. + model_class: Optional HuggingFace model class to use instead of the default auto-detected + class. When the class name matches a key in SUPPORTED_ARCHITECTURES, the corresponding + adapter is selected automatically (e.g., BertForNextSentencePrediction). + hf_model: Optional pre-loaded HuggingFace model to use instead of loading one. Useful for + models loaded with custom configurations (e.g., quantization via BitsAndBytesConfig). + When provided, load_weights is ignored. + device_map: HuggingFace-style device map (``"auto"``, ``"balanced"``, dict, etc.) for + multi-GPU inference. Passed straight to ``from_pretrained``. Mutually exclusive + with ``device``. + n_devices: Convenience: split the model across this many CUDA devices (translated to a + ``max_memory`` dict internally). Requires CUDA with at least this many visible devices. + max_memory: Optional per-device memory budget for HF's dispatcher. + n_ctx: Optional context length override. The bridge normally uses the model's documented + max context from the HF config. Setting this writes to whichever HF field the model + uses (n_positions / max_position_embeddings / etc.), so callers don't need to know + the field name. If larger than the model's default, a warning is emitted — quality + may degrade past the trained length for rotary models. + revision: Optional HF revision string (branch, tag, or commit). Forwarded to + ``AutoConfig.from_pretrained`` and ``AutoModelForCausalLM.from_pretrained``. + Mutually exclusive with ``checkpoint_index`` and ``checkpoint_value``. + checkpoint_index: Index into the available training checkpoints for the model family. + Convenience over ``revision`` for checkpointed models like EleutherAI/pythia* and + stanford-crfm/*. Resolved to a revision string via the known per-family naming + conventions (``step{value}`` for Pythia, ``checkpoint-{value}`` for stanford-crfm). + checkpoint_value: Training step or token count of the desired checkpoint. Alternative to + ``checkpoint_index``; must be one of the labels returned by ``get_checkpoint_labels``. + + Returns: + The bridge to the loaded model. + """ + for official_name, aliases in MODEL_ALIASES.items(): + if model_name in aliases: + logging.warning( + f"DEPRECATED: You are using a deprecated, model_name alias '{model_name}'. TransformerLens will now load the official transformers model name, '{official_name}' instead.\n Please update your code to use the official name by changing model_name from '{model_name}' to '{official_name}'.\nSince TransformerLens v3, all model names should be the official transformers model names.\nThe aliases will be removed in the next version of TransformerLens, so please do the update now." + ) + model_name = official_name + break + if checkpoint_index is not None or checkpoint_value is not None: + if revision is not None: + raise ValueError( + "Specify either revision= or checkpoint_index/checkpoint_value, not both." + ) + revision = _resolve_checkpoint_to_revision(model_name, checkpoint_index, checkpoint_value) + # Pass HF token for gated model access (e.g. meta-llama/*) + from transformer_lens.utilities.hf_utils import get_hf_token + + _hf_token = get_hf_token() + if hf_model is not None: + # Reuse the pre-loaded model's config to avoid a Hub call when model_name + # is a Hub repo ID but the model is already loaded locally. + hf_config = copy.deepcopy(hf_model.config) + else: + hf_config = AutoConfig.from_pretrained( + model_name, + output_attentions=True, + trust_remote_code=trust_remote_code, + token=_hf_token, + revision=revision, + ) + _n_ctx_field: str | None = None + if n_ctx is not None: + if n_ctx <= 0: + raise ValueError(f"n_ctx must be a positive integer, got n_ctx={n_ctx}.") + # Resolve n_ctx to whichever HF config field this model uses. Mirrors the order in + # map_default_transformer_lens_config so the TL config derivation picks up the override. + for _field in ( + "n_positions", + "max_position_embeddings", + "max_context_length", + "max_length", + "seq_length", + ): + if hasattr(hf_config, _field): + _n_ctx_field = _field + break + if _n_ctx_field is None: + raise ValueError( + f"Cannot apply n_ctx={n_ctx}: no recognized context-length field on " + f"HF config for {model_name}. Use hf_config_overrides instead." + ) + _default_n_ctx = getattr(hf_config, _n_ctx_field) + if _default_n_ctx is not None and n_ctx > _default_n_ctx: + logging.warning( + "Setting n_ctx=%d which is larger than the model's default " + "context length of %d. The model was not trained on sequences " + "this long and may produce unreliable results (especially for " + "rotary models without RoPE scaling).", + n_ctx, + _default_n_ctx, + ) + # Warn if the caller also set the same field via hf_config_overrides — explicit n_ctx wins. + if hf_config_overrides and _n_ctx_field in hf_config_overrides: + _conflicting_value = hf_config_overrides[_n_ctx_field] + if _conflicting_value != n_ctx: + logging.warning( + "Both n_ctx=%d and hf_config_overrides['%s']=%s were provided. " + "The explicit n_ctx takes precedence.", + n_ctx, + _n_ctx_field, + _conflicting_value, + ) + hf_config_overrides = dict(hf_config_overrides or {}) + hf_config_overrides[_n_ctx_field] = n_ctx + if hf_config_overrides: + hf_config.__dict__.update(hf_config_overrides) + architecture = determine_architecture_from_hf_config(hf_config) + bridge_config = build_bridge_config_from_hf(hf_config, architecture, model_name, dtype) + adapter = ArchitectureAdapterFactory.select_architecture_adapter(bridge_config) + # Pre-loaded models carry their own weight placement (possibly set by the caller via + # device_map). Passing device_map / n_devices / max_memory alongside hf_model= is ambiguous + # and would silently be ignored, so fail loudly. + if hf_model is not None and ( + device_map is not None or n_devices is not None or max_memory is not None + ): + raise ValueError( + "device_map / n_devices / max_memory are only supported when the bridge loads " + "the HF model itself. When passing hf_model=..., apply device_map via " + "AutoModel.from_pretrained before handing the model to the bridge." + ) + # Stateful/SSM (Mamba) models keep a per-layer recurrent cache that must live on that + # layer's device. The bridge allocates the stateful cache on a single cfg.device, so + # cross-device splits would silently misplace the cache. Blocked until v2. + if (n_devices is not None and n_devices > 1) or device_map is not None: + if getattr(bridge_config, "is_stateful", False): + raise ValueError( + "Multi-device splits are not yet supported for stateful (SSM / Mamba) " + "architectures: the stateful cache allocation is single-device. " + "Load on one device, or wait for v2 support." + ) + # Resolve device_map before defaulting `device` — the two are mutually exclusive and the + # resolver raises on conflict. If n_devices>1 is passed it's translated into a device_map + + # max_memory pair here so downstream code only needs to check the resolved values. + from transformer_lens.utilities.multi_gpu import ( + count_unique_devices, + find_embedding_device, + resolve_device_map, + ) + + resolved_device_map, resolved_max_memory = resolve_device_map( + n_devices, device_map, device, max_memory + ) + if resolved_device_map is None: + if device is None: + device = get_device() + adapter.cfg.device = str(device) + else: + # cfg.device set from hf_device_map after the model is loaded; provisionally None. + adapter.cfg.device = None + if model_class is None: + model_class = get_hf_model_class_for_architecture(architecture) + # Ensure pad_token_id exists (v5 raises AttributeError if missing). + if not hasattr(hf_config, "pad_token_id") or "pad_token_id" not in hf_config.__dict__: + fallback_pad = getattr(hf_config, "eos_token_id", None) + # eos_token_id can be a list (Gemma3 uses [1, 106]); take the first. + if isinstance(fallback_pad, list): + fallback_pad = fallback_pad[0] if fallback_pad else None + hf_config.pad_token_id = fallback_pad + model_kwargs = {"config": hf_config, "torch_dtype": dtype} + if _hf_token: + model_kwargs["token"] = _hf_token + if trust_remote_code: + model_kwargs["trust_remote_code"] = True + if revision is not None: + model_kwargs["revision"] = revision + if resolved_device_map is not None: + model_kwargs["device_map"] = resolved_device_map + if resolved_max_memory is not None: + model_kwargs["max_memory"] = resolved_max_memory + if hasattr(adapter.cfg, "attn_implementation") and adapter.cfg.attn_implementation is not None: + model_kwargs["attn_implementation"] = adapter.cfg.attn_implementation + else: + # Eager is required for output_attentions hooks. + model_kwargs["attn_implementation"] = "eager" + adapter.prepare_loading(model_name, model_kwargs) + if hf_model is not None: + # Use the pre-loaded model as-is (quantized models with custom device_map, etc). + pass + elif not load_weights: + from_config_kwargs = {} + if trust_remote_code: + from_config_kwargs["trust_remote_code"] = True + # adapter.prepare_loading may have replaced model_kwargs["config"] (e.g. Qwen3.5 + # text-only extraction); honor that here so the no-weights path uses the + # same config the load-weights path would. + prepared_config = model_kwargs.get("config", hf_config) + with contextlib.redirect_stdout(None): + hf_model = model_class.from_config(prepared_config, **from_config_kwargs) + else: + try: + hf_model = model_class.from_pretrained(model_name, **model_kwargs) + except RuntimeError as e: + # HF refuses to load when positional-weight shapes don't match. If the user + # requested an n_ctx that conflicts with the saved weights (common for + # learned-pos-embed models like GPT-2), re-raise with a clearer message. + if n_ctx is not None and "ignore_mismatched_sizes" in str(e): + raise RuntimeError( + f"Failed to load {model_name} with n_ctx={n_ctx}: the pretrained " + f"weights' positional-embedding shape does not match the requested " + f"context length. This affects models with learned positional " + f"embeddings (e.g. GPT-2, OPT). Options: (1) use the model's " + f"default n_ctx, (2) pass load_weights=False if you only need " + f"config inspection, or (3) choose a rotary-embedding model " + f"(e.g. Llama, Mistral) which supports n_ctx changes without " + f"weight mismatch." + ) from e + raise + # Skip explicit .to(device) when accelerate has placed weights via device_map. + if resolved_device_map is None and device is not None: + hf_model = hf_model.to(device) + # Cast params to dtype; preserve float32 buffers (e.g. RotaryEmbedding.inv_freq). + for param in hf_model.parameters(): + if param.is_floating_point() and param.dtype != dtype: + param.data = param.data.to(dtype=dtype) + # Derive cfg.device / cfg.n_devices from hf_device_map when present. Covers fresh loads + # with a resolved device_map AND pre-loaded models with caller-dispatched device_map="auto". + hf_device_map_post = getattr(hf_model, "hf_device_map", None) + if hf_device_map_post: + # Pre-loaded path can smuggle CPU/disk offload in; validate. + offload_values = {str(v).lower() for v in hf_device_map_post.values() if isinstance(v, str)} + forbidden = offload_values & {"cpu", "disk", "meta"} + if forbidden and ((n_devices is not None and n_devices > 1) or device_map is not None): + raise ValueError( + f"hf_device_map contains unsupported offload targets: {sorted(forbidden)}. " + "v1 multi-device support is GPU-only." + ) + embedding_device = find_embedding_device(hf_model) + if embedding_device is not None: + adapter.cfg.device = str(embedding_device) + adapter.cfg.n_devices = count_unique_devices(hf_model) + elif adapter.cfg.device is None: + # Pre-loaded single-device model with no hf_device_map — fall back to first param. + try: + adapter.cfg.device = str(next(hf_model.parameters()).device) + except StopIteration: + adapter.cfg.device = "cpu" + # Verify the n_ctx override actually took effect on the loaded model. If HF's config class + # silently dropped or normalized the value, warn so the user isn't misled. + if n_ctx is not None and _n_ctx_field is not None and hf_model is not None: + _actual = getattr(hf_model.config, _n_ctx_field, None) + if _actual != n_ctx: + logging.warning( + "n_ctx=%d was requested but hf_model.config.%s=%s after load. " + "The override may not have taken effect; the model may not " + "accept sequences longer than %s.", + n_ctx, + _n_ctx_field, + _actual, + _actual, + ) + adapter.prepare_model(hf_model) + default_padding_side = getattr(adapter.cfg, "default_padding_side", None) + use_fast = getattr(adapter.cfg, "use_fast", True) + # Audio models use feature extractors, not text tokenizers. + _is_audio = getattr(adapter.cfg, "is_audio_model", False) + if _is_audio and tokenizer is None: + tokenizer = None + elif tokenizer is not None: + tokenizer = setup_tokenizer(tokenizer, default_padding_side=default_padding_side) + else: + token_arg = get_hf_token() + # Some adapters override tokenizer source (e.g. OpenELM has no tokenizer of its own). + tokenizer_source = model_name + if hasattr(adapter.cfg, "tokenizer_name") and adapter.cfg.tokenizer_name is not None: + tokenizer_source = adapter.cfg.tokenizer_name + # Encoder-decoder models like T5 don't have a BOS token and raise on add_bos_token=True. + try: + base_tokenizer = AutoTokenizer.from_pretrained( + tokenizer_source, + add_bos_token=True, + use_fast=use_fast, + token=token_arg, + trust_remote_code=trust_remote_code, + ) + except ValueError: + base_tokenizer = AutoTokenizer.from_pretrained( + tokenizer_source, + use_fast=use_fast, + token=token_arg, + trust_remote_code=trust_remote_code, + ) + tokenizer = setup_tokenizer( + base_tokenizer, + default_padding_side=default_padding_side, + ) + if tokenizer is not None: + ( + adapter.cfg.tokenizer_prepends_bos, + adapter.cfg.tokenizer_appends_eos, + ) = detect_tokenizer_bos_eos(tokenizer) + from transformer_lens.model_bridge.sources.transformers_driver import ( + TransformersDriver, + ) + + driver = TransformersDriver(hf_model, adapter, tokenizer) + bridge = TransformerBridge(hf_model, adapter, tokenizer, driver=driver) + + # Multimodal models: load the image preprocessor. + if getattr(adapter.cfg, "is_multimodal", False): + try: + from transformers import AutoProcessor + + huggingface_token = os.environ.get("HF_TOKEN", "") + token_arg = huggingface_token if len(huggingface_token) > 0 else None + bridge.processor = AutoProcessor.from_pretrained( + model_name, + token=token_arg, + trust_remote_code=trust_remote_code, + ) + except Exception: + # Some processors need torchvision (e.g. LlavaOnevision); install if missing. + _torchvision_available = False + try: + import torchvision # noqa: F401 + + _torchvision_available = True + except Exception: + import shutil + import subprocess + import sys + + try: + if shutil.which("uv"): + subprocess.check_call( + ["uv", "pip", "install", "torchvision", "-q"], + ) + else: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "torchvision", "-q"], + ) + import importlib + + importlib.invalidate_caches() + _torchvision_available = True + except Exception: + pass + + if _torchvision_available: + try: + from transformers import AutoProcessor + + huggingface_token = os.environ.get("HF_TOKEN", "") + token_arg = huggingface_token if len(huggingface_token) > 0 else None + bridge.processor = AutoProcessor.from_pretrained( + model_name, + token=token_arg, + trust_remote_code=trust_remote_code, + ) + except Exception: + pass + + # Audio models: load the feature extractor. + if getattr(adapter.cfg, "is_audio_model", False): + try: + from transformers import AutoFeatureExtractor + + huggingface_token = os.environ.get("HF_TOKEN", "") + token_arg = huggingface_token if len(huggingface_token) > 0 else None + bridge.processor = AutoFeatureExtractor.from_pretrained( + model_name, + token=token_arg, + trust_remote_code=trust_remote_code, + ) + except Exception: + pass + + return bridge diff --git a/transformer_lens/model_bridge/sources/transformers_driver.py b/transformer_lens/model_bridge/sources/transformers_driver.py new file mode 100644 index 0000000000..c745788e2b --- /dev/null +++ b/transformer_lens/model_bridge/sources/transformers_driver.py @@ -0,0 +1,75 @@ +"""HuggingFace transformers Driver.""" +from __future__ import annotations + +from typing import Any, Iterator, Mapping + +import torch +from torch import nn + +from transformer_lens.model_bridge.driver_protocol import ( + ForwardResult, + Intervention, + TensorLike, +) +from transformer_lens.model_bridge.sources._driver_base import DriverBase + + +class TransformersDriver(DriverBase): + """Wraps an HF ``nn.Module``. PyTorch hooks fire via module replacement during the + real forward; this driver just runs the engine and threads the native output back.""" + + _supported_features = frozenset( + {"gradients", "parameters", "state_dict", "weight_access", "intervention_callbacks"} + ) + + def __init__(self, model: nn.Module, adapter: Any, tokenizer: Any) -> None: + super().__init__(adapter.cfg, tokenizer) + self._model = model + self._adapter = adapter + + def forward( + self, + input_ids: TensorLike | None = None, + *, + capture: tuple[str, ...] = (), + intervene: Mapping[str, Intervention] | None = None, + max_new_tokens: int = 1, + return_logits: bool = True, + **kwargs: Any, + ) -> ForwardResult: + if input_ids is not None: + raw = self._model(input_ids, **kwargs) + else: + raw = self._model(**kwargs) + + logits = None + if return_logits: + if hasattr(raw, "logits"): + logits = raw.logits + elif isinstance(raw, tuple) and len(raw) > 0: + logits = raw[0] + else: + logits = raw + + return ForwardResult(logits=logits, raw_output=raw) + + def parameters(self) -> Iterator[torch.Tensor]: + return self._model.parameters() + + def named_parameters( + self, + prefix: str = "", + recurse: bool = True, + remove_duplicate: bool = True, + ) -> Iterator[tuple[str, torch.Tensor]]: + return self._model.named_parameters(prefix, recurse, remove_duplicate) + + @property + def underlying_model(self) -> nn.Module: + """Escape hatch for code that needs the raw HF module. Driver-specific.""" + return self._model + + def set_underlying_model(self, value: nn.Module) -> None: + """Used by weight-processing paths that move the model to a different + device. Non-torch drivers don't implement this.""" + self._model = value diff --git a/transformer_lens/model_bridge/sources/vllm/__init__.py b/transformer_lens/model_bridge/sources/vllm/__init__.py new file mode 100644 index 0000000000..ee059a6e69 --- /dev/null +++ b/transformer_lens/model_bridge/sources/vllm/__init__.py @@ -0,0 +1,12 @@ +"""vLLM source for TransformerBridge. + +Provides :func:`boot_vllm`, which constructs a vLLM ``LLM`` and wraps its +inner ``nn.Module`` in a :class:`TransformerBridge`. vLLM drives the forward +pass (PagedAttention, ``torch.compile``, CUDA graphs); the bridge surface is +populated from GPU buffers written by hooks the plugin installs pre-compile. +""" +from __future__ import annotations + +from .source import boot_vllm + +__all__ = ["boot_vllm"] diff --git a/transformer_lens/model_bridge/sources/vllm/driver.py b/transformer_lens/model_bridge/sources/vllm/driver.py new file mode 100644 index 0000000000..e6733c3ef1 --- /dev/null +++ b/transformer_lens/model_bridge/sources/vllm/driver.py @@ -0,0 +1,207 @@ +"""vLLM Driver: forward dispatches via ``llm.generate``; captures via ``collective_rpc``.""" +from __future__ import annotations + +import logging +from typing import Any, Mapping + +import torch + +from transformer_lens.model_bridge.driver_protocol import ( + ForwardResult, + Intervention, + TensorLike, +) +from transformer_lens.model_bridge.sources._driver_base import DriverBase + +from .intervention_specs import SUPPORTED_OPS + + +class VLLMDriver(DriverBase): + """Driver wrapping a vLLM ``LLM``; captures via ``collective_rpc``.""" + + # vLLM owns the model in a worker — no torch surface (parameters/state_dict/grads). + _supported_features = frozenset() + + def __init__( + self, + llm: Any, + adapter: Any, + tokenizer: Any, + overlay: Any, + hf_config: Any, + max_num_batched_tokens: int, + ) -> None: + super().__init__(adapter.cfg, tokenizer) + self._llm = llm + self._max_num_batched_tokens = max_num_batched_tokens + + self.supported_hook_points = frozenset(overlay.capture_specs(hf_config).keys()) + + n_layers = getattr(hf_config, "num_hidden_layers", 0) + nonfiring: list[str] = [] + for tmpl in overlay.nonfiring_hooks(): + if "{i}" in tmpl and isinstance(n_layers, int) and n_layers > 0: + nonfiring.extend(tmpl.replace("{i}", str(i)) for i in range(n_layers)) + else: + nonfiring.append(tmpl) + self.non_fireable_hook_points = frozenset(nonfiring) + + def forward( + self, + input_ids: TensorLike | None = None, + *, + capture: tuple[str, ...] = (), + intervene: Mapping[str, Intervention] | None = None, + max_new_tokens: int = 1, + return_logits: bool = True, + **kwargs: Any, + ) -> ForwardResult: + if input_ids is None: + raise ValueError("VLLMDriver requires input_ids") + if int(max_new_tokens) != 1: + raise NotImplementedError( + "VLLMDriver supports max_new_tokens=1 only — decode-step writes " + "overwrite the prefill buffer; multi-step capture is multi-buffer work." + ) + intervene_specs = self._validate_interventions(intervene or {}) + + ids_list = self._normalize_input_ids(input_ids) + if len(ids_list) > self._max_num_batched_tokens: + # Worker buffers silently clamp on overflow — fail loud here instead. + raise ValueError( + f"Prompt length {len(ids_list)} exceeds max_num_batched_tokens=" + f"{self._max_num_batched_tokens}; raise the boot_vllm kwarg or " + "shorten the prompt." + ) + + from vllm import SamplingParams + from vllm.inputs import TokensPrompt + + # Push intervention state (possibly empty) before generate — this also + # resets stale interventions from prior forwards. + self._llm.collective_rpc("tl_set_interventions", args=(intervene_specs,)) + # Request full-vocab logprobs so the driver can populate position -1 of + # the synthesized logits with the real next-token distribution. vLLM's + # ``max_logprobs`` was set to d_vocab at boot to make this legal. + outputs = self._llm.generate( + prompts=[TokensPrompt(prompt_token_ids=ids_list)], + sampling_params=SamplingParams( + max_tokens=int(max_new_tokens), + temperature=0.0, + logprobs=self.bridge_config.d_vocab if return_logits else None, + ), + ) + + n_tokens = len(ids_list) + # collective_rpc returns one result per worker; single-rank, so [0]. + worker_captures = self._llm.collective_rpc("tl_read_captures", args=([n_tokens],))[0] + # Add batch dim: vLLM hands back (n_tokens, width); bridge expects (1, n_tokens, width). + captured = {name: t.unsqueeze(0) for name, t in worker_captures.items()} + + logits: torch.Tensor | None = None + if return_logits: + logits = self._synthesize_logits(outputs[0], n_tokens, self.bridge_config.d_vocab) + + return ForwardResult(logits=logits, captured=captured, raw_output=outputs[0]) + + def close(self) -> None: + # Detach hooks before dropping the LLM so they don't stay registered on + # worker modules for the life of the process (long-running notebooks). + log = logging.getLogger("transformer_lens.vllm") + if self._llm is not None: + try: + self._llm.collective_rpc("tl_remove_hooks") + except Exception as e: + # Best-effort: engine may already be torn down or the RPC surface + # gone. Log so hook-leak debugging has a thread to pull. + log.debug("tl_remove_hooks failed during close(): %s", e) + self._llm = None + # vLLM 0.20.2 has no LLM.shutdown() — model weights and KV cache stay + # resident until process exit unless we explicitly tear down the + # distributed environment vLLM set up at construction. Both calls are + # best-effort: they're no-ops if there's no distributed state. After + # this, the caller still needs gc.collect() + torch.cuda.empty_cache() + # to drop PyTorch's caching allocator entries. + try: + from vllm.distributed.parallel_state import ( + destroy_distributed_environment, + destroy_model_parallel, + ) + + destroy_model_parallel() + destroy_distributed_environment() + except Exception as e: + log.debug("vLLM distributed teardown failed during close(): %s", e) + + @staticmethod + def _synthesize_logits(request_output: Any, n_tokens: int, d_vocab: int) -> torch.Tensor: + """Build a (1, n_tokens, d_vocab) logits-like tensor from vLLM's sampler output. + + vLLM's lm_head bypass means our hook never fires; the sampler returns + full-vocab logprobs (we set ``max_logprobs=d_vocab`` at boot to allow this). + Position -1 — the input's last token, = next-token prediction — is populated + from those logprobs. Earlier positions stay at ``-inf`` so any argmax there + is loud rather than silently misleading; populating them would need + ``prompt_logprobs`` requested per call (much more expensive). + """ + logits = torch.full((1, n_tokens, d_vocab), float("-inf"), dtype=torch.float16) + gen = request_output.outputs[0] if request_output.outputs else None + if gen is None: + return logits + # Prefer real logprobs; fall back to the generated token id (one-hot-ish) + # if logprobs weren't requested (e.g. return_logits=False elsewhere). + if gen.logprobs: + for token_id, lp_obj in gen.logprobs[0].items(): + logits[0, -1, int(token_id)] = float(lp_obj.logprob) + elif gen.token_ids: + logits[0, -1, int(gen.token_ids[0])] = 0.0 + return logits + + def _validate_interventions(self, intervene: Mapping[str, Any]) -> dict: + """Reject callables, validate spec format and hook names; return a plain dict.""" + out: dict = {} + for hook_name, spec in intervene.items(): + if callable(spec): + raise NotImplementedError( + "VLLMDriver requires intervention specs (dict), not callables. " + "Supported ops: suppress, scale (factor: float), add (value: scalar or width-shaped), " + "set (value: scalar or width-shaped)." + ) + if not isinstance(spec, Mapping) or "op" not in spec: + raise ValueError( + f"Intervention spec for {hook_name!r} must be a dict with 'op' key; got {spec!r}" + ) + op = spec["op"] + if op not in SUPPORTED_OPS: + raise ValueError( + f"Unsupported intervention op {op!r} for {hook_name!r}. " + f"Supported: {sorted(SUPPORTED_OPS)}." + ) + if op == "scale" and "factor" not in spec: + raise ValueError( + f"Intervention {hook_name!r}: op='scale' requires 'factor' (float)." + ) + if op in ("add", "set") and "value" not in spec: + raise ValueError( + f"Intervention {hook_name!r}: op={op!r} requires 'value' " + "(scalar or width-shaped tensor/list)." + ) + if hook_name not in self.supported_hook_points: + raise ValueError( + f"Cannot intervene on {hook_name!r}: not in supported_hook_points." + ) + out[hook_name] = dict(spec) + return out + + @staticmethod + def _normalize_input_ids(input_ids: Any) -> list: + """Coerce input_ids to a flat list[int] for ``TokensPrompt``; batch_size=1 only.""" + if isinstance(input_ids, torch.Tensor): + ids_list = input_ids.tolist() + else: + ids_list = list(input_ids) + if ids_list and isinstance(ids_list[0], list): + if len(ids_list) != 1: + raise NotImplementedError("VLLMDriver supports batch_size=1 only.") + ids_list = ids_list[0] + return ids_list diff --git a/transformer_lens/model_bridge/sources/vllm/internals.py b/transformer_lens/model_bridge/sources/vllm/internals.py new file mode 100644 index 0000000000..bdabf5455a --- /dev/null +++ b/transformer_lens/model_bridge/sources/vllm/internals.py @@ -0,0 +1,24 @@ +"""Single chokepoint for vLLM internal API access. + +vLLM rearranges its internal class paths every 4-6 weeks. Centralize every +``llm.llm_engine.…`` walk here so version drift is patched in one place. + +**Validated against ``vllm==0.20.2``** (also the version pinned in +``demos/vLLM_Bridge_Integration_Test.ipynb``). The patched-load-model path in +``plugin.py`` and the ``hf_config`` walk below have been confirmed on that +release; newer releases may move attributes — re-validate before bumping. +""" +from __future__ import annotations + +from typing import Any + + +def extract_hf_config(llm: Any) -> Any: + """Return the HF config that vLLM loaded the model from.""" + try: + return llm.llm_engine.model_config.hf_config + except AttributeError as e: + raise RuntimeError( + "Could not locate hf_config under llm.llm_engine.model_config. " + "vLLM may have moved it; update extract_hf_config() to match." + ) from e diff --git a/transformer_lens/model_bridge/sources/vllm/intervention_specs.py b/transformer_lens/model_bridge/sources/vllm/intervention_specs.py new file mode 100644 index 0000000000..ab35dac72d --- /dev/null +++ b/transformer_lens/model_bridge/sources/vllm/intervention_specs.py @@ -0,0 +1,9 @@ +"""Intervention op vocabulary shared by the driver (spec validation) and the +worker extension (spec → buffer translation). + +Adding a new op requires (a) listing it here, (b) handling it in +``worker_extension._apply_intervention``. +""" +from __future__ import annotations + +SUPPORTED_OPS = frozenset({"suppress", "scale", "add", "set"}) diff --git a/transformer_lens/model_bridge/sources/vllm/overlays/__init__.py b/transformer_lens/model_bridge/sources/vllm/overlays/__init__.py new file mode 100644 index 0000000000..37d3650d75 --- /dev/null +++ b/transformer_lens/model_bridge/sources/vllm/overlays/__init__.py @@ -0,0 +1,21 @@ +"""vLLM overlay registry. + +One :class:`DecoderOnlyOverlay` handles every vLLM decoder-only model +(Llama / Qwen / Mistral / Gemma / Phi3 / Qwen3 / Kimi / GLM / …). It's the +default for any architecture; per-architecture overlays would only land if a +model breaks vLLM's conventional decoder-only shape. +""" +from __future__ import annotations + +from .base import AdapterOverlay +from .decoder_only import DecoderOnlyOverlay + +DEFAULT_VLLM_OVERLAY: AdapterOverlay = DecoderOnlyOverlay() + + +def get_overlay(architecture: str) -> AdapterOverlay: + """Return the overlay for an architecture; falls back to the decoder-only default.""" + return DEFAULT_VLLM_OVERLAY + + +__all__ = ["AdapterOverlay", "DEFAULT_VLLM_OVERLAY", "DecoderOnlyOverlay", "get_overlay"] diff --git a/transformer_lens/model_bridge/sources/vllm/overlays/base.py b/transformer_lens/model_bridge/sources/vllm/overlays/base.py new file mode 100644 index 0000000000..e59bad5392 --- /dev/null +++ b/transformer_lens/model_bridge/sources/vllm/overlays/base.py @@ -0,0 +1,25 @@ +"""Base class for vLLM overlays.""" +from __future__ import annotations + +from typing import Any, Dict, List, Tuple + + +class AdapterOverlay: + """A vLLM overlay for a single architecture family. + + :meth:`capture_specs` is called BEFORE ``LLM(...)`` to register the dot-paths + and output widths the plugin should pre-allocate GPU buffers for. The plugin + reads these during ``Worker.load_model`` so capture hooks are present when + ``torch.compile`` traces the model. + + :meth:`nonfiring_hooks` enumerates hooks that vLLM's fused kernels prevent + from firing; surfaced as a single boot-time warning. + """ + + def capture_specs(self, hf_config: Any) -> Dict[str, Tuple[str, int]]: + """Return ``{canonical_hook_name: (dot_path_in_vllm_model, output_width)}``.""" + raise NotImplementedError + + def nonfiring_hooks(self) -> List[str]: + """Canonical hook names that vLLM's fused kernels cannot expose.""" + return [] diff --git a/transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py b/transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py new file mode 100644 index 0000000000..fbed07c8c4 --- /dev/null +++ b/transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py @@ -0,0 +1,65 @@ +"""Generic overlay for any decoder-only model vLLM supports. + +vLLM's decoder-only models all share the same internal structure: +``model.embed_tokens`` / ``model.layers.{i}`` (each with ``self_attn`` and +``mlp`` submodules) / ``model.norm`` / ``lm_head``. This overlay hooks that +shared abstraction, so one file works for Llama, Qwen, Mistral, Gemma, Phi3, +Qwen3, Kimi, GLM, and every other model that inherits the standard shape. + +Non-decoder-only architectures (Mamba SSM, T5 encoder-decoder, BERT, MoE +per-expert) break the convention and would need their own overlays. + +Two hooks capture different points than HF/HookedTransformer: + +- ``blocks.{i}.hook_out``: vLLM's layer returns ``(mlp_delta, residual)`` + separately (fused-residual). The plugin's hook materializes the sum so the + captured value matches HF's "post-MLP residual stream". +- ``ln_final.hook_normalized``: vLLM exposes ``x * rsqrt(var+eps) * weight``; + HF/HT exposes the pre-weight value. Divide the vLLM capture by + ``model.norm.weight`` (or ``1 + weight`` for Gemma) for HT-equivalent output. + +Non-decoder-only architectures (Mamba SSM, T5 encoder-decoder, BERT, MoE +per-expert) break the convention and would need their own overlays. +""" +from __future__ import annotations + +from typing import Any, Dict, List, Tuple + +from .base import AdapterOverlay + + +class DecoderOnlyOverlay(AdapterOverlay): + """Default overlay for vLLM decoder-only models.""" + + def capture_specs(self, hf_config: Any) -> Dict[str, Tuple[str, int]]: + d_model = hf_config.hidden_size + n_layers = hf_config.num_hidden_layers + # unembed.hook_out is intentionally NOT captured here — vLLM's sampler + # computes logits via a direct matmul on the final hidden state and + # never invokes lm_head.__call__, so register_forward_hook on lm_head + # would install but never fire. See nonfiring_hooks() below; the + # driver synthesizes the next-token logits from vLLM's sampler output. + specs: Dict[str, Tuple[str, int]] = { + "embed.hook_out": ("model.embed_tokens", d_model), + "ln_final.hook_normalized": ("model.norm", d_model), + } + for i in range(n_layers): + specs[f"blocks.{i}.hook_out"] = (f"model.layers.{i}", d_model) + specs[f"blocks.{i}.attn.hook_out"] = (f"model.layers.{i}.self_attn", d_model) + specs[f"blocks.{i}.mlp.hook_out"] = (f"model.layers.{i}.mlp", d_model) + return specs + + def nonfiring_hooks(self) -> List[str]: + # vLLM's universal fused-kernel limitations — PagedAttention fuses the + # QK^T → softmax → attn-weight path; QKVParallelLinear fuses RoPE inside + # the projection. Same restriction on every decoder-only model. + return [ + "blocks.{i}.attn.hook_pattern", + "blocks.{i}.attn.hook_attn_scores", + "blocks.{i}.attn.hook_rot_q", + "blocks.{i}.attn.hook_rot_k", + # vLLM's sampler bypasses lm_head.__call__ — capture-via-forward-hook + # never fires; driver synthesizes argmax-matching logits from the + # sampler's returned token. + "unembed.hook_out", + ] diff --git a/transformer_lens/model_bridge/sources/vllm/plugin.py b/transformer_lens/model_bridge/sources/vllm/plugin.py new file mode 100644 index 0000000000..02ddf9a586 --- /dev/null +++ b/transformer_lens/model_bridge/sources/vllm/plugin.py @@ -0,0 +1,170 @@ +"""vLLM plugin entry point. + +Monkey-patches ``Worker.load_model`` to install capture hooks after weights +load and before ``compile_or_warm_up_model`` — the only window where hooks +make it into the compiled FX graph (PyTorch #117758). + +Hook body invariants under ``torch.compile``: in-place writes to a +pre-allocated GPU tensor; no ``.cpu()`` (illegal during CUDA-graph capture); +SymInt-indexed slicing only (Python ``.shape`` access forces specialization). + +Interventions ride the same hook. Each hook applies an affine transform +``output = output * scale_buf + bias_buf`` before capturing. Defaults are +``scale=ones`` / ``bias=zeros`` (identity). The driver swaps buffer contents +between forwards via ``tl_set_interventions`` — the FX graph references the +buffers, so swaps take effect on the next dispatch without recompiling. + +Memory cost: the affine transform allocates a transient output-shape tensor +per hook per forward, even in identity mode. Peak forward memory is roughly +1.5× the prior capture-only design — the caching allocator reuses the slot +but the peak pressure rises. Branching the hook to skip the affine in +identity mode would defeat the swap-via-buffer trick and break the FX graph. +""" +from __future__ import annotations + +import re +from typing import Any, Dict, Tuple + +import torch + +# Matches dot-paths like "model.layers.0", "model.layers.15" — vLLM's decoder +# layers, which return the fused-residual (mlp_delta, residual) 2-tuple. Hooks +# on these need to materialize the sum so the capture matches HF's +# blocks.{i}.hook_out semantics (full residual stream). +_DECODER_LAYER_PATH = re.compile(r"^model\.layers\.\d+$") + +# Transient signal driver → worker during LLM construction. Per-Worker buffers +# live on Worker instances so concurrent boot_vllm calls don't collide. +_config: Dict[str, Any] = {} +_install_patched = False +_orig_load_model = None + + +def configure( + capture_specs: Dict[str, Tuple[str, int]], + max_num_batched_tokens: int, + dtype: torch.dtype, +) -> None: + """Set capture specs, buffer length, and dtype before ``LLM(...)``.""" + _config["capture_specs"] = capture_specs + _config["max_num_batched_tokens"] = max_num_batched_tokens + _config["dtype"] = dtype + + +def register() -> None: + """Idempotent monkey-patch of ``Worker.load_model``. + + vLLM calls ``register()`` once per process at entry-points discovery. Idempotent + so re-imports (notebook restarts, repeated ``boot_vllm`` in the same process) + don't double-wrap. + + No ``unregister()`` symmetry: the patch stays for process lifetime. Benign + because ``patched_load_model`` no-ops when ``_config["capture_specs"]`` is + absent — and ``boot_vllm`` clears ``_config`` after each ``LLM(...)``, so + any subsequent non-TL ``LLM(...)`` in the same process hits the no-op path. + """ + global _install_patched, _orig_load_model + if _install_patched: + return + from vllm.v1.worker.gpu_worker import Worker + + _orig_load_model = Worker.load_model + + def patched_load_model(self): + _orig_load_model(self) + specs = _config.get("capture_specs") + if not specs: + return # not a TL-driven LLM; no hooks to install + max_n = _config["max_num_batched_tokens"] + dtype = _config["dtype"] + device = next(self.model_runner.model.parameters()).device + + # Detach prior handles before reassigning — vLLM doesn't double-load + # today, but unconditional reassignment would orphan hooks if it ever did. + for handle in getattr(self, "_tl_hook_handles", []): + handle.remove() + self._tl_buffers = {} + self._tl_scale_buffers = {} + self._tl_bias_buffers = {} + self._tl_hook_handles = [] + for canonical_name, (dot_path, width) in specs.items(): + target = self.model_runner.model + for seg in dot_path.split("."): + target = target[int(seg)] if seg.isdigit() else getattr(target, seg) + capture_buf = torch.zeros(max_n, width, device=device, dtype=dtype) + # Affine identity at install. Driver swaps via tl_set_interventions + # to enable suppress/scale/add/set ops between forwards. + scale_buf = torch.ones(width, device=device, dtype=dtype) + bias_buf = torch.zeros(width, device=device, dtype=dtype) + self._tl_buffers[canonical_name] = capture_buf + self._tl_scale_buffers[canonical_name] = scale_buf + self._tl_bias_buffers[canonical_name] = bias_buf + # Decoder layers return vLLM's (mlp_delta, residual) tuple; the + # hook materializes their sum so the capture semantically matches + # HF's full residual stream. Other modules use the default path. + materialize = bool(_DECODER_LAYER_PATH.match(dot_path)) + handle = target.register_forward_hook( + _make_capture_hook(capture_buf, scale_buf, bias_buf, materialize=materialize) + ) + self._tl_hook_handles.append(handle) + + Worker.load_model = patched_load_model + _install_patched = True + + +def _make_capture_hook( + capture_buf: torch.Tensor, + scale_buf: torch.Tensor, + bias_buf: torch.Tensor, + *, + materialize: bool = False, +): + """GPU-only, dynamic-shape-safe affine + capture into pre-allocated buffers. + + When ``materialize=True`` (decoder layers), treat the module's output as + vLLM's fused-residual ``(mlp_delta, residual)`` tuple: capture + ``mlp_delta + residual`` (the full residual stream, matching HF's + blocks.{i}.hook_out semantics) and return ``(modified - residual, residual)`` + so the next layer's input_layernorm sees the same fused sum. Mutations + propagate through both the capture and the downstream graph. + """ + + @torch.no_grad() + def hook(_module, _inputs, output): + if materialize and isinstance(output, tuple) and len(output) == 2: + hidden, residual = output + if isinstance(hidden, torch.Tensor) and isinstance(residual, torch.Tensor): + t = hidden + residual + modified = t * scale_buf + bias_buf + n = t.shape[0] + capture_buf.narrow(0, 0, n).copy_(modified) + # ``(modified - residual) + residual`` reconstructs ``modified`` in + # the next layer's fused input_layernorm. Identity case is exact; + # for interventions, fp16 precision loss is bounded by one ulp at + # the residual stream's magnitude — small relative to mutation effect. + return (modified - residual, residual) + + tuple_tail: tuple = () + if isinstance(output, tuple): + t = output[0] + tuple_tail = output[1:] + else: + t = output + if not isinstance(t, torch.Tensor): + return None + # Affine transform; default scale=1 / bias=0 means identity. Driver + # swaps buffer contents to enable interventions. + modified = t * scale_buf + bias_buf + # ``narrow`` produces an explicit-shape view ``(n, width)`` that Dynamo + # traces correctly under dynamic shapes; the ``[:n]`` getitem form gets + # erased under fake-tensor tracing and ``copy_`` then sees the full + # buffer as destination, raising "expand s72 -> max_n". + n = t.shape[0] + capture_buf.narrow(0, 0, n).copy_(modified) + # Preserve the input wrapping — a 1-tuple input must come back as a 1-tuple + # (``tuple_tail`` is falsy in that case, so don't gate on it). + if isinstance(output, tuple): + return (modified,) + tuple_tail + return modified + + return hook diff --git a/transformer_lens/model_bridge/sources/vllm/source.py b/transformer_lens/model_bridge/sources/vllm/source.py new file mode 100644 index 0000000000..e5b7c083fe --- /dev/null +++ b/transformer_lens/model_bridge/sources/vllm/source.py @@ -0,0 +1,207 @@ +"""``boot_vllm`` — construct a vLLM LLM, wrap it in a RemoteBridge via VLLMDriver.""" +from __future__ import annotations + +import logging +import os +import warnings +from typing import Any, Dict, Optional + +import torch + +from transformer_lens.factories.architecture_adapter_factory import ( + ArchitectureAdapterFactory, +) +from transformer_lens.model_bridge.remote_bridge import RemoteBridge +from transformer_lens.model_bridge.sources._bridge_builder import ( + build_bridge_config_from_hf, +) +from transformer_lens.utilities.hf_utils import get_hf_token + +from . import plugin +from .driver import VLLMDriver +from .internals import extract_hf_config +from .overlays import get_overlay + +# Forced LLM(...) kwargs that the capture-hook design depends on. Caller override → ValueError. +_LOCKED_KWARGS = { + "tensor_parallel_size": 1, + "pipeline_parallel_size": 1, + "skip_tokenizer_init": True, + "disable_log_stats": True, +} + + +def boot_vllm( + model_name: str, + tokenizer: Optional[Any] = None, + dtype: Optional[torch.dtype] = None, + gpu_memory_utilization: float = 0.5, + max_model_len: Optional[int] = None, + max_num_batched_tokens: int = 2048, + **vllm_kwargs: Any, +) -> RemoteBridge: + """Boot a model via vLLM and wrap it in a :class:`RemoteBridge` via :class:`VLLMDriver`. + + vLLM drives the forward pass (PagedAttention + ``torch.compile`` + CUDA graphs). + Capture buffers are populated by hooks the plugin installs pre-compile inside + the worker; they come back via ``collective_rpc`` and replay through the + bridge's HookPoint tree. + + **Scope vs vllm-lens:** vllm-lens is observation-only. This source extends to + observation + spec-vocabulary *mutation* — each capture hook also applies an + affine transform ``output = output * scale + bias`` (default identity), so + interventions (``suppress`` / ``scale`` / ``add`` / ``set``) propagate to + downstream layers. The hook's return value replaces the module output per + PyTorch ``register_forward_hook`` semantics. **The mutation path under + torch.compile + CUDA graphs is verified end-to-end by** + ``demos/vLLM_Bridge_Integration_Test.ipynb``; unit tests cover the dispatch + protocol only. + + Some captures use vLLM-native conventions that differ from HF/HT; see + :mod:`transformer_lens.model_bridge.sources.vllm.overlays.decoder_only` for + which hooks diverge and the conversion to apply for HT-equivalent values. + + GPU memory cost: each capture buffer is ``max_num_batched_tokens × width`` at + the model's dtype. For Llama-3.2-1B at fp16 with ``max_num_batched_tokens=2048``, + the unembed buffer alone is ~525 MB (2048 × 128256 × 2 bytes); residual-stream + buffers add ~8 MB per hook. The affine intervention hook also allocates a + transient output-shape tensor per forward (even in identity mode), so peak + forward memory is ~1.5× the capture buffers' resident size. + + KV-cache footprint: vLLM reserves KV cache sized for ``max_model_len`` × layers + × heads × head_dim. If ``max_model_len`` is left as ``None``, vLLM uses the + model's native context (e.g. 131072 for Llama-3.2-1B) — easily 4+ GiB even + on a 1B model. Pass an explicit ``max_model_len`` (e.g. ``2048`` for typical + mech-interp prompts) to keep the budget on smaller GPUs. + """ + _reject_locked_overrides(vllm_kwargs) + + from transformers import AutoConfig, AutoTokenizer + + # Resolve architecture WITHOUT loading weights so we can tell the plugin + # which dot-paths to hook before LLM(...) constructs the worker. + hf_token = get_hf_token() + hf_config_preview = AutoConfig.from_pretrained(model_name, token=hf_token) + architecture = hf_config_preview.architectures[0] + overlay = get_overlay(architecture) + + resolved_dtype = dtype or _dtype_from_hf_config(hf_config_preview) + plugin.configure( + capture_specs=overlay.capture_specs(hf_config_preview), + max_num_batched_tokens=max_num_batched_tokens, + dtype=resolved_dtype, + ) + plugin.register() + + # The plugin's _config singleton must be visible to the worker, which only + # holds with single-process execution. Multi-GPU is unsupported. Override + # any user setting — silent capture failure otherwise. + existing_mp = os.environ.get("VLLM_ENABLE_V1_MULTIPROCESSING") + if existing_mp not in (None, "0"): + warnings.warn( + f"VLLM_ENABLE_V1_MULTIPROCESSING={existing_mp!r} overridden to '0' — " + "boot_vllm needs single-process execution for capture hooks to install. " + "Multi-GPU vLLM is unsupported.", + UserWarning, + stacklevel=2, + ) + os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0" + + from vllm import LLM + + llm = LLM( + model=model_name, + gpu_memory_utilization=gpu_memory_utilization, + max_model_len=max_model_len, + # Critical: vLLM defaults max_num_batched_tokens to 8192 for chunked prefill. + # We size our capture buffer to this same value, so vLLM must compile for + # the matching dynamic-shape range — otherwise Dynamo's symbolic-shape + # hint exceeds the buffer dim and narrow() fails at compile time. + max_num_batched_tokens=max_num_batched_tokens, + # Register TLWorkerExtension so its tl_* methods are reachable via + # collective_rpc. Passed as a dotted path; vLLM imports the class at + # worker construction and mixes it into the Worker via multiple + # inheritance (asserts no attribute name collisions — hence the tl_ prefix). + worker_extension_cls="transformer_lens.model_bridge.sources.vllm.worker_extension.TLWorkerExtension", + # Allow full-vocab logprobs so the driver can synthesize real logits for + # the generated position (vLLM caps logprobs to this value; default 20 is + # too small for mech-interp). One ~512 KB buffer per call; negligible. + max_logprobs=hf_config_preview.vocab_size, + dtype=str(resolved_dtype).replace("torch.", "") if dtype is not None else "auto", + **_LOCKED_KWARGS, + **vllm_kwargs, + ) + + # Capture-path validity is enforced inside patched_load_model during + # LLM(...) above — any missing dot-path raises AttributeError there. By + # the time we reach this line every spec has already been walked successfully. + hf_config = extract_hf_config(llm) + # _config has been consumed into per-Worker state; clear so a non-TL + # vllm.LLM(...) in the same process doesn't inherit our specs. + plugin._config.clear() + if tokenizer is None: + tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token) + + # Build the adapter (RemoteBridge skips adapter.prepare_model — there's no + # local model tree to walk). Use the shared HF→TL config builder so + # bridge_config is a real TransformerBridgeConfig with all dataclass + # defaults (d_vocab_out=-1, etc.) — not a deep-copied HF config with + # extra fields, which is missing the TL-only attributes BridgeCore reads. + bridge_config = build_bridge_config_from_hf(hf_config, architecture, model_name, resolved_dtype) + adapter = ArchitectureAdapterFactory.select_architecture_adapter(bridge_config) + + driver = VLLMDriver( + llm=llm, + adapter=adapter, + tokenizer=tokenizer, + overlay=overlay, + hf_config=hf_config, + max_num_batched_tokens=max_num_batched_tokens, + ) + bridge = RemoteBridge(adapter=adapter, tokenizer=tokenizer, driver=driver) + _log_hook_summary(model_name, architecture, driver) + return bridge + + +def _reject_locked_overrides(vllm_kwargs: Dict[str, Any]) -> None: + for key, locked in _LOCKED_KWARGS.items(): + if key in vllm_kwargs and vllm_kwargs[key] != locked: + raise ValueError( + f"boot_vllm forces {key}={locked}; caller passed {key}={vllm_kwargs[key]}. " + "Multi-device / continuous batching / vLLM-owned tokenizer are unsupported." + ) + + +def _dtype_from_hf_config(hf_config: Any) -> torch.dtype: + raw = getattr(hf_config, "torch_dtype", None) + if isinstance(raw, torch.dtype): + return raw + if isinstance(raw, str): + return getattr(torch, raw, torch.float16) + return torch.float16 + + +def _log_hook_summary(model_name: str, architecture: str, driver: VLLMDriver) -> None: + """Log the fireable and non-fireable hook sets so users don't have to grep.""" + log = logging.getLogger("transformer_lens.vllm") + fireable = sorted(driver.supported_hook_points) + log.info( + "vLLM source on %s (%s) captures %d hook(s): %s", + model_name, + architecture, + len(fireable), + ", ".join(fireable), + ) + nonfiring = sorted(driver.non_fireable_hook_points) + if nonfiring: + log.info( + "vLLM source on %s (%s) cannot fire %d hook(s) (vLLM fuses these): %s. " + "Use boot_transformers() if you need them.", + model_name, + architecture, + len(nonfiring), + ", ".join(nonfiring), + ) + + +__all__ = ["boot_vllm"] diff --git a/transformer_lens/model_bridge/sources/vllm/worker_extension.py b/transformer_lens/model_bridge/sources/vllm/worker_extension.py new file mode 100644 index 0000000000..70a98ad39a --- /dev/null +++ b/transformer_lens/model_bridge/sources/vllm/worker_extension.py @@ -0,0 +1,101 @@ +"""Worker extension exposed to collective_rpc for capture-buffer reads and +intervention-buffer writes. + +Hook *installation* lives in :mod:`plugin` (must happen pre-compile). This class +only exposes the post-compile read/write surface. Buffers are per-Worker +(``self._tl_buffers`` / ``self._tl_scale_buffers`` / ``self._tl_bias_buffers``) +so concurrent ``boot_vllm`` calls don't collide. All methods prefixed ``tl_`` +to avoid colliding with vLLM ``Worker`` attributes. +""" +from __future__ import annotations + +from typing import Any, Dict, List + +import torch + +from .intervention_specs import SUPPORTED_OPS + + +class TLWorkerExtension: + """Mixed into vLLM's ``Worker`` via ``worker_extension_cls``.""" + + _tl_hook_handles: list + _tl_buffers: Dict[str, torch.Tensor] + _tl_scale_buffers: Dict[str, torch.Tensor] + _tl_bias_buffers: Dict[str, torch.Tensor] + + def tl_read_captures(self, prompt_lens: List[int]) -> Dict[str, torch.Tensor]: + """Slice each capture buffer back to ``sum(prompt_lens)`` rows; CPU copies. + + Caller (VLLMDriver.forward) gates ``sum(prompt_lens) <= max_num_batched_tokens`` + before the RPC, so ``total`` is always within buffer bounds. + """ + total = sum(prompt_lens) + buffers: Dict[str, torch.Tensor] = getattr(self, "_tl_buffers", {}) + return {name: buf[:total].detach().cpu().clone() for name, buf in buffers.items()} + + def tl_set_interventions(self, specs: Dict[str, Dict[str, Any]]) -> None: + """Reset all affine buffers to identity, then apply each spec. + + Driver pushes the full spec set every forward (or ``{}`` to reset). + Spec format: ``{hook_name: {"op": , ...op-specific params>}}``. + Supported ops: suppress, scale (``factor``: float), add and set + (``value``: scalar broadcast across width, or 1-D shape ``(width,)``). + """ + scale_bufs: Dict[str, torch.Tensor] = getattr(self, "_tl_scale_buffers", {}) + bias_bufs: Dict[str, torch.Tensor] = getattr(self, "_tl_bias_buffers", {}) + # Reset every hook to identity first — clears any stale state from + # the previous forward. + for name, sb in scale_bufs.items(): + sb.fill_(1.0) + bias_bufs[name].zero_() + for hook_name, spec in specs.items(): + if hook_name not in scale_bufs: + raise KeyError(f"Unknown hook for intervention: {hook_name!r}") + _apply_intervention(scale_bufs[hook_name], bias_bufs[hook_name], spec) + + def tl_remove_hooks(self) -> None: + """Detach all capture hooks and drop buffer references. Idempotent.""" + for handle in getattr(self, "_tl_hook_handles", []): + handle.remove() + self._tl_hook_handles = [] + self._tl_buffers = {} + self._tl_scale_buffers = {} + self._tl_bias_buffers = {} + + +def _apply_intervention( + scale_buf: torch.Tensor, bias_buf: torch.Tensor, spec: Dict[str, Any] +) -> None: + """Translate a spec dict to in-place buffer writes.""" + op = spec.get("op") + if op not in SUPPORTED_OPS: + raise ValueError(f"Unsupported intervention op: {op!r}. Supported: {sorted(SUPPORTED_OPS)}") + if op == "suppress": + scale_buf.zero_() + bias_buf.zero_() + return + if op == "scale": + scale_buf.fill_(float(spec["factor"])) + bias_buf.zero_() + return + value = torch.as_tensor(spec["value"], device=bias_buf.device, dtype=bias_buf.dtype) + # 0-d broadcasts across width (e.g. "shift all dims by 0.5"); width-shaped + # writes element-wise (e.g. SAE steering vector). Anything else is an error. + if value.ndim == 0: + bias_buf.fill_(value.item()) + elif value.shape == bias_buf.shape: + bias_buf.copy_(value) + else: + raise ValueError( + f"Intervention 'value' must be a scalar or shape {tuple(bias_buf.shape)}; " + f"got shape {tuple(value.shape)}" + ) + if op == "add": + scale_buf.fill_(1.0) + elif op == "set": + scale_buf.zero_() + else: + raise RuntimeError( + f"op {op!r} is in SUPPORTED_OPS but _apply_intervention has no branch for it." + ) diff --git a/transformer_lens/model_bridge/transformer_bridge.py b/transformer_lens/model_bridge/transformer_bridge.py new file mode 100644 index 0000000000..ae3830a911 --- /dev/null +++ b/transformer_lens/model_bridge/transformer_bridge.py @@ -0,0 +1,2991 @@ +"""Bridge module for connecting different model architectures. + +This module provides the bridge components that wrap remote model components and provide +a consistent interface for accessing their weights and performing operations. +""" +import logging +import re +import warnings +from collections.abc import Generator +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Dict, + Iterator, + List, + Literal, + Optional, + Tuple, + Union, + cast, +) + +import einops +import numpy as np +import torch +import tqdm +from torch import nn + +from transformer_lens import utilities as utils +from transformer_lens.FactoredMatrix import FactoredMatrix +from transformer_lens.hook_points import HookIntrospectionMixin, HookPoint +from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter +from transformer_lens.model_bridge.bridge_core import BridgeCore +from transformer_lens.model_bridge.component_setup import set_original_components +from transformer_lens.model_bridge.composition_scores import CompositionScores +from transformer_lens.model_bridge.driver_protocol import ( + TensorLike, + to_torch, + validate_driver, +) +from transformer_lens.model_bridge.exceptions import StopAtLayerException +from transformer_lens.model_bridge.generalized_components.base import ( + GeneralizedComponent, +) +from transformer_lens.model_bridge.generalized_components.block import ( + _BLOCK_INTERNAL_MODULES, + _NORM_PREFIXES, + _VARIANT_SUBMODULE_SET, + VARIANT_SUBMODULE_NAMES, +) +from transformer_lens.model_bridge.get_params_util import get_bridge_params +from transformer_lens.utilities.devices import move_to_and_update_config + +if TYPE_CHECKING: + pass + +_BLOCK_PATTERN = re.compile("blocks\\.(\\d+)") + + +def _resolve_attr_path(obj: nn.Module, attr_path: str) -> torch.Tensor: + """Walk a dot-separated attribute path and return the final tensor.""" + result = obj + for attr in attr_path.split("."): + result = getattr(result, attr) + return cast(torch.Tensor, result) + + +# build_alias_to_canonical_map lives in bridge_core.py; re-import for the module's +# internal use (run_with_cache, hooks() context manager in this file). +from transformer_lens.model_bridge.bridge_core import ( # noqa: E402 + build_alias_to_canonical_map, +) + + +class TransformerBridge(BridgeCore, HookIntrospectionMixin, nn.Module): + """Torch-backed bridge: HF, vLLM-via-torch, anything that wraps an ``nn.Module``. + + Provides a standardized interface to access components of a transformer + model, regardless of the underlying architecture. It uses an architecture adapter + to map between the TransformerLens and HuggingFace model structures. + + Tokenization notes + ------------------ + + :meth:`to_tokens`, :meth:`to_str_tokens`, :meth:`get_token_position`, + :meth:`forward` (string input), and :meth:`generate` accept ``prepend_bos`` + to control BOS prepending. Resolution: explicit arg → + ``cfg.default_prepend_bos`` (defaults ``True``, even for non-BOS-trained + models — attention heads tend to use position 0 as a resting state). + **Pass ``prepend_bos=False`` when tokenizing a fragment of a larger + prompt** — off-by-one position errors usually trace back here. + + Reconciliation with ``cfg.tokenizer_prepends_bos`` (tokenizers that add + BOS automatically) is handled internally — pass the value you want; + the bridge adds or strips manually as needed. When + ``cfg.tokenizer_appends_eos=True`` (OLMo, Apertus, etc.), + :meth:`to_tokens` also strips trailing EOS tokens so the model receives + a continuation rather than a terminated sequence; this path is + bridge-specific. + + BPE/SentencePiece tokenizers treat ``"hello"``, ``" hello"``, and + ``"Hello"`` as distinct tokens. Concatenated prompts may not tokenize + as the sum of parts — inspect with :meth:`to_str_tokens` when in doubt. + """ + + # hook_aliases inherited from BridgeCore + + def __init__( + self, + model: nn.Module, + adapter: ArchitectureAdapter, + tokenizer: Any, + *, + driver: Any = None, + ): + """Initialize the bridge. + + Args: + model: The model to bridge (must be a PyTorch nn.Module or PreTrainedModel) + adapter: The architecture adapter to use + tokenizer: The tokenizer to use (required) + driver: Optional pre-built :class:`Driver`. Sources that construct + exotic drivers (vLLM, Inspect) pass them here. When ``None``, + a :class:`TransformersDriver` is built from the supplied + ``model``/``adapter``/``tokenizer`` — kept for backward + compatibility with direct ``TransformerBridge(...)`` callers. + """ + nn.Module.__init__(self) + self.__dict__["original_model"] = model + # Production sources construct their Driver and pass it via ``driver=``. + # The fallback covers tests / direct callers with a hand-rolled triple. + if driver is None: + from transformer_lens.model_bridge.sources.transformers_driver import ( + TransformersDriver, + ) + + driver = TransformersDriver(model, adapter, tokenizer) + BridgeCore.__init__(self, adapter, tokenizer, driver) + # real_components maps TL keys to (remote_path, actual_instance) tuples; + # for list components, actual_instance is a list of instances. + self.real_components: Dict[str, tuple] = {} + if not hasattr(self.cfg, "device") or self.cfg.device is None: + try: + self.cfg.device = str(next(self.original_model.parameters()).device) + except StopIteration: + self.cfg.device = "cpu" + set_original_components(self, self.adapter, self.__dict__["original_model"]) + self._initialize_hook_registry() + self._register_aliases() + self._register_all_aliases_recursive() + # Re-scan after alias registration so alias names (hook_resid_pre, …) + # join the registry alongside their canonical targets. Shared HookPoint + # instances, so no double-firing. + self._scan_existing_hooks(self, "") + self._setup_hook_compatibility() + # Backfill supported_hook_points = registry − non_fireable. Whitelist- + # semantic drivers (Inspect) declared their own non-empty set and skip. + if not self._driver.supported_hook_points: + self._driver.supported_hook_points = ( + frozenset(self._hook_registry) - self._driver.non_fireable_hook_points + ) + # Fail fast on a misshapen driver here, not at first capture. + validate_driver(self._driver, after_bridge_construction=True) + self.processor = None + + @classmethod + def boot_transformers( + cls, + model_name: str, + hf_config_overrides: Optional[dict] = None, + device: Optional[Union[str, torch.device]] = None, + dtype: torch.dtype = torch.float32, + tokenizer: Optional[Any] = None, + load_weights: bool = True, + trust_remote_code: bool = False, + model_class: Optional[type] = None, + hf_model: Optional[Any] = None, + device_map: Optional[Union[str, Dict[str, Union[str, int]]]] = None, + n_devices: Optional[int] = None, + max_memory: Optional[Dict[Union[str, int], str]] = None, + n_ctx: Optional[int] = None, + revision: Optional[str] = None, + checkpoint_index: Optional[int] = None, + checkpoint_value: Optional[int] = None, + ) -> "TransformerBridge": + """Boot a model from HuggingFace (alias for sources.transformers.boot). + + Returns raw HF weights by default — logits/activations match HF, *not* + legacy ``HookedTransformer`` (which folds LayerNorm + centers weights). + Call ``enable_compatibility_mode()`` on the result for HookedTransformer- + equivalent numerics. Generation, argmax, and CE loss are unaffected. + + Attention implementation is forced to ``"eager"`` so hooks can capture scores + and patterns. For an apples-to-apples HF comparison, load the HF model with + ``attn_implementation="eager"`` too; comparing against the default ``"sdpa"`` + shows ~1e-3 fp32 drift from kernel-level op reordering, not a bridge bug. + + Args: + model_name: The name of the model to load. + hf_config_overrides: Optional overrides applied to the HuggingFace config before model load. + device: The device to use. If None, will be determined automatically. Mutually exclusive + with ``device_map``. + dtype: The dtype to use for the model. + tokenizer: Optional pre-initialized tokenizer to use; if not provided one will be created. + load_weights: If False, load model without weights (on meta device) for config inspection only. + trust_remote_code: Whether to trust remote code for custom model architectures. + model_class: Optional HuggingFace model class to use instead of the default + auto-detected class (e.g., BertForNextSentencePrediction). + hf_model: Optional pre-loaded HuggingFace model to use instead of loading one. Useful + for models loaded with custom configurations (e.g., quantization via + BitsAndBytesConfig). When provided, load_weights is ignored. If the pre-loaded + model was built with a ``device_map``, ``cfg.device`` and ``cfg.n_devices`` are + derived from its ``hf_device_map`` automatically. + device_map: HuggingFace-style device map for multi-GPU inference. Pass ``"auto"``, + ``"balanced"``, ``"sequential"``, or an explicit ``{submodule_path: device}`` dict. + Mutually exclusive with ``device``. + n_devices: Convenience shortcut: split the model across this many CUDA devices. + Translated to a ``max_memory`` dict over devices 0..n_devices-1 and passed as + ``device_map`` to HF. Requires CUDA with at least this many visible devices. + max_memory: Optional per-device memory budget, passed through to HF's dispatcher. + Only used when ``device_map`` or ``n_devices`` is in effect. + n_ctx: Optional context length override. Writes to the appropriate HF config field + for this model automatically (callers don't need to know the field name). + Warns if larger than the model's default context length. + revision: Optional HF revision (branch, tag, or commit). Forwarded to the underlying + ``AutoConfig.from_pretrained`` and ``AutoModelForCausalLM.from_pretrained`` calls. + Mutually exclusive with ``checkpoint_index`` / ``checkpoint_value``. + checkpoint_index: Index into the available training checkpoints for the model family + (currently ``EleutherAI/pythia*`` and ``stanford-crfm/*``). Resolved to a revision + string via known per-family naming conventions. + checkpoint_value: Training step or token count of the desired checkpoint. Alternative + to ``checkpoint_index``; must match an entry in the family's checkpoint label list. + + Returns: + The bridge to the loaded model. + """ + from transformer_lens.model_bridge.sources.transformers import boot + + return boot( + model_name=model_name, + hf_config_overrides=hf_config_overrides, + device=device, + dtype=dtype, + tokenizer=tokenizer, + load_weights=load_weights, + trust_remote_code=trust_remote_code, + model_class=model_class, + hf_model=hf_model, + device_map=device_map, + n_devices=n_devices, + max_memory=max_memory, + n_ctx=n_ctx, + revision=revision, + checkpoint_index=checkpoint_index, + checkpoint_value=checkpoint_value, + ) + + @property + def original_model(self) -> nn.Module: + """The wrapped ``nn.Module``. Raises :class:`AttributeError` for + non-torch drivers (vLLM, Inspect) that don't expose a local module.""" + underlying = getattr(self._driver, "underlying_model", None) + if underlying is None: + raise AttributeError( + f"{type(self._driver).__name__} does not expose an nn.Module — " + "non-torch drivers (vLLM, Inspect) operate without a local module." + ) + return underlying + + @original_model.setter + def original_model(self, value: nn.Module) -> None: + """Used by weight-processing paths that move the model across devices.""" + self.__dict__["original_model"] = value + # Sync via the driver's public API; non-torch drivers don't implement it. + setter = getattr(self._driver, "set_underlying_model", None) + if callable(setter): + setter(value) + + def _set_processed_weight_attributes(self) -> None: + """Create 3D processed weight attributes for attention components. + + For each attention component, if it has 2D weights (q.weight, k.weight, v.weight), + reshape them to 3D format [n_heads, d_model, d_head] and set as: + - _processed_W_Q + - _processed_W_K + - _processed_W_V + - _processed_b_Q + - _processed_b_K + - _processed_b_V + + This allows property aliases (W_Q, W_K, W_V) to return 3D format for + HookedTransformer compatibility while keeping 2D format for calculations. + """ + + n_heads = self.cfg.n_heads + d_head = self.cfg.d_head + d_model = self.cfg.d_model + if not hasattr(self, "blocks"): + return + for block in self.blocks: + if "attn" not in block._modules: + continue + attn = block.attn + if not (hasattr(attn, "q") and hasattr(attn.q, "weight")): + continue + try: + w_q_2d = attn.q.weight.data + w_k_2d = attn.k.weight.data + w_v_2d = attn.v.weight.data + attn._processed_W_Q = einops.rearrange( + w_q_2d, "m (i h) -> i m h", i=n_heads, h=d_head + ) + attn._processed_W_K = einops.rearrange( + w_k_2d, "m (i h) -> i m h", i=n_heads, h=d_head + ) + attn._processed_W_V = einops.rearrange( + w_v_2d, "m (i h) -> i m h", i=n_heads, h=d_head + ) + if hasattr(attn.q, "bias") and attn.q.bias is not None: + b_q_2d = attn.q.bias.data + b_k_2d = attn.k.bias.data + b_v_2d = attn.v.bias.data + attn._processed_b_Q = einops.rearrange( + b_q_2d, "(i h) -> i h", i=n_heads, h=d_head + ) + attn._processed_b_K = einops.rearrange( + b_k_2d, "(i h) -> i h", i=n_heads, h=d_head + ) + attn._processed_b_V = einops.rearrange( + b_v_2d, "(i h) -> i h", i=n_heads, h=d_head + ) + if hasattr(attn, "o") and hasattr(attn.o, "weight"): + w_o_2d = attn.o.weight.data + w_o_transposed = w_o_2d.T + attn._processed_W_O = einops.rearrange( + w_o_transposed, "m (i h) -> i h m", i=n_heads, h=d_head + ) + if hasattr(attn.o, "bias") and attn.o.bias is not None: + attn._processed_b_O = attn.o.bias.data + except Exception: + pass + + def _register_all_aliases_recursive(self) -> None: + """Recursively register aliases on all bridge components. + + This walks through all components and calls _register_aliases() on each one. + Used after weight processing to ensure aliases point to processed weights. + """ + if hasattr(self, "_register_aliases"): + self._register_aliases() + for module in self.modules(): + if module is not self and hasattr(module, "_register_aliases"): + getattr(module, "_register_aliases")() + + def __setattr__(self, name: str, value: Any) -> None: + """Override setattr to track HookPoint objects dynamically.""" + super().__setattr__(name, value) + if isinstance(value, HookPoint): + value.name = name + self._hook_registry[name] = value + elif hasattr(value, "get_hooks") and callable(getattr(value, "get_hooks")): + component_hooks = value.get_hooks() + for hook_name, hook in component_hooks.items(): + full_name = f"{name}.{hook_name}" + hook.name = full_name + self._hook_registry[full_name] = hook + + def _scan_existing_hooks(self, module: nn.Module, prefix: str = "") -> None: + """Scan existing modules for hooks and add them to registry.""" + visited = set() + # Protect canonical HookPoint names from alias overwrites + named_hook_ids: set = set() + + def scan_module(mod: nn.Module, path: str = "") -> None: + obj_id = id(mod) + if obj_id in visited: + return + visited.add(obj_id) + if hasattr(mod, "get_hooks") and callable(getattr(mod, "get_hooks")): + component_hooks = mod.get_hooks() # type: ignore[operator] + if isinstance(component_hooks, dict): + hooks_dict = cast(Dict[str, HookPoint], component_hooks) + for hook_name, hook in hooks_dict.items(): + full_name = f"{path}.{hook_name}" if path else hook_name + hook_id = id(hook) + if hook_id not in named_hook_ids: + hook.name = full_name + named_hook_ids.add(hook_id) + self._hook_registry[full_name] = hook + for attr_name in dir(mod): + if attr_name.startswith("_"): + continue + if attr_name == "original_component" or attr_name == "original_model": + continue + if attr_name in [ + "OV", + "QK", + "W_V", + "W_O", + "W_Q", + "W_K", + "W_in", + "W_gate", + "W_out", + "b_V", + "b_O", + "b_Q", + "b_K", + "b_in", + "b_out", + ]: + continue + try: + attr = getattr(mod, attr_name) + except (AttributeError, NameError, RuntimeError, TypeError): + continue + name = f"{path}.{attr_name}" if path else attr_name + if isinstance(attr, HookPoint): + hook_id = id(attr) + if hook_id not in named_hook_ids: + attr.name = name + named_hook_ids.add(hook_id) + self._hook_registry[name] = attr + for child_name, child_module in mod.named_children(): + if ( + child_name == "original_component" + or child_name == "_original_component" + or child_name == "original_model" + ): + continue + child_path = f"{path}.{child_name}" if path else child_name + scan_module(child_module, child_path) + + scan_module(module, prefix) + + @property + def n_params_total(self) -> int: + """Total number of parameters in the model, including embeddings, biases, + and layer norm weights. + + Mirrors :attr:`HookedTransformer.n_params_total`. Use this when you want + the actual parameter count for memory budgeting, comparison with + HuggingFace's ``model.num_parameters()``, or alignment with reported + model sizes in papers (e.g. the Pythia suite). + + Returns: + int: ``sum(p.numel() for p in self.parameters())`` + """ + return sum(p.numel() for p in self.parameters()) + + def __getattr__(self, name: str) -> Any: + """Provide a clear error message for missing attributes.""" + # Re-invoke original_model's property so its descriptive AttributeError + # for non-torch drivers isn't shadowed by the __dict__ fallback below. + if name == "original_model": + prop = type(self).__dict__.get("original_model") + if isinstance(prop, property) and prop.fget is not None: + return prop.fget(self) + if name in self.__dict__: # type: ignore[arg-type] + return self.__dict__[name] + # Use __dict__ directly to avoid recursion + if "_modules" in self.__dict__ and name in self.__dict__["_modules"]: # type: ignore[arg-type] + return self.__dict__["_modules"][name] + if "original_model" in self.__dict__ and self.__dict__["original_model"] is not None: + try: + name_split = name.split(".") + if len(name_split) > 1: + current = getattr(self.__dict__["original_model"], name_split[0]) + for part in name_split[1:]: # type: ignore[operator] + current = getattr(current, part) + return current + else: + return getattr(self.__dict__["original_model"], name) + except AttributeError: + pass # type: ignore[operator,assignment] + raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") + + def __str__(self) -> str: + """Get a string representation of the bridge. + # type: ignore[operator] + Returns: + A string describing the bridge's components # type: ignore[operator] + """ + lines = ["TransformerBridge:"] + mapping = self.adapter.get_component_mapping() + lines.extend(self._format_component_mapping(mapping, indent=1)) + return "\n".join(lines) + + def enable_compatibility_mode( + self, + disable_warnings: bool = False, + no_processing: bool = False, + fold_ln: bool = True, + center_writing_weights: bool = True, + center_unembed: bool = True, + fold_value_biases: bool = True, + refactor_factored_attn_matrices: bool = False, + ) -> None: + """Apply HookedTransformer-equivalent weight processing and legacy hook compatibility. + + Defaults match HookedTransformer's load-time processing (fold_ln + weight + centering) — required for analyses that reason in HookedTransformer's + post-processed coordinate system: logit lens, direct logit attribution, + residual-stream norms. Also enables legacy hook/component name aliases. + + Args: + disable_warnings: Whether to disable warnings about legacy components/hooks + no_processing: Whether to disable ALL pre-processing steps of the model. + If True, overrides fold_ln, center_writing_weights, and center_unembed to False. + fold_ln: Whether to fold layer norm weights into the subsequent linear layers. + Default: True. Ignored if no_processing=True. + center_writing_weights: Whether to center the writing weights (W_out in attention and MLPs). + Default: True. Ignored if no_processing=True. + center_unembed: Whether to center the unembedding matrix. + Default: True. Ignored if no_processing=True. + fold_value_biases: Whether to fold value biases into output bias. + Default: True. Ignored if no_processing=True. + refactor_factored_attn_matrices: Whether to refactor factored attention matrices. + Default: False. Ignored if no_processing=True. + """ + from transformer_lens.utilities.bridge_components import ( + apply_fn_to_all_components, + ) + + self.compatibility_mode = True + + def set_compatibility_mode(component: Any) -> None: + """Set compatibility mode on a component.""" + component.compatibility_mode = True + component.disable_warnings = disable_warnings + + apply_fn_to_all_components(self, set_compatibility_mode) + self.clear_hook_registry() + try: + if not no_processing: + self.process_weights( + fold_ln=fold_ln, + center_writing_weights=center_writing_weights, + center_unembed=center_unembed, + fold_value_biases=fold_value_biases, + refactor_factored_attn_matrices=refactor_factored_attn_matrices, + ) + finally: + # Re-initialize hooks even on failure so bridge stays usable + self._initialize_hook_registry() + self._setup_hook_compatibility() + self._register_all_aliases_recursive() + + def _setup_hook_compatibility(self) -> None: + """Setup hook compatibility transformations to match HookedTransformer behavior. + + This method sets up hook conversions and wrappers that ensure Bridge hooks + have the same shapes and behavior as HookedTransformer hooks. This includes: + 1. hook_z reshaping from [batch, seq, d_model] to [batch, seq, n_heads, d_head] + 2. Wrapping HF attention forward to inject position embeddings/attention masks + 3. Architecture-specific setup (e.g., rotary embedding references) + + This is called during __init__ and should always be run, regardless of whether + compatibility mode or weight processing is enabled. + + Note: This method is idempotent - can be called multiple times safely. + """ + if hasattr(self.adapter, "setup_hook_compatibility"): + self.adapter.setup_hook_compatibility(self) + elif hasattr(self.adapter, "setup_no_processing_hooks"): + self.adapter.setup_no_processing_hooks(self) + blocks_to_process = [] + if hasattr(self, "blocks"): + blocks_to_process.extend(self.blocks) + if hasattr(self, "encoder_blocks"): + blocks_to_process.extend(self.encoder_blocks) + if hasattr(self, "decoder_blocks"): + blocks_to_process.extend(self.decoder_blocks) + for block in blocks_to_process: + for attn_name in ["attn", "self_attn", "cross_attn"]: + if hasattr(block, attn_name): + attn = getattr(block, attn_name) + if hasattr(attn, "setup_hook_compatibility"): + attn.setup_hook_compatibility() + elif hasattr(attn, "setup_no_processing_hooks"): + attn.setup_no_processing_hooks() + + def process_weights( + self, + verbose: bool = False, + fold_ln: bool = True, + center_writing_weights: bool = True, + center_unembed: bool = True, + fold_value_biases: bool = True, + refactor_factored_attn_matrices: bool = False, + ) -> None: + """Process weights directly using ProcessWeights and architecture adapter. + + This method applies weight processing transformations to improve model interpretability + without requiring a reference HookedTransformer model. Works with all architectures + supported by TransformerBridge, including GPT-OSS and other new models. + + Args: + verbose: If True, print detailed progress messages. Default: False + fold_ln: Fold LayerNorm weights/biases into subsequent layers. Default: True + center_writing_weights: Center weights that write to residual stream. Default: True + center_unembed: Center unembedding weights (translation invariant). Default: True + fold_value_biases: Fold value biases into output bias. Default: True + refactor_factored_attn_matrices: Experimental QK/OV factorization. Default: False + """ + from transformer_lens.weight_processing import ProcessWeights + + if verbose: + print(f"Processing weights for {self.cfg.model_name}...") + + # Soft capping (tanh) is not translation-invariant; centering would change output. + if center_unembed and getattr(self.cfg, "output_logits_soft_cap", -1.0) > 0.0: + import logging + + logging.warning( + "center_unembed=True is incompatible with logit softcapping " + "(output_logits_soft_cap=%.1f). Disabling center_unembed.", + self.cfg.output_logits_soft_cap, + ) + center_unembed = False + + if verbose: + print(" Extracting state dict from existing model...") + state_dict = self.state_dict() + adapter = self.adapter + + # Untie embed/unembed weights (GPT-2) so centering affects only unembed + embed_key = "embed.weight" + unembed_key = "unembed.weight" + + if embed_key in state_dict and unembed_key in state_dict: + # Check if they point to the same tensor (weight tying) + if state_dict[embed_key].data_ptr() == state_dict[unembed_key].data_ptr(): + if verbose: + print(" Breaking weight tying between embed and unembed in state dict...") + # Clone the unembed weight to break the tie + state_dict[unembed_key] = state_dict[unembed_key].clone() + + if adapter and hasattr(adapter, "preprocess_weights"): + adapter._fold_ln_requested = fold_ln # type: ignore[union-attr] + state_dict = adapter.preprocess_weights(state_dict) + + # Use unified ProcessWeights.process_weights() like HookedTransformer does. + # Float32 upcasting for precision is handled centrally in process_weights(). + if verbose: + print(" Processing weights (fold_ln, center_writing_weights, etc.)...") + state_dict = ProcessWeights.process_weights( + state_dict, + self.cfg, + fold_ln=fold_ln, + center_writing_weights=center_writing_weights, + center_unembed=center_unembed, + fold_value_biases=fold_value_biases, + refactor_factored_attn_matrices=refactor_factored_attn_matrices, + adapter=adapter, + ) + + # Normalize HF-prefix keys to TL format for weight routing + import re + + hf_to_tl_prefix = {} + for tl_name, (remote_path, _component) in self.real_components.items(): + if remote_path and remote_path != tl_name: + hf_to_tl_prefix[remote_path] = tl_name + + normalized_state_dict = {} + for key, value in state_dict.items(): + new_key = key + for hf_prefix, tl_prefix in hf_to_tl_prefix.items(): + if key.startswith(hf_prefix + "."): + suffix = key[len(hf_prefix) + 1 :] + new_key = f"{tl_prefix}.{suffix}" + break + normalized_state_dict[new_key] = value + state_dict = normalized_state_dict + + if verbose: + print(" Distributing weights to generalized components...") + ProcessWeights.distribute_weights_to_components( + state_dict=state_dict, + component_mapping=self.real_components, + ) + + def _calculate_loss(self, logits, tokens, loss_per_token=False): + """Calculate cross-entropy loss.""" + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = tokens[..., 1:].contiguous() + loss_fct = torch.nn.CrossEntropyLoss(reduction="none" if loss_per_token else "mean") + flat_logits = shift_logits.view(-1, shift_logits.size(-1)) + flat_labels = shift_labels.view(-1) + loss = loss_fct(flat_logits, flat_labels) + if loss_per_token: + return loss.view(shift_labels.shape) + else: + return loss + + def _extract_hf_weights(self): + """Extract weights from the original HuggingFace model.""" + hf_state_dict = self.state_dict() + for layer_idx in range(self.cfg.n_layers): + combined_qkv_key = f"transformer.h.{layer_idx}.attn.c_attn.weight" + combined_qkv_bias_key = f"transformer.h.{layer_idx}.attn.c_attn.bias" + if combined_qkv_key in hf_state_dict: + separate_keys_to_remove = [ + f"transformer.h.{layer_idx}.attn.q.weight", + f"transformer.h.{layer_idx}.attn.q.bias", + f"transformer.h.{layer_idx}.attn.k.weight", + f"transformer.h.{layer_idx}.attn.k.bias", + f"transformer.h.{layer_idx}.attn.v.weight", + f"transformer.h.{layer_idx}.attn.v.bias", + ] + for key_to_remove in separate_keys_to_remove: + if key_to_remove in hf_state_dict: + del hf_state_dict[key_to_remove] + return hf_state_dict + + def to_tokens( + self, + input: Union[str, List[str]], + prepend_bos: Optional[bool] = None, + padding_side: Optional[str] = None, + move_to_device: bool = True, + truncate: bool = True, + ) -> torch.Tensor: + """Converts a string to a tensor of tokens. + + See the class-level "Tokenization notes" for full ``prepend_bos`` + semantics, the ``default_prepend_bos`` / + ``tokenizer_prepends_bos`` interaction, and the whitespace- + sensitivity gotcha. **Pass ``prepend_bos=False`` whenever you're + tokenizing only part of a prompt.** + + Args: + input: The input to tokenize. + prepend_bos: Overrides ``self.cfg.default_prepend_bos``. Defaults + to ``None`` (use the cfg setting). Pass ``True`` or ``False`` + to override locally. + padding_side: Which side to pad on when tokenizing multiple + strings of different lengths. Defaults to the tokenizer's + ``padding_side``. + move_to_device: Whether to move the result to ``cfg.device``. + truncate: Whether to truncate inputs longer than ``cfg.n_ctx``. + + Returns: + Token tensor of shape ``[batch, pos]``. + """ + assert self.tokenizer is not None, "Cannot use to_tokens without a tokenizer" + if prepend_bos is None: + prepend_bos = getattr(self.cfg, "default_prepend_bos", True) + if padding_side is None: + padding_side = getattr(self.tokenizer, "padding_side", "right") + tokenizer_prepends_bos = getattr(self.cfg, "tokenizer_prepends_bos", True) + if prepend_bos and (not tokenizer_prepends_bos): + input = utils.get_input_with_manually_prepended_bos(self.tokenizer.bos_token, input) + if isinstance(input, str): + input = [input] + tokens = self.tokenizer( + input, + return_tensors="pt", + padding=True, + truncation=truncate, + max_length=self.cfg.n_ctx if truncate else None, + )["input_ids"] + # Strip auto-appended EOS tokens (e.g., OLMo) + if ( + getattr(self.cfg, "tokenizer_appends_eos", False) + and self.tokenizer.eos_token_id is not None + ): + # Remove trailing EOS, keep at least 1 token + while tokens.shape[-1] > 1 and (tokens[:, -1] == self.tokenizer.eos_token_id).all(): + tokens = tokens[:, :-1] + if not prepend_bos and tokenizer_prepends_bos: + tokens = utils.get_tokens_with_bos_removed(self.tokenizer, tokens) + if move_to_device: + tokens = tokens.to(self.cfg.device) + return tokens + + def to_string( + self, tokens: Union[List[int], torch.Tensor, np.ndarray] + ) -> Union[str, List[str]]: + """Convert tokens to string(s). + + Args: + tokens: Tokens to convert + + Returns: + Decoded string(s) + """ + if not isinstance(tokens, torch.Tensor): + tokens = torch.tensor(tokens) + if len(tokens.shape) == 2: + return self.tokenizer.batch_decode(tokens, clean_up_tokenization_spaces=False) + elif len(tokens.shape) <= 1: + return self.tokenizer.decode(tokens, clean_up_tokenization_spaces=False) + else: + raise ValueError(f"Invalid shape passed in: {tokens.shape}") + + def to_str_tokens( + self, + input: Union[str, torch.Tensor, np.ndarray, List], + prepend_bos: Optional[bool] = None, + padding_side: Optional[str] = None, + ) -> Union[List[str], List[List[str]]]: + """Map text or tokens to a list of tokens as strings. + + See the class-level "Tokenization notes" for full ``prepend_bos`` + semantics. **Pass ``prepend_bos=False`` whenever you're tokenizing + only part of a prompt.** When ``input`` is already a tensor or + array, ``prepend_bos`` and ``padding_side`` are ignored. + + Args: + input: A string, list of strings, or tensor/array of token IDs. + prepend_bos: Overrides ``self.cfg.default_prepend_bos``. Only + applies when ``input`` is a string. Defaults to ``None`` + (use the cfg setting). + padding_side: Which side to pad on. Only applies when ``input`` + is a string. + + Returns: + List of token strings. + """ + if isinstance(input, list): + return cast( + List[List[str]], + [self.to_str_tokens(item, prepend_bos, padding_side) for item in input], + ) + elif isinstance(input, str): + tokens = self.to_tokens(input, prepend_bos=prepend_bos, padding_side=padding_side)[0] + elif isinstance(input, torch.Tensor): + tokens = input.squeeze() + if tokens.dim() == 0: + tokens = tokens.unsqueeze(0) + assert ( + tokens.dim() == 1 + ), f"Invalid tokens input to to_str_tokens, has shape: {tokens.shape}" + elif isinstance(input, np.ndarray): + tokens_np = input.squeeze() + if tokens_np.ndim == 0: + tokens_np = np.expand_dims(tokens_np, axis=0) + assert ( + tokens_np.ndim == 1 + ), f"Invalid tokens input to to_str_tokens, has shape: {tokens_np.shape}" + tokens = torch.tensor(tokens_np) + else: + raise ValueError(f"Invalid input type to to_str_tokens: {type(input)}") + # v5 compat: wrap each token so batch_decode decodes them individually + tokens_list = [[int(t)] for t in tokens.tolist()] + str_tokens = self.tokenizer.batch_decode(tokens_list, clean_up_tokenization_spaces=False) + return str_tokens + + def to_single_token(self, string: str) -> int: + """Map a string that makes up a single token to the id for that token. + + Args: + string: The string to convert + + Returns: + Token ID + + Raises: + AssertionError: If string is not a single token + """ + token = self.to_tokens(string, prepend_bos=False).squeeze() + if token.numel() != 1: + raise AssertionError(f"Input string: {string} is not a single token!") + return int(token.item()) + + def get_token_position( + self, + single_token: Union[str, int], + input: Union[str, torch.Tensor], + mode="first", + prepend_bos: Optional[Union[bool, None]] = None, + padding_side: Optional[Union[Literal["left", "right"], None]] = None, + ): + """Get the position of a single_token in a string or sequence of tokens. + + Raises an error if the token is not present. + + When ``input`` is a string it's tokenized internally — see the + class-level "Tokenization notes" for ``prepend_bos`` semantics. + Off-by-one position errors usually mean ``prepend_bos`` is on + when it shouldn't be (or vice versa); pass ``prepend_bos=False`` + when ``input`` is a fragment of a larger prompt. + + Args: + single_token (Union[str, int]): The token to search for. Can + be a token index, or a string (but the string must correspond to a single token). + input (Union[str, torch.Tensor]): The sequence to + search in. Can be a string or a rank 1 tensor of tokens or a rank 2 tensor of tokens + with a dummy batch dimension. + mode (str, optional): If there are multiple matches, which match to return. Supports + "first" or "last". Defaults to "first". + prepend_bos (bool, optional): Overrides ``self.cfg.default_prepend_bos``. Only + applies when ``input`` is a string. Defaults to ``None`` (use the cfg setting). + padding_side (Union[Literal["left", "right"], None], optional): Specifies which + side to pad when tokenizing multiple strings of different lengths. + """ + if isinstance(input, str): + tokens = self.to_tokens(input, prepend_bos=prepend_bos, padding_side=padding_side) + else: + tokens = input + if len(tokens.shape) == 2: + assert ( + tokens.shape[0] == 1 + ), f"If tokens are rank two, they must have shape [1, seq_len], not {tokens.shape}" + tokens = tokens[0] + if isinstance(single_token, str): + single_token = self.to_single_token(single_token) + elif isinstance(single_token, torch.Tensor): + single_token = single_token.item() + indices = torch.arange(len(tokens), device=tokens.device)[tokens == single_token] + assert len(indices) > 0, "The token does not occur in the prompt" + if mode == "first": + return indices[0].item() + elif mode == "last": + return indices[-1].item() + else: + raise ValueError(f"mode must be 'first' or 'last', not {mode}") + + def to_single_str_token(self, int_token: int) -> str: + """Get the single token corresponding to an int in string form. + + Args: + int_token: The token ID + + Returns: + The token string + """ + assert isinstance(int_token, int) + token = self.to_str_tokens(torch.tensor([int_token])) + if isinstance(token, list) and len(token) == 1: + return str(token[0]) + raise AssertionError("Expected a single string token.") + + def blocks_with(self, submodule: str) -> List[Tuple[int, "GeneralizedComponent"]]: + """Return (index, block) pairs for blocks with the named bridged submodule. + + Checks _modules (not hasattr) so HF-internal attrs don't match. + Use instead of assuming blocks[0] is representative on hybrid models. + """ + if not hasattr(self, "blocks"): + return [] + return [(i, block) for i, block in enumerate(self.blocks) if submodule in block._modules] + + def stack_params_for( + self, submodule: str, attr_path: str, reshape_fn: Optional[Callable] = None + ) -> Tuple[List[int], torch.Tensor]: + """Stack a parameter across matching blocks only. Returns (layer_indices, tensor). + + Use for hybrid models where not all blocks have the submodule. + """ + matching = self.blocks_with(submodule) + if not matching: + raise ValueError( + f"No blocks have submodule '{submodule}'. " + f"Available submodules can be checked with blocks_with()." + ) + indices: List[int] = [] + weights: List[torch.Tensor] = [] + for idx, block in matching: + w = _resolve_attr_path(block, attr_path) + if reshape_fn is not None: + w = reshape_fn(w) + weights.append(w) + indices.append(idx) + return indices, torch.stack(weights, dim=0) + + def _stack_block_params( + self, attr_path: str, reshape_fn: Optional[Callable] = None + ) -> torch.Tensor: + """Stack a parameter across all blocks; falls back to matching-only on hybrids. + + On hybrid models, logs a warning about index mapping and returns only + blocks that have the submodule. First path segment is checked against + _modules; deeper segments resolve via getattr (intentional — W_Q etc. + are exposed via __getattr__ delegation). + """ + first_attr = attr_path.split(".")[0] + matching_blocks = [ + (i, block) for i, block in enumerate(self.blocks) if first_attr in block._modules + ] + + if len(matching_blocks) == 0: + raise AttributeError( + f"No blocks have submodule '{first_attr}'. " + f"Use bridge.blocks_with('{first_attr}') to check availability." + ) + + if len(matching_blocks) < len(self.blocks): + indices = [i for i, _ in matching_blocks] + logging.warning( + "Hybrid model: only %d/%d blocks have '%s'. Returning stacked tensor " + "for layers %s only. Tensor index i corresponds to original layer " + "indices[i], not layer i. For explicit index mapping, use " + "bridge.stack_params_for('%s', '%s').", + len(matching_blocks), + len(self.blocks), + first_attr, + indices, + first_attr, + attr_path, + ) + + weights: List[torch.Tensor] = [] + for _, block in matching_blocks: + w = _resolve_attr_path(block, attr_path) + if reshape_fn is not None: + w = reshape_fn(w) + weights.append(w) + # Under a device_map split, per-block tensors live on different devices. + # torch.stack requires a common device; gather onto cfg.device (the embedding / + # input device — a natural "home" for cross-layer reductions). + if getattr(self.cfg, "n_devices", 1) > 1 and weights and self.cfg.device is not None: + target_device = torch.device(self.cfg.device) + weights = [w.to(target_device) for w in weights] + return torch.stack(weights, dim=0) + + def _reshape_qkv(self, w: torch.Tensor) -> torch.Tensor: + """Reshape 2D [d_model, d_model] QKV weight to 3D [n_heads, d_model, d_head].""" + if w.shape == (self.cfg.d_model, self.cfg.d_model): + d_head = self.cfg.d_model // self.cfg.n_heads + return w.reshape(self.cfg.n_heads, self.cfg.d_model, d_head) + return w + + def _reshape_o(self, w: torch.Tensor) -> torch.Tensor: + """Reshape 2D [d_model, d_model] O weight to 3D [n_heads, d_head, d_model].""" + if w.shape == (self.cfg.d_model, self.cfg.d_model): + d_head = self.cfg.d_model // self.cfg.n_heads + return w.reshape(self.cfg.n_heads, d_head, self.cfg.d_model) + return w + + @property + def W_K(self) -> torch.Tensor: + """Stack the key weights across all layers.""" + return self._stack_block_params("attn.W_K", self._reshape_qkv) + + @property + def W_Q(self) -> torch.Tensor: + """Stack the query weights across all layers.""" + return self._stack_block_params("attn.W_Q", self._reshape_qkv) + + @property + def W_V(self) -> torch.Tensor: + """Stack the value weights across all layers.""" + return self._stack_block_params("attn.W_V", self._reshape_qkv) + + @property + def W_O(self) -> torch.Tensor: + """Stack the attn output weights across all layers.""" + return self._stack_block_params("attn.W_O", self._reshape_o) + + @property + def W_in(self) -> torch.Tensor: + """Stack the MLP input weights across all layers.""" + return self._stack_block_params("mlp.W_in") + + @property + def W_gate(self) -> Union[torch.Tensor, None]: + """Stack the MLP gate weights across all layers (gated MLPs only).""" + if getattr(self.cfg, "gated_mlp", False): + return self._stack_block_params("mlp.W_gate") + return None + + @property + def W_out(self) -> torch.Tensor: + """Stack the MLP output weights across all layers.""" + return self._stack_block_params("mlp.W_out") + + @property + def b_K(self) -> torch.Tensor: + """Stack the key biases across all layers.""" + return self._stack_block_params("attn.b_K") + + @property + def b_Q(self) -> torch.Tensor: + """Stack the query biases across all layers.""" + return self._stack_block_params("attn.b_Q") + + @property + def b_V(self) -> torch.Tensor: + """Stack the value biases across all layers.""" + return self._stack_block_params("attn.b_V") + + @property + def b_O(self) -> torch.Tensor: + """Stack the attn output biases across all layers.""" + return self._stack_block_params("attn.b_O") + + @property + def b_in(self) -> torch.Tensor: + """Stack the MLP input biases across all layers.""" + return self._stack_block_params("mlp.b_in") + + @property + def b_out(self) -> torch.Tensor: + """Stack the MLP output biases across all layers.""" + return self._stack_block_params("mlp.b_out") + + @property + def W_U(self) -> torch.Tensor: + """Unembedding matrix (d_model, d_vocab). Maps residual stream to logits.""" + return self.unembed.W_U + + @property + def b_U(self) -> torch.Tensor: + """Unembedding bias (d_vocab).""" + return self.unembed.b_U + + @property + def W_E(self) -> torch.Tensor: + """Token embedding matrix (d_vocab, d_model).""" + return self.embed.W_E + + @property + def QK(self): + """QK circuit. On hybrids, returns attn layers only (with warning). See QK_for_attn_layers().""" + return FactoredMatrix(self.W_Q, self.W_K.transpose(-2, -1)) + + @property + def OV(self): + """OV circuit. On hybrids, returns attn layers only (with warning). See OV_for_attn_layers().""" + return FactoredMatrix(self.W_V, self.W_O) + + def QK_for_attn_layers(self) -> Tuple[List[int], FactoredMatrix]: + """QK circuit for attention layers only. Returns (layer_indices, FactoredMatrix).""" + q_indices, W_Q = self.stack_params_for("attn", "attn.W_Q", self._reshape_qkv) + _, W_K = self.stack_params_for("attn", "attn.W_K", self._reshape_qkv) + return q_indices, FactoredMatrix(W_Q, W_K.transpose(-2, -1)) + + def OV_for_attn_layers(self) -> Tuple[List[int], FactoredMatrix]: + """OV circuit for attention layers only. Returns (layer_indices, FactoredMatrix).""" + v_indices, W_V = self.stack_params_for("attn", "attn.W_V", self._reshape_qkv) + _, W_O = self.stack_params_for("attn", "attn.W_O", self._reshape_o) + return v_indices, FactoredMatrix(W_V, W_O) + + # ------------------------------------------------------------------ + # Mechanistic interpretability analysis methods + # ------------------------------------------------------------------ + + def tokens_to_residual_directions( + self, + tokens: Union[str, int, torch.Tensor], + ) -> torch.Tensor: + """Map tokens to their unembedding vectors (residual stream directions). + + Returns the columns of W_U corresponding to the given tokens — i.e. the + directions in the residual stream that the model dots with to produce the + logit for each token. + + WARNING: If you use this without folding in LayerNorm (compatibility mode), + the results will be misleading because LN weights change the unembed map. + + Args: + tokens: A single token (str, int, or scalar tensor), a 1-D tensor of + token IDs, or a 2-D batch of token IDs. + + Returns: + Tensor of unembedding vectors with shape matching the input token shape + plus a trailing d_model dimension. + """ + if isinstance(tokens, torch.Tensor) and tokens.numel() > 1: + residual_directions = self.W_U[:, tokens] + residual_directions = einops.rearrange( + residual_directions, "d_model ... -> ... d_model" + ) + return residual_directions + else: + if isinstance(tokens, str): + token = self.to_single_token(tokens) + elif isinstance(tokens, int): + token = tokens + elif isinstance(tokens, torch.Tensor) and tokens.numel() == 1: + token = int(tokens.item()) + else: + raise ValueError(f"Invalid token type: {type(tokens)}") + residual_direction = self.W_U[:, token] + return residual_direction + + # Variant → attr paths for the output bias that feeds the residual stream. + _VARIANT_OUTPUT_BIAS_ATTRS: Dict[str, tuple] = { + "attn": ("b_O",), + "linear_attn": ("out_proj.bias",), + "mamba": ("out_proj.bias",), + "mixer": ("out_proj.bias",), + "ssm": ("out_proj.bias",), + } + + def _get_block_variant_bias(self, block: "GeneralizedComponent") -> Optional[torch.Tensor]: + """Return the output bias from this block's variant submodule, or None.""" + for name in VARIANT_SUBMODULE_NAMES: + if name not in block._modules: + continue + variant = block._modules[name] + for attr_path in self._VARIANT_OUTPUT_BIAS_ATTRS.get(name, ()): + obj = variant + try: + for attr in attr_path.split("."): + obj = getattr(obj, attr) + except AttributeError: + continue + if obj is not None and isinstance(obj, torch.Tensor): + return obj + return None + + def accumulated_bias( + self, + layer: int, + mlp_input: bool = False, + include_mlp_biases: bool = True, + ) -> torch.Tensor: + """Sum of variant + MLP output biases through the residual stream up to `layer`. + + Includes all layer types (attn, SSM, linear-attn). Set mlp_input=True + to include the variant bias of the target layer itself. + """ + accumulated = torch.zeros(self.cfg.d_model, device=self.cfg.device) + for i in range(layer): + block = self.blocks[i] + b_O = self._get_block_variant_bias(block) + if b_O is not None: + accumulated = accumulated + b_O.to(accumulated.device) + if include_mlp_biases and "mlp" in block._modules: + b_out = getattr(block.mlp, "b_out", None) + if b_out is not None: + accumulated = accumulated + b_out.to(accumulated.device) + if mlp_input: + assert layer < self.cfg.n_layers, "Cannot include attn_bias from beyond the final layer" + block = self.blocks[layer] + b_O = self._get_block_variant_bias(block) + if b_O is not None: + accumulated = accumulated + b_O.to(accumulated.device) + return accumulated + + def all_composition_scores(self, mode: str) -> CompositionScores: + """Composition scores for all attention head pairs. Returns CompositionScores. + + See https://transformer-circuits.pub/2021/framework/index.html + On hybrid models, only attention layers are included; layer_indices + maps tensor position i to original layer number. + """ + attn_blocks = self.blocks_with("attn") + if not attn_blocks: + raise ValueError("No attention layers found — cannot compute composition scores.") + + indices = [idx for idx, _ in attn_blocks] + blocks_list = [block for _, block in attn_blocks] + + def _stack(attr_path: str, reshape_fn: Optional[Callable] = None) -> torch.Tensor: + weights: List[torch.Tensor] = [] + for block in blocks_list: + w = _resolve_attr_path(block, attr_path) + if reshape_fn is not None: + w = reshape_fn(w) + weights.append(w) + # See _stack_block_params: gather per-block tensors onto cfg.device when split. + if getattr(self.cfg, "n_devices", 1) > 1 and weights and self.cfg.device is not None: + target_device = torch.device(self.cfg.device) + weights = [w.to(target_device) for w in weights] + return torch.stack(weights, dim=0) + + W_V = _stack("attn.W_V", self._reshape_qkv) + W_O = _stack("attn.W_O", self._reshape_o) + left = FactoredMatrix(W_V, W_O) + + if mode == "Q": + W_Q = _stack("attn.W_Q", self._reshape_qkv) + W_K = _stack("attn.W_K", self._reshape_qkv) + right = FactoredMatrix(W_Q, W_K.transpose(-2, -1)) + elif mode == "K": + W_Q = _stack("attn.W_Q", self._reshape_qkv) + W_K = _stack("attn.W_K", self._reshape_qkv) + right = FactoredMatrix(W_Q, W_K.transpose(-2, -1)).T + elif mode == "V": + right = left + else: + raise ValueError(f"mode must be one of ['Q', 'K', 'V'] not {mode}") + + scores = utils.composition_scores(left, right, broadcast_dims=True) + n_attn = len(indices) + idx_tensor = torch.arange(n_attn, device=self.cfg.device) + mask = idx_tensor[:, None, None, None] < idx_tensor[None, None, :, None] + scores = torch.where(mask, scores, torch.zeros_like(scores)) + + labels = [f"L{l}H{h}" for l in indices for h in range(self.cfg.n_heads)] + return CompositionScores(scores=scores, layer_indices=indices, head_labels=labels) + + def composition_layer_indices(self) -> List[int]: + """Original layer indices for attention layers (maps composition score positions).""" + return [idx for idx, _ in self.blocks_with("attn")] + + def block_hooks(self, layer_idx: int) -> List[str]: + """Sorted hook names available on block `layer_idx` (block-relative paths).""" + prefix = f"blocks.{layer_idx}." + return sorted(name[len(prefix) :] for name in self.hook_dict if name.startswith(prefix)) + + def block_submodules(self, layer_idx: int) -> List[str]: + """Return bridged submodule names on block `layer_idx`.""" + block = self.blocks[layer_idx] + return [name for name in block._modules if name not in _BLOCK_INTERNAL_MODULES] + + def layer_types(self) -> List[str]: + """Per-block type labels, e.g. ["attn+mlp", "ssm+mlp", ...]. Deterministic order.""" + types = [] + for block in self.blocks: + variants = [n for n in VARIANT_SUBMODULE_NAMES if n in block._modules] + universals = sorted( + n + for n in block._modules + if n not in _VARIANT_SUBMODULE_SET + and n not in _BLOCK_INTERNAL_MODULES + and not n.startswith(_NORM_PREFIXES) + ) + parts = variants + universals + types.append("+".join(parts) if parts else "unknown") + return types + + @property + def all_head_labels(self) -> list[str]: + """Human-readable labels for all attention heads, e.g. ['L0H0', 'L0H1', ...].""" + return [f"L{l}H{h}" for l in range(self.cfg.n_layers) for h in range(self.cfg.n_heads)] + + @property + def attn_head_labels(self) -> list[str]: + """Head labels for attention layers only — matches all_composition_scores() dims.""" + return [ + f"L{l}H{h}" for l in self.composition_layer_indices() for h in range(self.cfg.n_heads) + ] + + def parameters(self, recurse: bool = True) -> Iterator[nn.Parameter]: + """Returns parameters following standard PyTorch semantics. + + This method delegates to the underlying HuggingFace model's parameters(). + For TransformerLens-style parameter generator, use tl_parameters() instead. + + Args: + recurse: If True, yields parameters of this module and all submodules + + Returns: + Iterator of nn.Parameter objects + """ + return self.original_model.parameters(recurse=recurse) + + def named_parameters( + self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True + ) -> Iterator[tuple[str, nn.Parameter]]: + """Returns named parameters following standard PyTorch semantics. + + This method delegates to the underlying HuggingFace model's named_parameters(). + For TransformerLens-style generator, use tl_named_parameters() instead. + + Args: + prefix: Prefix to prepend to all parameter names + recurse: If True, yields parameters of this module and all submodules + remove_duplicate: If True, removes duplicate parameters + + Returns: + Iterator of (name, parameter) tuples + """ + return self.original_model.named_parameters(prefix, recurse, remove_duplicate) + + def tl_parameters(self) -> dict[str, torch.Tensor]: + """Returns TransformerLens-style parameter dictionary. + + Parameter names follow TransformerLens conventions (e.g., 'blocks.0.attn.W_Q') and may + include processed weights (non-leaf tensors). This format is expected by SVDInterpreter + among other analysis tools. + + Returns: + Dictionary mapping TransformerLens parameter names to tensors + + Example: + >>> bridge = TransformerBridge.boot_transformers("gpt2") + >>> tl_params = bridge.tl_parameters() + >>> W_Q = tl_params["blocks.0.attn.W_Q"] # Shape: [n_heads, d_model, d_head] + """ + return self.get_params() + + def tl_named_parameters(self) -> Iterator[tuple[str, torch.Tensor]]: + """Returns iterator of TransformerLens-style named parameters. + + This provides the same parameters as tl_parameters() but as an iterator + for consistency with PyTorch's named_parameters() API pattern. + + Returns: + Iterator of (name, tensor) tuples with TransformerLens naming conventions + + Example: + >>> bridge = TransformerBridge.boot_transformers("gpt2") + >>> for name, param in bridge.tl_named_parameters(): + ... if "attn.W_Q" in name: + ... print(f"{name}: {param.shape}") # doctest: +ELLIPSIS + blocks.0.attn.W_Q: torch.Size([12, 768, 64]) + ... + """ + return iter(self.get_params().items()) + + def forward( + self, + input: Union[str, List[str], torch.Tensor], + return_type: Optional[str] = "logits", + loss_per_token: bool = False, + prepend_bos: Optional[bool] = None, + padding_side: Optional[str] = None, + attention_mask: Optional[torch.Tensor] = None, + start_at_layer: Optional[int] = None, + stop_at_layer: Optional[int] = None, + pixel_values: Optional[torch.Tensor] = None, + input_values: Optional[torch.Tensor] = None, + **kwargs, + ) -> Any: + """Forward pass through the model. + + Args: + input: Input to the model + return_type: Type of output to return ('logits', 'loss', 'both', 'predictions', None) + loss_per_token: Whether to return loss per token + prepend_bos: Whether to prepend BOS token + padding_side: Which side to pad on + start_at_layer: Not implemented in TransformerBridge. The bridge delegates + to HuggingFace's model.forward() which owns the layer iteration loop, + making start_at_layer infeasible without monkey-patching HF internals + (fragile across HF versions) or exception-based layer skipping (corrupts + model state). Raises NotImplementedError if a non-None value is passed. + stop_at_layer: Layer to stop forward pass at + pixel_values: Optional image tensor for multimodal models (e.g., LLaVA, Gemma3). + The tensor is passed directly to the underlying HuggingFace model. + Only valid when cfg.is_multimodal is True. + input_values: Optional audio waveform tensor for audio models (e.g., HuBERT). + The tensor is passed directly to the underlying HuggingFace model. + Only valid when cfg.is_audio_model is True. + **kwargs: Additional arguments passed to model + + Returns: + Model output based on return_type + """ + + if start_at_layer is not None: + raise NotImplementedError( + "start_at_layer is not supported in TransformerBridge. " + "The bridge delegates to HuggingFace's model.forward() which controls " + "the layer iteration loop. See the TransformerBridge review plan for a " + "detailed analysis of implementation approaches and their tradeoffs." + ) + + # Set stop_at_layer flag on all blocks if requested + if stop_at_layer is not None and hasattr(self, "blocks"): + for block in self.blocks: + block._stop_at_layer_idx = stop_at_layer + + # Map HookedEncoderDecoder-style kwargs to HF-compatible names + if "decoder_input" in kwargs: + kwargs["decoder_input_ids"] = kwargs.pop("decoder_input") + if "one_zero_attention_mask" in kwargs: + if attention_mask is None: + attention_mask = kwargs.pop("one_zero_attention_mask") + else: + kwargs.pop("one_zero_attention_mask") + + # Detect batched list input that will need padding. For this case we force + # left-padding internally and auto-compute attention_mask + position_ids + # (unless the caller passed them explicitly) so pad tokens don't contaminate + # attention or position embeddings. + _is_batched_list = ( + isinstance(input, list) + and len(input) > 1 + and not getattr(self.cfg, "is_audio_model", False) + ) + + try: + if isinstance(input, (str, list)): + if getattr(self.cfg, "is_audio_model", False): + raise ValueError( + "Audio models require tensor input (raw waveform), not text. " + "Pass a torch.Tensor or use the input_values parameter." + ) + if _is_batched_list and padding_side is None: + # Force left-padding so real tokens are flush-right. + _orig_padding_side = self.tokenizer.padding_side + self.tokenizer.padding_side = "left" + try: + input_ids = self.to_tokens( + input, prepend_bos=prepend_bos, padding_side=padding_side + ) + finally: + self.tokenizer.padding_side = _orig_padding_side + else: + input_ids = self.to_tokens( + input, prepend_bos=prepend_bos, padding_side=padding_side + ) + else: + input_ids = input + # Promote 1D integer token tensors to 2D [batch=1, seq] to match + # HookedTransformer's contract. Float tensors (inputs_embeds, + # audio waveforms) are passed through unchanged. + if ( + isinstance(input_ids, torch.Tensor) + and input_ids.ndim == 1 + and not input_ids.is_floating_point() + ): + input_ids = input_ids.unsqueeze(0) + + # Detect inputs_embeds: if the tensor is floating point, it's pre-computed + # embeddings (e.g., from multimodal models) rather than token IDs. + _is_inputs_embeds = ( + isinstance(input_ids, torch.Tensor) and input_ids.is_floating_point() + ) + + # Auto-compute attention_mask + position_ids for batched list input + # when the caller didn't supply them. Matches HF generation convention. + if ( + _is_batched_list + and attention_mask is None + and self.tokenizer is not None + and self.tokenizer.pad_token_id is not None + and not _is_inputs_embeds + ): + _prev_side = self.tokenizer.padding_side + self.tokenizer.padding_side = "left" + try: + attention_mask = utils.get_attention_mask( + self.tokenizer, + input_ids, + prepend_bos=getattr(self.cfg, "default_prepend_bos", True), + ).to(self.cfg.device) + finally: + self.tokenizer.padding_side = _prev_side + if "position_ids" not in kwargs: + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + kwargs["position_ids"] = position_ids + + if attention_mask is not None: + kwargs["attention_mask"] = attention_mask + if kwargs.pop("use_past_kv_cache", False) or kwargs.get("use_cache", False): + kwargs["use_cache"] = True + # Auto-generate decoder_input_ids for encoder-decoder models + if ( + "decoder_input_ids" not in kwargs + and hasattr(self.original_model, "config") + and getattr(self.original_model.config, "is_encoder_decoder", False) + ): + decoder_start_token_id = getattr( + self.original_model.config, "decoder_start_token_id", None + ) + if decoder_start_token_id is not None: + shifted = input_ids[:, :-1] + start_tokens = torch.full( + (input_ids.shape[0], 1), + decoder_start_token_id, + dtype=input_ids.dtype, + device=input_ids.device, + ) + kwargs["decoder_input_ids"] = torch.cat([start_tokens, shifted], dim=1) + else: + kwargs["decoder_input_ids"] = input_ids + + # Tell PosEmbedBridge to expand batch=1 position_ids to full batch. + if hasattr(self, "pos_embed"): + self.pos_embed._current_batch_size = input_ids.shape[0] + + # Handle pixel_values for multimodal models + if pixel_values is not None: + if not getattr(self.cfg, "is_multimodal", False): + raise ValueError( + "pixel_values can only be passed to multimodal models " + "(cfg.is_multimodal must be True)" + ) + kwargs["pixel_values"] = pixel_values + + # Handle input_values for audio models + if input_values is not None: + if not getattr(self.cfg, "is_audio_model", False): + raise ValueError( + "input_values can only be passed to audio models " + "(cfg.is_audio_model must be True)" + ) + kwargs["input_values"] = input_values + + # Audio models use input_values (waveform), not input_ids + if getattr(self.cfg, "is_audio_model", False): + if input_values is not None: + result = self._driver.forward(**kwargs) + elif isinstance(input, torch.Tensor): + kwargs["input_values"] = input + result = self._driver.forward(**kwargs) + else: + raise ValueError( + "Audio models require tensor input (raw waveform). " + "Pass a torch.Tensor or use input_values parameter." + ) + elif _is_inputs_embeds: + result = self._driver.forward(inputs_embeds=input_ids, **kwargs) + else: + # By keyword so kw-only ``input_ids`` drivers don't TypeError. + result = self._driver.forward(input_ids=input_ids, **kwargs) + output = result.raw_output + # No-op for HF (its hooks already fired); load-bearing for vLLM/Inspect. + if result.captured: + self._replay_captures(result.captured) + # Convert TensorLike to torch at the boundary; let weird shapes + # (audio/CTC dataclasses, tuple-of-tuples) pass through unchanged — + # downstream return_type branches catch them with specific errors. + logits = result.logits + if isinstance(logits, torch.Tensor): + pass + elif logits is not None and isinstance(logits, TensorLike): + logits = to_torch(logits) + # Stash only the cache object (not the full output) for generate(). + if getattr(self, "_capture_hf_cache", False): + self._last_hf_cache = getattr(output, "past_key_values", None) + return self._finalize_return( + return_type, + logits, + input_ids, + is_audio_model=getattr(self.cfg, "is_audio_model", False), + inputs_embeds_was_used=_is_inputs_embeds, + loss_per_token=loss_per_token, + ) + except StopAtLayerException as e: + # Execution stopped at the requested layer + return e.layer_output + finally: + # Clean up state that may be inconsistent after StopAtLayerException + if stop_at_layer is not None and hasattr(self, "blocks"): + # Reset the stop flag on all blocks + for block in self.blocks: + block._stop_at_layer_idx = None + + # Clear any stale KV cache — layers after the stop point didn't + # execute, so the cache is incomplete and would corrupt subsequent + # generate() calls that expect a full cache. + if hasattr(self, "_last_hf_cache"): + del self._last_hf_cache + + # loss_fn inherited from BridgeCore + + def _generate_tokens( + self, + current_tokens: torch.Tensor, + input_tokens: torch.Tensor, + batch_size: int, + *, + max_new_tokens: int, + do_sample: bool, + top_k: Optional[int], + top_p: Optional[float], + temperature: float, + freq_penalty: float, + repetition_penalty: float, + stop_at_eos: bool, + stop_tokens: List[int], + eos_token_for_padding: int, + finished_sequences: torch.Tensor, + use_past_kv_cache: bool, + use_stateful_cache: bool, + mamba_cache: Any, + mamba_conv_kernel: int, + is_encoder_decoder: bool, + _is_batched_list: bool, + _generate_from_embeds: bool, + encoder_input: Optional[torch.Tensor], + decoder_tokens: Optional[torch.Tensor], + generated_token_ids: Optional[List[torch.Tensor]], + pixel_values: Optional[torch.Tensor], + multimodal_kwargs: Dict[str, Any], + verbose: bool, + ) -> Generator[Tuple[torch.Tensor, torch.Tensor, bool], None, None]: + """Core generation loop. Yields (sampled_tokens, final_logits, all_finished) per step. + + Owns the forward pass, sampling, EOS handling, token accumulation, and + KV cache management. Callers are responsible for try/finally cleanup of + ``_capture_hf_cache``. + """ + _hf_kv_cache = None + + for gen_step_idx in tqdm.tqdm(range(max_new_tokens), disable=not verbose): + with torch.no_grad(): + if is_encoder_decoder: + logits = self( + encoder_input, + return_type="logits", + decoder_input=decoder_tokens, + ) + else: + forward_kwargs: Dict[str, Any] = {} + # Compute attention mask and position_ids for batched + # inputs with padding. + if ( + _is_batched_list + and self.tokenizer is not None + and self.tokenizer.pad_token_id is not None + ): + _prev_side = self.tokenizer.padding_side + self.tokenizer.padding_side = "left" + attn_mask = utils.get_attention_mask( + self.tokenizer, + current_tokens, + prepend_bos=getattr(self.cfg, "default_prepend_bos", True), + ).to(self.cfg.device) + self.tokenizer.padding_side = _prev_side + forward_kwargs["attention_mask"] = attn_mask + position_ids = attn_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attn_mask == 0, 1) + forward_kwargs["position_ids"] = position_ids + if gen_step_idx == 0: + if pixel_values is not None: + forward_kwargs["pixel_values"] = pixel_values + if multimodal_kwargs: + forward_kwargs.update(multimodal_kwargs) + if use_stateful_cache: + forward_kwargs["cache_params"] = mamba_cache + forward_kwargs["use_cache"] = True + if gen_step_idx == 0: + cache_position = torch.arange( + 0, mamba_conv_kernel, device=self.cfg.device + ) + forward_kwargs["cache_position"] = cache_position + logits = self( + current_tokens, + return_type="logits", + **forward_kwargs, + ) + else: + input_seq_pos = input_tokens.shape[1] + gen_step_idx - 1 + cache_position = torch.tensor([input_seq_pos], device=self.cfg.device) + forward_kwargs["cache_position"] = cache_position + if "position_ids" in forward_kwargs: + forward_kwargs["position_ids"] = forward_kwargs["position_ids"][ + :, -1: + ] + logits = self( + current_tokens[:, -1:], + return_type="logits", + **forward_kwargs, + ) + elif use_past_kv_cache: + forward_kwargs["use_cache"] = True + if _hf_kv_cache is not None: + forward_kwargs["past_key_values"] = _hf_kv_cache + if "position_ids" in forward_kwargs: + forward_kwargs["position_ids"] = forward_kwargs["position_ids"][ + :, -1: + ] + logits = self( + current_tokens[:, -1:], + return_type="logits", + **forward_kwargs, + ) + else: + logits = self( + current_tokens, + return_type="logits", + **forward_kwargs, + ) + else: + logits = self(current_tokens, return_type="logits", **forward_kwargs) + if use_past_kv_cache and hasattr(self, "_last_hf_cache"): + _hf_kv_cache = self._last_hf_cache or _hf_kv_cache + del self._last_hf_cache + final_logits = logits[:, -1, :] + + # Sample next token + penalty_tokens = ( + torch.stack(generated_token_ids, dim=1) + if _generate_from_embeds and generated_token_ids + else None + ) + if do_sample: + sampled_tokens = utils.sample_logits( + final_logits, + top_k=top_k, + top_p=top_p, + temperature=temperature, + freq_penalty=freq_penalty, + repetition_penalty=repetition_penalty, + tokens=penalty_tokens + if _generate_from_embeds + else (decoder_tokens if is_encoder_decoder else current_tokens), + ).to(self.cfg.device) + else: + sampled_tokens = utils.sample_logits( + final_logits, + temperature=0.0, + repetition_penalty=repetition_penalty, + tokens=penalty_tokens + if _generate_from_embeds + else (decoder_tokens if is_encoder_decoder else current_tokens), + ).to(self.cfg.device) + + # Handle EOS + if stop_at_eos: + sampled_tokens[finished_sequences] = eos_token_for_padding + finished_sequences.logical_or_( + torch.isin( + sampled_tokens.to(self.cfg.device), + torch.tensor(stop_tokens).to(self.cfg.device), + ) + ) + + # Update token sequences + if is_encoder_decoder: + assert decoder_tokens is not None + decoder_tokens = torch.cat([decoder_tokens, sampled_tokens.unsqueeze(1)], dim=1) + elif _generate_from_embeds: + assert generated_token_ids is not None + generated_token_ids.append(sampled_tokens) + embed_fn = self.original_model.get_input_embeddings() # type: ignore[operator] + assert embed_fn is not None + new_embed = embed_fn(sampled_tokens.unsqueeze(1)).to(current_tokens.dtype) + current_tokens = torch.cat([current_tokens, new_embed], dim=1) + else: + current_tokens = torch.cat([current_tokens, sampled_tokens.unsqueeze(1)], dim=1) + + all_finished = bool(stop_at_eos and finished_sequences.all().item()) + + yield sampled_tokens, final_logits, all_finished + + if all_finished: + return + + def generate( + self, + input: Union[str, List[str], torch.Tensor] = "", + max_new_tokens: int = 10, + stop_at_eos: bool = True, + eos_token_id: Optional[int] = None, + do_sample: bool = True, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + temperature: float = 1.0, + freq_penalty: float = 0.0, + repetition_penalty: float = 1.0, + use_past_kv_cache: bool = True, + prepend_bos: Optional[bool] = None, + padding_side: Optional[str] = None, + return_type: Optional[str] = "input", + verbose: bool = True, + output_logits: bool = False, + pixel_values: Optional[torch.Tensor] = None, + **multimodal_kwargs, + ) -> str | list[str] | torch.Tensor | Any: # Any for transformers.utils.ModelOutput + # Any: beartype forward ref limitation (beartype#546) + """Sample tokens from the model. + + Sample tokens from the model until the model outputs eos_token or max_new_tokens is reached. + This implementation is based on HookedTransformer.generate() to ensure consistent behavior. + + Args: + input: Text string, list of strings, or tensor of tokens + max_new_tokens: Maximum number of tokens to generate + stop_at_eos: If True, stop generating tokens when the model outputs eos_token + eos_token_id: The token ID to use for end of sentence + do_sample: If True, sample from the model's output distribution. Otherwise, use greedy search + top_k: Number of tokens to sample from. If None, sample from all tokens + top_p: Probability mass to sample from. If 1.0, sample from all tokens + temperature: Temperature for sampling. Higher values will make the model more random + freq_penalty: Frequency penalty for sampling - how much to penalise previous tokens + repetition_penalty: HuggingFace-style repetition penalty. Values > 1.0 discourage + repetition by dividing positive logits and multiplying negative logits for + previously seen tokens. Default 1.0 (no penalty). + use_past_kv_cache: If True, use KV caching for faster generation + prepend_bos: Accepted for API compatibility but not applied during generation. + The HF model expects tokens in its native format (tokenizer defaults). + Overriding BOS can silently degrade generation quality. + padding_side: Which side to pad when tokenizing multiple strings of different + lengths. For batched list inputs, left-padding is forced internally for + correct generation behavior. Defaults to None (tokenizer default). + return_type: The type of output to return - 'input', 'str', or 'tokens' + verbose: Not used in Bridge (kept for API compatibility) + output_logits: If True, return a ModelOutput with sequences and logits tuple + pixel_values: Optional image tensor for multimodal models. Only passed on the + first generation step (the vision encoder processes the image once, then + embeddings are part of the token sequence for subsequent steps). + + Returns: + Generated sequence as string, list of strings, or tensor depending on input type and return_type. + If output_logits=True, returns a ModelOutput-like object with 'sequences' and 'logits' attributes. + """ + # prepend_bos is intentionally not applied during generation. + # The HF model expects tokens in its native format. Overriding BOS can silently + # degrade quality. + if prepend_bos is not None: + import warnings + + warnings.warn( + "prepend_bos is ignored during TransformerBridge.generate(). " + "The HF model expects tokens with the tokenizer's default BOS handling. " + "To control BOS, tokenize with to_tokens(prepend_bos=...) and pass the " + "resulting tensor to generate().", + stacklevel=2, + ) + # padding_side is handled internally: for batched list inputs, left-padding + # is forced to ensure correct generation. See _is_batched_list logic below. + + # Stateful dispatch is decided after input parsing so we can fall back + # to hf_generate() for input types the stateful loop doesn't handle. + is_stateful_model = getattr(self.cfg, "is_stateful", False) + + _is_batched_list = isinstance(input, list) and len(input) > 1 + + _generate_from_embeds = False + if isinstance(input, str): + input_tokens = self.to_tokens(input, move_to_device=True, truncate=False) + input_type = "str" + elif isinstance(input, list): + # Force left-padding for batched generation so real tokens are + # flush-right and logits[:, -1, :] is always the last real token. + if _is_batched_list: + _orig_padding_side = self.tokenizer.padding_side + self.tokenizer.padding_side = "left" + input_tokens = self.to_tokens(input, move_to_device=True, truncate=False) + if _is_batched_list: + self.tokenizer.padding_side = _orig_padding_side + input_type = "list" + elif isinstance(input, torch.Tensor) and input.is_floating_point(): + # inputs_embeds: pre-computed embeddings (e.g., from multimodal models) + input_tokens = input.to(self.cfg.device) + input_type = "embeds" + _generate_from_embeds = True + else: + input_tokens = input.to(self.cfg.device) + input_type = "tokens" + + # Determine return type + if return_type == "input": + if input_type in ["str", "list"]: + return_type = "str" + elif input_type == "embeds": + return_type = "tokens" + else: + return_type = "tokens" + + batch_size = input_tokens.shape[0] + + # Setup EOS token handling + stop_tokens = [] + eos_token_for_padding = 0 + if stop_at_eos: + tokenizer_has_eos_token = ( + self.tokenizer is not None and self.tokenizer.eos_token_id is not None + ) + if eos_token_id is None: + assert ( + tokenizer_has_eos_token + ), "Must pass eos_token_id if stop_at_eos is True and tokenizer is None or has no eos_token_id" + assert self.tokenizer is not None + eos_token_id = self.tokenizer.eos_token_id + + if isinstance(eos_token_id, int): + stop_tokens = [eos_token_id] + eos_token_for_padding = eos_token_id + else: + stop_tokens = list(eos_token_id) + if tokenizer_has_eos_token: + assert self.tokenizer is not None + eos_token_for_padding = self.tokenizer.eos_token_id + else: + eos_token_for_padding = eos_token_id[0] + + # Track which sequences have finished + finished_sequences = torch.zeros(batch_size, dtype=torch.bool, device=self.cfg.device) + + # Optionally collect logits at each generation step for downstream tooling/tests + logits_seq_list: list[torch.Tensor] | None = [] if output_logits else None + + # Detect encoder-decoder models (T5, BART, etc.) + is_encoder_decoder = hasattr(self.original_model, "config") and getattr( + self.original_model.config, "is_encoder_decoder", False + ) + + # HF cache flows opaquely through the component chain via + # _reconstruct_attention() → _update_kv_cache() on each layer. + _hf_kv_cache = None + if use_past_kv_cache and is_encoder_decoder: + # Encoder-decoder models (T5, BART) don't support the opaque + # cache path — silently disable rather than crash, since + # use_past_kv_cache=True is the default. + use_past_kv_cache = False + + # SSMs (Mamba/Mamba-2) run through a dedicated cache path so hooks + # fire on every step. Unsupported input types fall back to hf_generate(). + use_stateful_cache = ( + is_stateful_model + and use_past_kv_cache + and not is_encoder_decoder + and not _generate_from_embeds + and pixel_values is None + and not multimodal_kwargs + ) + if is_stateful_model and not use_stateful_cache: + hf_kwargs: dict[str, Any] = { + "max_new_tokens": max_new_tokens, + "do_sample": do_sample, + "temperature": temperature, + } + if top_k is not None: + hf_kwargs["top_k"] = top_k + if top_p is not None: + hf_kwargs["top_p"] = top_p + if eos_token_id is not None: + hf_kwargs["eos_token_id"] = eos_token_id + return self.hf_generate(input, **hf_kwargs) + + # SSM cache is built once and mutated in place across forward calls. + # Adapter owns the cache-type choice; new SSMs just override + # create_stateful_cache(). + mamba_cache: Any = None + mamba_conv_kernel: int = 0 + if use_stateful_cache: + hf_model: Any = self.original_model + mamba_conv_kernel = int(getattr(hf_model.config, "conv_kernel", 4)) + cache_dtype = self.cfg.dtype or torch.float32 + mamba_cache = self.adapter.create_stateful_cache( + hf_model=hf_model, + batch_size=batch_size, + device=self.cfg.device, + dtype=cache_dtype, + ) + + if use_past_kv_cache and not use_stateful_cache: + self._capture_hf_cache = True # Signal forward() to stash cache + + # Generate tokens + current_tokens = input_tokens.clone() + # For inputs_embeds generation, also track generated token IDs for decoding + if _generate_from_embeds: + generated_token_ids: list[torch.Tensor] = [] + sampled_tokens_list = [] + + # For encoder-decoder models, keep encoder input fixed and grow decoder input + if is_encoder_decoder: + encoder_input = input_tokens.clone() + decoder_start_token_id = getattr( + self.original_model.config, "decoder_start_token_id", 0 + ) + decoder_tokens = torch.full( + (batch_size, 1), + decoder_start_token_id, + dtype=input_tokens.dtype, + device=self.cfg.device, + ) + + try: + for sampled_tokens, final_logits, all_finished in self._generate_tokens( + current_tokens, + input_tokens, + batch_size, + max_new_tokens=max_new_tokens, + do_sample=do_sample, + top_k=top_k, + top_p=top_p, + temperature=temperature, + freq_penalty=freq_penalty, + repetition_penalty=repetition_penalty, + stop_at_eos=stop_at_eos, + stop_tokens=stop_tokens, + eos_token_for_padding=eos_token_for_padding, + finished_sequences=finished_sequences, + use_past_kv_cache=use_past_kv_cache, + use_stateful_cache=use_stateful_cache, + mamba_cache=mamba_cache, + mamba_conv_kernel=mamba_conv_kernel, + is_encoder_decoder=is_encoder_decoder, + _is_batched_list=_is_batched_list, + _generate_from_embeds=_generate_from_embeds, + encoder_input=encoder_input if is_encoder_decoder else None, + decoder_tokens=decoder_tokens if is_encoder_decoder else None, + generated_token_ids=generated_token_ids if _generate_from_embeds else None, + pixel_values=pixel_values, + multimodal_kwargs=multimodal_kwargs if multimodal_kwargs else {}, + verbose=verbose, + ): + sampled_tokens_list.append(sampled_tokens.unsqueeze(1)) + if logits_seq_list is not None: + logits_seq_list.append(final_logits.clone()) + if all_finished: + break + finally: + self._capture_hf_cache = False + if hasattr(self, "_last_hf_cache"): + del self._last_hf_cache + + # Concatenate all sampled tokens + sampled_tokens = torch.cat(sampled_tokens_list, dim=1) + if is_encoder_decoder: + # Reconstruct full decoder sequence: start token + generated tokens + output_tokens = torch.cat([decoder_tokens[:, :1], sampled_tokens], dim=1) + elif _generate_from_embeds: + # For inputs_embeds, we only have the generated token IDs (no input token IDs) + output_tokens = sampled_tokens + else: + output_tokens = torch.cat([input_tokens, sampled_tokens], dim=1) + + # Return ModelOutput if output_logits was requested + if output_logits and logits_seq_list is not None: + from transformers.utils import ModelOutput # type: ignore + + def _logits_to_tuple(logits_list: list[torch.Tensor]) -> tuple[torch.Tensor, ...]: + assert logits_list is not None + # Convert list of [batch, vocab] tensors to tuple + return tuple(logits_list) + + try: + from transformers.generation.utils import GenerateDecoderOnlyOutput + + # Return a HF-compatible ModelOutput structure + # GenerateDecoderOnlyOutput expects: sequences, scores (optional), logits (optional) + return GenerateDecoderOnlyOutput( + sequences=cast(torch.LongTensor, output_tokens), + # HF's type hint says tuple[FloatTensor] but should be tuple[FloatTensor, ...] + # (variable-length tuple with one element per generated token) + logits=_logits_to_tuple(logits_seq_list), # type: ignore[arg-type] + ) + except (ImportError, AttributeError): + # Fallback if GenerateDecoderOnlyOutput not available in this transformers version + return ModelOutput( + sequences=output_tokens, + logits=_logits_to_tuple(logits_seq_list), + ) + + # Format output + if return_type == "str": + assert self.tokenizer is not None + if input_type == "str": + return self.tokenizer.decode(output_tokens[0], skip_special_tokens=True) + else: + decoded_texts = [ + self.tokenizer.decode(tokens, skip_special_tokens=True) + for tokens in output_tokens + ] + return decoded_texts[0] if len(decoded_texts) == 1 else decoded_texts + else: # return_type == "tokens" + return output_tokens + + @torch.no_grad() + def generate_stream( + self, + input: Union[str, List[str], torch.Tensor] = "", + max_new_tokens: int = 10, + max_tokens_per_yield: int = 25, + stop_at_eos: bool = True, + eos_token_id: Optional[int] = None, + do_sample: bool = True, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + temperature: float = 1.0, + freq_penalty: float = 0.0, + repetition_penalty: float = 1.0, + use_past_kv_cache: bool = True, + prepend_bos: Optional[bool] = None, + padding_side: Optional[str] = None, + return_type: Optional[str] = "input", + verbose: bool = True, + ) -> Generator[Union[torch.Tensor, str], None, None]: + """Stream tokens from the model as they are generated. + + Yields batches of tokens progressively during generation rather than + waiting for the entire sequence. Uses the same core loop as generate(). + + Args: + input: Text string, list of strings, or tensor of tokens. + max_new_tokens: Maximum number of tokens to generate. + max_tokens_per_yield: Yield accumulated tokens every this many steps. + stop_at_eos: If True, stop when eos_token is produced. + eos_token_id: Token ID(s) for end of sentence. Defaults to tokenizer's. + do_sample: If True, sample; otherwise greedy. + top_k: Top-k sampling. None means no filtering. + top_p: Nucleus sampling threshold. + temperature: Sampling temperature. + freq_penalty: Frequency penalty for previous tokens. + repetition_penalty: HF-style repetition penalty (>1.0 discourages repeats). + use_past_kv_cache: Use KV caching for faster generation. + prepend_bos: Not applied (API compatibility). See generate() docstring. + padding_side: Which side to pad for batched list inputs. Left-padding + is forced internally for batched generation. + return_type: 'input' (match input type), 'str', or 'tokens'. + verbose: Show progress bar. + + Yields: + Token tensors [batch, seq_len] or strings, accumulated up to + max_tokens_per_yield tokens between yields. First yield includes + the input tokens; subsequent yields contain only new tokens. + """ + if prepend_bos is not None: + warnings.warn( + "prepend_bos is ignored during TransformerBridge.generate_stream(). " + "The HF model expects tokens with the tokenizer's default BOS handling.", + stacklevel=2, + ) + + # --- Input parsing (mirrors generate()) --- + _is_batched_list = isinstance(input, list) and len(input) > 1 + + if isinstance(input, str): + input_tokens = self.to_tokens(input, move_to_device=True, truncate=False) + input_type = "str" + elif isinstance(input, list): + if _is_batched_list: + _orig_ps = self.tokenizer.padding_side + self.tokenizer.padding_side = "left" + try: + input_tokens = self.to_tokens(input, move_to_device=True, truncate=False) + finally: + if _is_batched_list: + self.tokenizer.padding_side = _orig_ps + input_type = "list" + else: + input_tokens = input.to(self.cfg.device) + input_type = "tokens" + + if return_type == "input": + return_type = "str" if input_type in ["str", "list"] else "tokens" + + batch_size = input_tokens.shape[0] + + # --- EOS setup --- + stop_tokens: List[int] = [] + eos_token_for_padding = 0 + if stop_at_eos: + tokenizer_has_eos_token = ( + self.tokenizer is not None and self.tokenizer.eos_token_id is not None + ) + if eos_token_id is None: + assert ( + tokenizer_has_eos_token + ), "Must pass eos_token_id if stop_at_eos is True and tokenizer is None or has no eos_token_id" + assert self.tokenizer is not None + eos_token_id = self.tokenizer.eos_token_id + if isinstance(eos_token_id, int): + stop_tokens = [eos_token_id] + eos_token_for_padding = eos_token_id + else: + stop_tokens = list(eos_token_id) + if tokenizer_has_eos_token: + assert self.tokenizer is not None + eos_token_for_padding = self.tokenizer.eos_token_id + else: + eos_token_for_padding = eos_token_id[0] + + finished_sequences = torch.zeros(batch_size, dtype=torch.bool, device=self.cfg.device) + + # --- Cache setup --- + if use_past_kv_cache: + self._capture_hf_cache = True + + current_tokens = input_tokens.clone() + + # --- Streaming loop --- + # All yields are token tensors [batch, seq_len]. Each yield contains + # only the newly generated tokens since the previous yield (the first + # yield additionally prepends the input tokens for context). + accumulated_tokens: Optional[torch.Tensor] = None + tokens_since_last_yield = 0 + + def _maybe_decode( + tokens: torch.Tensor, + ) -> Union[torch.Tensor, str]: + if return_type == "str": + assert self.tokenizer is not None + return self.tokenizer.decode(tokens[0], skip_special_tokens=True) + return tokens + + try: + for step_idx, (sampled_tokens, _, all_finished) in enumerate( + self._generate_tokens( + current_tokens, + input_tokens, + batch_size, + max_new_tokens=max_new_tokens, + do_sample=do_sample, + top_k=top_k, + top_p=top_p, + temperature=temperature, + freq_penalty=freq_penalty, + repetition_penalty=repetition_penalty, + stop_at_eos=stop_at_eos, + stop_tokens=stop_tokens, + eos_token_for_padding=eos_token_for_padding, + finished_sequences=finished_sequences, + use_past_kv_cache=use_past_kv_cache, + use_stateful_cache=False, + mamba_cache=None, + mamba_conv_kernel=0, + is_encoder_decoder=False, + _is_batched_list=_is_batched_list, + _generate_from_embeds=False, + encoder_input=None, + decoder_tokens=None, + generated_token_ids=None, + pixel_values=None, + multimodal_kwargs={}, + verbose=verbose, + ) + ): + new_tokens = sampled_tokens.unsqueeze(-1) + + if step_idx == 0: + accumulated_tokens = torch.cat([input_tokens, new_tokens], dim=-1) + tokens_since_last_yield = accumulated_tokens.shape[1] + else: + if accumulated_tokens is None: + accumulated_tokens = new_tokens + else: + accumulated_tokens = torch.cat([accumulated_tokens, new_tokens], dim=-1) + tokens_since_last_yield += 1 + + if tokens_since_last_yield >= max_tokens_per_yield: + yield _maybe_decode(accumulated_tokens) + tokens_since_last_yield = 0 + accumulated_tokens = None + + if all_finished: + if accumulated_tokens is not None: + yield _maybe_decode(accumulated_tokens) + break + + # Yield remainder after loop completes without break + if accumulated_tokens is not None: + yield _maybe_decode(accumulated_tokens) + finally: + self._capture_hf_cache = False + if hasattr(self, "_last_hf_cache"): + del self._last_hf_cache + + def hf_generate( + self, + input: str | list[str] | torch.Tensor = "", + max_new_tokens: int = 10, + stop_at_eos: bool = True, + eos_token_id: int | None = None, + do_sample: bool = True, + top_k: int | None = None, + top_p: float | None = None, + temperature: float = 1.0, + use_past_kv_cache: bool = True, + return_type: str | None = "input", + pixel_values: torch.Tensor | None = None, + **generation_kwargs, + ) -> str | list[str] | torch.Tensor | Any: # Any for HF ModelOutput types + # Any: beartype forward ref limitation (beartype#546) + """Generate text using the underlying HuggingFace model with full HF API support. + + This method provides direct access to HuggingFace's generation API, forwarding all + generation parameters (including output_scores, output_logits, output_attentions, + output_hidden_states) directly to the underlying HF model. Use this when you need + full HuggingFace generation features not supported by the standard generate() method. + + For standard generation compatible with HookedTransformer, use generate() instead. + + Args: + input: Text string, list of strings, or tensor of tokens + max_new_tokens: Maximum number of tokens to generate + stop_at_eos: If True, stop generating tokens when the model outputs eos_token + eos_token_id: The token ID to use for end of sentence + do_sample: If True, sample from the model's output distribution + top_k: Number of tokens to sample from + top_p: Probability mass to sample from + temperature: Temperature for sampling + use_past_kv_cache: If True, use KV caching for faster generation + return_type: The type of output to return - 'input', 'str', or 'tokens' + **generation_kwargs: Additional HuggingFace generation parameters including: + - output_scores: Return generation scores + - output_logits: Return generation logits + - output_attentions: Return attention weights + - output_hidden_states: Return hidden states + - return_dict_in_generate: Return ModelOutput object + - And any other HF generation parameters + + Returns: + Generated sequence as string, list of strings, tensor, or HF ModelOutput + depending on input type, return_type, and generation_kwargs. + + Example:: + + # Get full HF ModelOutput with logits and attentions + from transformer_lens import HookedTransformer + model = HookedTransformer.from_pretrained("tiny-stories-1M") + result = model.hf_generate( + "Hello world", + max_new_tokens=5, + output_logits=True, + output_attentions=True, + return_dict_in_generate=True + ) + print(result.sequences) # Generated tokens + print(result.logits) # Logits for each generation step + print(result.attentions) # Attention weights + """ + # Handle string input by tokenizing it + if isinstance(input, str): + inputs = self.tokenizer(input, return_tensors="pt", padding=False, truncation=False).to( + self.cfg.device + ) + input_ids = inputs["input_ids"] + input_type = "str" + elif isinstance(input, list): + inputs = self.tokenizer(input, return_tensors="pt", padding=True, truncation=False).to( + self.cfg.device + ) + input_ids = inputs["input_ids"] + input_type = "list" + else: + input_ids = input + if input_ids.device != self.cfg.device: + input_ids = input_ids.to(self.cfg.device) + input_type = "tokens" + + # Build generation_kwargs from explicit args and kwargs + generation_kwargs = dict(generation_kwargs) if generation_kwargs is not None else {} + generation_kwargs.update( + { + "max_new_tokens": max_new_tokens, + "do_sample": do_sample, + "temperature": temperature, + "pad_token_id": self.tokenizer.eos_token_id, + } + ) + + if top_k is not None: + generation_kwargs["top_k"] = top_k + if top_p is not None: + generation_kwargs["top_p"] = top_p + if eos_token_id is not None: + generation_kwargs["eos_token_id"] = eos_token_id + elif stop_at_eos and self.tokenizer.eos_token_id is not None: + generation_kwargs["eos_token_id"] = self.tokenizer.eos_token_id + + if pixel_values is not None: + generation_kwargs["pixel_values"] = pixel_values + + if use_past_kv_cache: + generation_kwargs["use_cache"] = True + + # HF dict flags that trigger ModelOutput returns + hf_dict_flags = ( + "output_scores", + "output_logits", + "output_attentions", + "output_hidden_states", + ) + + # If any HF-style output flags are provided, ensure return_dict_in_generate is set + any_flag_set = False + for f in hf_dict_flags: + if generation_kwargs.get(f) is not None: + generation_kwargs[f] = bool(generation_kwargs[f]) + any_flag_set = True + + if any_flag_set: + generation_kwargs.setdefault("return_dict_in_generate", True) + + # Generate using the original HuggingFace model + with torch.no_grad(): + outputs = self.original_model.generate(input_ids, **generation_kwargs) # type: ignore[operator] + + # Check if output is a ModelOutput + try: + from transformers.utils import ModelOutput # type: ignore + + is_model_output = isinstance(outputs, ModelOutput) + except Exception: + is_model_output = False + + # Return based on return_type and input format + if return_type == "input" or return_type is None: + if input_type == "str": + # Decode the full output back to string + if is_model_output and hasattr(outputs, "sequences"): + return self.tokenizer.decode(outputs.sequences[0], skip_special_tokens=True) + return self.tokenizer.decode(outputs[0], skip_special_tokens=True) + elif input_type == "list": + # Decode each sequence in the batch + if is_model_output and hasattr(outputs, "sequences"): + return [ + self.tokenizer.decode(seq, skip_special_tokens=True) + for seq in outputs.sequences + ] + return [self.tokenizer.decode(seq, skip_special_tokens=True) for seq in outputs] + else: + # Return the full token sequence including input + return outputs + elif return_type == "tokens": + return outputs + else: + # For other return types, default to the decoded text + if input_type == "str": + if is_model_output and hasattr(outputs, "sequences"): + return self.tokenizer.decode(outputs.sequences[0], skip_special_tokens=True) + return self.tokenizer.decode(outputs[0], skip_special_tokens=True) + elif input_type == "list": + if is_model_output and hasattr(outputs, "sequences"): + return [ + self.tokenizer.decode(seq, skip_special_tokens=True) + for seq in outputs.sequences + ] + return [self.tokenizer.decode(seq, skip_special_tokens=True) for seq in outputs] + else: + return outputs + + def prepare_multimodal_inputs( + self, + text: Union[str, List[str]], + images: Optional[Any] = None, + ) -> Dict[str, torch.Tensor]: + """Prepare multimodal inputs using the model's processor. + + Converts text and images into model-ready tensors (input_ids, pixel_values, + attention_mask, etc.) using the HuggingFace processor loaded during boot(). + + Args: + text: Text prompt(s), typically containing image placeholder tokens + (e.g., "" for LLaVA). + images: PIL Image or list of PIL Images to process. Pass None for + text-only inputs on a multimodal model. + + Returns: + Dictionary with 'input_ids', 'pixel_values', 'attention_mask', etc. + All tensors are moved to the model's device. + + Raises: + ValueError: If model is not multimodal or processor is not available. + """ + if not getattr(self.cfg, "is_multimodal", False): + raise ValueError( + "prepare_multimodal_inputs() requires a multimodal model " + "(cfg.is_multimodal must be True)" + ) + if self.processor is None: + raise ValueError( + "No processor available. Load model with boot_transformers() or " + "set bridge.processor = AutoProcessor.from_pretrained(...) manually." + ) + inputs = self.processor(text=text, images=images, return_tensors="pt") + return {k: v.to(self.cfg.device) if hasattr(v, "to") else v for k, v in inputs.items()} + + def to(self, *args, **kwargs) -> "TransformerBridge": + """Move model to device and/or change dtype. + + Args: + args: Positional arguments for nn.Module.to + kwargs: Keyword arguments for nn.Module.to + print_details: Whether to print details about device/dtype changes (default: True) + + Returns: + Self for chaining + """ + # Extract print_details if provided + print_details = kwargs.pop("print_details", True) + + # Handle both device and dtype changes + # torch.nn.Module.to() supports: to(device), to(dtype), to(device, dtype), + # to(device=...), to(dtype=...), to(device=..., dtype=...) + target_device, target_dtype = None, None + + if len(args) >= 1: + first_arg = args[0] + if isinstance(first_arg, (torch.device, str)): + target_device = first_arg + elif isinstance(first_arg, torch.dtype): + target_dtype = first_arg + if len(args) >= 2: + second_arg = args[1] + if isinstance(second_arg, torch.dtype): + target_dtype = second_arg + + # these override positional args + if "device" in kwargs: + target_device = kwargs["device"] + if "dtype" in kwargs: + target_dtype = kwargs["dtype"] + + # Moving a multi-device (device_map-dispatched) model to a single device would + # collapse the split and break accelerate's hook routing. Warn and drop the + # device move; still honor dtype changes. + if target_device is not None and getattr(self.cfg, "n_devices", 1) > 1: + warnings.warn( + f"TransformerBridge.to({target_device!r}) ignored: model is dispatched " + f"across {self.cfg.n_devices} devices via device_map. Reload with " + "device=... (and no device_map/n_devices) to move to a single device.", + stacklevel=2, + ) + target_device = None + + if target_device is not None: + move_to_and_update_config(self, target_device, print_details) + if target_dtype is not None: + move_to_and_update_config(self, target_dtype, print_details) + + # Move the original model with all original args/kwargs (with print_details removed). + # When we've nulled target_device for multi-GPU safety, strip device args so the + # underlying module isn't moved either. + if target_device is None and (len(args) > 0 or "device" in kwargs): + kwargs.pop("device", None) + # Filter positional args: drop devices/strings, keep dtypes. + args = tuple(a for a in args if not isinstance(a, (torch.device, str))) + self.original_model = self.original_model.to(*args, **kwargs) + return self + + def cuda(self, device: Optional[Union[int, torch.device]] = None) -> "TransformerBridge": + """Move model to CUDA. + + Args: + device: CUDA device + + Returns: + Self for chaining + """ + if isinstance(device, int): + return self.to(f"cuda:{device}") + elif device is None: + return self.to("cuda") + else: + return self.to(device) + + def cpu(self) -> "TransformerBridge": + """Move model to CPU. + + Returns: + Self for chaining + """ + return self.to(torch.device("cpu")) + + def mps(self) -> "TransformerBridge": + """Move model to MPS. + + Returns: + Self for chaining + """ + return self.to(torch.device("mps")) + + def set_use_attn_result(self, use_attn_result: bool): + """Toggle whether to explicitly calculate and expose the result for each attention head. + + Useful for interpretability but can easily burn through GPU memory. + """ + if use_attn_result: + self._validate_attention_fork_supported("use_attn_result") + self.cfg.use_attn_result = use_attn_result + self._propagate_attention_flag("use_attn_result", use_attn_result) + + def set_use_split_qkv_input(self, use_split_qkv_input: bool): + """Toggle independent residual copies for Q/K/V so each path can be patched alone. + + Mutually exclusive with `use_attn_in` — set that flag off first if it's on. + """ + if use_split_qkv_input: + if bool(getattr(self.cfg, "use_attn_in", False)): + raise ValueError( + "use_split_qkv_input and use_attn_in are mutually exclusive. " + "Call set_use_attn_in(False) before enabling use_split_qkv_input." + ) + self._validate_attention_fork_supported("use_split_qkv_input") + self.cfg.use_split_qkv_input = use_split_qkv_input + self._propagate_attention_flag("use_split_qkv_input", use_split_qkv_input) + + def set_use_attn_in(self, use_attn_in: bool): + """Toggle a single 4D residual copy feeding all three Q/K/V projections. + + Mutually exclusive with `use_split_qkv_input` — set that flag off first + if it's on. When on, `hook_attn_in` fires at + `[batch, pos, n_heads, d_model]`, enabling coarse-grained interventions + on the residual-stream copy shared across Q/K/V. + """ + if use_attn_in: + if bool(getattr(self.cfg, "use_split_qkv_input", False)): + raise ValueError( + "use_attn_in and use_split_qkv_input are mutually exclusive. " + "Call set_use_split_qkv_input(False) before enabling use_attn_in." + ) + self._validate_attention_fork_supported("use_attn_in") + self.cfg.use_attn_in = use_attn_in + self._propagate_attention_flag("use_attn_in", use_attn_in) + + def _propagate_attention_flag(self, flag_name: str, value: bool) -> None: + """Mirror `bridge.cfg.` onto every block's attention config. + + Some adapters (Llama family) deep-copy the block template during + `setup_blocks_bridge`, cloning the attention bridge's config along + with it. Others (Pythia, GPT-2) override `__deepcopy__` to share the + config. Setting the flag only on `self.cfg` silently misses the + cloned-config case. Propagating explicitly keeps both patterns + honest — a no-op when configs are shared, a correctness fix when + they aren't. + """ + if not hasattr(self, "blocks"): + return + for block in self.blocks: + attn = block._modules.get("attn") if hasattr(block, "_modules") else None + if attn is None: + continue + attn_cfg = getattr(attn, "config", None) + if attn_cfg is not None and attn_cfg is not self.cfg: + try: + setattr(attn_cfg, flag_name, value) + except Exception: + # Some cfg objects may be frozen/immutable. Skip silently — + # the block simply won't honor the flag, which is the + # same outcome as before this fix. + pass + + def _validate_attention_fork_supported(self, flag_name: str) -> None: + """Raise / warn if the model can't honor a fine-grained attention flag. + + The post-ln1 fork path lives on JointQKVAttentionBridge and + PositionEmbeddingsAttentionBridge. Plain AttentionBridge delegates to + HF and exposes no fork point; we raise rather than setting the flag + silently. For hybrid models (some attention layers, some not), we warn + and list which layers will honor the flag. + """ + # Deferred imports: tight circular dependency with bridge setup. + from transformer_lens.model_bridge.generalized_components.joint_qkv_attention import ( + JointQKVAttentionBridge, + ) + from transformer_lens.model_bridge.generalized_components.position_embeddings_attention import ( + PositionEmbeddingsAttentionBridge, + ) + + if not hasattr(self, "blocks"): + raise NotImplementedError( + f"{flag_name}: this bridge has no `blocks` attribute, so no " + "attention bridges to apply the flag to." + ) + supported_classes = (JointQKVAttentionBridge, PositionEmbeddingsAttentionBridge) + supporting_layers: list[int] = [] + attn_classes: set[str] = set() + total_with_attn = 0 + for idx, block in enumerate(self.blocks): + attn = block._modules.get("attn") if hasattr(block, "_modules") else None + if attn is None: + continue + total_with_attn += 1 + attn_classes.add(type(attn).__name__) + if isinstance(attn, supported_classes): + supporting_layers.append(idx) + if total_with_attn == 0: + raise NotImplementedError(f"{flag_name}: no attention bridges found on self.blocks.") + if not supporting_layers: + raise NotImplementedError( + f"{flag_name}: none of this model's attention bridges support " + "the fine-grained Q/K/V hook fork. Found attention classes: " + f"{sorted(attn_classes)}. Supported classes: " + f"{[c.__name__ for c in supported_classes]}. Plain " + "AttentionBridge delegates to HuggingFace and exposes no hook " + "point before the Q/K/V projection." + ) + if len(supporting_layers) < total_with_attn: + skipped = total_with_attn - len(supporting_layers) + warnings.warn( + f"{flag_name}: {skipped} of {total_with_attn} attention layers " + "use an attention-bridge class that cannot honor this flag " + f"(attention classes present: {sorted(attn_classes)}). " + f"The flag will affect layers: {supporting_layers}.", + stacklevel=3, + ) + + def _is_valid_bridge_path(self, hf_path: str) -> bool: + """Check if a HuggingFace path corresponds to a valid bridge component. + + This validates that the path follows the bridge component structure and doesn't + contain nested HuggingFace components that should have been wrapped. + + Args: + hf_path: HuggingFace path after removing _original_component + + Returns: + True if the path is valid, False if it contains nested HF components + """ + # Split the path into parts + parts = hf_path.split(".") + + # Get the component mapping for validation + component_mapping = self.adapter.component_mapping + if not component_mapping: + return True # If no mapping, accept all keys + + # Walk through the path and check if each level is a registered bridge component + # For example, transformer.h.0.mlp.in.weight should be valid + # but transformer.h.0.mlp.c_fc.weight should be invalid (c_fc is nested HF component) + + # Start from the root + current_component = None + idx = 0 + + # Find which top-level component this belongs to + for tl_name, component in component_mapping.items(): + if component.name and hf_path.startswith(component.name + "."): + current_component = component + # Skip past the HF prefix + remaining_path = hf_path[len(component.name) + 1 :] + parts = remaining_path.split(".") + idx = 0 + break + + if current_component is None: + return True # Path doesn't match any component, let it through + + # Special handling for blocks + if hasattr(current_component, "is_list_item") and current_component.is_list_item: + # Skip the layer index + if idx < len(parts) and parts[idx].isdigit(): + idx += 1 + + # Now validate the rest of the path against submodules + while idx < len(parts): + part = parts[idx] + + # If we hit 'weight' or 'bias', we're at a parameter - this is valid + if part in ("weight", "bias"): + return True + + # Check if this part is a registered submodule + if hasattr(current_component, "submodules") and current_component.submodules: + if part in current_component.submodules: + current_component = current_component.submodules[part] + idx += 1 + continue + else: + # This part is not a registered bridge component + # It's likely a nested HF component (like c_fc, c_proj, c_attn) + return False + else: + # No submodules to check, but not at a parameter yet + # Check if next is weight/bias + if idx + 1 < len(parts) and parts[idx + 1] in ("weight", "bias"): + return True + # Otherwise this is likely a nested HF component + return False + + idx += 1 + + return True + + def _normalize_bridge_key_to_hf(self, key: str) -> str: + """Normalize a key that uses bridge attribute names to use HF module names. + + PyTorch's state_dict uses the Python attribute names (e.g., 'ln1') + but the conversion logic expects HF module names (e.g., 'ln_1'). This + function only replaces non-nested component names, leaving bridge + subcomponents (like 'in', 'out', 'q', 'k', 'v') unchanged since they're + handled by the component structure. + + Args: + key: Key that may use bridge attribute names + + Returns: + Key with attribute names replaced by module names where needed + """ + component_mapping = self.adapter.component_mapping + if not component_mapping: + return key + + # Build a mapping of only the direct module attribute names to HF names + # We only care about top-level and block-level component names, NOT subcomponents + attr_to_hf = {} + + # Map top-level components + for tl_name, component in component_mapping.items(): + if component.name and tl_name != "blocks": + # Skip if TL name is already a suffix of the HF path (avoids doubling). + if tl_name != component.name and not component.name.endswith("." + tl_name): + attr_to_hf[tl_name] = component.name + + # Map block-level components (ln1, ln2, attn, mlp) + blocks_component = component_mapping.get("blocks") + if blocks_component and hasattr(blocks_component, "submodules"): + for tl_subname, subcomponent in blocks_component.submodules.items(): + if subcomponent.name: + # Only map if the names differ (e.g., ln1 -> ln_1, but attn -> attn) + if tl_subname != subcomponent.name: + attr_to_hf[tl_subname] = subcomponent.name + + # Replace only these specific attribute names in the key + # We need to be careful to only replace whole path components, not substrings + parts = key.split(".") + result_parts = [] + + for part in parts: + if part in attr_to_hf: + result_parts.append(attr_to_hf[part]) + else: + result_parts.append(part) + + return ".".join(result_parts) + + def state_dict(self, destination=None, prefix="", keep_vars=False): + """Get state dict with TransformerLens format keys. + + Converts HuggingFace format keys to TransformerLens format and filters out + _original_component references and nested HuggingFace components. + + This returns a clean state dict with only bridge component paths converted to TL format, + excluding nested HF components (like c_fc, c_proj, c_attn) that exist inside + original_component modules. + + Args: + destination: Optional dict to store state dict in + prefix: Optional prefix to add to all keys + keep_vars: Whether to keep variables as Variables instead of tensors + + Returns: + Dict containing the state dict with TransformerLens format keys + """ + if destination is not None: + raw_state_dict = self.original_model.state_dict( + destination=destination, prefix=prefix, keep_vars=keep_vars + ) + else: + raw_state_dict = self.original_model.state_dict(prefix=prefix, keep_vars=keep_vars) + + # Clean _original_component references and convert to TL format + # Also filter out nested HuggingFace components that are wrapped by bridge components + tl_state_dict = {} + + for key, value in raw_state_dict.items(): + # Skip _original_component keys + if key == "_original_component" or key.startswith("_original_component."): + continue + + # Remove all _original_component from the key + clean_key = key.replace("._original_component", "") + + # Check if this is a valid bridge path (not a nested HF component) + if not self._is_valid_bridge_path(clean_key): + continue + + # Normalize bridge component names to HF names for conversion + # (e.g., 'ln1' -> 'ln_1', 'mlp.in' -> 'mlp.c_fc') + hf_key = self._normalize_bridge_key_to_hf(clean_key) + + # Convert to TL format - this uses the adapter's component_mapping + tl_key = self.adapter.convert_hf_key_to_tl_key(hf_key) + + # Only add if we haven't seen this TL key yet (handles duplicates) + if tl_key not in tl_state_dict: + tl_state_dict[tl_key] = value + + return tl_state_dict + + def load_state_dict(self, state_dict, strict=True, assign=False): + """Load state dict into the model, handling both clean keys and original keys with _original_component references. + + Args: + state_dict: Dictionary containing a whole state of the module + strict: Whether to strictly enforce that the keys in state_dict match the keys returned by this module's state_dict() function + assign: Whether to assign items in the state dictionary to their corresponding keys in the module instead of copying them + + Returns: + NamedTuple with missing_keys and unexpected_keys fields + """ + current_state_dict = self.original_model.state_dict() + clean_to_actual = {} + actual_to_clean = {} + for actual_key in current_state_dict.keys(): + if actual_key != "_original_component": + clean_key = actual_key.replace("._original_component", "") + clean_to_actual[clean_key] = actual_key + actual_to_clean[actual_key] = clean_key + mapped_state_dict = {} + for input_key, value in state_dict.items(): + if input_key in current_state_dict: + mapped_state_dict[input_key] = value + else: + if input_key in clean_to_actual: + actual_key = clean_to_actual[input_key] + mapped_state_dict[actual_key] = value + else: + mapped_state_dict[input_key] = value + effective_strict = strict and len(mapped_state_dict) == len(current_state_dict) + return self.original_model.load_state_dict( + mapped_state_dict, strict=effective_strict, assign=assign + ) + + def get_params(self): + """Access to model parameters in the format expected by SVDInterpreter. + + For missing weights, returns zero tensors of appropriate shape instead of raising exceptions. + This ensures compatibility across different model architectures. + + Returns: + dict: Dictionary of parameter tensors with TransformerLens naming convention + + Raises: + ValueError: If configuration is inconsistent (e.g., cfg.n_layers != len(blocks)) + """ + return get_bridge_params(self) + + # NOTE: list_supported_models and check_model_support are attached to this class + # dynamically by transformer_lens.model_bridge.sources.transformers module. + # These are HuggingFace-specific methods that belong in the transformers source module. From 2e236da53144797f1a2c8f04d4c9ea707e76f101 Mon Sep 17 00:00:00 2001 From: Jonah Larson Date: Thu, 28 May 2026 08:53:52 -0500 Subject: [PATCH 02/43] vLLM Batches (#1338) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Refactored `transformers` so that bridge configuration code is source-agnostic * Initial breakup of Bridge into core and bridge only components. Setup remote driver in prep * Initial setup of the vllm source Cherry-picked from 98bbb835 on feature/vllm-integration. Resolved against dev-4.x's Phase A type split: - bridge.py: kept the shim (TransformerBridge content lives in transformer_bridge.py now). - sources/transformers.py: accepted the scaffold's package restructure (transformers.py → transformers/{__init__,helpers,source}.py + _hf_format.py). - sources/transformers/source.py: re-applied Phase A's explicit TransformersDriver construction in boot(). sources/vllm/source.py's boot_vllm currently sets bridge._forward_impl, which Phase A removed. The function is dead until the next chunk lifts it onto VLLMDriver + RemoteBridge — landing as-is so the scaffold tree is on dev-4.x for incremental Phase B work. Co-Authored-By: Claude Opus 4.7 (1M context) * Revision round 1 of the vLLM system * Full vLLM integration initial completion * jupiter patch * dynamo patching * pass through batch size * add max model length to remote bridge boot * Updating config construction to properly pass through to vLLM * Wiring TLWorkerExtension * Additional testing + diagnostic * Another spot check * Remove logging * Fix issue with logit generation on hook_out * Adjust to use token_ids directly instead of logprobs * Updating Step 5 and driver bridge cleanup * Add decoder layer for materializing the residual stream * Updating tests due to changes on `dev` * Fix pip install path * Testing ln_final divergence * documenting differences in ln_final * Format cleanup * Initial batching test * Add version print * Setup batching for vllm --------- Co-authored-by: Claude Opus 4.7 (1M context) --- demos/vLLM_Bridge_Integration_Test.ipynb | 326 +++++++++++++----- tests/unit/model_bridge/test_vllm_driver.py | 182 ++++++++++ .../unit/model_bridge/test_vllm_internals.py | 51 +++ .../test_vllm_worker_extension.py | 155 +++++++++ .../model_bridge/sources/vllm/driver.py | 123 +++++++ .../model_bridge/sources/vllm/internals.py | 48 +++ .../model_bridge/sources/vllm/plugin.py | 169 ++++++--- .../model_bridge/sources/vllm/source.py | 13 + .../sources/vllm/worker_extension.py | 78 ++++- 9 files changed, 1008 insertions(+), 137 deletions(-) create mode 100644 tests/unit/model_bridge/test_vllm_internals.py create mode 100644 tests/unit/model_bridge/test_vllm_worker_extension.py diff --git a/demos/vLLM_Bridge_Integration_Test.ipynb b/demos/vLLM_Bridge_Integration_Test.ipynb index e712db1c41..106ec40986 100644 --- a/demos/vLLM_Bridge_Integration_Test.ipynb +++ b/demos/vLLM_Bridge_Integration_Test.ipynb @@ -4,15 +4,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# vLLM Bridge \u2014 Integration Test\n", + "# vLLM Bridge — Integration Test\n", "\n", "End-to-end validation of `boot_vllm` on a real GPU. Runs the v4 Driver protocol's vLLM backend against `meta-llama/Llama-3.2-1B`, compares per-hook activations and next-token argmax against the HF transformers backend, and exercises the affine intervention path.\n", "\n", - "**Branch:** `dev-4.x`. **Hardware:** any CUDA GPU with \u226510 GB VRAM (free Colab T4 is sufficient).\n", + "**Branch:** `dev-4.x`. **Hardware:** any CUDA GPU with ≥10 GB VRAM (free Colab T4 is sufficient).\n", "\n", "## Scope: observation + mutation\n", "\n", - "This source extends past vllm-lens's observation-only scope. Each capture hook applies an affine transform (`output = output * scale + bias`, default identity) and returns the modified tensor, so interventions propagate to downstream layers. Step 6 below is the load-bearing verification that this works end-to-end under `torch.compile` + CUDA graphs \u2014 unit tests can't reach the compiled-graph path.\n", + "This source extends past vllm-lens's observation-only scope. Each capture hook applies an affine transform (`output = output * scale + bias`, default identity) and returns the modified tensor, so interventions propagate to downstream layers. Step 6 below is the load-bearing verification that this works end-to-end under `torch.compile` + CUDA graphs — unit tests can't reach the compiled-graph path.\n", "\n", "## What this validates\n", "1. `boot_vllm` returns a `RemoteBridge` end-to-end.\n", @@ -30,8 +30,8 @@ "source": [ "## Setup\n", "\n", - "1. **Runtime \u2192 Change runtime type \u2192 GPU** (T4 / L4 / A100 all work).\n", - "2. **Secrets \u2192 add `HF_TOKEN`** with a token that has access to `meta-llama/Llama-3.2-1B` (gated).\n", + "1. **Runtime → Change runtime type → GPU** (T4 / L4 / A100 all work).\n", + "2. **Secrets → add `HF_TOKEN`** with a token that has access to `meta-llama/Llama-3.2-1B` (gated).\n", "\n", "The environment cell below patches `sys.stdout.fileno` because ipykernel's captured stdout doesn't expose a real file descriptor and vLLM's worker init calls `fileno()`. Without the patch, Step 2 fails with `UnsupportedOperation: fileno`." ], @@ -42,14 +42,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# Install vllm and TransformerLens @ feature/driver-system. ~3-5 minutes.\n", - "# vllm pinned to 0.20.2 \u2014 the version the internal-API walks in\n", - "# transformer_lens/model_bridge/sources/vllm/{plugin,internals}.py were validated against.\n", - "# vLLM rearranges its internal class paths every 4-6 weeks; re-validate before bumping.\n", - "%pip install -q \"vllm==0.20.2\"\n", - "%pip install -q git+https://github.com/TransformerLensOrg/TransformerLens.git@feature/driver-system" - ], + "source": "# Install vllm and TransformerLens @ feature/vllm-batched. ~3-5 minutes.\n# Branch must match this notebook: feature/vllm-batched has the hook-fire counter\n# (Step 7) and the batched capture surface (Step 10). feature/driver-system lacks\n# both, so collective_rpc raises NotImplementedError on tl_reset_counter / the\n# batched RPCs.\n# vllm pinned to 0.20.2 — the version the internal-API walks in\n# transformer_lens/model_bridge/sources/vllm/{plugin,internals}.py were validated against.\n# vLLM rearranges its internal class paths every 4-6 weeks; re-validate before bumping.\n%pip install -q \"vllm==0.20.2\"\n%pip install -q git+https://github.com/TransformerLensOrg/TransformerLens.git@feature/vllm-batched", "id": "4ab1eb60e6b1" }, { @@ -57,45 +50,14 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "import gc\n", - "import os\n", - "import sys\n", - "\n", - "import torch\n", - "\n", - "# HF_TOKEN comes from Colab secrets. Falls back to env var for non-Colab runs.\n", - "try:\n", - " from google.colab import userdata\n", - " os.environ.setdefault(\"HF_TOKEN\", userdata.get(\"HF_TOKEN\"))\n", - "except (ImportError, Exception):\n", - " pass\n", - "assert os.environ.get(\"HF_TOKEN\"), \"Set HF_TOKEN in Colab Secrets (gear icon, left sidebar).\"\n", - "\n", - "# Colab/Jupyter compatibility: ipykernel's stdout doesn't expose a fileno();\n", - "# vLLM's worker init calls sys.stdout.fileno() during parallel-state setup\n", - "# and crashes with UnsupportedOperation: fileno. Patch fileno to return the\n", - "# underlying process FDs (1, 2) \u2014 Colab writes back to those anyway.\n", - "if \"ipykernel\" in sys.modules:\n", - " sys.stdout.fileno = lambda: 1 # type: ignore[method-assign]\n", - " sys.stderr.fileno = lambda: 2 # type: ignore[method-assign]\n", - "\n", - "MODEL = \"meta-llama/Llama-3.2-1B\"\n", - "PROMPT = \"The quick brown fox jumps over the\"\n", - "DTYPE = torch.float16\n", - "torch.manual_seed(0)\n", - "\n", - "print(f\"CUDA available: {torch.cuda.is_available()}\")\n", - "print(f\"Device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU only \u2014 abort'}\")\n", - "assert torch.cuda.is_available(), \"GPU runtime required.\"" - ], + "source": "import gc\nimport os\nimport sys\n\nimport torch\n\n# HF_TOKEN comes from Colab secrets. Falls back to env var for non-Colab runs.\ntry:\n from google.colab import userdata\n os.environ.setdefault(\"HF_TOKEN\", userdata.get(\"HF_TOKEN\"))\nexcept (ImportError, Exception):\n pass\nassert os.environ.get(\"HF_TOKEN\"), \"Set HF_TOKEN in Colab Secrets (gear icon, left sidebar).\"\n\n# Colab/Jupyter compatibility: ipykernel's stdout doesn't expose a fileno();\n# vLLM's worker init calls sys.stdout.fileno() during parallel-state setup\n# and crashes with UnsupportedOperation: fileno. Patch fileno to return the\n# underlying process FDs (1, 2) — Colab writes back to those anyway.\nif \"ipykernel\" in sys.modules:\n sys.stdout.fileno = lambda: 1 # type: ignore[method-assign]\n sys.stderr.fileno = lambda: 2 # type: ignore[method-assign]\n\n# Read the installed vllm version from package metadata, NOT `import vllm` —\n# importing vllm loads its CUDA C extension (vllm._C), which is exactly what\n# fails with `libcudart.so.NN not found` when a newer wheel built for a CUDA\n# version Colab doesn't ship gets installed. Metadata read works regardless.\nfrom importlib.metadata import PackageNotFoundError\nfrom importlib.metadata import version as _pkg_version\n\n_PINNED_VLLM = \"0.20.2\"\ntry:\n _vllm_ver = _pkg_version(\"vllm\")\n print(f\"vllm version: {_vllm_ver}\")\n if _vllm_ver != _PINNED_VLLM:\n print(\n f\"⚠ expected vllm=={_PINNED_VLLM} (the version the capture plugin is validated \"\n f\"against); got {_vllm_ver}. Newer wheels target a CUDA the Colab image may not \"\n \"ship (→ libcudart.so error at boot) and may move vLLM internals the plugin walks. \"\n f\"Re-pin with %pip install -q 'vllm=={_PINNED_VLLM}' and restart the runtime.\"\n )\nexcept PackageNotFoundError:\n print(\"⚠ vllm is not installed — run the install cell above.\")\n\nMODEL = \"meta-llama/Llama-3.2-1B\"\nPROMPT = \"The quick brown fox jumps over the\"\nDTYPE = torch.float16\ntorch.manual_seed(0)\n\nprint(f\"CUDA available: {torch.cuda.is_available()}\")\nprint(f\"Device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU only — abort'}\")\nassert torch.cuda.is_available(), \"GPU runtime required.\"", "id": "1748b73ca8b0" }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 1 \u2014 HF reference\n", + "## Step 1 — HF reference\n", "\n", "Boot the transformers backend first, capture activations and argmax, then drop it so vLLM has the GPU to itself." ], @@ -132,7 +94,7 @@ "\n", "cache_hf_cpu = {name: t.detach().cpu().clone() for name, t in cache_hf.cache_dict.items()}\n", "next_token_hf = bridge_hf.tokenizer.decode([argmax_hf])\n", - "print(f\"HF argmax token id: {argmax_hf} \u2192 {next_token_hf!r}\")\n", + "print(f\"HF argmax token id: {argmax_hf} → {next_token_hf!r}\")\n", "print(f\"HF cache: {len(cache_hf_cpu)} entries (filtered to overlay's fireable set)\")\n", "\n", "# Move parameters to CPU before deletion to force release even if a reference\n", @@ -148,7 +110,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 2 \u2014 Boot vLLM bridge\n", + "## Step 2 — Boot vLLM bridge\n", "\n", "`boot_vllm` constructs the LLM, monkey-patches `Worker.load_model` pre-compile to install capture hooks, then wraps it in a `RemoteBridge`." ], @@ -163,7 +125,7 @@ "from transformer_lens.model_bridge.remote_bridge import RemoteBridge\n", "\n", "# max_model_len=2048 caps the KV cache reservation. Llama-3.2-1B's native\n", - "# context is 131072 (128k) \u2014 the default reservation is ~4 GiB and overshoots\n", + "# context is 131072 (128k) — the default reservation is ~4 GiB and overshoots\n", "# the free T4 budget. The test prompt is ~10 tokens, so 2048 is plenty.\n", "bridge = RemoteBridge.boot_vllm(\n", " MODEL,\n", @@ -182,9 +144,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 3 \u2014 Capture pipeline\n", + "## Step 3 — Capture pipeline\n", "\n", - "Run a single forward, populate the cache via `collective_rpc \u2192 tl_read_captures`, and sanity-check shapes." + "Run a single forward, populate the cache via `collective_rpc → tl_read_captures`, and sanity-check shapes." ], "id": "284538a2c556" }, @@ -199,7 +161,7 @@ "argmax_vllm = int(logits_vllm[0, -1].argmax().item())\n", "next_token_vllm = bridge.tokenizer.decode([argmax_vllm])\n", "\n", - "print(f\"vLLM argmax token id: {argmax_vllm} \u2192 {next_token_vllm!r}\")\n", + "print(f\"vLLM argmax token id: {argmax_vllm} → {next_token_vllm!r}\")\n", "print(f\"vLLM cache entries: {len(cache_vllm.cache_dict)}\")\n", "\n", "for name in sorted(bridge._driver.supported_hook_points)[:5]:\n", @@ -213,7 +175,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 4 \u2014 Greedy parity \n", + "## Step 4 — Greedy parity \n", "\n", "vLLM and HF must produce the same next-token argmax on the same prompt." ], @@ -226,9 +188,9 @@ "outputs": [], "source": [ "parity = argmax_vllm == argmax_hf\n", - "status = \"\u2705 PASS\" if parity else \"\u274c FAIL\"\n", - "print(f\"{status}: HF\u2192{argmax_hf} ({next_token_hf!r}) vs vLLM\u2192{argmax_vllm} ({next_token_vllm!r})\")\n", - "assert parity, \"Greedy parity failed \u2014 kernel divergence or overlay misconfiguration.\"" + "status = \"✅ PASS\" if parity else \"❌ FAIL\"\n", + "print(f\"{status}: HF→{argmax_hf} ({next_token_hf!r}) vs vLLM→{argmax_vllm} ({next_token_vllm!r})\")\n", + "assert parity, \"Greedy parity failed — kernel divergence or overlay misconfiguration.\"" ], "id": "a59824b0e3be" }, @@ -236,11 +198,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 5 \u2014 Per-hook L2 (acceptance gate)\n", + "## Step 5 — Per-hook L2 (acceptance gate)\n", "\n", "Target is relative L2 < 5e-3 in fp16 for every fireable hook. One hook remains exempted from the strict gate:\n", "\n", - "- **`ln_final.hook_normalized`** \u2014 vLLM's `model.norm` is invoked as part of the fused-residual norm kernel and the captured value scales ~2\u00d7 HF's. Open investigation (likely a residual-fusion semantic discrepancy at the model boundary rather than a real divergence; argmax + downstream parity work correctly).\n", + "- **`ln_final.hook_normalized`** — vLLM's `model.norm` is invoked as part of the fused-residual norm kernel and the captured value scales ~2× HF's. Open investigation (likely a residual-fusion semantic discrepancy at the model boundary rather than a real divergence; argmax + downstream parity work correctly).\n", "\n", "All other fireable hooks (including `blocks.{i}.hook_out`, which is now materialized from vLLM's `(mlp_delta, residual)` tuple) must be within target." ], @@ -272,21 +234,21 @@ " base = t_hf.norm().item() or 1.0\n", " rel = diff / base\n", " if rel < TARGET_REL_L2:\n", - " mark = \"\u2705\"\n", + " mark = \"✅\"\n", " note = \"\"\n", " elif name in _RESIDUAL_FUSION_DIVERGENT:\n", - " mark = \"\u26a0\"\n", + " mark = \"⚠\"\n", " note = \"residual-fusion (expected)\"\n", " else:\n", - " mark = \"\u274c\"\n", + " mark = \"❌\"\n", " note = \"\"\n", " rows.append((name, rel, mark, note))\n", "\n", "print(f\"{'hook':<40} {'rel L2':>10} status note\")\n", "print(\"-\" * 80)\n", "for name, rel, mark, note in rows:\n", - " rel_str = f\"{rel:.3e}\" if isinstance(rel, float) else \"\u2014\"\n", - " print(f\"{name:<40} {rel_str:>10} {mark or '\u2014':<6} {note}\")\n", + " rel_str = f\"{rel:.3e}\" if isinstance(rel, float) else \"—\"\n", + " print(f\"{name:<40} {rel_str:>10} {mark or '—':<6} {note}\")\n", "\n", "# Strict gate: only the non-residual-fusion hooks need to be within target.\n", "failed = [(name, rel) for name, rel, _, _ in rows\n", @@ -301,7 +263,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 6 \u2014 Intervention smoke (load-bearing)\n", + "## Step 6 — Intervention smoke (load-bearing)\n", "\n", "**This cell is the load-bearing verification for the vLLM source's mutation claim.** Unit tests only exercise the dispatch protocol (mocked LLM); they cannot prove that the affine math (`output = output * scale + bias`) traces correctly through `torch.compile` and propagates to downstream layers under CUDA-graph replay. That guarantee depends on this cell passing.\n", "\n", @@ -315,41 +277,102 @@ "metadata": {}, "outputs": [], "source": [ - "# Suppress (zero) the embedding output. Forward should behave very differently.\n", + "# (a) suppress: zero the embedding output. Multiplicative op (scale=0).\n", "logits_suppressed, cache_suppressed = bridge.run_with_cache(\n", " tokens,\n", " intervene={\"embed.hook_out\": {\"op\": \"suppress\"}},\n", ")\n", "argmax_suppressed = int(logits_suppressed[0, -1].argmax().item())\n", - "\n", - "# Embed cache should be all zeros after suppress.\n", "embed_norm = cache_suppressed[\"embed.hook_out\"].abs().max().item()\n", - "print(f\"Embed |max| after suppress: {embed_norm:.6f} (should be 0.0)\")\n", - "assert embed_norm == 0.0, \"Suppress did not zero embed.hook_out \u2014 intervention path broken.\"\n", - "\n", - "# Argmax should differ from the clean run.\n", - "argmax_shifted = argmax_suppressed != argmax_vllm\n", - "print(f\"Clean argmax: {argmax_vllm} Suppressed argmax: {argmax_suppressed} Shifted: {argmax_shifted}\")\n", + "print(f\"suppress: embed |max|={embed_norm:.6f} (→0) argmax {argmax_vllm} → {argmax_suppressed}\")\n", + "assert embed_norm == 0.0, \"suppress did not zero embed.hook_out\"\n", + "assert argmax_suppressed != argmax_vllm, \"suppress did not change the prediction\"\n", + "\n", + "# (b) set: force embed to a constant vector. Additive op (scale=0, bias=value).\n", + "d_model = bridge.cfg.d_model\n", + "set_value = [0.5] * d_model\n", + "logits_set, cache_set = bridge.run_with_cache(\n", + " tokens,\n", + " intervene={\"embed.hook_out\": {\"op\": \"set\", \"value\": set_value}},\n", + ")\n", + "set_max = cache_set[\"embed.hook_out\"].abs().max().item()\n", + "print(f\"set: embed |max|={set_max:.6f} (→0.5) argmax {argmax_vllm} → {int(logits_set[0, -1].argmax().item())}\")\n", + "assert abs(set_max - 0.5) < 1e-2, \"set did not write the constant value to embed.hook_out\"\n", "\n", - "# Verify the next forward (no intervene) reverts \u2014 interventions are not sticky.\n", + "# (c) reset: a clean forward must revert — interventions are not sticky.\n", "logits_revert, _ = bridge.run_with_cache(tokens)\n", "argmax_revert = int(logits_revert[0, -1].argmax().item())\n", - "print(f\"Revert argmax: {argmax_revert} matches clean: {argmax_revert == argmax_vllm}\")\n", - "assert argmax_revert == argmax_vllm, \"Intervention persisted across calls \u2014 reset path broken.\"" + "print(f\"reset: argmax → {argmax_revert} matches clean: {argmax_revert == argmax_vllm}\")\n", + "assert argmax_revert == argmax_vllm, \"intervention persisted across calls — reset path broken\"" ], "id": "8c94e6f538f4" }, + { + "cell_type": "markdown", + "id": "a21f3155a525", + "metadata": {}, + "source": [ + "## Step 7 — Hook fires exactly once\n", + "\n", + "A correctness gate for the capture mechanism: under `torch.compile` + CUDA graphs, each capture hook must fire exactly once per forward. A double-fire would silently overwrite the buffer (and with multi-token decode, write the wrong step's activation). The plugin increments a shared GPU counter on every hook fire; after one forward it should equal the number of installed hooks." + ] + }, + { + "cell_type": "code", + "id": "53ee0d1f31e7", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "bridge._driver._llm.collective_rpc(\"tl_reset_counter\")\n", + "bridge.run_with_cache(tokens)\n", + "fire_count = bridge._driver._llm.collective_rpc(\"tl_read_counter\")[0]\n", + "expected = len(bridge._driver.supported_hook_points)\n", + "print(f\"Hook fires: {fire_count} expected (one per hook): {expected}\")\n", + "assert fire_count == expected, (\n", + " f\"Expected {expected} fires (one per installed hook), got {fire_count} — \"\n", + " \"a hook fired more than once under compile, or some didn't fire.\"\n", + ")\n", + "print(\"✅ every capture hook fired exactly once.\")" + ] + }, + { + "cell_type": "markdown", + "id": "2795d60c55a9", + "metadata": {}, + "source": [ + "## Step 8 — Stream safety\n", + "\n", + "Capturing the same prompt repeatedly must be deterministic — back-to-back forwards through the compiled graph should produce bitwise-identical captures. Drift here would indicate a stale-buffer race or non-deterministic kernel path in the capture machinery." + ] + }, + { + "cell_type": "code", + "id": "8213e889960f", + "metadata": {}, + "execution_count": null, + "outputs": [], + "source": [ + "refs = []\n", + "for _ in range(5):\n", + " _, cache_i = bridge.run_with_cache(tokens)\n", + " refs.append(cache_i[\"embed.hook_out\"].detach().cpu().clone())\n", + "for i, c in enumerate(refs[1:], start=1):\n", + " assert torch.equal(refs[0], c), f\"Run {i} differs from run 0 — non-deterministic capture\"\n", + "print(\"✅ 5 back-to-back captures are bitwise identical.\")" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Step 7 \u2014 Lifetime\n", + "## Step 9 — Lifetime\n", "\n", "`bridge.close()` is responsible for releasing **our** resources:\n", "- detaches the per-Worker capture hooks via `tl_remove_hooks`\n", "- tears down vLLM's distributed environment (`destroy_distributed_environment`)\n", "\n", - "It cannot release **vLLM's** internal state in 0.20.2 \u2014 there's no `LLM.shutdown()` API. Model weights, KV cache pool, and Inductor compile cache stay resident in PyTorch's caching allocator until process exit. A clean Colab `Runtime \u2192 Restart session` is the only way to fully reclaim GPU memory.\n", + "It cannot release **vLLM's** internal state in 0.20.2 — there's no `LLM.shutdown()` API. Model weights, KV cache pool, and Inductor compile cache stay resident in PyTorch's caching allocator until process exit. A clean Colab `Runtime → Restart session` is the only way to fully reclaim GPU memory.\n", "\n", "What we *can* verify: `bridge.close()` ran without error and didn't leak more memory. The residual is informational, not a gate." ], @@ -371,31 +394,154 @@ "print(f\"GPU memory released: {released:.2f} GB\")\n", "print()\n", "if released > 0.05:\n", - " print(f\"\u2705 close() released ~{released:.2f} GB (hooks + capture buffers + distributed state).\")\n", + " print(f\"✅ close() released ~{released:.2f} GB (hooks + capture buffers + distributed state).\")\n", "else:\n", - " print(\"\u26a0 close() released < 50 MB \u2014 likely vLLM's model weights and KV cache pool\")\n", + " print(\"⚠ close() released < 50 MB — likely vLLM's model weights and KV cache pool\")\n", " print(\" staying resident. This is expected on vLLM 0.20.2; restart the runtime\")\n", " print(\" for a clean GPU.\")" ], "id": "51dbe16a57b2" }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Step 10 — Batched capture (`enable_batching=True`)\n\nThe default path above is compile-validated but single-prompt. The throughput\npath for SAE/probe data collection is `enable_batching=True`: `enforce_eager`\n(no torch.compile), per-request accumulation across chunked prefill, and\n`batch_size > 1`.\n\n> **⚠ Restart the runtime, and use a GPU bigger than a T4, before running this\n> section.** Two reasons:\n> 1. It boots a *second* vLLM engine, and vLLM 0.20.2 can't release the first\n> in-process (no shutdown API; weights + KV pool stay resident — see Step 9).\n> 2. The multi-prompt batched forward **deadlocked on a 16 GB T4** under memory\n> pressure; an L4/A100 ran 10a–10c clean. Larger GPUs (compute ≥ 8) also use\n> the FlashAttention backend, which exposes query offsets more directly than\n> the T4's FlashInfer fallback.\n>\n> **Runtime → Restart session**, switch to L4/A100, then run **only**: the\n> install cell, the Setup cell, and this Step 10 section (each Step 10 cell\n> re-imports what it needs — you do not need to re-run Steps 1–9).", + "id": "5ff55aa42ae6" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "# Self-contained: re-imports RemoteBridge so this section runs standalone after a\n# runtime restart (only the Setup cell needs to have run first, for MODEL/DTYPE).\nfrom transformer_lens.model_bridge.remote_bridge import RemoteBridge\n\nbridge_b = RemoteBridge.boot_vllm(\n MODEL,\n dtype=DTYPE,\n gpu_memory_utilization=0.5,\n max_model_len=2048,\n # Small cap so a long prompt below forces chunked prefill (multi-forward).\n max_num_batched_tokens=512,\n enable_batching=True,\n)\nprint(f\"Batched bridge booted (enforce_eager). Fireable hooks: {len(bridge_b._driver.supported_hook_points)}\")\nSAMPLE_HOOK = sorted(bridge_b._driver.supported_hook_points)[0]\nprint(f\"Sampling correctness on hook: {SAMPLE_HOOK!r}\")", + "id": "e81f834a7b60" + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Summary\n", + "### 10a — Batch parity\n", "\n", - "If all asserts above passed, the v4 Driver-protocol vLLM backend is sound on this architecture:\n", + "Each row's real-token activations must equal the same prompt run alone. vLLM\n", + "computes each request independently under PagedAttention, so right-padding the\n", + "batch is a pure cache-assembly artifact — real tokens are padding-independent." + ], + "id": "5f6cae085def" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "PROMPTS = [\"The capital of France is\", \"The quick brown fox jumps over the\", \"Hello\"]\n", + "batch_tokens = [bridge_b.to_tokens(p)[0].tolist() for p in PROMPTS]\n", "\n", - "- `boot_vllm` returns `RemoteBridge` end-to-end.\n", - "- `collective_rpc \u2192 tl_read_captures` populates the cache.\n", - "- Greedy argmax matches HF (greedy parity).\n", - "- Per-hook L2 < 5e-3 vs HF across every fireable hook (hook-fidelity gate).\n", - "- Affine interventions (suppress / scale / add / set) apply per-forward and reset cleanly.\n", - "- GPU lifetime is well-behaved.\n", + "_, cache_batch = bridge_b.run_with_cache(batch_tokens)\n", + "print(f\"batched cache '{SAMPLE_HOOK}' shape: {tuple(cache_batch[SAMPLE_HOOK].shape)} (batch, max_seq, d_model)\")\n", "\n", - "Next: extend to other architectures (Qwen / Mistral / Gemma) via the `DecoderOnlyOverlay`, or add `clamp` to the intervention vocabulary." + "TARGET_REL_L2 = 5e-3\n", + "for k, ids in enumerate(batch_tokens):\n", + " L = len(ids)\n", + " _, cache_single = bridge_b.run_with_cache([ids])\n", + " bt = cache_batch[SAMPLE_HOOK][k, :L].float()\n", + " st = cache_single[SAMPLE_HOOK][0, :L].float()\n", + " rel = (bt - st).norm().item() / (st.norm().item() or 1.0)\n", + " print(f\" row {k} ({L:>2} tok): rel L2 = {rel:.3e}\")\n", + " assert rel < TARGET_REL_L2, f\"row {k} batched != single (rel {rel:.3e})\"\n", + " # Pad positions past the real length must be zero.\n", + " assert torch.equal(cache_batch[SAMPLE_HOOK][k, L:], torch.zeros_like(cache_batch[SAMPLE_HOOK][k, L:]))\n", + "print(\"✅ batched rows match single-prompt runs; pad positions zeroed.\")" ], + "id": "0ea345b5b8fc" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10b — Chunked-prefill accumulation\n", + "\n", + "A prompt longer than `max_num_batched_tokens` splits across forwards. The eager\n", + "hook accumulates per-request slices and `torch.cat`s them — a single-buffer\n", + "overwrite (the compiled design) would keep only the last chunk. Assert the\n", + "captured sequence length equals the full prompt length." + ], + "id": "808190b7edfc" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "base_ids = bridge_b.to_tokens(\"The quick brown fox jumps over the lazy dog. \")[0].tolist()\n", + "long_ids = (base_ids * 60)[:600] # > max_num_batched_tokens=512 → forces chunking\n", + "print(f\"prompt length {len(long_ids)} tok > max_num_batched_tokens=512\")\n", + "\n", + "_, cache_long = bridge_b.run_with_cache([long_ids])\n", + "seq = cache_long[SAMPLE_HOOK].shape[1]\n", + "print(f\"captured seq length: {seq}\")\n", + "assert seq == len(long_ids), f\"chunked prefill lost tokens: captured {seq} of {len(long_ids)} — accumulation broken\"\n", + "assert torch.isfinite(cache_long[SAMPLE_HOOK]).all(), \"non-finite values in accumulated capture\"\n", + "print(\"✅ chunked-prefill accumulation reconstructs the full sequence.\")" + ], + "id": "c5ede5236742" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10c — Batched interventions (global)\n", + "\n", + "Interventions in batched mode are global across the batch. A `suppress` on the\n", + "embedding shifts every row's prediction; a clean batch reverts." + ], + "id": "97f65eadc7ad" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "logits_clean, _ = bridge_b.run_with_cache(batch_tokens)\n", + "clean_argmax = [int(logits_clean[k, len(ids) - 1].argmax().item()) for k, ids in enumerate(batch_tokens)]\n", + "\n", + "logits_supp, cache_supp = bridge_b.run_with_cache(\n", + " batch_tokens, intervene={\"embed.hook_out\": {\"op\": \"suppress\"}}\n", + ")\n", + "supp_argmax = [int(logits_supp[k, len(ids) - 1].argmax().item()) for k, ids in enumerate(batch_tokens)]\n", + "embed_max = cache_supp[\"embed.hook_out\"].abs().max().item()\n", + "print(f\"clean argmax: {clean_argmax}\")\n", + "print(f\"suppress argmax: {supp_argmax} embed |max|={embed_max:.6f} (→0)\")\n", + "assert embed_max == 0.0, \"suppress did not zero embed across the batch\"\n", + "assert supp_argmax != clean_argmax, \"suppress did not shift any row's prediction\"\n", + "\n", + "logits_revert, _ = bridge_b.run_with_cache(batch_tokens)\n", + "revert_argmax = [int(logits_revert[k, len(ids) - 1].argmax().item()) for k, ids in enumerate(batch_tokens)]\n", + "print(f\"revert argmax: {revert_argmax} matches clean: {revert_argmax == clean_argmax}\")\n", + "assert revert_argmax == clean_argmax, \"intervention persisted across batched calls\"\n", + "print(\"✅ batched interventions apply globally and reset cleanly.\")" + ], + "id": "b58d8362010e" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bridge_b.close()\n", + "del bridge_b, cache_batch, cache_long, logits_clean, logits_supp, cache_supp, logits_revert\n", + "gc.collect(); torch.cuda.empty_cache()\n", + "print(f\"GPU memory after batched-bridge close: {torch.cuda.memory_allocated() / 1e9:.2f} GB\")" + ], + "id": "aac4cf7b621d" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": "## Summary\n\nIf all asserts above passed, the v4 Driver-protocol vLLM backend is sound on this architecture:\n\n- `boot_vllm` returns `RemoteBridge` end-to-end.\n- `collective_rpc → tl_read_captures` populates the cache.\n- Greedy argmax matches HF (greedy parity).\n- Per-hook L2 < 5e-3 vs HF across every fireable hook (hook-fidelity gate; `ln_final.hook_normalized` exempted — see overlay docstring).\n- Affine interventions (suppress / scale / add / set) apply per-forward and reset cleanly.\n- Each hook fires exactly once per forward under compile (no double-fire / silent overwrite).\n- Back-to-back captures are deterministic (stream-safe).\n- GPU lifetime behaves as expected for vLLM 0.20.x (full release needs a runtime restart).\n- **Batched mode** (`enable_batching=True`, Step 10): `batch_size > 1` parity vs single-prompt runs, chunked-prefill accumulation reconstructs full sequences, and global interventions apply + reset across the batch.\n\nKnown divergences (documented, not bugs): `ln_final.hook_normalized` post-weight vs pre-weight, Gemma `embed.hook_out` scaling. Both reconciled by conversions noted in `sources.vllm.overlays.decoder_only`.\n\nOut of scope: multi-token generation (`max_new_tokens > 1`), per-request (non-global) batched interventions, tensor/pipeline parallelism.", "id": "fedb17c9a539" } ], @@ -417,4 +563,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/tests/unit/model_bridge/test_vllm_driver.py b/tests/unit/model_bridge/test_vllm_driver.py index 8a46a63938..964a761116 100644 --- a/tests/unit/model_bridge/test_vllm_driver.py +++ b/tests/unit/model_bridge/test_vllm_driver.py @@ -243,6 +243,188 @@ def test_forward_rejects_prompt_exceeding_buffer(self): _driver(captures={}, max_num_batched_tokens=4).forward(torch.tensor([[1, 2, 3, 4, 5]])) +def _batched_request_output(request_id, generated_token=None, top_logprobs=None): + """RequestOutput-shaped mock carrying a request_id for the accumulator join.""" + completion = MagicMock() + completion.token_ids = [generated_token] if generated_token is not None else [] + completion.logprobs = ( + [{tid: MagicMock(logprob=lp) for tid, lp in top_logprobs.items()}] + if top_logprobs is not None + else [] + ) + ro = MagicMock() + ro.request_id = request_id + ro.outputs = [completion] + return ro + + +def _batched_driver(*, outputs, captures_by_req, hf_config=None) -> VLLMDriver: + """Batched-mode VLLMDriver. ``outputs`` is the llm.generate return (in submission + order, each carrying .request_id); ``captures_by_req`` is the + tl_read_batched_captures payload keyed by req_id.""" + llm = MagicMock() + llm.generate = MagicMock(return_value=outputs) + # collective_rpc("tl_read_batched_captures")[0] is the only indexed call; the + # reset/set calls ignore the return value. + llm.collective_rpc = MagicMock(return_value=[captures_by_req]) + return VLLMDriver( + llm=llm, + adapter=_adapter(), + tokenizer=None, + overlay=_overlay(), + hf_config=hf_config or _hf_config(), + max_num_batched_tokens=2048, + enable_batching=True, + ) + + +class TestNormalizeInputIdsBatched: + """_normalize_input_ids_batched accepts tensors, flat lists, and ragged lists.""" + + def test_1d_tensor_is_single_prompt(self): + assert VLLMDriver._normalize_input_ids_batched(torch.tensor([1, 2, 3])) == [[1, 2, 3]] + + def test_2d_tensor_is_per_row(self): + assert VLLMDriver._normalize_input_ids_batched(torch.tensor([[1, 2], [3, 4]])) == [ + [1, 2], + [3, 4], + ] + + def test_flat_list_is_single_prompt(self): + assert VLLMDriver._normalize_input_ids_batched([1, 2, 3]) == [[1, 2, 3]] + + def test_ragged_list_of_lists_preserved(self): + assert VLLMDriver._normalize_input_ids_batched([[1, 2, 3], [4, 5]]) == [[1, 2, 3], [4, 5]] + + +class TestBatchedForward: + """Batched path: req_id join, right-padded assembly, per-row logit synthesis.""" + + def test_req_id_join_not_positional(self): + """Accumulator keys are out-of-order req_ids; join via outputs[k].request_id.""" + pytest.importorskip("vllm") + outputs = [ + _batched_request_output("req-A", top_logprobs={7: 2.0}), + _batched_request_output("req-B", top_logprobs={3: 2.0}), + ] + # Captures keyed by req_id, deliberately reverse insertion order. + captures_by_req = { + "req-B": {"embed.hook_out": torch.full((2, 4), 2.0)}, + "req-A": {"embed.hook_out": torch.full((3, 4), 1.0)}, + } + result = _batched_driver(outputs=outputs, captures_by_req=captures_by_req).forward( + [[1, 2, 3], [4, 5]] + ) + emb = result.captured["embed.hook_out"] + assert tuple(emb.shape) == (2, 3, 4) # (batch, max_seq, width) + # Row 0 = req-A (3 real tokens, value 1.0); row 1 = req-B (2 real, value 2.0). + assert torch.equal(emb[0, :3], torch.full((3, 4), 1.0)) + assert torch.equal(emb[1, :2], torch.full((2, 4), 2.0)) + + def test_join_strips_internal_req_id_suffix(self): + """vLLM keys the worker accumulator by f'{public}-{hash}'; RequestOutput + carries only the public id. Join must match exact-or-prefix.""" + pytest.importorskip("vllm") + outputs = [ + _batched_request_output("10", top_logprobs={7: 2.0}), + _batched_request_output("11", top_logprobs={3: 2.0}), + ] + # Worker keys carry the engine-internal "-" suffix. + captures_by_req = { + "10-83c3532c": {"embed.hook_out": torch.full((3, 4), 1.0)}, + "11-a1b2c3d4": {"embed.hook_out": torch.full((2, 4), 2.0)}, + } + result = _batched_driver(outputs=outputs, captures_by_req=captures_by_req).forward( + [[1, 2, 3], [4, 5]] + ) + emb = result.captured["embed.hook_out"] + assert tuple(emb.shape) == (2, 3, 4) + assert torch.equal(emb[0, :3], torch.full((3, 4), 1.0)) + assert torch.equal(emb[1, :2], torch.full((2, 4), 2.0)) + + def test_join_prefix_does_not_collide_on_numeric_ids(self): + """Public '1' must not match worker key '10-...'; the '-' delimiter guards it.""" + pytest.importorskip("vllm") + outputs = [ + _batched_request_output("1", top_logprobs={7: 2.0}), + _batched_request_output("10", top_logprobs={3: 2.0}), + ] + captures_by_req = { + "1-aaaaaaaa": {"embed.hook_out": torch.full((2, 4), 1.0)}, + "10-bbbbbbbb": {"embed.hook_out": torch.full((3, 4), 9.0)}, + } + result = _batched_driver(outputs=outputs, captures_by_req=captures_by_req).forward( + [[1, 2], [1, 2, 3]] + ) + emb = result.captured["embed.hook_out"] + assert torch.equal(emb[0, :2], torch.full((2, 4), 1.0)) # req "1" + assert torch.equal(emb[1, :3], torch.full((3, 4), 9.0)) # req "10" + + def test_shorter_rows_right_padded_with_zeros(self): + pytest.importorskip("vllm") + outputs = [ + _batched_request_output("r0", top_logprobs={1: 1.0}), + _batched_request_output("r1", top_logprobs={1: 1.0}), + ] + captures_by_req = { + "r0": {"embed.hook_out": torch.ones(3, 4)}, + "r1": {"embed.hook_out": torch.ones(1, 4)}, + } + result = _batched_driver(outputs=outputs, captures_by_req=captures_by_req).forward( + [[1, 2, 3], [9]] + ) + emb = result.captured["embed.hook_out"] + # Row 1 has 1 real token; positions 1,2 are zero pad. + assert torch.equal(emb[1, 1:], torch.zeros(2, 4)) + + def test_logits_at_per_row_last_token(self): + """Next-token logits land at prompt_len-1 per row, never -1 (a pad row).""" + pytest.importorskip("vllm") + outputs = [ + _batched_request_output("r0", top_logprobs={7: 5.0}), + _batched_request_output("r1", top_logprobs={3: 5.0}), + ] + captures_by_req = { + "r0": {"embed.hook_out": torch.ones(3, 4)}, + "r1": {"embed.hook_out": torch.ones(1, 4)}, + } + result = _batched_driver(outputs=outputs, captures_by_req=captures_by_req).forward( + [[1, 2, 3], [9]] + ) + logits = result.logits + assert logits is not None and tuple(logits.shape) == (2, 3, 16) + # Row 0 last token at pos 2; row 1 last token at pos 0 (not 2, which is pad). + assert int(logits[0, 2].argmax().item()) == 7 + assert int(logits[1, 0].argmax().item()) == 3 + assert torch.isinf(logits[1, 2]).all() # pad position stays -inf + + def test_resets_accumulator_before_generate(self): + """tl_reset_accumulators must fire so prior-forward chunks don't leak.""" + pytest.importorskip("vllm") + driver = _batched_driver( + outputs=[_batched_request_output("r0", top_logprobs={1: 1.0})], + captures_by_req={"r0": {"embed.hook_out": torch.ones(2, 4)}}, + ) + driver.forward([[1, 2]]) + methods = [c.args[0] for c in driver._llm.collective_rpc.call_args_list] + assert "tl_reset_accumulators" in methods + assert "tl_set_batched_interventions" in methods + + def test_batched_intervention_spec_pushed(self): + pytest.importorskip("vllm") + driver = _batched_driver( + outputs=[_batched_request_output("r0", top_logprobs={1: 1.0})], + captures_by_req={"r0": {"embed.hook_out": torch.ones(2, 4)}}, + ) + driver.forward([[1, 2]], intervene={"embed.hook_out": {"op": "suppress"}}) + calls = [c.args for c in driver._llm.collective_rpc.call_args_list] + assert any( + a[0] == "tl_set_batched_interventions" + and a[1] == ({"embed.hook_out": {"op": "suppress"}},) + for a in calls + ) + + class TestVLLMDriverThroughBridge: """End-to-end via RemoteBridge — drivers' captures flow into the HookPoint tree.""" diff --git a/tests/unit/model_bridge/test_vllm_internals.py b/tests/unit/model_bridge/test_vllm_internals.py new file mode 100644 index 0000000000..cbca9315e8 --- /dev/null +++ b/tests/unit/model_bridge/test_vllm_internals.py @@ -0,0 +1,51 @@ +"""Unit tests for the vLLM internals chokepoint. + +``segment_by_request``'s primary path reads ``model_runner.query_start_loc`` and +needs no vLLM install (backend-agnostic). The attn-metadata fallback imports +vLLM's forward context, so it's exercised on GPU via the Colab notebook instead. +""" +from __future__ import annotations + +from types import SimpleNamespace + +import numpy as np +import torch + +from transformer_lens.model_bridge.sources.vllm.internals import segment_by_request + + +def _model_runner(query_start_loc, req_ids): + return SimpleNamespace( + query_start_loc=query_start_loc, + input_batch=SimpleNamespace(req_ids=req_ids), + ) + + +class TestSegmentByRequest: + """Per-request query offsets come from model_runner.query_start_loc.""" + + def test_plain_tensor_sliced_to_num_reqs_plus_one(self): + # The buffer is padded past the active batch; only the first n+1 entries + # are this step's offsets ([0, len0, len0+len1, ...]). + mr = _model_runner(torch.tensor([0, 6, 14, 16, 99, 99]), ["21", "22", "23"]) + offsets, req_ids = segment_by_request(mr) + assert req_ids == ["21", "22", "23"] + assert offsets.tolist() == [0, 6, 14, 16] + + def test_cpu_gpu_buffer_via_cpu_accessor(self): + """vLLM's CpuGpuBuffer exposes the data tensor as ``.cpu``.""" + buf = SimpleNamespace(cpu=torch.tensor([0, 3, 5, 100])) + offsets, _ = segment_by_request(_model_runner(buf, ["a", "b"])) + assert offsets.tolist() == [0, 3, 5] + + def test_cpu_gpu_buffer_via_np_accessor(self): + """Some buffers surface a numpy view as ``.np``.""" + buf = SimpleNamespace(np=np.array([0, 2, 7, 9, 50])) + offsets, _ = segment_by_request(_model_runner(buf, ["a", "b", "c"])) + assert offsets.tolist() == [0, 2, 7, 9] + + def test_single_request(self): + mr = _model_runner(torch.tensor([0, 5]), ["only"]) + offsets, req_ids = segment_by_request(mr) + assert offsets.tolist() == [0, 5] + assert req_ids == ["only"] diff --git a/tests/unit/model_bridge/test_vllm_worker_extension.py b/tests/unit/model_bridge/test_vllm_worker_extension.py new file mode 100644 index 0000000000..adf120e4d1 --- /dev/null +++ b/tests/unit/model_bridge/test_vllm_worker_extension.py @@ -0,0 +1,155 @@ +"""Unit tests for TLWorkerExtension — the post-compile read/write RPC surface. + +These methods are pure torch + Python (no vLLM install needed). Hook installation +and the in-compile counter increment live in plugin.py and can only be exercised +on a real GPU via demos/vLLM_Bridge_Integration_Test.ipynb. +""" +from __future__ import annotations + +import torch + +from transformer_lens.model_bridge.sources.vllm.worker_extension import ( + TLWorkerExtension, + _apply_intervention, + _apply_op, +) + + +class TestFireCounter: + """tl_reset_counter / tl_read_counter operate on the shared GPU counter.""" + + def test_read_counter_reflects_value(self): + ext = TLWorkerExtension() + ext._tl_fire_counter = torch.tensor([7], dtype=torch.int64) + assert ext.tl_read_counter() == 7 + + def test_reset_counter_zeros(self): + ext = TLWorkerExtension() + ext._tl_fire_counter = torch.tensor([42], dtype=torch.int64) + ext.tl_reset_counter() + assert ext.tl_read_counter() == 0 + + def test_missing_counter_reads_zero(self): + """Before patched_load_model runs, the attribute doesn't exist yet.""" + ext = TLWorkerExtension() + assert ext.tl_read_counter() == 0 + ext.tl_reset_counter() # no-op, must not raise + + +class TestApplyIntervention: + """_apply_intervention translates spec dicts to affine buffer writes.""" + + def _buffers(self, width=4): + return torch.ones(width), torch.zeros(width) + + def test_suppress_zeros_both(self): + scale, bias = self._buffers() + _apply_intervention(scale, bias, {"op": "suppress"}) + assert torch.equal(scale, torch.zeros(4)) + assert torch.equal(bias, torch.zeros(4)) + + def test_scale_sets_factor(self): + scale, bias = self._buffers() + _apply_intervention(scale, bias, {"op": "scale", "factor": 0.5}) + assert torch.equal(scale, torch.full((4,), 0.5)) + assert torch.equal(bias, torch.zeros(4)) + + def test_add_scalar_broadcasts(self): + scale, bias = self._buffers() + _apply_intervention(scale, bias, {"op": "add", "value": 0.5}) + assert torch.equal(scale, torch.ones(4)) + assert torch.equal(bias, torch.full((4,), 0.5)) + + def test_add_vector_elementwise(self): + scale, bias = self._buffers() + vec = [1.0, 2.0, 3.0, 4.0] + _apply_intervention(scale, bias, {"op": "add", "value": vec}) + assert torch.equal(bias, torch.tensor(vec)) + + def test_set_zeros_scale(self): + scale, bias = self._buffers() + _apply_intervention(scale, bias, {"op": "set", "value": 2.0}) + assert torch.equal(scale, torch.zeros(4)) + assert torch.equal(bias, torch.full((4,), 2.0)) + + def test_value_shape_mismatch_raises(self): + import pytest + + scale, bias = self._buffers(width=4) + with pytest.raises(ValueError, match="must be a scalar or shape"): + _apply_intervention(scale, bias, {"op": "add", "value": [1.0, 2.0]}) + + +class TestApplyOp: + """_apply_op is the eager batched path's tensor-level intervention.""" + + def _t(self): + return torch.ones(3, 4) + + def test_suppress_zeros(self): + assert torch.equal(_apply_op(self._t(), {"op": "suppress"}), torch.zeros(3, 4)) + + def test_scale_multiplies(self): + assert torch.equal( + _apply_op(self._t(), {"op": "scale", "factor": 0.5}), torch.full((3, 4), 0.5) + ) + + def test_add_scalar_broadcasts(self): + assert torch.equal( + _apply_op(self._t(), {"op": "add", "value": 1.0}), torch.full((3, 4), 2.0) + ) + + def test_add_vector_elementwise(self): + out = _apply_op(self._t(), {"op": "add", "value": [0.0, 1.0, 2.0, 3.0]}) + assert torch.equal(out[0], torch.tensor([1.0, 2.0, 3.0, 4.0])) + + def test_set_replaces(self): + assert torch.equal( + _apply_op(self._t(), {"op": "set", "value": 9.0}), torch.full((3, 4), 9.0) + ) + + def test_unsupported_op_raises(self): + import pytest + + with pytest.raises(ValueError, match="Unsupported intervention op"): + _apply_op(self._t(), {"op": "clamp", "value": 1.0}) + + def test_value_shape_mismatch_raises(self): + import pytest + + with pytest.raises(ValueError, match="must be a scalar or shape"): + _apply_op(self._t(), {"op": "add", "value": [1.0, 2.0]}) + + +class TestBatchedAccumulators: + """tl_reset_accumulators / tl_read_batched_captures / tl_set_batched_interventions.""" + + def test_read_concatenates_chunks_in_order(self): + ext = TLWorkerExtension() + ext._tl_accum = { + ("req-A", "embed.hook_out"): [torch.ones(2, 4), torch.full((1, 4), 2.0)], + ("req-B", "embed.hook_out"): [torch.full((3, 4), 5.0)], + } + out = ext.tl_read_batched_captures() + # req-A's two chunks cat to (3, 4); chunk order is token order. + assert tuple(out["req-A"]["embed.hook_out"].shape) == (3, 4) + assert torch.equal(out["req-A"]["embed.hook_out"][2], torch.full((4,), 2.0)) + assert tuple(out["req-B"]["embed.hook_out"].shape) == (3, 4) + + def test_reset_clears_accumulator(self): + ext = TLWorkerExtension() + ext._tl_accum = {("r", "h"): [torch.ones(1, 4)]} + ext.tl_reset_accumulators() + assert ext.tl_read_batched_captures() == {} + + def test_set_batched_interventions_validates_op(self): + import pytest + + ext = TLWorkerExtension() + with pytest.raises(ValueError, match="Unsupported intervention op"): + ext.tl_set_batched_interventions({"h": {"op": "clamp"}}) + + def test_set_batched_interventions_stores_specs(self): + ext = TLWorkerExtension() + ext.tl_set_batched_interventions({"h": {"op": "suppress"}}) + assert ext._tl_intervention_specs == {"h": {"op": "suppress"}} diff --git a/transformer_lens/model_bridge/sources/vllm/driver.py b/transformer_lens/model_bridge/sources/vllm/driver.py index e6733c3ef1..a9fa167121 100644 --- a/transformer_lens/model_bridge/sources/vllm/driver.py +++ b/transformer_lens/model_bridge/sources/vllm/driver.py @@ -30,10 +30,12 @@ def __init__( overlay: Any, hf_config: Any, max_num_batched_tokens: int, + enable_batching: bool = False, ) -> None: super().__init__(adapter.cfg, tokenizer) self._llm = llm self._max_num_batched_tokens = max_num_batched_tokens + self._enable_batching = enable_batching self.supported_hook_points = frozenset(overlay.capture_specs(hf_config).keys()) @@ -65,6 +67,9 @@ def forward( ) intervene_specs = self._validate_interventions(intervene or {}) + if self._enable_batching: + return self._forward_batched(input_ids, intervene_specs, return_logits) + ids_list = self._normalize_input_ids(input_ids) if len(ids_list) > self._max_num_batched_tokens: # Worker buffers silently clamp on overflow — fail loud here instead. @@ -104,6 +109,111 @@ def forward( return ForwardResult(logits=logits, captured=captured, raw_output=outputs[0]) + def _forward_batched( + self, + input_ids: TensorLike, + intervene_specs: dict, + return_logits: bool, + ) -> ForwardResult: + """Eager batched path: per-request capture, right-padded to (B, S, W). + + No per-prompt length gate — chunked prefill accumulates long prompts + across forwards. Interventions are global across the batch. + """ + from vllm import SamplingParams + from vllm.inputs import TokensPrompt + + prompts_ids = self._normalize_input_ids_batched(input_ids) + prompt_lens = [len(ids) for ids in prompts_ids] + + # Reset accumulators so prior-forward chunks don't leak into the cat. + self._llm.collective_rpc("tl_reset_accumulators") + self._llm.collective_rpc("tl_reset_counter") + self._llm.collective_rpc("tl_set_batched_interventions", args=(intervene_specs,)) + + d_vocab = self.bridge_config.d_vocab + outputs = self._llm.generate( + prompts=[TokensPrompt(prompt_token_ids=ids) for ids in prompts_ids], + sampling_params=SamplingParams( + max_tokens=1, + temperature=0.0, + logprobs=d_vocab if return_logits else None, + ), + ) + + # Keyed by req_id (no guaranteed order) — _assemble_padded joins to slot + # k via outputs[k].request_id, not by position. + worker_captures = self._llm.collective_rpc("tl_read_batched_captures")[0] + captured = self._assemble_padded(outputs, worker_captures, prompt_lens) + + logits: torch.Tensor | None = None + if return_logits: + logits = self._synthesize_logits_batched(outputs, prompt_lens, d_vocab) + + return ForwardResult(logits=logits, captured=captured, raw_output=outputs) + + @staticmethod + def _assemble_padded( + outputs: list, + worker_captures: Mapping[str, Mapping[str, torch.Tensor]], + prompt_lens: list[int], + ) -> dict[str, torch.Tensor]: + """Stack per-request captures into right-padded ``(batch, max_seq, width)``. + + Pad is a cache-assembly artifact only: vLLM computes each request + independently, so real-token activations don't depend on the padding. + """ + batch = len(outputs) + max_seq = max(prompt_lens) if prompt_lens else 0 + # Worker keys are engine-internal req_ids ("10-83c3532c"); RequestOutput + # carries only the public id ("10"). Join exact-or-prefix; the "-" keeps + # "1" from matching "10-...". + worker_keys = list(worker_captures.keys()) + + def _captures_for(public_rid: str) -> Mapping[str, torch.Tensor]: + if public_rid in worker_captures: + return worker_captures[public_rid] + matches = [k for k in worker_keys if k.startswith(f"{public_rid}-")] + return worker_captures[matches[0]] if len(matches) == 1 else {} + + per_slot = [_captures_for(o.request_id) for o in outputs] + + hook_names: set[str] = set() + for caps in per_slot: + hook_names |= set(caps.keys()) + + assembled: dict[str, torch.Tensor] = {} + for name in hook_names: + sample = next(caps[name] for caps in per_slot if name in caps) + buf = torch.zeros(batch, max_seq, sample.shape[-1], dtype=sample.dtype) + for k, caps in enumerate(per_slot): + t = caps.get(name) + if t is not None: + buf[k, : t.shape[0]] = t + assembled[name] = buf + return assembled + + @staticmethod + def _synthesize_logits_batched( + outputs: list, prompt_lens: list[int], d_vocab: int + ) -> torch.Tensor: + """Build ``(batch, max_seq, d_vocab)`` logits; next-token dist at each row's + ``prompt_lens[k] - 1``, never ``-1`` (a pad position for shorter prompts).""" + batch = len(outputs) + max_seq = max(prompt_lens) if prompt_lens else 0 + logits = torch.full((batch, max_seq, d_vocab), float("-inf"), dtype=torch.float16) + for k, request_output in enumerate(outputs): + gen = request_output.outputs[0] if request_output.outputs else None + if gen is None: + continue + pos = prompt_lens[k] - 1 + if gen.logprobs: + for token_id, lp_obj in gen.logprobs[0].items(): + logits[k, pos, int(token_id)] = float(lp_obj.logprob) + elif gen.token_ids: + logits[k, pos, int(gen.token_ids[0])] = 0.0 + return logits + def close(self) -> None: # Detach hooks before dropping the LLM so they don't stay registered on # worker modules for the life of the process (long-running notebooks). @@ -205,3 +315,16 @@ def _normalize_input_ids(input_ids: Any) -> list: raise NotImplementedError("VLLMDriver supports batch_size=1 only.") ids_list = ids_list[0] return ids_list + + @staticmethod + def _normalize_input_ids_batched(input_ids: Any) -> list[list[int]]: + """Coerce to ``list[list[int]]`` (one per prompt); accepts 1-D/2-D tensor, + flat list (single prompt), or ragged list-of-lists.""" + if isinstance(input_ids, torch.Tensor): + if input_ids.dim() == 1: + return [input_ids.tolist()] + return [row.tolist() for row in input_ids] + seq = list(input_ids) + if seq and isinstance(seq[0], (list, tuple)): + return [list(row) for row in seq] + return [list(seq)] diff --git a/transformer_lens/model_bridge/sources/vllm/internals.py b/transformer_lens/model_bridge/sources/vllm/internals.py index bdabf5455a..185a5a9a4b 100644 --- a/transformer_lens/model_bridge/sources/vllm/internals.py +++ b/transformer_lens/model_bridge/sources/vllm/internals.py @@ -22,3 +22,51 @@ def extract_hf_config(llm: Any) -> Any: "Could not locate hf_config under llm.llm_engine.model_config. " "vLLM may have moved it; update extract_hf_config() to match." ) from e + + +# Cumulative per-request query offsets: FlashAttention/Triton name them +# query_start_loc, FlashInfer names them qo_indptr. Request i = rows i:i+1. +_QUERY_OFFSET_ATTRS = ("query_start_loc", "qo_indptr") + + +def _to_cpu_offsets(buf: Any) -> Any: + """Coerce a CpuGpuBuffer (``.cpu``/``.np``/``.gpu``) or tensor to a CPU tensor.""" + import torch + + for accessor in ("cpu", "np", "gpu"): + data = getattr(buf, accessor, None) + if data is not None and hasattr(data, "__len__"): + return torch.as_tensor(data) + return torch.as_tensor(buf) if hasattr(buf, "shape") else None + + +def segment_by_request(model_runner: Any) -> Any: + """Return ``(query_offsets_cpu, req_ids)``; request i = rows offsets[i]:offsets[i+1]. + + Only valid inside a forward. ``req_ids`` is row order, NOT submission order — + join on it. Reads ``model_runner.query_start_loc`` (backend-agnostic; the + runner builds it before any attention backend, whereas FlashInfer buries its + offsets in an opaque C++ wrapper), falling back to attn metadata for backends + that surface them directly. ``(None, req_ids)`` ⇒ caller single-slices. + """ + req_ids = list(model_runner.input_batch.req_ids) + n = len(req_ids) + + qsl = getattr(model_runner, "query_start_loc", None) + if qsl is not None: + offsets = _to_cpu_offsets(qsl) + if offsets is not None and len(offsets) >= n + 1: # buffer is padded to max batch + return offsets[: n + 1].detach().cpu(), req_ids + + from vllm.forward_context import get_forward_context + + attn_metadata = get_forward_context().attn_metadata + if isinstance(attn_metadata, list): # dual-batch-overlap returns a list of dicts + attn_metadata = attn_metadata[0] + if isinstance(attn_metadata, dict): + for meta in attn_metadata.values(): + for attr in _QUERY_OFFSET_ATTRS: + off = getattr(meta, attr, None) + if off is not None: + return off.detach().cpu(), req_ids + return None, req_ids diff --git a/transformer_lens/model_bridge/sources/vllm/plugin.py b/transformer_lens/model_bridge/sources/vllm/plugin.py index 02ddf9a586..b3b0dd4e2b 100644 --- a/transformer_lens/model_bridge/sources/vllm/plugin.py +++ b/transformer_lens/model_bridge/sources/vllm/plugin.py @@ -4,21 +4,24 @@ load and before ``compile_or_warm_up_model`` — the only window where hooks make it into the compiled FX graph (PyTorch #117758). -Hook body invariants under ``torch.compile``: in-place writes to a -pre-allocated GPU tensor; no ``.cpu()`` (illegal during CUDA-graph capture); -SymInt-indexed slicing only (Python ``.shape`` access forces specialization). - -Interventions ride the same hook. Each hook applies an affine transform -``output = output * scale_buf + bias_buf`` before capturing. Defaults are -``scale=ones`` / ``bias=zeros`` (identity). The driver swaps buffer contents -between forwards via ``tl_set_interventions`` — the FX graph references the -buffers, so swaps take effect on the next dispatch without recompiling. - -Memory cost: the affine transform allocates a transient output-shape tensor -per hook per forward, even in identity mode. Peak forward memory is roughly -1.5× the prior capture-only design — the caching allocator reuses the slot -but the peak pressure rises. Branching the hook to skip the affine in -identity mode would defeat the swap-via-buffer trick and break the FX graph. +Two hook flavors, selected by ``configure(enable_batching=...)``: + +* Compiled (default): in-place writes to a pre-allocated GPU tensor; no + ``.cpu()`` (illegal during CUDA-graph capture); SymInt-indexed slicing only + (Python ``.shape`` access forces specialization). Single prompt. + Interventions ride the same hook as an affine transform + ``output = output * scale_buf + bias_buf`` (defaults identity). The driver + swaps buffer contents between forwards via ``tl_set_interventions`` — the FX + graph references the buffers, so swaps take effect without recompiling. + Memory cost: the affine allocates a transient output-shape tensor per hook + per forward, even at identity — peak forward memory ~1.5× capture-only. + Branching to skip the affine would defeat the swap trick and break the graph. + +* Batched (``enable_batching=True``, runs ``enforce_eager``): the hook reads + per-request token boundaries via ``segment_by_request`` (only valid inside a + forward, untraceable under compile — hence eager), slices each request's rows + to CPU, and appends to per-(req_id, hook) accumulators across chunked-prefill + forwards. Interventions apply directly to the tensor via ``_apply_op``. """ from __future__ import annotations @@ -27,10 +30,11 @@ import torch -# Matches dot-paths like "model.layers.0", "model.layers.15" — vLLM's decoder -# layers, which return the fused-residual (mlp_delta, residual) 2-tuple. Hooks -# on these need to materialize the sum so the capture matches HF's -# blocks.{i}.hook_out semantics (full residual stream). +from .internals import segment_by_request +from .worker_extension import _apply_op + +# Decoder layers return the fused-residual (mlp_delta, residual) 2-tuple, so +# their hooks materialize the sum (see _make_capture_hook); other modules don't. _DECODER_LAYER_PATH = re.compile(r"^model\.layers\.\d+$") # Transient signal driver → worker during LLM construction. Per-Worker buffers @@ -44,11 +48,13 @@ def configure( capture_specs: Dict[str, Tuple[str, int]], max_num_batched_tokens: int, dtype: torch.dtype, + enable_batching: bool = False, ) -> None: - """Set capture specs, buffer length, and dtype before ``LLM(...)``.""" + """Set capture specs, buffer length, dtype, and hook flavor before ``LLM(...)``.""" _config["capture_specs"] = capture_specs _config["max_num_batched_tokens"] = max_num_batched_tokens _config["dtype"] = dtype + _config["enable_batching"] = enable_batching def register() -> None: @@ -77,6 +83,7 @@ def patched_load_model(self): return # not a TL-driven LLM; no hooks to install max_n = _config["max_num_batched_tokens"] dtype = _config["dtype"] + enable_batching = _config.get("enable_batching", False) device = next(self.model_runner.model.parameters()).device # Detach prior handles before reassigning — vLLM doesn't double-load @@ -87,25 +94,46 @@ def patched_load_model(self): self._tl_scale_buffers = {} self._tl_bias_buffers = {} self._tl_hook_handles = [] + # Batched-mode per-(req_id, hook) accumulators + global spec dict. + self._tl_accum = {} + self._tl_intervention_specs = {} + # Shared counter — surfaces hook double-fire under compile via tl_read_counter. + self._tl_fire_counter = torch.zeros(1, device=device, dtype=torch.int64) for canonical_name, (dot_path, width) in specs.items(): target = self.model_runner.model for seg in dot_path.split("."): target = target[int(seg)] if seg.isdigit() else getattr(target, seg) - capture_buf = torch.zeros(max_n, width, device=device, dtype=dtype) - # Affine identity at install. Driver swaps via tl_set_interventions - # to enable suppress/scale/add/set ops between forwards. - scale_buf = torch.ones(width, device=device, dtype=dtype) - bias_buf = torch.zeros(width, device=device, dtype=dtype) - self._tl_buffers[canonical_name] = capture_buf - self._tl_scale_buffers[canonical_name] = scale_buf - self._tl_bias_buffers[canonical_name] = bias_buf # Decoder layers return vLLM's (mlp_delta, residual) tuple; the # hook materializes their sum so the capture semantically matches # HF's full residual stream. Other modules use the default path. materialize = bool(_DECODER_LAYER_PATH.match(dot_path)) - handle = target.register_forward_hook( - _make_capture_hook(capture_buf, scale_buf, bias_buf, materialize=materialize) - ) + if enable_batching: + handle = target.register_forward_hook( + _make_batched_hook( + self, + canonical_name, + self._tl_fire_counter, + materialize=materialize, + ) + ) + else: + capture_buf = torch.zeros(max_n, width, device=device, dtype=dtype) + # Affine identity at install. Driver swaps via tl_set_interventions + # to enable suppress/scale/add/set ops between forwards. + scale_buf = torch.ones(width, device=device, dtype=dtype) + bias_buf = torch.zeros(width, device=device, dtype=dtype) + self._tl_buffers[canonical_name] = capture_buf + self._tl_scale_buffers[canonical_name] = scale_buf + self._tl_bias_buffers[canonical_name] = bias_buf + handle = target.register_forward_hook( + _make_capture_hook( + capture_buf, + scale_buf, + bias_buf, + self._tl_fire_counter, + materialize=materialize, + ) + ) self._tl_hook_handles.append(handle) Worker.load_model = patched_load_model @@ -116,6 +144,7 @@ def _make_capture_hook( capture_buf: torch.Tensor, scale_buf: torch.Tensor, bias_buf: torch.Tensor, + fire_counter: torch.Tensor, *, materialize: bool = False, ): @@ -127,10 +156,13 @@ def _make_capture_hook( blocks.{i}.hook_out semantics) and return ``(modified - residual, residual)`` so the next layer's input_layernorm sees the same fused sum. Mutations propagate through both the capture and the downstream graph. + + ``fire_counter`` is incremented per call for the fire-once check. """ @torch.no_grad() def hook(_module, _inputs, output): + fire_counter.add_(1) if materialize and isinstance(output, tuple) and len(output) == 2: hidden, residual = output if isinstance(hidden, torch.Tensor) and isinstance(residual, torch.Tensor): @@ -138,10 +170,8 @@ def hook(_module, _inputs, output): modified = t * scale_buf + bias_buf n = t.shape[0] capture_buf.narrow(0, 0, n).copy_(modified) - # ``(modified - residual) + residual`` reconstructs ``modified`` in - # the next layer's fused input_layernorm. Identity case is exact; - # for interventions, fp16 precision loss is bounded by one ulp at - # the residual stream's magnitude — small relative to mutation effect. + # Reconstructs ``modified`` in the next layer's fused norm: exact at + # identity, bounded fp16 error under intervention. return (modified - residual, residual) tuple_tail: tuple = () @@ -155,14 +185,71 @@ def hook(_module, _inputs, output): # Affine transform; default scale=1 / bias=0 means identity. Driver # swaps buffer contents to enable interventions. modified = t * scale_buf + bias_buf - # ``narrow`` produces an explicit-shape view ``(n, width)`` that Dynamo - # traces correctly under dynamic shapes; the ``[:n]`` getitem form gets - # erased under fake-tensor tracing and ``copy_`` then sees the full - # buffer as destination, raising "expand s72 -> max_n". + # narrow() keeps the dynamic shape; [:n] gets erased under fake-tensor + # tracing and copy_ then sees the full buffer ("expand s72 -> max_n"). n = t.shape[0] capture_buf.narrow(0, 0, n).copy_(modified) - # Preserve the input wrapping — a 1-tuple input must come back as a 1-tuple - # (``tuple_tail`` is falsy in that case, so don't gate on it). + # Gate on isinstance, not truthy tuple_tail — a 1-tuple has an empty tail. + if isinstance(output, tuple): + return (modified,) + tuple_tail + return modified + + return hook + + +def _make_batched_hook( + worker: Any, + canonical_name: str, + fire_counter: torch.Tensor, + *, + materialize: bool = False, +): + """Eager hook: intervene, then append each request's rows to ``worker._tl_accum``. + + Chunked prefill fires this once per chunk; appends are token-order for the + later cat. ``materialize`` mirrors the compiled hook (fused-residual sum). + """ + + @torch.no_grad() + def hook(_module, _inputs, output): + fire_counter.add_(1) + residual = None + if materialize and isinstance(output, tuple) and len(output) == 2: + hidden, residual = output + if isinstance(hidden, torch.Tensor) and isinstance(residual, torch.Tensor): + t = hidden + residual + else: + return None + tuple_tail: tuple = () + elif isinstance(output, tuple): + t = output[0] + tuple_tail = output[1:] + else: + t = output + tuple_tail = () + if not isinstance(t, torch.Tensor): + return None + + modified = t + for spec in getattr(worker, "_tl_intervention_specs", {}).values(): + modified = _apply_op(modified, spec) + + qsl, req_ids = segment_by_request(worker.model_runner) + accum: Dict[tuple, list] = worker._tl_accum + if qsl is None: + # No per-request boundaries available — treat the batch as one request. + req_id = req_ids[0] if req_ids else "0" + accum.setdefault((req_id, canonical_name), []).append(modified.detach().cpu()) + else: + for i, req_id in enumerate(req_ids): + start, end = int(qsl[i]), int(qsl[i + 1]) + if end <= start: + continue + chunk = modified[start:end].detach().cpu() + accum.setdefault((req_id, canonical_name), []).append(chunk) + + if residual is not None: + return (modified - residual, residual) if isinstance(output, tuple): return (modified,) + tuple_tail return modified diff --git a/transformer_lens/model_bridge/sources/vllm/source.py b/transformer_lens/model_bridge/sources/vllm/source.py index e5b7c083fe..cfafc5d63d 100644 --- a/transformer_lens/model_bridge/sources/vllm/source.py +++ b/transformer_lens/model_bridge/sources/vllm/source.py @@ -38,6 +38,7 @@ def boot_vllm( gpu_memory_utilization: float = 0.5, max_model_len: Optional[int] = None, max_num_batched_tokens: int = 2048, + enable_batching: bool = False, **vllm_kwargs: Any, ) -> RemoteBridge: """Boot a model via vLLM and wrap it in a :class:`RemoteBridge` via :class:`VLLMDriver`. @@ -73,6 +74,11 @@ def boot_vllm( model's native context (e.g. 131072 for Llama-3.2-1B) — easily 4+ GiB even on a 1B model. Pass an explicit ``max_model_len`` (e.g. ``2048`` for typical mech-interp prompts) to keep the budget on smaller GPUs. + + ``enable_batching`` switches to the eager batched path (``enforce_eager``, + ``batch_size > 1``) — the throughput path for SAE/probe data collection. + Default ``False`` keeps the compile-validated single-prompt path. Batched + caches are right-padded with zeros to the longest sequence. """ _reject_locked_overrides(vllm_kwargs) @@ -90,6 +96,7 @@ def boot_vllm( capture_specs=overlay.capture_specs(hf_config_preview), max_num_batched_tokens=max_num_batched_tokens, dtype=resolved_dtype, + enable_batching=enable_batching, ) plugin.register() @@ -109,6 +116,10 @@ def boot_vllm( from vllm import LLM + # Batched capture reads query_start_loc from the forward context, untraceable + # under torch.compile — so the batched path must run eager. + eager_kwargs: Dict[str, Any] = {"enforce_eager": True} if enable_batching else {} + llm = LLM( model=model_name, gpu_memory_utilization=gpu_memory_utilization, @@ -128,6 +139,7 @@ def boot_vllm( # too small for mech-interp). One ~512 KB buffer per call; negligible. max_logprobs=hf_config_preview.vocab_size, dtype=str(resolved_dtype).replace("torch.", "") if dtype is not None else "auto", + **eager_kwargs, **_LOCKED_KWARGS, **vllm_kwargs, ) @@ -157,6 +169,7 @@ def boot_vllm( overlay=overlay, hf_config=hf_config, max_num_batched_tokens=max_num_batched_tokens, + enable_batching=enable_batching, ) bridge = RemoteBridge(adapter=adapter, tokenizer=tokenizer, driver=driver) _log_hook_summary(model_name, architecture, driver) diff --git a/transformer_lens/model_bridge/sources/vllm/worker_extension.py b/transformer_lens/model_bridge/sources/vllm/worker_extension.py index 70a98ad39a..17a8dc1f84 100644 --- a/transformer_lens/model_bridge/sources/vllm/worker_extension.py +++ b/transformer_lens/model_bridge/sources/vllm/worker_extension.py @@ -1,11 +1,18 @@ -"""Worker extension exposed to collective_rpc for capture-buffer reads and -intervention-buffer writes. +"""Worker extension exposed to collective_rpc for capture reads and +intervention writes. Hook *installation* lives in :mod:`plugin` (must happen pre-compile). This class -only exposes the post-compile read/write surface. Buffers are per-Worker -(``self._tl_buffers`` / ``self._tl_scale_buffers`` / ``self._tl_bias_buffers``) -so concurrent ``boot_vllm`` calls don't collide. All methods prefixed ``tl_`` -to avoid colliding with vLLM ``Worker`` attributes. +only exposes the read/write surface. State is per-Worker so concurrent +``boot_vllm`` calls don't collide. All methods prefixed ``tl_`` to avoid +colliding with vLLM ``Worker`` attributes. + +Two capture modes, selected at boot: + * Compiled (default): per-hook GPU buffers + affine scale/bias swap. Single + prompt. ``tl_read_captures`` / ``tl_set_interventions``. + * Batched (``enable_batching=True``, eager): per-(req_id, hook) CPU + accumulators filled in the hook via query_start_loc segmentation; arbitrary + batch + chunked prefill. ``tl_read_batched_captures`` / + ``tl_set_batched_interventions`` / ``tl_reset_accumulators``. """ from __future__ import annotations @@ -23,6 +30,10 @@ class TLWorkerExtension: _tl_buffers: Dict[str, torch.Tensor] _tl_scale_buffers: Dict[str, torch.Tensor] _tl_bias_buffers: Dict[str, torch.Tensor] + _tl_fire_counter: torch.Tensor + # Batched-mode state (eager). + _tl_accum: Dict[tuple, List[torch.Tensor]] + _tl_intervention_specs: Dict[str, Dict[str, Any]] def tl_read_captures(self, prompt_lens: List[int]) -> Dict[str, torch.Tensor]: """Slice each capture buffer back to ``sum(prompt_lens)`` rows; CPU copies. @@ -54,6 +65,39 @@ def tl_set_interventions(self, specs: Dict[str, Dict[str, Any]]) -> None: raise KeyError(f"Unknown hook for intervention: {hook_name!r}") _apply_intervention(scale_bufs[hook_name], bias_bufs[hook_name], spec) + def tl_reset_counter(self) -> None: + """Zero the shared hook-fire counter before a forward.""" + counter = getattr(self, "_tl_fire_counter", None) + if counter is not None: + counter.zero_() + + def tl_read_counter(self) -> int: + """Total hook fires since the last reset.""" + counter = getattr(self, "_tl_fire_counter", None) + return int(counter.item()) if counter is not None else 0 + + def tl_reset_accumulators(self) -> None: + """Clear capture chunks before each generate, else prior chunks leak into the cat.""" + self._tl_accum = {} + + def tl_read_batched_captures(self) -> Dict[str, Dict[str, torch.Tensor]]: + """Cat per-request chunks into ``{req_id: {hook: (seq, width)}}`` (chunks are token-order).""" + accum: Dict[tuple, List[torch.Tensor]] = getattr(self, "_tl_accum", {}) + out: Dict[str, Dict[str, torch.Tensor]] = {} + for (req_id, name), chunks in accum.items(): + out.setdefault(req_id, {})[name] = torch.cat(chunks, dim=0) + return out + + def tl_set_batched_interventions(self, specs: Dict[str, Dict[str, Any]]) -> None: + """Store the global spec dict the eager hook reads; ``{}`` clears.""" + for spec in specs.values(): + op = spec.get("op") + if op not in SUPPORTED_OPS: + raise ValueError( + f"Unsupported intervention op: {op!r}. Supported: {sorted(SUPPORTED_OPS)}" + ) + self._tl_intervention_specs = dict(specs) + def tl_remove_hooks(self) -> None: """Detach all capture hooks and drop buffer references. Idempotent.""" for handle in getattr(self, "_tl_hook_handles", []): @@ -62,6 +106,28 @@ def tl_remove_hooks(self) -> None: self._tl_buffers = {} self._tl_scale_buffers = {} self._tl_bias_buffers = {} + self._tl_accum = {} + self._tl_intervention_specs = {} + + +def _apply_op(t: torch.Tensor, spec: Dict[str, Any]) -> torch.Tensor: + """Apply a spec to a tensor in-line (eager path; no GPU buffer to swap).""" + op = spec.get("op") + if op not in SUPPORTED_OPS: + raise ValueError(f"Unsupported intervention op: {op!r}. Supported: {sorted(SUPPORTED_OPS)}") + if op == "suppress": + return torch.zeros_like(t) + if op == "scale": + return t * float(spec["factor"]) + value = torch.as_tensor(spec["value"], device=t.device, dtype=t.dtype) + if value.ndim != 0 and value.shape != (t.shape[-1],): + raise ValueError( + f"Intervention 'value' must be a scalar or shape {(t.shape[-1],)}; " + f"got shape {tuple(value.shape)}" + ) + if op == "add": + return t + value + return torch.zeros_like(t) + value # set def _apply_intervention( From d8b471e534a3732678bb04442398318bedd35bf2 Mon Sep 17 00:00:00 2001 From: Jonah Larson Date: Thu, 28 May 2026 12:17:37 -0500 Subject: [PATCH 03/43] vLLM Driver Bugs (#1343) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Refactored `transformers` so that bridge configuration code is source-agnostic * Initial breakup of Bridge into core and bridge only components. Setup remote driver in prep * Initial setup of the vllm source Cherry-picked from 98bbb835 on feature/vllm-integration. Resolved against dev-4.x's Phase A type split: - bridge.py: kept the shim (TransformerBridge content lives in transformer_bridge.py now). - sources/transformers.py: accepted the scaffold's package restructure (transformers.py → transformers/{__init__,helpers,source}.py + _hf_format.py). - sources/transformers/source.py: re-applied Phase A's explicit TransformersDriver construction in boot(). sources/vllm/source.py's boot_vllm currently sets bridge._forward_impl, which Phase A removed. The function is dead until the next chunk lifts it onto VLLMDriver + RemoteBridge — landing as-is so the scaffold tree is on dev-4.x for incremental Phase B work. Co-Authored-By: Claude Opus 4.7 (1M context) * Revision round 1 of the vLLM system * Full vLLM integration initial completion * jupiter patch * dynamo patching * pass through batch size * add max model length to remote bridge boot * Updating config construction to properly pass through to vLLM * Wiring TLWorkerExtension * Additional testing + diagnostic * Another spot check * Remove logging * Fix issue with logit generation on hook_out * Adjust to use token_ids directly instead of logprobs * Updating Step 5 and driver bridge cleanup * Add decoder layer for materializing the residual stream * Updating tests due to changes on `dev` * Fix pip install path * Testing ln_final divergence * documenting differences in ln_final * Format cleanup * Initial batching test * Add version print * Setup batching for vllm * Additional documentation and coverage * CI Failure cleanup --------- Co-authored-by: Claude Opus 4.7 (1M context) --- tests/unit/model_bridge/test_vllm_driver.py | 80 +++++++++++++++++++ tests/unit/model_bridge/test_vllm_plugin.py | 56 +++++++++++++ .../test_vllm_worker_extension.py | 55 +++++++++++++ .../model_bridge/remote_bridge.py | 72 ++++++++++++++++- .../model_bridge/sources/_driver_base.py | 5 ++ .../model_bridge/sources/vllm/driver.py | 68 +++++++++++----- .../sources/vllm/overlays/decoder_only.py | 10 +-- .../model_bridge/sources/vllm/plugin.py | 5 +- .../model_bridge/sources/vllm/source.py | 7 ++ .../sources/vllm/worker_extension.py | 42 ++++++++-- 10 files changed, 366 insertions(+), 34 deletions(-) create mode 100644 tests/unit/model_bridge/test_vllm_plugin.py diff --git a/tests/unit/model_bridge/test_vllm_driver.py b/tests/unit/model_bridge/test_vllm_driver.py index 964a761116..f676a005d7 100644 --- a/tests/unit/model_bridge/test_vllm_driver.py +++ b/tests/unit/model_bridge/test_vllm_driver.py @@ -133,6 +133,31 @@ def test_non_fireable_expanded_per_layer(self): ) +class TestVLLMDriverConfig: + """Boot-time config: logprobs request must match boot's max_logprobs.""" + + def test_n_logprobs_uses_vocab_size(self): + # hf_config.vocab_size (16) is the request count, matching boot's + # max_logprobs — not bridge_config.d_vocab, which may be padded larger. + assert _driver()._n_logprobs == 16 + + +class TestVLLMDriverGetParam: + """get_param fetches a named worker tensor via collective_rpc.""" + + def test_returns_rpc_result(self): + driver = _driver(captures={}) + driver._llm.collective_rpc = MagicMock(return_value=[torch.ones(4)]) + out = driver.get_param("model.norm.weight") + assert torch.equal(out, torch.ones(4)) + assert driver._llm.collective_rpc.call_args.args[0] == "tl_get_param" + + def test_returns_none_when_closed(self): + driver = _driver(captures={}) + driver._llm = None + assert driver.get_param("model.norm.weight") is None + + class TestVLLMDriverForward: """forward dispatches via llm.generate and surfaces captures via ForwardResult.""" @@ -398,6 +423,23 @@ def test_logits_at_per_row_last_token(self): assert int(logits[1, 0].argmax().item()) == 3 assert torch.isinf(logits[1, 2]).all() # pad position stays -inf + def test_assemble_padded_raises_on_missing_join(self): + """A request with no matching worker key must raise, not zero-fill silently.""" + outputs = [SimpleNamespace(request_id="99")] + worker_captures = {"10-83c3532c": {"embed.hook_out": torch.ones(2, 4)}} + with pytest.raises(RuntimeError, match="Cannot join request '99'"): + VLLMDriver._assemble_padded(outputs, worker_captures, [2]) + + def test_assemble_padded_raises_on_ambiguous_join(self): + """Two worker keys sharing the public-id prefix is ambiguous — raise.""" + outputs = [SimpleNamespace(request_id="1")] + worker_captures = { + "1-aaaaaaaa": {"embed.hook_out": torch.ones(2, 4)}, + "1-bbbbbbbb": {"embed.hook_out": torch.ones(2, 4)}, + } + with pytest.raises(RuntimeError, match="found 2 key"): + VLLMDriver._assemble_padded(outputs, worker_captures, [2]) + def test_resets_accumulator_before_generate(self): """tl_reset_accumulators must fire so prior-forward chunks don't leak.""" pytest.importorskip("vllm") @@ -438,3 +480,41 @@ def test_bridge_replays_vllm_captures(self): bridge.forward(torch.tensor([[1, 2, 3]])) assert len(fired) == 1 assert tuple(fired[0].shape) == (1, 3, 4) + + def test_forward_rejects_loss_return_types(self): + """Synthesized logits are last-position-only; loss/both would be nan, so refuse. + + The guard is the first thing forward() does, so this needs no vllm install. + """ + bridge = RemoteBridge(adapter=_adapter(), tokenizer=None, driver=_driver(captures={})) + for rt in ("loss", "both"): + with pytest.raises(NotImplementedError, match="return_type"): + bridge.forward(torch.tensor([[1, 2, 3]]), return_type=rt) + + def test_run_with_hooks_rejects_bwd_hooks(self): + """No backward pass on a remote driver — bwd_hooks raise before any forward.""" + bridge = RemoteBridge(adapter=_adapter(), tokenizer=None, driver=_driver(captures={})) + with pytest.raises(NotImplementedError, match="backward"): + bridge.run_with_hooks( + torch.tensor([[1, 2, 3]]), + bwd_hooks=[("embed.hook_out", lambda a, hook: a)], + ) + + def test_context_manager_closes_engine(self): + """`with bridge:` releases the engine on exit (close() nulls the LLM).""" + driver = _driver(captures={}) + bridge = RemoteBridge(adapter=_adapter(), tokenizer=None, driver=driver) + with bridge as entered: + assert entered is bridge + assert driver._llm is None # close() ran + + def test_run_with_hooks_warns_fwd_hooks_are_read_only(self): + """A mutating fwd_hook is a no-op on a remote driver — warn loudly.""" + pytest.importorskip("vllm") + driver = _driver(captures={"embed.hook_out": torch.randn(3, 4)}) + bridge = RemoteBridge(adapter=_adapter(), tokenizer=None, driver=driver) + with pytest.warns(UserWarning, match="read-only"): + bridge.run_with_hooks( + torch.tensor([[1, 2, 3]]), + fwd_hooks=[("embed.hook_out", lambda a, hook: a)], + ) diff --git a/tests/unit/model_bridge/test_vllm_plugin.py b/tests/unit/model_bridge/test_vllm_plugin.py new file mode 100644 index 0000000000..4de4383589 --- /dev/null +++ b/tests/unit/model_bridge/test_vllm_plugin.py @@ -0,0 +1,56 @@ +"""Unit tests for the eager batched capture hook (plugin._make_batched_hook). + +The hook closure is pure torch + the segmentation helper, so it runs without a +vLLM install or GPU: a mock worker carries ``query_start_loc`` (segment_by_request's +backend-agnostic source) and the per-(req_id, hook) accumulator. +""" +from __future__ import annotations + +from types import SimpleNamespace + +import torch + +from transformer_lens.model_bridge.sources.vllm.plugin import _make_batched_hook + + +def _worker(specs): + """Single-request worker mock: query_start_loc=[0, 2], one req id.""" + return SimpleNamespace( + _tl_accum={}, + _tl_intervention_specs=specs, + model_runner=SimpleNamespace( + query_start_loc=torch.tensor([0, 2]), + input_batch=SimpleNamespace(req_ids=["r0"]), + ), + ) + + +class TestBatchedHookInterventionTargeting: + """A batched hook applies ONLY the spec keyed to its own hook name.""" + + def test_non_targeted_hook_is_unmodified(self): + # Spec targets embed; a hook belonging to blocks.0.hook_out must NOT apply it. + worker = _worker({"embed.hook_out": {"op": "suppress"}}) + counter = torch.zeros(1, dtype=torch.int64) + hook = _make_batched_hook(worker, "blocks.0.hook_out", counter) + + out = hook(None, None, torch.ones(2, 4)) + assert torch.equal(out, torch.ones(2, 4)), "spec leaked onto a non-targeted hook" + assert torch.equal(worker._tl_accum[("r0", "blocks.0.hook_out")][0], torch.ones(2, 4)) + + def test_targeted_hook_is_modified(self): + worker = _worker({"embed.hook_out": {"op": "suppress"}}) + counter = torch.zeros(1, dtype=torch.int64) + hook = _make_batched_hook(worker, "embed.hook_out", counter) + + out = hook(None, None, torch.ones(2, 4)) + assert torch.equal(out, torch.zeros(2, 4)), "suppress did not apply to its target" + assert torch.equal(worker._tl_accum[("r0", "embed.hook_out")][0], torch.zeros(2, 4)) + + def test_no_spec_leaves_output_unchanged(self): + worker = _worker({}) + counter = torch.zeros(1, dtype=torch.int64) + hook = _make_batched_hook(worker, "embed.hook_out", counter) + + out = hook(None, None, torch.ones(2, 4) * 3) + assert torch.equal(out, torch.ones(2, 4) * 3) diff --git a/tests/unit/model_bridge/test_vllm_worker_extension.py b/tests/unit/model_bridge/test_vllm_worker_extension.py index adf120e4d1..5d06ca40a6 100644 --- a/tests/unit/model_bridge/test_vllm_worker_extension.py +++ b/tests/unit/model_bridge/test_vllm_worker_extension.py @@ -6,6 +6,8 @@ """ from __future__ import annotations +from types import SimpleNamespace + import torch from transformer_lens.model_bridge.sources.vllm.worker_extension import ( @@ -15,6 +17,59 @@ ) +class TestGetParam: + """tl_get_param resolves a dotted path to a CPU tensor clone, or None.""" + + def test_reads_named_tensor(self): + ext = TLWorkerExtension() + weight = torch.ones(4) + ext.model_runner = SimpleNamespace( # type: ignore[attr-defined] + model=SimpleNamespace(norm=SimpleNamespace(weight=weight)) + ) + out = ext.tl_get_param("norm.weight") + assert torch.equal(out, weight) + assert out is not weight # cloned, not a live reference + + def test_missing_path_returns_none(self): + ext = TLWorkerExtension() + ext.model_runner = SimpleNamespace(model=SimpleNamespace()) # type: ignore[attr-defined] + assert ext.tl_get_param("norm.weight") is None + + def test_non_tensor_target_returns_none(self): + ext = TLWorkerExtension() + ext.model_runner = SimpleNamespace( # type: ignore[attr-defined] + model=SimpleNamespace(norm=SimpleNamespace()) + ) + assert ext.tl_get_param("norm") is None + + +class TestReadCapturesFiltering: + """names restricts the GPU→CPU read; None reads all.""" + + def _ext(self): + ext = TLWorkerExtension() + ext._tl_buffers = {"a": torch.ones(3, 4), "b": torch.zeros(3, 4)} + return ext + + def test_names_filters(self): + out = self._ext().tl_read_captures([2], names=["a"]) + assert set(out) == {"a"} + assert tuple(out["a"].shape) == (2, 4) + + def test_none_reads_all(self): + out = self._ext().tl_read_captures([2]) + assert set(out) == {"a", "b"} + + def test_batched_names_filters(self): + ext = TLWorkerExtension() + ext._tl_accum = { + ("r", "a"): [torch.ones(2, 4)], + ("r", "b"): [torch.zeros(2, 4)], + } + out = ext.tl_read_batched_captures(names=["a"]) + assert set(out["r"]) == {"a"} + + class TestFireCounter: """tl_reset_counter / tl_read_counter operate on the shared GPU counter.""" diff --git a/transformer_lens/model_bridge/remote_bridge.py b/transformer_lens/model_bridge/remote_bridge.py index b41fa10352..0c8cc5b25d 100644 --- a/transformer_lens/model_bridge/remote_bridge.py +++ b/transformer_lens/model_bridge/remote_bridge.py @@ -1,7 +1,8 @@ """Non-torch bridge: vLLM workers, Inspect remote providers.""" from __future__ import annotations -from typing import Any +import warnings +from typing import Any, Callable, List, Optional, Tuple, Union from transformer_lens.hook_points import HookIntrospectionMixin, HookPoint from transformer_lens.model_bridge.bridge_core import BridgeCore @@ -68,6 +69,16 @@ def forward( **kwargs: Any, ) -> Any: """Tokenize → driver.forward → replay captures → finalize per return_type.""" + if return_type in ("loss", "both") and not getattr( + self._driver, "provides_sequence_logits", True + ): + # Final-position-only logits ⇒ loss over the -inf earlier positions is nan. + raise NotImplementedError( + f"RemoteBridge does not support return_type={return_type!r} on this driver: " + "it provides next-token logits for the final position only, so loss over " + "earlier positions is undefined. Use return_type='logits' and read " + "logits[..., -1, :] (or the per-row last token in batched mode)." + ) if isinstance(input, str): assert self.tokenizer is not None, "Tokenizer must be set for string input." tokens = self.tokenizer.encode(input, return_tensors="pt") @@ -93,8 +104,67 @@ def forward( loss_per_token=loss_per_token, ) + def run_with_hooks( + self, + input: Any, + fwd_hooks: List[Tuple[Union[str, Callable], Callable]] = [], + bwd_hooks: List[Tuple[Union[str, Callable], Callable]] = [], + reset_hooks_end: bool = True, + clear_contexts: bool = False, + return_type: Optional[str] = "logits", + names_filter: Optional[Union[str, List[str], Callable[[str], bool]]] = None, + stop_at_layer: Optional[int] = None, + remove_batch_dim: bool = False, + **kwargs: Any, + ) -> Any: + """Run with hooks. Remote fwd_hooks fire post-forward on captured + activations (read-only) — they can't alter the computation, so warn; use + ``intervene=`` specs to mutate. bwd_hooks are unsupported (no backward).""" + if bwd_hooks: + raise NotImplementedError( + "RemoteBridge has no backward pass; bwd_hooks are unsupported." + ) + if fwd_hooks: + warnings.warn( + "RemoteBridge fwd_hooks fire on already-captured activations (read-only): " + "a hook that returns a modified tensor does NOT change the forward " + "computation or logits — the return is discarded. To intervene on the " + "computation, pass intervene={hook_name: {'op': ...}} to " + "forward()/run_with_cache().", + UserWarning, + stacklevel=2, + ) + return super().run_with_hooks( + input, + fwd_hooks=fwd_hooks, + bwd_hooks=bwd_hooks, + reset_hooks_end=reset_hooks_end, + clear_contexts=clear_contexts, + return_type=return_type, + names_filter=names_filter, + stop_at_layer=stop_at_layer, + remove_batch_dim=remove_batch_dim, + **kwargs, + ) + def to_tokens(self, text: Any, *args: Any, **kwargs: Any) -> Any: """Tokenize via ``self.tokenizer``. BOS/padding handling lives on :class:`TransformerBridge`.""" assert self.tokenizer is not None, "Tokenizer must be set." return self.tokenizer.encode(text, return_tensors="pt") + + def __enter__(self) -> "RemoteBridge": + """Use as a context manager so the engine is released on exit: + ``with RemoteBridge.boot_vllm(...) as bridge: ...``.""" + return self + + def __exit__(self, *exc: Any) -> None: + self.close() + + def __del__(self) -> None: + # Best-effort safety net for notebooks that drop the bridge without + # close() — repeated boot_vllm would otherwise OOM. close() is idempotent. + try: + self.close() + except Exception: + pass diff --git a/transformer_lens/model_bridge/sources/_driver_base.py b/transformer_lens/model_bridge/sources/_driver_base.py index 175d3fee24..ac3be638ff 100644 --- a/transformer_lens/model_bridge/sources/_driver_base.py +++ b/transformer_lens/model_bridge/sources/_driver_base.py @@ -29,6 +29,11 @@ class DriverBase(ABC): # Subclasses override with the capability strings they actually serve. _supported_features: frozenset[str] = frozenset() + # True if forward() returns logits for every position, so loss is computable. + # Drivers that synthesize only the final position set this False; the bridge + # then refuses return_type=loss/both rather than return nan. + provides_sequence_logits: bool = True + def __init__( self, bridge_config: TransformerBridgeConfig, diff --git a/transformer_lens/model_bridge/sources/vllm/driver.py b/transformer_lens/model_bridge/sources/vllm/driver.py index a9fa167121..cf231c3cc7 100644 --- a/transformer_lens/model_bridge/sources/vllm/driver.py +++ b/transformer_lens/model_bridge/sources/vllm/driver.py @@ -1,6 +1,7 @@ """vLLM Driver: forward dispatches via ``llm.generate``; captures via ``collective_rpc``.""" from __future__ import annotations +import gc import logging from typing import Any, Mapping @@ -21,6 +22,8 @@ class VLLMDriver(DriverBase): # vLLM owns the model in a worker — no torch surface (parameters/state_dict/grads). _supported_features = frozenset() + # Logits synthesized for the final position only (sampler bypass). + provides_sequence_logits = False def __init__( self, @@ -36,6 +39,9 @@ def __init__( self._llm = llm self._max_num_batched_tokens = max_num_batched_tokens self._enable_batching = enable_batching + # Logprobs per forward = real vocab (boot's max_logprobs). d_vocab can be + # padded larger, which vLLM would reject; the logits tensor stays d_vocab. + self._n_logprobs = int(getattr(hf_config, "vocab_size", self.bridge_config.d_vocab)) self.supported_hook_points = frozenset(overlay.capture_specs(hf_config).keys()) @@ -67,8 +73,12 @@ def forward( ) intervene_specs = self._validate_interventions(intervene or {}) + # Restrict the GPU→CPU read to these hooks (None = all). run_with_cache + # doesn't derive this from names_filter yet — only explicit forward(capture=). + names = list(capture) or None + if self._enable_batching: - return self._forward_batched(input_ids, intervene_specs, return_logits) + return self._forward_batched(input_ids, intervene_specs, return_logits, names) ids_list = self._normalize_input_ids(input_ids) if len(ids_list) > self._max_num_batched_tokens: @@ -85,21 +95,19 @@ def forward( # Push intervention state (possibly empty) before generate — this also # resets stale interventions from prior forwards. self._llm.collective_rpc("tl_set_interventions", args=(intervene_specs,)) - # Request full-vocab logprobs so the driver can populate position -1 of - # the synthesized logits with the real next-token distribution. vLLM's - # ``max_logprobs`` was set to d_vocab at boot to make this legal. + # Full-vocab logprobs → position -1 of the synthesized logits (see _n_logprobs). outputs = self._llm.generate( prompts=[TokensPrompt(prompt_token_ids=ids_list)], sampling_params=SamplingParams( max_tokens=int(max_new_tokens), temperature=0.0, - logprobs=self.bridge_config.d_vocab if return_logits else None, + logprobs=self._n_logprobs if return_logits else None, ), ) n_tokens = len(ids_list) # collective_rpc returns one result per worker; single-rank, so [0]. - worker_captures = self._llm.collective_rpc("tl_read_captures", args=([n_tokens],))[0] + worker_captures = self._llm.collective_rpc("tl_read_captures", args=([n_tokens], names))[0] # Add batch dim: vLLM hands back (n_tokens, width); bridge expects (1, n_tokens, width). captured = {name: t.unsqueeze(0) for name, t in worker_captures.items()} @@ -114,11 +122,13 @@ def _forward_batched( input_ids: TensorLike, intervene_specs: dict, return_logits: bool, + names: list[str] | None = None, ) -> ForwardResult: """Eager batched path: per-request capture, right-padded to (B, S, W). No per-prompt length gate — chunked prefill accumulates long prompts - across forwards. Interventions are global across the batch. + across forwards. Interventions are global across the batch. ``names`` + restricts the returned hooks (``None`` = all). """ from vllm import SamplingParams from vllm.inputs import TokensPrompt @@ -137,13 +147,13 @@ def _forward_batched( sampling_params=SamplingParams( max_tokens=1, temperature=0.0, - logprobs=d_vocab if return_logits else None, + logprobs=self._n_logprobs if return_logits else None, ), ) # Keyed by req_id (no guaranteed order) — _assemble_padded joins to slot # k via outputs[k].request_id, not by position. - worker_captures = self._llm.collective_rpc("tl_read_batched_captures")[0] + worker_captures = self._llm.collective_rpc("tl_read_batched_captures", args=(names,))[0] captured = self._assemble_padded(outputs, worker_captures, prompt_lens) logits: torch.Tensor | None = None @@ -174,7 +184,16 @@ def _captures_for(public_rid: str) -> Mapping[str, torch.Tensor]: if public_rid in worker_captures: return worker_captures[public_rid] matches = [k for k in worker_keys if k.startswith(f"{public_rid}-")] - return worker_captures[matches[0]] if len(matches) == 1 else {} + # Raise, never silently zero-fill the row: a missing or ambiguous join + # is indistinguishable from a genuine zero activation, which is silent + # data loss on the collection path. + if len(matches) != 1: + raise RuntimeError( + f"Cannot join request {public_rid!r} to worker captures: found " + f"{len(matches)} key(s) in {worker_keys}. Expected exactly one " + f"(exact or '{public_rid}-')." + ) + return worker_captures[matches[0]] per_slot = [_captures_for(o.request_id) for o in outputs] @@ -214,6 +233,14 @@ def _synthesize_logits_batched( logits[k, pos, int(gen.token_ids[0])] = 0.0 return logits + def get_param(self, dotted_name: str) -> torch.Tensor | None: + """Fetch a named worker tensor (e.g. ``model.norm.weight``) for conversions + the bridge can't otherwise do (ln_final post→pre-weight; see the overlay). + None if closed or the path is missing.""" + if self._llm is None: + return None + return self._llm.collective_rpc("tl_get_param", args=(dotted_name,))[0] + def close(self) -> None: # Detach hooks before dropping the LLM so they don't stay registered on # worker modules for the life of the process (long-running notebooks). @@ -229,9 +256,7 @@ def close(self) -> None: # vLLM 0.20.2 has no LLM.shutdown() — model weights and KV cache stay # resident until process exit unless we explicitly tear down the # distributed environment vLLM set up at construction. Both calls are - # best-effort: they're no-ops if there's no distributed state. After - # this, the caller still needs gc.collect() + torch.cuda.empty_cache() - # to drop PyTorch's caching allocator entries. + # best-effort: they're no-ops if there's no distributed state. try: from vllm.distributed.parallel_state import ( destroy_distributed_environment, @@ -242,17 +267,22 @@ def close(self) -> None: destroy_distributed_environment() except Exception as e: log.debug("vLLM distributed teardown failed during close(): %s", e) + # Free the caching allocator's blocks here so the caller doesn't have to. + gc.collect() + try: + if torch.cuda.is_available(): + torch.cuda.empty_cache() + except Exception as e: + log.debug("torch.cuda.empty_cache failed during close(): %s", e) @staticmethod def _synthesize_logits(request_output: Any, n_tokens: int, d_vocab: int) -> torch.Tensor: """Build a (1, n_tokens, d_vocab) logits-like tensor from vLLM's sampler output. - vLLM's lm_head bypass means our hook never fires; the sampler returns - full-vocab logprobs (we set ``max_logprobs=d_vocab`` at boot to allow this). - Position -1 — the input's last token, = next-token prediction — is populated - from those logprobs. Earlier positions stay at ``-inf`` so any argmax there - is loud rather than silently misleading; populating them would need - ``prompt_logprobs`` requested per call (much more expensive). + Values are **log-probs**, not raw logits (vLLM returns log_softmax): fine + for argmax/next-token, wrong for absolute scale (temperature, logit-lens). + lm_head is bypassed so only position -1 is filled (next-token); earlier + positions stay -inf — populating them needs prompt_logprobs per call. """ logits = torch.full((1, n_tokens, d_vocab), float("-inf"), dtype=torch.float16) gen = request_output.outputs[0] if request_output.outputs else None diff --git a/transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py b/transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py index fbed07c8c4..0ce38efb37 100644 --- a/transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py +++ b/transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py @@ -15,11 +15,11 @@ separately (fused-residual). The plugin's hook materializes the sum so the captured value matches HF's "post-MLP residual stream". - ``ln_final.hook_normalized``: vLLM exposes ``x * rsqrt(var+eps) * weight``; - HF/HT exposes the pre-weight value. Divide the vLLM capture by - ``model.norm.weight`` (or ``1 + weight`` for Gemma) for HT-equivalent output. - -Non-decoder-only architectures (Mamba SSM, T5 encoder-decoder, BERT, MoE -per-expert) break the convention and would need their own overlays. + HF/HT exposes the pre-weight value. They are NOT auto-converted — the cache + carries vLLM's post-weight value under this name, so a direct diff against + ``boot_transformers`` will mismatch here. To convert, fetch the weight via + ``bridge._driver.get_param("model.norm.weight")`` and divide the capture by it + (or by ``1 + weight`` for Gemma). """ from __future__ import annotations diff --git a/transformer_lens/model_bridge/sources/vllm/plugin.py b/transformer_lens/model_bridge/sources/vllm/plugin.py index b3b0dd4e2b..d2ee620b9b 100644 --- a/transformer_lens/model_bridge/sources/vllm/plugin.py +++ b/transformer_lens/model_bridge/sources/vllm/plugin.py @@ -230,8 +230,11 @@ def hook(_module, _inputs, output): if not isinstance(t, torch.Tensor): return None + # Only this hook's spec — keyed by name like the compiled per-hook buffers. + # Iterating all specs would apply every intervention to every hook. modified = t - for spec in getattr(worker, "_tl_intervention_specs", {}).values(): + spec = getattr(worker, "_tl_intervention_specs", {}).get(canonical_name) + if spec is not None: modified = _apply_op(modified, spec) qsl, req_ids = segment_by_request(worker.model_runner) diff --git a/transformer_lens/model_bridge/sources/vllm/source.py b/transformer_lens/model_bridge/sources/vllm/source.py index cfafc5d63d..bb6d03d501 100644 --- a/transformer_lens/model_bridge/sources/vllm/source.py +++ b/transformer_lens/model_bridge/sources/vllm/source.py @@ -62,6 +62,13 @@ def boot_vllm( :mod:`transformer_lens.model_bridge.sources.vllm.overlays.decoder_only` for which hooks diverge and the conversion to apply for HT-equivalent values. + **Returned logits are log-probs, not raw logits.** vLLM bypasses ``lm_head``; + the driver fills the final position from the sampler's ``log_softmax`` output. + Correct for argmax / next-token, but absolute scale is off — temperature + scaling and logit-lens magnitude analysis will be wrong. Only the final + position is populated (earlier positions are ``-inf``); ``return_type`` in + ``{"loss", "both"}`` is rejected for that reason. + GPU memory cost: each capture buffer is ``max_num_batched_tokens × width`` at the model's dtype. For Llama-3.2-1B at fp16 with ``max_num_batched_tokens=2048``, the unembed buffer alone is ~525 MB (2048 × 128256 × 2 bytes); residual-stream diff --git a/transformer_lens/model_bridge/sources/vllm/worker_extension.py b/transformer_lens/model_bridge/sources/vllm/worker_extension.py index 17a8dc1f84..a462e826fd 100644 --- a/transformer_lens/model_bridge/sources/vllm/worker_extension.py +++ b/transformer_lens/model_bridge/sources/vllm/worker_extension.py @@ -16,7 +16,7 @@ """ from __future__ import annotations -from typing import Any, Dict, List +from typing import Any, Dict, List, Optional import torch @@ -35,15 +35,19 @@ class TLWorkerExtension: _tl_accum: Dict[tuple, List[torch.Tensor]] _tl_intervention_specs: Dict[str, Dict[str, Any]] - def tl_read_captures(self, prompt_lens: List[int]) -> Dict[str, torch.Tensor]: - """Slice each capture buffer back to ``sum(prompt_lens)`` rows; CPU copies. + def tl_read_captures( + self, prompt_lens: List[int], names: Optional[List[str]] = None + ) -> Dict[str, torch.Tensor]: + """Slice each capture buffer to ``sum(prompt_lens)`` rows; CPU copies. - Caller (VLLMDriver.forward) gates ``sum(prompt_lens) <= max_num_batched_tokens`` - before the RPC, so ``total`` is always within buffer bounds. + ``names`` restricts the read (``None`` = all) — this is the only GPU→CPU + crossing, so it's where a names_filtered run saves bandwidth. Caller gates + ``sum(prompt_lens) <= max_num_batched_tokens``, so ``total`` is in bounds. """ total = sum(prompt_lens) buffers: Dict[str, torch.Tensor] = getattr(self, "_tl_buffers", {}) - return {name: buf[:total].detach().cpu().clone() for name, buf in buffers.items()} + wanted = buffers.keys() if names is None else [n for n in names if n in buffers] + return {name: buffers[name][:total].detach().cpu().clone() for name in wanted} def tl_set_interventions(self, specs: Dict[str, Dict[str, Any]]) -> None: """Reset all affine buffers to identity, then apply each spec. @@ -65,6 +69,19 @@ def tl_set_interventions(self, specs: Dict[str, Dict[str, Any]]) -> None: raise KeyError(f"Unknown hook for intervention: {hook_name!r}") _apply_intervention(scale_bufs[hook_name], bias_bufs[hook_name], spec) + def tl_get_param(self, dotted_name: str) -> Optional[torch.Tensor]: + """Read a named model tensor (e.g. ``model.norm.weight``) as a CPU clone. + + ``None`` if the path doesn't resolve to a tensor. The bridge has no general + weight surface, so this is how callers reach e.g. the ln_final weight. + """ + target = getattr(self, "model_runner").model + for seg in dotted_name.split("."): + target = target[int(seg)] if seg.isdigit() else getattr(target, seg, None) + if target is None: + return None + return target.detach().cpu().clone() if isinstance(target, torch.Tensor) else None + def tl_reset_counter(self) -> None: """Zero the shared hook-fire counter before a forward.""" counter = getattr(self, "_tl_fire_counter", None) @@ -80,11 +97,20 @@ def tl_reset_accumulators(self) -> None: """Clear capture chunks before each generate, else prior chunks leak into the cat.""" self._tl_accum = {} - def tl_read_batched_captures(self) -> Dict[str, Dict[str, torch.Tensor]]: - """Cat per-request chunks into ``{req_id: {hook: (seq, width)}}`` (chunks are token-order).""" + def tl_read_batched_captures( + self, names: Optional[List[str]] = None + ) -> Dict[str, Dict[str, torch.Tensor]]: + """Cat per-request chunks into ``{req_id: {hook: (seq, width)}}`` (token-order). + + ``names`` restricts to those hooks (``None`` = all). Note the per-chunk + GPU→CPU copy already happened in the hook, so this only saves the cat. + """ accum: Dict[tuple, List[torch.Tensor]] = getattr(self, "_tl_accum", {}) + nameset = None if names is None else set(names) out: Dict[str, Dict[str, torch.Tensor]] = {} for (req_id, name), chunks in accum.items(): + if nameset is not None and name not in nameset: + continue out.setdefault(req_id, {})[name] = torch.cat(chunks, dim=0) return out From 59a1a97819a24e903ebc8587aa102cd7a482ba33 Mon Sep 17 00:00:00 2001 From: jlarson4 Date: Fri, 29 May 2026 18:05:55 -0500 Subject: [PATCH 04/43] Pass through bridge changes --- transformer_lens/model_bridge/bridge_core.py | 78 +++++----- .../model_bridge/transformer_bridge.py | 137 ++++++++++++++++-- 2 files changed, 164 insertions(+), 51 deletions(-) diff --git a/transformer_lens/model_bridge/bridge_core.py b/transformer_lens/model_bridge/bridge_core.py index 301432dbfe..96f30b94a5 100644 --- a/transformer_lens/model_bridge/bridge_core.py +++ b/transformer_lens/model_bridge/bridge_core.py @@ -421,6 +421,20 @@ def add_hook( else: raise AttributeError(f"Hook point '{hook_name}' not found on component") + def add_perma_hook( + self, + name: Union[str, Callable[[str], bool]], + hook_fn: Callable, + dir: Literal["fwd", "bwd"] = "fwd", + ) -> None: + """Add a permanent hook that survives ``reset_hooks()`` calls. + + Convenience wrapper for ``add_hook(..., is_permanent=True)``. To remove, + call ``reset_hooks(including_permanent=True)`` or remove from the + underlying ``HookPoint`` directly. + """ + self.add_hook(name, hook_fn, dir=dir, is_permanent=True) + def reset_hooks(self, clear_contexts: bool = True) -> None: """Remove all hooks. Registry is canonical; nn.Module children walked additively.""" for hp in self._hook_registry.values(): @@ -455,7 +469,7 @@ def hooks( @contextmanager def _hooks_context() -> Iterator["BridgeCore"]: - added_hooks: List[Tuple[HookPoint, str]] = [] + added_hooks: List[Tuple[HookPoint, Literal["fwd", "bwd"]]] = [] def add_hook_to_point( hook_point: HookPoint, @@ -471,7 +485,7 @@ def add_hook_to_point( hook_point.add_hook(hook_fn, dir=dir, alias_names=alias_names_list) else: hook_point.add_hook(hook_fn, dir=dir) - added_hooks.append((hook_point, name)) + added_hooks.append((hook_point, dir)) def apply_hooks(hook_list: List[Tuple[Any, Callable]], is_fwd: bool) -> None: direction: Literal["fwd", "bwd"] = "fwd" if is_fwd else "bwd" @@ -504,8 +518,8 @@ def apply_hooks(hook_list: List[Tuple[Any, Callable]], is_fwd: bool) -> None: yield self finally: if reset_hooks_end: - for hook_point, _ in added_hooks: - hook_point.remove_hooks() + for hook_point, direction in added_hooks: + hook_point.remove_hooks(dir=direction) return _hooks_context() @@ -530,7 +544,7 @@ def run_with_hooks( (KV cache cleaned up on stop). ``remove_batch_dim`` squeezes/unsqueezes the batch dim around hook callbacks (batch_size==1 only). """ - added_hooks: List[Tuple[HookPoint, str]] = [] + added_hooks: List[Tuple[HookPoint, Literal["fwd", "bwd"]]] = [] effective_stop_layer = None if stop_at_layer is not None and hasattr(self, "blocks"): if stop_at_layer < 0: @@ -559,7 +573,7 @@ def add_hook_to_point( hook_point.add_hook(hook_fn, dir=dir, alias_names=alias_names_list) else: hook_point.add_hook(hook_fn, dir=dir) - added_hooks.append((hook_point, name)) + added_hooks.append((hook_point, dir)) if stop_at_layer is not None and hasattr(self, "blocks"): if stop_at_layer < 0: @@ -629,8 +643,8 @@ def wrapped_hook_fn(tensor, hook, _orig_fn=original_hook_fn): return output finally: if reset_hooks_end: - for hook_point, _ in added_hooks: - hook_point.remove_hooks() + for hook_point, direction in added_hooks: + hook_point.remove_hooks(dir=direction) # ---- high-level execution: run_with_cache ---- @@ -668,6 +682,8 @@ def run_with_cache( """Run the model and cache activations. Returns ``(output, cache)``. ``stop_at_layer`` raises :class:`StopAtLayerException` to stop early. + ``device`` offloads cached activations (matches ``ActivationCache.to``); the + model and inputs stay where the caller put them. """ aliases = build_alias_to_canonical_map(self.hook_dict) @@ -776,37 +792,19 @@ def stop_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: hook_dict[block_hook_name].add_hook(stop_hook) hooks.append((hook_dict[block_hook_name], block_hook_name)) filtered_kwargs = kwargs.copy() - if cache_device is not None: - if getattr(self.cfg, "n_devices", 1) > 1: - # Moving a dispatched model to a single device collapses accelerate's - # split and breaks its routing hooks. The cache will stay spread across - # the per-layer devices; callers can .to(cache_device) on cache entries - # after the fact if they need a single-device cache. - warnings.warn( - f"run_with_cache(device={cache_device!r}) ignored: model is dispatched " - f"across {self.cfg.n_devices} devices via device_map. Cached activations " - "will remain on their per-layer devices.", - stacklevel=2, - ) - else: - try: - # original_model / its setter exist on TransformerBridge; on - # non-torch bridges the property raises AttributeError. - underlying = getattr(self, "original_model") - setattr(self, "original_model", underlying.to(cache_device)) - except AttributeError: - # Non-torch driver: cache_device doesn't apply to the model. - warnings.warn( - f"run_with_cache(device={cache_device!r}) ignored: driver does not " - "expose a local model. Cached activations will stay on driver-managed " - "devices.", - stacklevel=2, - ) - if processed_args and isinstance(processed_args[0], torch.Tensor): - processed_args = [processed_args[0].to(cache_device)] + list(processed_args[1:]) - for key, value in filtered_kwargs.items(): - if isinstance(value, torch.Tensor): - filtered_kwargs[key] = value.to(cache_device) + # ``cache_device`` is honored by ``make_cache_hook`` above (``tensor.detach().to(cache_device)``); + # the model and inputs stay where the caller put them, matching ``ActivationCache.to``. + if cache_device is not None and getattr(self.cfg, "n_devices", 1) > 1: + # Moving a dispatched model to a single device collapses accelerate's + # split and breaks its routing hooks. The cache will stay spread across + # the per-layer devices; callers can .to(cache_device) on cache entries + # after the fact if they need a single-device cache. + warnings.warn( + f"run_with_cache(device={cache_device!r}) ignored: model is dispatched " + f"across {self.cfg.n_devices} devices via device_map. Cached activations " + "will remain on their per-layer devices.", + stacklevel=2, + ) try: if "output_attentions" not in filtered_kwargs: filtered_kwargs["output_attentions"] = True @@ -827,7 +825,7 @@ def stop_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: raise e finally: for hp, _ in hooks: - hp.remove_hooks() + hp.remove_hooks(dir="fwd") if self.compatibility_mode == True: reverse_aliases = {} for old_name, new_name in aliases.items(): diff --git a/transformer_lens/model_bridge/transformer_bridge.py b/transformer_lens/model_bridge/transformer_bridge.py index ae3830a911..138c8d7d75 100644 --- a/transformer_lens/model_bridge/transformer_bridge.py +++ b/transformer_lens/model_bridge/transformer_bridge.py @@ -28,6 +28,7 @@ from torch import nn from transformer_lens import utilities as utils +from transformer_lens.ActivationCache import ActivationCache from transformer_lens.FactoredMatrix import FactoredMatrix from transformer_lens.hook_points import HookIntrospectionMixin, HookPoint from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter @@ -504,6 +505,14 @@ def enable_compatibility_mode( post-processed coordinate system: logit lens, direct logit attribution, residual-stream norms. Also enables legacy hook/component name aliases. + Hook semantic parity (issue #1317): ``hook_q_input``, ``hook_k_input``, + ``hook_v_input``, ``hook_attn_in``, and ``hook_mlp_in`` fire on the + pre-norm residual. Carve-outs: post-norm architectures (OLMo 2, + BERT-style) read the post-attention residual instead, and MLA blocks + (DeepSeek V2/V3/R1) do not expose the split-qkv aliases. ``hook_mlp_in`` + is gated on ``cfg.use_hook_mlp_in``; toggle it via + :py:meth:`set_use_hook_mlp_in`. + Args: disable_warnings: Whether to disable warnings about legacy components/hooks no_processing: Whether to disable ALL pre-processing steps of the model. @@ -532,6 +541,11 @@ def set_compatibility_mode(component: Any) -> None: apply_fn_to_all_components(self, set_compatibility_mode) self.clear_hook_registry() + # Drop pre-ln capture handles from any prior call so they don't accumulate. + if hasattr(self, "blocks"): + for block in self.blocks: + if hasattr(block, "_teardown_pre_ln_capture"): + block._teardown_pre_ln_capture() try: if not no_processing: self.process_weights( @@ -1739,10 +1753,30 @@ def _generate_tokens( forward_kwargs["use_cache"] = True if _hf_kv_cache is not None: forward_kwargs["past_key_values"] = _hf_kv_cache + # HF v5 + macOS-arm64 NaNs when these are inferred + # from cache state alone. Mirror HF generate(): pass + # both an (batch, total_len) attention_mask and a + # (batch, 1) position_ids for the new token. + batch_size = current_tokens.shape[0] + total_len = current_tokens.shape[1] + device = current_tokens.device + if "attention_mask" not in forward_kwargs: + forward_kwargs["attention_mask"] = torch.ones( + (batch_size, total_len), + dtype=torch.long, + device=device, + ) if "position_ids" in forward_kwargs: forward_kwargs["position_ids"] = forward_kwargs["position_ids"][ :, -1: ] + else: + forward_kwargs["position_ids"] = torch.full( + (batch_size, 1), + total_len - 1, + dtype=torch.long, + device=device, + ) logits = self( current_tokens[:, -1:], return_type="logits", @@ -1838,9 +1872,14 @@ def generate( return_type: Optional[str] = "input", verbose: bool = True, output_logits: bool = False, + return_cache: bool = False, + names_filter: Optional[Union[str, List[str], Callable[[str], bool]]] = None, + device: Optional[Union[str, torch.device]] = None, pixel_values: Optional[torch.Tensor] = None, **multimodal_kwargs, - ) -> str | list[str] | torch.Tensor | Any: # Any for transformers.utils.ModelOutput + ) -> ( + str | list[str] | torch.Tensor | Any | tuple[Any, ActivationCache] + ): # Any for transformers.utils.ModelOutput # Any: beartype forward ref limitation (beartype#546) """Sample tokens from the model. @@ -1870,6 +1909,18 @@ def generate( return_type: The type of output to return - 'input', 'str', or 'tokens' verbose: Not used in Bridge (kept for API compatibility) output_logits: If True, return a ModelOutput with sequences and logits tuple + return_cache: If True, also return an ActivationCache for the full prompt + + generated sequence, identical to ``run_with_cache(output)``, and the call + returns an ``(output, cache)`` tuple. Implemented as one extra clean forward + over the output, so the cache includes every hook point (attention patterns + included). Supported only for single-sequence, decoder-only text generation; + encoder-decoder, SSM, multimodal, batched, and inputs_embeds inputs raise + NotImplementedError. The cache spans prompt + max_new_tokens and can be large, + use ``names_filter`` to scope it and/or ``device`` to offload it. + names_filter: Passed to ``run_with_cache`` when ``return_cache=True``; restricts + which activations are cached (str, list of str, or callable). + device: Passed through when ``return_cache=True`` to offload the cached tensors + to this device (e.g. "cpu") to save accelerator memory. pixel_values: Optional image tensor for multimodal models. Only passed on the first generation step (the vision encoder processes the image once, then embeddings are part of the token sequence for subsequent steps). @@ -1877,6 +1928,13 @@ def generate( Returns: Generated sequence as string, list of strings, or tensor depending on input type and return_type. If output_logits=True, returns a ModelOutput-like object with 'sequences' and 'logits' attributes. + If return_cache=True, returns an ``(output, ActivationCache)`` tuple where ``output`` is the + value that would otherwise be returned and the cache equals ``run_with_cache(output)``. + + Example: + ``out, cache = model.generate(prompt, max_new_tokens=20, return_cache=True)`` returns a + normal ActivationCache over the full prompt + generated sequence (equivalent to + ``run_with_cache(out)``). """ # prepend_bos is intentionally not applied during generation. # The HF model expects tokens in its native format. Overriding BOS can silently @@ -1970,6 +2028,37 @@ def generate( self.original_model.config, "is_encoder_decoder", False ) + # return_cache recomputes run_with_cache on the generated output (see issue #697). + # That is well-defined only for single-sequence, decoder-only text generation, so + # reject the paths whose cache would be wrong/undefined, with a clear pointer to the + # run_with_cache workaround. Fail fast here, before any generation work. + if return_cache: + if is_encoder_decoder: + raise NotImplementedError( + "generate(return_cache=True) is not supported for encoder-decoder " + "models yet. Run run_with_cache on the generated output instead." + ) + if is_stateful_model: + raise NotImplementedError( + "generate(return_cache=True) is not supported for stateful/SSM models " + "(e.g. Mamba); they do not expose standard transformer hook points." + ) + if pixel_values is not None or multimodal_kwargs: + raise NotImplementedError( + "generate(return_cache=True) is not supported for multimodal generation " + "yet. Run run_with_cache on the generated output instead." + ) + if _generate_from_embeds: + raise NotImplementedError( + "generate(return_cache=True) requires token input, not inputs_embeds." + ) + if batch_size > 1: + raise NotImplementedError( + "generate(return_cache=True) is not supported for batched/multi-prompt " + "generation yet. Pass a single prompt, or run run_with_cache on each " + "output sequence." + ) + # HF cache flows opaquely through the component chain via # _reconstruct_attention() → _update_kv_cache() on each layer. _hf_kv_cache = None @@ -2093,7 +2182,8 @@ def generate( else: output_tokens = torch.cat([input_tokens, sampled_tokens], dim=1) - # Return ModelOutput if output_logits was requested + # Build the formatted output (shape unchanged: ModelOutput / str / list[str] / tokens). + result: Any if output_logits and logits_seq_list is not None: from transformers.utils import ModelOutput # type: ignore @@ -2105,9 +2195,9 @@ def _logits_to_tuple(logits_list: list[torch.Tensor]) -> tuple[torch.Tensor, ... try: from transformers.generation.utils import GenerateDecoderOnlyOutput - # Return a HF-compatible ModelOutput structure + # HF-compatible ModelOutput structure. # GenerateDecoderOnlyOutput expects: sequences, scores (optional), logits (optional) - return GenerateDecoderOnlyOutput( + result = GenerateDecoderOnlyOutput( sequences=cast(torch.LongTensor, output_tokens), # HF's type hint says tuple[FloatTensor] but should be tuple[FloatTensor, ...] # (variable-length tuple with one element per generated token) @@ -2115,24 +2205,32 @@ def _logits_to_tuple(logits_list: list[torch.Tensor]) -> tuple[torch.Tensor, ... ) except (ImportError, AttributeError): # Fallback if GenerateDecoderOnlyOutput not available in this transformers version - return ModelOutput( + result = ModelOutput( sequences=output_tokens, logits=_logits_to_tuple(logits_seq_list), ) - - # Format output - if return_type == "str": + elif return_type == "str": assert self.tokenizer is not None if input_type == "str": - return self.tokenizer.decode(output_tokens[0], skip_special_tokens=True) + result = self.tokenizer.decode(output_tokens[0], skip_special_tokens=True) else: decoded_texts = [ self.tokenizer.decode(tokens, skip_special_tokens=True) for tokens in output_tokens ] - return decoded_texts[0] if len(decoded_texts) == 1 else decoded_texts + result = decoded_texts[0] if len(decoded_texts) == 1 else decoded_texts else: # return_type == "tokens" - return output_tokens + result = output_tokens + + if not return_cache: + return result + + # return_cache: recompute one clean forward over the full generated sequence so the + # cache is identical to run_with_cache(output_tokens) - all hook points, including + # attention patterns. The guards above restrict this to single-sequence, decoder-only + # text generation (see issue #697). + _, cache = self.run_with_cache(output_tokens, names_filter=names_filter, device=device) + return result, cache @torch.no_grad() def generate_stream( @@ -2673,6 +2771,23 @@ def set_use_attn_in(self, use_attn_in: bool): self.cfg.use_attn_in = use_attn_in self._propagate_attention_flag("use_attn_in", use_attn_in) + def set_use_hook_mlp_in(self, use_hook_mlp_in: bool) -> None: + """Toggle the pre-ln2 ``hook_mlp_in`` HookPoint, matching legacy semantics. + + See :py:meth:`HookedTransformer.set_use_hook_mlp_in`. + """ + self.cfg.use_hook_mlp_in = use_hook_mlp_in + if not hasattr(self, "blocks"): + return + for block in self.blocks: + block_cfg = getattr(block, "config", None) + if block_cfg is not None and block_cfg is not self.cfg: + try: + block_cfg.use_hook_mlp_in = use_hook_mlp_in + except Exception: + pass + block._use_hook_mlp_in = use_hook_mlp_in + def _propagate_attention_flag(self, flag_name: str, value: bool) -> None: """Mirror `bridge.cfg.` onto every block's attention config. From 91ea21dedc0ddf31333eefbfe3fddfda54761037 Mon Sep 17 00:00:00 2001 From: Jonah Larson Date: Mon, 8 Jun 2026 16:32:09 -0500 Subject: [PATCH 05/43] Inspect Driver (#1367) * INitial Inspect Driver setup * Additional improvements to inspect * Cleaning up inspect driver flaws * Adding additional coverage to ensure verify interventions & CUDA support * Improving architecture verification process * extended inspect tests to cover more model types * Clean up leaking rand change * Fix inspect memoize bug * Additional inspect improvements * Initial setup of inspect vllm provider * Additional inspect driver feature tightening * migrating dev 4.x changes * format checks * docstring cleanup * Fixing CI issues --- demos/Inspect_Bridge_Demo.ipynb | 3115 +++++++++++++++++ demos/Inspect_vLLM_Provider_Demo.ipynb | 360 ++ pyproject.toml | 11 + scripts/inspect_parity_report.py | 157 + tests/acceptance/model_bridge/conftest.py | 9 +- .../model_bridge/test_inspect_provider.py | 637 ++++ .../unit/model_bridge/test_inspect_driver.py | 438 +++ .../test_inspect_vllm_provider.py | 564 +++ .../model_bridge/remote_bridge.py | 52 +- .../model_bridge/sources/inspect/__init__.py | 25 + .../sources/inspect/_provider_base.py | 201 ++ .../model_bridge/sources/inspect/conftest.py | 19 + .../model_bridge/sources/inspect/driver.py | 182 + .../model_bridge/sources/inspect/eval.py | 118 + .../model_bridge/sources/inspect/hooks.py | 86 + .../sources/inspect/intervention.py | 60 + .../model_bridge/sources/inspect/profiles.py | 134 + .../model_bridge/sources/inspect/source.py | 151 + .../sources/inspect/transformers_provider.py | 522 +++ .../sources/inspect/vllm_provider.py | 439 +++ .../model_bridge/sources/inspect/wire.py | 70 + .../model_bridge/sources/vllm/driver.py | 4 + .../model_bridge/sources/vllm/plugin.py | 38 +- .../sources/vllm/worker_extension.py | 14 + uv.lock | 1023 ++++-- 25 files changed, 8219 insertions(+), 210 deletions(-) create mode 100644 demos/Inspect_Bridge_Demo.ipynb create mode 100644 demos/Inspect_vLLM_Provider_Demo.ipynb create mode 100644 scripts/inspect_parity_report.py create mode 100644 tests/acceptance/model_bridge/test_inspect_provider.py create mode 100644 tests/unit/model_bridge/test_inspect_driver.py create mode 100644 tests/unit/model_bridge/test_inspect_vllm_provider.py create mode 100644 transformer_lens/model_bridge/sources/inspect/__init__.py create mode 100644 transformer_lens/model_bridge/sources/inspect/_provider_base.py create mode 100644 transformer_lens/model_bridge/sources/inspect/conftest.py create mode 100644 transformer_lens/model_bridge/sources/inspect/driver.py create mode 100644 transformer_lens/model_bridge/sources/inspect/eval.py create mode 100644 transformer_lens/model_bridge/sources/inspect/hooks.py create mode 100644 transformer_lens/model_bridge/sources/inspect/intervention.py create mode 100644 transformer_lens/model_bridge/sources/inspect/profiles.py create mode 100644 transformer_lens/model_bridge/sources/inspect/source.py create mode 100644 transformer_lens/model_bridge/sources/inspect/transformers_provider.py create mode 100644 transformer_lens/model_bridge/sources/inspect/vllm_provider.py create mode 100644 transformer_lens/model_bridge/sources/inspect/wire.py diff --git a/demos/Inspect_Bridge_Demo.ipynb b/demos/Inspect_Bridge_Demo.ipynb new file mode 100644 index 0000000000..802106b236 --- /dev/null +++ b/demos/Inspect_Bridge_Demo.ipynb @@ -0,0 +1,3115 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b7e48565", + "metadata": {}, + "source": [ + "# Inspect Driver — Demo\n", + "\n", + "Turn a model served through [`inspect_ai`](https://inspect.aisi.org.uk) into a\n", + "TransformerLens `HookedTransformer`: `run_with_cache`, named hook points, and\n", + "interventions all work over the Inspect boundary.\n", + "\n", + "`boot_inspect` boots the model behind an `inspect_ai` provider and wraps it in a\n", + "bridge with the standard TL hook contract.\n", + "\n", + "**Scope (v1):**\n", + "- Captures residual / attention / MLP outputs under their TransformerBridge names\n", + " (the same canonical names the vLLM driver uses):\n", + " `blocks.{i}.hook_in` (resid_pre) / `blocks.{i}.ln2.hook_in` (resid_mid) /\n", + " `blocks.{i}.hook_out` (resid_post), `blocks.{i}.attn.hook_out`,\n", + " `blocks.{i}.mlp.hook_out`. Head-split hooks (`hook_q/k/v/z`, `hook_pattern`)\n", + " aren't available — use `boot_transformers()`.\n", + "- Which of those a given model actually serves is decided by a per-model structural\n", + " self-check: `resid_mid` is gated on parallel / norm-variant architectures, and\n", + " `attn_out`/`mlp_out` when their submodule isn't locatable. gpt2 (here) serves all five.\n", + "- Returns full-sequence logits, so `return_type=\"loss\"` works too.\n", + "\n", + "Runs on CPU; no GPU required." + ] + }, + { + "cell_type": "markdown", + "id": "91511cda", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Install TransformerLens with the `inspect` extra (pulls in `inspect_ai`):\n", + "\n", + "```bash\n", + "pip install \"transformer_lens[inspect]\"\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1b0c92d4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-29T18:38:08.301264Z", + "iopub.status.busy": "2026-05-29T18:38:08.301205Z", + "iopub.status.idle": "2026-05-29T18:38:11.378083Z", + "shell.execute_reply": "2026-05-29T18:38:11.377648Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "import torch\n", + "\n", + "from transformer_lens.model_bridge.remote_bridge import RemoteBridge\n", + "from transformer_lens.model_bridge.transformer_bridge import TransformerBridge\n", + "\n", + "MODEL = \"gpt2\"\n", + "PROMPT = \"The quick brown fox\"\n", + "torch.manual_seed(0)" + ] + }, + { + "cell_type": "markdown", + "id": "f5f730f7", + "metadata": {}, + "source": [ + "## Step 1 — Boot the model through Inspect\n", + "\n", + "`boot_inspect` resolves the architecture/config (no weights loaded TL-side), boots\n", + "the model behind an `inspect_ai` provider, and wraps it in a `RemoteBridge` with\n", + "the standard hook contract." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5ebf4efd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-29T18:38:11.379416Z", + "iopub.status.busy": "2026-05-29T18:38:11.379269Z", + "iopub.status.idle": "2026-05-29T18:38:13.454236Z", + "shell.execute_reply": "2026-05-29T18:38:13.453801Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "43666d4ba2304c08baff0f8809084935", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading weights: 0%| | 0/148 [00:00` is also a normal `inspect_ai` model, so it plugs into the eval\n", + "pipeline — not just the bridge capture path above. The next cells show: **(6)** eval-native\n", + "generation with logprobs + usage, **(7)** capturing activations alongside a scored eval\n", + "(full tensors to a side-artifact + a reduction in `samples_df`), **(8)** per-turn capture\n", + "across a rollout, and **(9)** tool-aware generation." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "709a4c77", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-29T18:38:14.255424Z", + "iopub.status.busy": "2026-05-29T18:38:14.255357Z", + "iopub.status.idle": "2026-05-29T18:38:15.661882Z", + "shell.execute_reply": "2026-05-29T18:38:15.661407Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7d0b2131829348a9a5c2309f34a71914", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading weights: 0%| | 0/148 [00:00 6 tokens\n", + "1st-token top-3: [(' the', -2.47), (' now', -3.04), (' a', -3.08)]\n" + ] + } + ], + "source": [ + "# (6) Eval-native generation: tl_bridge is a normal Inspect model — real multi-token\n", + "# generation with token logprobs + usage, read straight from the eval log.\n", + "import tempfile\n", + "\n", + "from inspect_ai import Task\n", + "from inspect_ai import eval as inspect_eval\n", + "from inspect_ai.dataset import Sample\n", + "from inspect_ai.solver import generate\n", + "\n", + "run = tempfile.mkdtemp()\n", + "gen_log = inspect_eval(\n", + " Task(dataset=[Sample(input=\"The capital of France is\")], solver=generate()),\n", + " model=\"tl_bridge/gpt2\",\n", + " max_tokens=6,\n", + " logprobs=True,\n", + " top_logprobs=3,\n", + " log_dir=f\"{run}/gen\",\n", + " display=\"none\",\n", + ")[0]\n", + "out = gen_log.samples[0].output\n", + "print(\"completion:\", repr(out.completion))\n", + "print(\"usage:\", out.usage.input_tokens, \"->\", out.usage.output_tokens, \"tokens\")\n", + "top3 = out.choices[0].logprobs.content[0].top_logprobs\n", + "print(\"1st-token top-3:\", [(t.token, round(t.logprob, 2)) for t in top3])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0c84386e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-29T18:38:15.662903Z", + "iopub.status.busy": "2026-05-29T18:38:15.662841Z", + "iopub.status.idle": "2026-05-29T18:38:16.703794Z", + "shell.execute_reply": "2026-05-29T18:38:16.703347Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "510fe4459254404599829fa646a82d57", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading weights: 0%| | 0/148 [00:00{\"name\": \"add\", \"arguments\": {\"a\": 2, \"b\": 2}}')\nprint(\"parsed tool calls:\", [(c.function, c.arguments) for c in calls])" + }, + { + "cell_type": "markdown", + "id": "81fc52b5", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "- `boot_inspect(\"gpt2\")` returns a bridge with the standard TL hook contract.\n", + "- Residual / attention / MLP activations cross the Inspect boundary and match\n", + " `boot_transformers` to fp tolerance (exact next-token argmax) — conversion to torch\n", + " happens at the bridge.\n", + "- Full-sequence logits, so `return_type=\"loss\"` matches `boot_transformers` too.\n", + "- Full affine interventions (suppress/scale/add/set) apply and revert.\n", + "- `tl_bridge/` is also a normal Inspect model: eval-native generation with\n", + " logprobs + usage, `capture_activations` alongside a scored eval (→ `.npz` + `samples_df`),\n", + " per-turn capture across rollouts (`turn_activations`), and tool-aware generation.\n", + "\n", + "**Out of v1 scope:** head-split hooks (`hook_q/k/v/z`, `hook_pattern`), `embed`/`ln_final`\n", + "(convention), batch > 1, and live agent loops with tool *execution* (need a tool-capable\n", + "model). For those, use `boot_transformers()`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "transformer-lens", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "039b65ce93724f1f8d988cf38f812441": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": 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/dev/null +++ b/demos/Inspect_vLLM_Provider_Demo.ipynb @@ -0,0 +1,360 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Inspect Driver — vLLM Provider Demo\n", + "\n", + "Same surface as the HF-backed `Inspect_Bridge_Demo`, but routes through `tl_bridge_vllm`\n", + "— vLLM under the hood instead of `AutoModelForCausalLM`. The point is dataset-scale\n", + "evals: vLLM's PagedAttention + continuous batching lets parallel `inspect_eval` samples\n", + "stream through a single resident model, where the HF provider serializes them.\n", + "\n", + "**Boundaries served (vLLM-specific):**\n", + "- ✅ `blocks.{i}.hook_out` (resid_post)\n", + "- ✅ `blocks.{i}.attn.hook_out` (attn_out)\n", + "- ✅ `blocks.{i}.mlp.hook_out` (mlp_out)\n", + "- ⛔ `blocks.{i}.hook_in` (resid_pre) — vLLM fuses the block-input read into the next\n", + " layer; no Python-visible hook point.\n", + "- ⛔ `blocks.{i}.ln2.hook_in` (resid_mid) — derived from resid_pre + attn_out.\n", + "- (Always non-fireable) head-split `hook_q/k/v/z`, `hook_pattern`, `embed`, `ln_final`,\n", + " `unembed`.\n", + "\n", + "Use `boot_inspect(provider=\"tl_bridge\")` for the gated boundaries (small models, CPU,\n", + "exact parity). Use `boot_inspect(provider=\"tl_bridge_vllm\")` for throughput on the\n", + "served boundaries.\n", + "\n", + "**Prerequisites:** an NVIDIA GPU with enough memory for the chosen model in fp16 +\n", + "vLLM's KV cache (≈4–8 GB for `meta-llama/Llama-3.2-1B`). vLLM is GPU-only; this\n", + "notebook will not run on CPU." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Install TransformerLens with both `inspect` and vLLM:\n", + "\n", + "```bash\n", + "pip install \"transformer_lens[inspect]\" vllm\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "import torch\n", + "\n", + "assert torch.cuda.is_available(), \"vLLM is GPU-only; this notebook needs CUDA.\"\n", + "\n", + "from transformer_lens.model_bridge.remote_bridge import RemoteBridge\n", + "from transformer_lens.model_bridge.transformer_bridge import TransformerBridge\n", + "\n", + "MODEL = \"meta-llama/Llama-3.2-1B\"\n", + "PROMPT = \"The quick brown fox\"\n", + "torch.manual_seed(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1 — Boot the model through Inspect (vLLM provider)\n", + "\n", + "`boot_inspect(..., provider=\"tl_bridge_vllm\")` constructs the same `RemoteBridge` you\n", + "get from the HF provider; the swap is invisible to downstream code. The provider's\n", + "structural self-check enumerates which boundary kinds vLLM can serve; the bridge profile\n", + "is filtered to that set, and any gated boundary surfaces as a UserWarning.\n", + "\n", + "First boot takes ~30–60s (vLLM loads weights, profiles KV cache, captures CUDA graphs)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": "bridge = RemoteBridge.boot_inspect(MODEL, provider=\"tl_bridge_vllm\")\nprint(\"bridge:\", type(bridge).__name__)\n# UserWarning surfaces on boot when boundaries are gated; print here for visibility.\nprint(\"capability note:\", bridge._driver._model.api.capability_note())" + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2 — Listing available hooks\n", + "\n", + "`bridge.hook_dict` is provider-agnostic. The fireable set vs the HF provider differs by\n", + "the gated kinds (resid_pre, resid_mid); everything else is identical." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"hook_dict entries:\", len(bridge.hook_dict))\n", + "fireable = sorted(bridge._driver.supported_hook_points)\n", + "print(\"fireable (native):\", len(fireable))\n", + "print(\"layer 0:\", [h for h in fireable if h.startswith(\"blocks.0.\")])\n", + "print(\"non-fireable (use boot_transformers):\", len(bridge._driver.non_fireable_hook_points))\n", + "# resid_pre / resid_mid live in non-fireable for this provider:\n", + "print(\"layer 0 non-fireable:\", [h for h in sorted(bridge._driver.non_fireable_hook_points)\n", + " if h.startswith(\"blocks.0.\")])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 3 — `run_with_cache` over the Inspect+vLLM boundary\n", + "\n", + "Activations cross the Inspect boundary as the same wire envelope the HF provider emits,\n", + "get converted to torch at the bridge boundary, and surface as a normal `ActivationCache`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tokens = bridge.to_tokens(PROMPT)\n", + "logits, cache = bridge.run_with_cache(tokens)\n", + "print(\"logits:\", tuple(logits.shape))\n", + "for hk in [\"blocks.0.hook_out\", \"blocks.0.attn.hook_out\", \"blocks.0.mlp.hook_out\"]:\n", + " print(f\"{hk}: shape={tuple(cache[hk].shape)} dtype={cache[hk].dtype}\")\n", + "nxt = int(logits[0, -1].argmax())\n", + "print(\"next-token:\", nxt, repr(bridge.tokenizer.decode([nxt])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4 — Parity vs `boot_transformers`\n", + "\n", + "vLLM runs the same weights as HF, but in fp16 with fused kernels (PagedAttention,\n", + "QKVParallelLinear). The residual stream matches to **fp16 tolerance** — looser than the\n", + "HF provider's exact match, tight enough that next-token argmax agrees on the served\n", + "boundaries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hf = TransformerBridge.boot_transformers(MODEL, dtype=torch.float16)\n", + "toks = hf.to_tokens(PROMPT) # shared, BOS-prepended\n", + "\n", + "hf_logits, hf_cache = hf.run_with_cache(toks)\n", + "i_logits, i_cache = bridge.run_with_cache(toks)\n", + "\n", + "print(\"argmax match:\", int(hf_logits[0, -1].argmax()) == int(i_logits[0, -1].argmax()))\n", + "# resid_post / attn_out / mlp_out across a sampling of layers — vLLM's served kinds.\n", + "for hk in [\"blocks.0.hook_out\", \"blocks.0.attn.hook_out\", \"blocks.0.mlp.hook_out\",\n", + " \"blocks.8.hook_out\", \"blocks.15.hook_out\"]:\n", + " a, b = hf_cache[hk].float(), i_cache[hk].float()\n", + " rel = (a - b).norm() / (a.norm() + 1e-6)\n", + " print(f\"{hk}: rel_L2={rel.item():.2e} maxdiff={(a - b).abs().max().item():.2e}\")\n", + "del hf # free HF before vLLM keeps going" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5 — Interventions\n", + "\n", + "Full affine vocabulary (`suppress` / `scale` / `add` / `set`) flows through to the vLLM\n", + "worker via the per-hook scale+bias buffer swap. Same surface as the HF provider — the\n", + "intervention dict is identical." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clean = int(bridge.forward(toks)[0, -1].argmax())\n", + "\n", + "hk = \"blocks.0.hook_out\" # resid_post\n", + "supp_logits, supp_cache = bridge.run_with_cache(\n", + " toks, intervene={hk: {\"op\": \"suppress\"}}\n", + ")\n", + "suppressed = int(supp_logits[0, -1].argmax())\n", + "print(f\"{hk} |max| after suppress:\", supp_cache[hk].abs().max().item())\n", + "print(f\"argmax: clean={clean} suppressed={suppressed} changed={clean != suppressed}\")\n", + "\n", + "revert = int(bridge.forward(toks)[0, -1].argmax())\n", + "print(f\"revert={revert} matches clean={revert == clean}\")\n", + "\n", + "# Tear down vLLM before the eval-pipeline section boots its own. vLLM has no\n", + "# LLM.shutdown(); the driver's close() destroys the distributed env so the next\n", + "# Inspect-launched LLM(...) starts clean.\n", + "bridge.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspect-native workflows\n", + "\n", + "`tl_bridge_vllm/` is also a normal `inspect_ai` model — the eval pipeline boots\n", + "vLLM, runs samples through it (parallel, batched), and reads activations off\n", + "`ModelOutput.metadata`. The next cells show the same four patterns as the HF demo:\n", + "**(6)** eval-native generation with logprobs + usage, **(7)** capture_activations\n", + "alongside a scored eval, **(8)** per-turn capture across a rollout, **(9)** tool-aware\n", + "generation. The helpers are provider-agnostic (they read the same wire envelope), so the\n", + "cells are essentially the HF cells with the model string swapped." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# (6) Eval-native generation: tl_bridge_vllm is a normal Inspect model — real multi-token\n", + "# generation with token logprobs + usage from vLLM's sampler.\n", + "import tempfile\n", + "\n", + "from inspect_ai import Task\n", + "from inspect_ai import eval as inspect_eval\n", + "from inspect_ai.dataset import Sample\n", + "from inspect_ai.solver import generate\n", + "\n", + "run = tempfile.mkdtemp()\n", + "gen_log = inspect_eval(\n", + " Task(dataset=[Sample(input=\"The capital of France is\")], solver=generate()),\n", + " model=f\"tl_bridge_vllm/{MODEL}\",\n", + " max_tokens=6,\n", + " logprobs=True,\n", + " top_logprobs=3,\n", + " log_dir=f\"{run}/gen\",\n", + " display=\"none\",\n", + ")[0]\n", + "out = gen_log.samples[0].output\n", + "print(\"completion:\", repr(out.completion))\n", + "print(\"usage:\", out.usage.input_tokens, \"->\", out.usage.output_tokens, \"tokens\")\n", + "top3 = out.choices[0].logprobs.content[0].top_logprobs\n", + "print(\"1st-token top-3:\", [(t.token, round(t.logprob, 2)) for t in top3])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# (7) Capture activations alongside a scored eval. Same helpers as the HF demo — they\n", + "# read metadata['activations'] which both providers emit in the same wire envelope.\n", + "import pathlib\n", + "\n", + "from inspect_ai.analysis import SampleSummary, samples_df\n", + "from inspect_ai.scorer import includes\n", + "\n", + "from transformer_lens.model_bridge.sources.inspect import activations_column, capture_activations\n", + "\n", + "cap_task = Task(\n", + " dataset=[\n", + " Sample(input=\"The capital of France is\", target=\"Paris\"),\n", + " Sample(input=\"The opposite of hot is\", target=\"cold\"),\n", + " ],\n", + " solver=[capture_activations([\"blocks.8.hook_out\"], output_dir=f\"{run}/acts\"), generate()],\n", + " scorer=includes(),\n", + ")\n", + "cap_log = inspect_eval(\n", + " cap_task, model=f\"tl_bridge_vllm/{MODEL}\", max_tokens=4, log_dir=f\"{run}/cap\", display=\"none\"\n", + ")[0]\n", + "print(\"status:\", cap_log.status, \"| npz:\", [p.name for p in pathlib.Path(f\"{run}/acts\").glob(\"*.npz\")])\n", + "df = samples_df(f\"{run}/cap\", columns=[*SampleSummary, activations_column()])\n", + "print(df[[\"id\", \"tl_activations\"]].to_string(index=False))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# (8) Per-turn capture during a multi-token generate. vLLM's compiled hooks overwrite\n", + "# buffer row 0 on each decode step, so the provider takes a single-token snapshot of the\n", + "# prompt activations BEFORE the eval generate (extra ~prompt prefill — small relative to\n", + "# typical multi-token completions).\n", + "from transformer_lens.model_bridge.sources.inspect import turn_activations\n", + "\n", + "turn_log = inspect_eval(\n", + " Task(dataset=[Sample(input=\"The capital of France is\")], solver=generate()),\n", + " model=f\"tl_bridge_vllm/{MODEL}\",\n", + " model_args={\"capture\": [\"blocks.8.hook_out\"]},\n", + " max_tokens=4,\n", + " log_dir=f\"{run}/turns\",\n", + " display=\"none\",\n", + ")[0]\n", + "turns = turn_activations(turn_log.samples[0])\n", + "print(\"model turns captured:\", len(turns))\n", + "print(\"turn 0:\", {k: tuple(v.shape) for k, v in turns[0].items()})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# (9) Tool-aware generation. Same parser as the HF provider — re-exported from the\n", + "# vLLM provider for symmetry. For a tool-capable instruct model the provider renders\n", + "# tool schemas into the chat template; here we just show the parser:\n", + "from transformer_lens.model_bridge.sources.inspect.vllm_provider import _parse_tool_calls\n", + "\n", + "calls = _parse_tool_calls('{\"name\": \"add\", \"arguments\": {\"a\": 2, \"b\": 2}}')\n", + "print(\"parsed tool calls:\", [(c.function, c.arguments) for c in calls])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "- `boot_inspect(MODEL, provider=\"tl_bridge_vllm\")` returns the same `RemoteBridge` as the\n", + " HF provider, backed by vLLM. The structural self-check gates `resid_pre`/`resid_mid`\n", + " (no Python-visible block-input hook under vLLM's fused execution).\n", + "- Residual / attn / mlp boundaries cross the Inspect boundary as the same wire envelope\n", + " the HF provider emits, match `boot_transformers` to fp16 tolerance (next-token argmax\n", + " agrees on served kinds), and convert to torch at the bridge.\n", + "- Full affine interventions (suppress / scale / add / set) flow through the per-hook\n", + " scale+bias buffer swap on the worker.\n", + "- `tl_bridge_vllm/` is also a normal Inspect model: eval-native generation,\n", + " `capture_activations`, per-turn capture, tool-call parsing — same helpers, same wire\n", + " format. Trade vs HF: gives up `resid_pre`/`resid_mid` and exact parity; gains vLLM's\n", + " throughput (PagedAttention + continuous batching) for dataset-scale evals." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index a98ab985c4..1f2c9a8569 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,12 +39,22 @@ # chardet<6 works around a `requests<=2.32` compatibility-check warning that fires # whenever chardet>=6 is installed. Remove the pin when psf/requests bumps the cap. evals=["lm-eval>=0.4", "chardet<6"] + inspect=["inspect_ai>=0.3"] lit=["lit-nlp>=1.3"] [project.scripts] build-docs="docs.make_docs:build_docs" docs-hot-reload="docs.make_docs:docs_hot_reload" + # Registers our HF-backed activation-extraction provider with inspect_ai so + # get_model("tl_bridge/") resolves it once the package is installed. + # (Named tl_bridge, not transformer_lens — inspect_ai ships a built-in by that name.) + # tl_bridge_vllm is the vLLM-backed sibling (lazy vllm import in __init__ so + # the module imports cleanly without vllm installed). + [project.entry-points.inspect_ai] + tl_bridge="transformer_lens.model_bridge.sources.inspect.transformers_provider" + tl_bridge_vllm="transformer_lens.model_bridge.sources.inspect.vllm_provider" + [dependency-groups] demo=["gradio>=4.0.0", "orjson>=3.11.7,<4.0"] dev=[ @@ -104,6 +114,7 @@ ] markers=[ "slow: marks tests as slow (deselect with '-m \"not slow\"')", + "inspect: requires inspect_ai (deselect with '-m \"not inspect\"')", ] pythonpath=["."] testpaths=["tests", "transformer_lens"] # Only test these directories diff --git a/scripts/inspect_parity_report.py b/scripts/inspect_parity_report.py new file mode 100644 index 0000000000..4d643f0c47 --- /dev/null +++ b/scripts/inspect_parity_report.py @@ -0,0 +1,157 @@ +"""Empirical parity report: boot_inspect vs boot_transformers per architecture. + +For each model, compares (at the first and last layer) the boundaries the provider's +structural self-check OFFERS — gated ones (e.g. resid_mid on parallel/norm-variant archs) +are correctly withheld and reported, not compared. Validates that self-check against real +models: everything offered must match boot_transformers; it is not a per-PR CI job. + +Run: uv run python scripts/inspect_parity_report.py +Override the model list with TL_PARITY_MODELS="id1,id2,...". +""" +from __future__ import annotations + +import gc +import os +import sys +import warnings + +warnings.filterwarnings("ignore") + +import torch + +# Broad coverage across adapter families: real small/gated checkpoints (HF token from +# .env) where available, tiny-random elsewhere. Structure (not weights) drives parity, +# so tiny-random is fine for classification; bad/404 ids land in "couldn't run". +DEFAULT_MODELS = [ + # --- sequential post-norm (GPT2 family) --- + "sshleifer/tiny-gpt2", # GPT2 + "gpt2", # GPT2 + "bigcode/gpt_bigcode-santacoder", # GPTBigCode + "roneneldan/TinyStories-33M", # GPTNeo + # --- sequential pre-norm (Llama family) --- + "meta-llama/Llama-3.2-1B", # Llama (gated) + "mistralai/Mistral-7B-v0.1", # Mistral + "Qwen/Qwen2.5-0.5B", # Qwen2 + "Qwen/Qwen3-0.6B", # Qwen3 + "microsoft/Phi-3-mini-4k-instruct", # Phi3 + "allenai/OLMo-2-0425-1B", # Olmo2 + "ibm-granite/granite-3.0-2b-base", # Granite + "stabilityai/stablelm-2-1_6b", # StableLm + "HuggingFaceTB/SmolLM2-135M", # Llama + # --- norm-variant (extra pre/post-FF norms → resid_mid gated by identity check) --- + "google/gemma-2-2b", # Gemma2 (gated) + "google/gemma-3-1b-pt", # Gemma3 (gated) + "ibm-granite/granite-3.0-2b-base", # Granite — residual-multiplier; even attn/mlp diverge + # --- parallel residual (attn + mlp both read resid_pre → resid_mid gated by causal) --- + "EleutherAI/pythia-160m", # GPTNeoX + "Salesforce/codegen-350M-mono", # CodeGen + "microsoft/phi-1_5", # Phi + "tiiuae/falcon-rw-1b", # Falcon (attn attr self_attention → attn_out gated) + # --- other / non-standard composition --- + "facebook/opt-125m", # OPT (fc1/fc2 → mlp_out gated) + "bigscience/bloom-560m", # Bloom + # --- tiny-random fallbacks for families lacking a small real checkpoint --- + "hf-internal-testing/tiny-random-GPTJForCausalLM", # GPTJ (parallel) + "trl-internal-testing/tiny-CohereForCausalLM", # Cohere (tiny) + "hf-internal-testing/tiny-random-MixtralForCausalLM", # Mixtral (MoE block → mlp_out gated) +] + +PROMPT = "The quick brown fox" +ATOL, RTOL = 1e-3, 1e-3 + + +# Boundary kind -> TransformerBridge-native hook suffix. +KIND_SUFFIX = { + "resid_pre": "hook_in", + "resid_mid": "ln2.hook_in", + "resid_post": "hook_out", + "attn_out": "attn.hook_out", + "mlp_out": "mlp.hook_out", +} + + +def verify(model_id: str) -> dict: + from transformer_lens.model_bridge.remote_bridge import RemoteBridge + from transformer_lens.model_bridge.transformer_bridge import TransformerBridge + + result: dict = {"model": model_id, "arch": "?", "status": "", "detail": ""} + hf = inspect = None + try: + # Force matched fp32 + (provider) eager attention so the comparison isolates the + # boundary mapping, not dtype/attn-impl differences. + hf = TransformerBridge.boot_transformers(model_id, device="cpu", dtype=torch.float32) + result["arch"] = getattr(hf.cfg, "architecture", "?") + n_layers = int(hf.cfg.n_layers) + toks = hf.to_tokens(PROMPT) + + inspect = RemoteBridge.boot_inspect(model_id, dtype=torch.float32) + # Validate the structural self-check: only the boundaries the provider OFFERS must + # match; ones it gated (e.g. resid_mid on parallel archs) are correctly withheld. + offered = inspect._driver.supported_hook_points + kinds_offered = {k for k, suf in KIND_SUFFIX.items() if f"blocks.0.{suf}" in offered} + gated = sorted(set(KIND_SUFFIX) - kinds_offered) + + _, hf_cache = hf.run_with_cache(toks) + _, i_cache = inspect.run_with_cache(toks) + + worst = 0.0 + mism = [] + for i in sorted({0, n_layers - 1}): + for kind in kinds_offered: + hk = f"blocks.{i}.{KIND_SUFFIX[kind]}" + if hk not in i_cache or hk not in hf_cache: + mism.append(f"{hk} missing") + continue + a, b = hf_cache[hk].float(), i_cache[hk].float() + if a.shape != b.shape: + mism.append(f"{hk} shape {tuple(a.shape)}!={tuple(b.shape)}") + continue + d = (a - b).abs().max().item() + worst = max(worst, d) + if not torch.allclose(a, b, atol=ATOL, rtol=RTOL): + mism.append(f"{hk} maxdiff={d:.2e}") + gated_str = ",".join(gated) if gated else "none" + if mism: + result["status"] = "FAIL" + result["detail"] = f"gated={gated_str}; " + "; ".join(mism[:3]) + else: + result["status"] = "PASS" + result["detail"] = f"maxdiff={worst:.2e} (L={n_layers}, gated={gated_str})" + except Exception as e: # download/gating/locate failures are not parity failures + result["status"] = "SKIP" + result["detail"] = f"{type(e).__name__}: {str(e).splitlines()[0][:120]}" + finally: + for bridge in (inspect, hf): + try: + if bridge is not None: + bridge.close() + except Exception: + pass + del hf, inspect + gc.collect() + return result + + +def main() -> None: + ids = os.environ.get("TL_PARITY_MODELS") + models = [m.strip() for m in ids.split(",")] if ids else DEFAULT_MODELS + rows = [] + for m in models: + r = verify(m) + rows.append(r) + print(f"[{r['status']:4}] {r['arch']:32} {r['model']:48} {r['detail']}", flush=True) + + print("\n================ PARITY REPORT CARD ================") + for status in ("PASS", "FAIL", "SKIP"): + sel = [r for r in rows if r["status"] == status] + print(f"\n{status} ({len(sel)}):") + for r in sel: + print(f" {r['arch']:32} {r['model']:48} {r['detail']}") + passed = sorted({r["arch"] for r in rows if r["status"] == "PASS"}) + print("\nPARITY-VERIFIED ARCHITECTURES (measured PASS):") + print(" " + ", ".join(passed)) + sys.exit(0) + + +if __name__ == "__main__": + main() diff --git a/tests/acceptance/model_bridge/conftest.py b/tests/acceptance/model_bridge/conftest.py index 870b38a7a3..505bcb20e3 100644 --- a/tests/acceptance/model_bridge/conftest.py +++ b/tests/acceptance/model_bridge/conftest.py @@ -1,7 +1,12 @@ """Session fixtures for model_bridge acceptance tests. -transformer_lens imports stay inside fixture bodies — jaxtyping's pytest_configure -hook must install before the package is first imported. +Session-scoped fixtures avoid redundant model loads across test files. +All models used here must be in the CI cache (see .github/workflows/checks.yml). + +Imports of ``transformer_lens`` are deferred into the fixtures: this conftest is +loaded during pytest's initial conftest collection (before plugins' pytest_configure), +and a module-level TL import would trip jaxtyping's "package already imported" guard +when a single test file under this directory is run in isolation. """ import pytest diff --git a/tests/acceptance/model_bridge/test_inspect_provider.py b/tests/acceptance/model_bridge/test_inspect_provider.py new file mode 100644 index 0000000000..6bc51e7259 --- /dev/null +++ b/tests/acceptance/model_bridge/test_inspect_provider.py @@ -0,0 +1,637 @@ +"""Acceptance gate for the Inspect driver: gpt2 through our HF provider must match +``boot_transformers`` to fp tolerance (same backend + dtype + eager attention), with +exact next-token argmax, and interventions must take effect. + +Gated on ``inspect_ai`` being installed (the ``inspect`` extra); runs on CPU. +""" +from __future__ import annotations + +import pytest +import torch + +try: + import inspect_ai # noqa: F401 + + INSPECT_AVAILABLE = True +except ImportError: + INSPECT_AVAILABLE = False + +pytestmark = [ + pytest.mark.inspect, + pytest.mark.skipif( + not INSPECT_AVAILABLE, reason="inspect_ai not installed (pip install '.[inspect]')" + ), +] + +MODEL = "gpt2" +PROMPT = "The quick brown fox" +# A spread across boundary kinds and layers — all must match boot_transformers to fp +# tolerance. TransformerBridge-native names (the bridge cache carries these too). +PARITY_HOOKS = [ + "blocks.0.hook_in", # resid_pre + "blocks.0.attn.hook_out", # attn_out + "blocks.0.ln2.hook_in", # resid_mid + "blocks.0.mlp.hook_out", # mlp_out + "blocks.0.hook_out", # resid_post + "blocks.6.attn.hook_out", + "blocks.11.hook_out", +] + +# Boundary kind -> TransformerBridge-native hook suffix. +KIND_SUFFIX = { + "resid_pre": "hook_in", + "resid_mid": "ln2.hook_in", + "resid_post": "hook_out", + "attn_out": "attn.hook_out", + "mlp_out": "mlp.hook_out", +} + +# Token-free tiny-random checkpoints, one per structural-check code path: standard +# sequential (nothing gated), parallel-residual (resid_mid gated by the causal probe), +# post-norm (resid_mid gated by the linear-identity probe). Pins cross-family behavior +# so a detector/load-path regression on non-gpt2 archs fails CI instead of shipping silently. +STRUCTURAL_FAMILIES = [ + ("hf-internal-testing/tiny-random-LlamaForCausalLM", frozenset()), + ("hf-internal-testing/tiny-random-GPTJForCausalLM", frozenset({"resid_mid"})), + ("hf-internal-testing/tiny-random-Gemma2ForCausalLM", frozenset({"resid_mid"})), +] + + +@pytest.fixture(scope="module") +def hf_bridge(): + from transformer_lens.model_bridge.transformer_bridge import TransformerBridge + + return TransformerBridge.boot_transformers(MODEL) + + +@pytest.fixture(scope="module") +def inspect_bridge(): + from transformer_lens.model_bridge.remote_bridge import RemoteBridge + + bridge = RemoteBridge.boot_inspect(MODEL) + yield bridge + bridge.close() + + +@pytest.fixture(scope="module") +def tokens(hf_bridge): + # Shared token ids so the only variable under test is the backend path, not + # tokenization (to_tokens BOS parity is covered by test_string_input_parity). + return hf_bridge.to_tokens(PROMPT) + + +class TestInspectParity: + def test_argmax_matches_boot_transformers(self, hf_bridge, inspect_bridge, tokens): + hf_logits = hf_bridge.forward(tokens) + i_logits = inspect_bridge.forward(tokens) + assert int(hf_logits[0, -1].argmax()) == int(i_logits[0, -1].argmax()) + + def test_string_input_parity(self, hf_bridge, inspect_bridge): + # Same STRING (not shared tokens): exercises RemoteBridge.to_tokens BOS handling. + # A bare encode (no BOS) would diverge from boot_transformers here. + hf_argmax = int(hf_bridge.forward(PROMPT)[0, -1].argmax()) + i_argmax = int(inspect_bridge.forward(PROMPT)[0, -1].argmax()) + assert hf_argmax == i_argmax + + def test_hooks_match(self, hf_bridge, inspect_bridge, tokens): + _, hf_cache = hf_bridge.run_with_cache(tokens) + _, i_cache = inspect_bridge.run_with_cache(tokens) + for hook in PARITY_HOOKS: + a, b = hf_cache[hook].float(), i_cache[hook].float() + assert a.shape == b.shape + assert torch.allclose( + a, b, atol=1e-3, rtol=1e-3 + ), f"{hook} diverges: max {(a - b).abs().max().item():.2e}" + + def test_loss_matches(self, hf_bridge, inspect_bridge, tokens): + # Full-sequence logits ⇒ loss is computable and matches boot_transformers. + hf_loss = hf_bridge.forward(tokens, return_type="loss") + i_loss = inspect_bridge.forward(tokens, return_type="loss") + assert torch.allclose(hf_loss, i_loss, atol=1e-3) + + +# Each intervenable boundary kind at layer 0, with whether suppress flips the top token +# (mlp_out shifts the logits but not the argmax at layer 0). resid_pre exercises the +# forward_pre_hook path; the others the forward_hook path. +INTERVENE_HOOKS = [ + ("blocks.0.hook_in", True), # resid_pre + ("blocks.0.attn.hook_out", True), # attn_out + ("blocks.0.mlp.hook_out", False), # mlp_out + ("blocks.0.hook_out", True), # resid_post +] + + +class TestInspectInterventions: + @pytest.mark.parametrize("hook,flips_argmax", INTERVENE_HOOKS) + def test_suppress_applies_at_each_boundary(self, inspect_bridge, tokens, hook, flips_argmax): + clean_logits = inspect_bridge.forward(tokens) + clean_argmax = int(clean_logits[0, -1].argmax()) + + supp_logits, supp_cache = inspect_bridge.run_with_cache( + tokens, intervene={hook: {"op": "suppress"}} + ) + assert supp_cache[hook].abs().max().item() == 0.0 # capture reflects the intervention + assert not torch.allclose(supp_logits, clean_logits) # and it propagated to the logits + if flips_argmax: + assert int(supp_logits[0, -1].argmax()) != clean_argmax + + def test_intervention_reverts(self, inspect_bridge, tokens): + clean_argmax = int(inspect_bridge.forward(tokens)[0, -1].argmax()) + inspect_bridge.forward(tokens, intervene={"blocks.0.hook_out": {"op": "suppress"}}) + assert int(inspect_bridge.forward(tokens)[0, -1].argmax()) == clean_argmax # not sticky + + +class TestPreHookKwargs: + """The resid_pre pre-hook runs with_kwargs=True so it modifies the right tensor + whether hidden_states arrives positionally (args[0]) or as a kwarg.""" + + CALL = "test_call" + + def _hook(self): + from transformer_lens.model_bridge.sources.inspect.transformers_provider import ( + _pre_hook, + ) + + return _pre_hook( + layer=0, want_capture=True, spec={"op": "suppress"}, raw={}, call_id=self.CALL + ) + + def _scope(self): + from transformer_lens.model_bridge.sources.inspect.transformers_provider import ( + _current_call_id, + ) + + return _current_call_id.set(self.CALL) + + def test_positional_hidden_states(self): + from transformer_lens.model_bridge.sources.inspect.transformers_provider import ( + _current_call_id, + ) + + hidden = torch.ones(1, 2, 4) + token = self._scope() + try: + new_args, new_kwargs = self._hook()(None, (hidden, "mask"), {}) + finally: + _current_call_id.reset(token) + assert torch.allclose(new_args[0], torch.zeros_like(hidden)) and new_args[1] == "mask" + assert new_kwargs == {} + + def test_kwarg_hidden_states(self): + from transformer_lens.model_bridge.sources.inspect.transformers_provider import ( + _current_call_id, + ) + + hidden = torch.ones(1, 2, 4) + token = self._scope() + try: + new_args, new_kwargs = self._hook()( + None, (), {"hidden_states": hidden, "use_cache": True} + ) + finally: + _current_call_id.reset(token) + assert new_args == () + assert torch.allclose(new_kwargs["hidden_states"], torch.zeros_like(hidden)) # suppressed + assert new_kwargs["use_cache"] is True # other kwargs preserved + + +class TestStructuralProbe: + """The boot-time structural self-check: resid_mid is offered only when attn feeds mlp. + Toy modules (no download) exercise the causal probe for sequential vs parallel blocks.""" + + def _toy_model(self, parallel: bool = False, resid_scale: float = 1.0): + import torch.nn as nn + + class Sub(nn.Module): + def __init__(self, k): + super().__init__() + self.lin = nn.Linear(4, 4) + self.k = k + + def forward(self, x): + return self.lin(x) * self.k + + class Block(nn.Module): + def __init__(self): + super().__init__() + self.self_attn = Sub(0.5) + self.mlp = Sub(0.1) + + def forward(self, x): + a = self.self_attn(x) + m = self.mlp(x) if parallel else self.mlp(x + a) # parallel reads block input + # resid_scale != 1 mimics post-norm/residual-multiplier archs (Gemma2/OLMo2/ + # Granite): outputs don't add linearly, so resid_pre + attn_out is wrong. + return x + resid_scale * a + resid_scale * m + + class Model(nn.Module): + def __init__(self): + super().__init__() + self.embed = nn.Embedding(16, 4) + self.layers = nn.ModuleList([Block()]) + + def forward(self, ids): + x = self.embed(ids) + for b in self.layers: + x = b(x) + return x + + torch.manual_seed(0) + return Model() + + def test_sequential_offers_resid_mid(self): + from transformer_lens.model_bridge.sources.inspect.transformers_provider import ( + _detect_capabilities, + ) + + m = self._toy_model() # standard sequential, linear residual + kinds, note = _detect_capabilities(m, m.layers) + assert "resid_mid" in kinds + assert note == "" # nothing gated + + def test_parallel_gates_resid_mid(self): + from transformer_lens.model_bridge.sources.inspect.transformers_provider import ( + _detect_capabilities, + ) + + m = self._toy_model(parallel=True) # mlp reads block input, not attn output + kinds, note = _detect_capabilities(m, m.layers) + assert "resid_mid" not in kinds + assert {"resid_pre", "resid_post", "attn_out", "mlp_out"} <= kinds # rest still served + assert "resid_mid" in note # note explains the gate + + def test_nonlinear_residual_gates_resid_mid(self): + from transformer_lens.model_bridge.sources.inspect.transformers_provider import ( + _detect_capabilities, + ) + + # Sequential (attn feeds mlp) but outputs are scaled before the residual add, so + # resid_post != resid_pre + attn_out + mlp_out — resid_mid must still be gated. + m = self._toy_model(resid_scale=2.0) + kinds, note = _detect_capabilities(m, m.layers) + assert "resid_mid" not in kinds + assert {"resid_pre", "resid_post", "attn_out", "mlp_out"} <= kinds + assert "resid_mid" in note + + def test_probe_leaves_global_rng_untouched(self): + # The probe's attn perturbation must use a local generator — booting a model + # should never reset the caller's torch RNG. + from transformer_lens.model_bridge.sources.inspect.transformers_provider import ( + _detect_capabilities, + ) + + m = self._toy_model() + before = torch.get_rng_state() + _detect_capabilities(m, m.layers) + assert torch.equal(torch.get_rng_state(), before) + + +class TestStructuralCheckAcrossFamilies: + """Real tiny-random models, one per detector code path, run in CI (no token, seconds). + Locks both the gating decision and offered-boundary parity vs boot_transformers, so a + structural-check or load-path regression on non-gpt2 architectures fails here.""" + + @pytest.mark.parametrize("model_id,expected_gated", STRUCTURAL_FAMILIES) + def test_gating_and_parity(self, model_id, expected_gated): + from transformer_lens.model_bridge.remote_bridge import RemoteBridge + from transformer_lens.model_bridge.transformer_bridge import TransformerBridge + + # Matched fp32 + eager attention (boot_inspect's defaults) so the only variable + # is the boundary mapping. + hf = TransformerBridge.boot_transformers(model_id, device="cpu", dtype=torch.float32) + inspect = RemoteBridge.boot_inspect(model_id, dtype=torch.float32) + try: + supported = inspect._driver.supported_hook_points + offered = {k for k, suf in KIND_SUFFIX.items() if f"blocks.0.{suf}" in supported} + gated = frozenset(set(KIND_SUFFIX) - offered) + assert gated == expected_gated, f"{model_id}: gated {sorted(gated)}" + # gated kinds are reported non-fireable, not silently dropped + for kind in expected_gated: + assert f"blocks.0.{KIND_SUFFIX[kind]}" in inspect._driver.non_fireable_hook_points + + n_layers = int(hf.cfg.n_layers) + toks = hf.to_tokens(PROMPT) + _, hf_cache = hf.run_with_cache(toks) + _, i_cache = inspect.run_with_cache(toks) + assert int(hf.forward(toks)[0, -1].argmax()) == int( + inspect.forward(toks)[0, -1].argmax() + ) + for layer in sorted({0, n_layers - 1}): + for kind in offered: + hk = f"blocks.{layer}.{KIND_SUFFIX[kind]}" + a, b = hf_cache[hk].float(), i_cache[hk].float() + assert torch.allclose( + a, b, atol=1e-3, rtol=1e-3 + ), f"{model_id} {hk} diverges: {(a - b).abs().max().item():.2e}" + finally: + inspect.close() + + +class TestRebootSemantics: + """inspect_ai memoizes get_model by name; boot_inspect passes memoize=False so each + boot honors its own args and independently owns (and frees) its model.""" + + def test_reboot_is_fresh_and_honors_dtype(self): + from transformer_lens.model_bridge.remote_bridge import RemoteBridge + + mid = "hf-internal-testing/tiny-random-LlamaForCausalLM" + b1 = RemoteBridge.boot_inspect(mid, dtype=torch.float32) + b2 = RemoteBridge.boot_inspect(mid, dtype=torch.float16) + try: + # Distinct provider objects — not inspect_ai's memoized singleton. + assert b1._driver._model is not b2._driver._model + # Each provider loaded at the dtype its boot requested (not a stale cache). + assert next(b1._driver._model.api._hf.parameters()).dtype == torch.float32 + assert next(b2._driver._model.api._hf.parameters()).dtype == torch.float16 + finally: + b1.close() + b2.close() + + +class TestEvalNativeGeneration: + """Beyond TL-driven capture, the provider works as a normal Inspect model: real + multi-token generation from chat input, with logprobs + usage, so an Inspect eval runs.""" + + def _api(self): + from inspect_ai.model import get_model + + from transformer_lens.model_bridge.sources.inspect import ( # noqa: F401 register + transformers_provider, + ) + + return get_model("tl_bridge/gpt2", memoize=False).api + + def test_multi_token_generation_with_logprobs(self): + import asyncio + + from inspect_ai.model import ChatMessageUser, GenerateConfig + + out = asyncio.run( + self._api().generate( + [ChatMessageUser(content="The capital of France is")], + None, + None, + GenerateConfig(max_tokens=5, logprobs=True, top_logprobs=3), + ) + ) + choice = out.choices[0] + assert choice.message.text # non-empty completion + assert choice.stop_reason in ("max_tokens", "stop") + assert len(choice.logprobs.content) == 5 # one logprob per generated token + assert len(choice.logprobs.content[0].top_logprobs) == 3 + assert out.usage.output_tokens == 5 + assert out.usage.total_tokens == out.usage.input_tokens + 5 + + def test_eval_runs_end_to_end(self, tmp_path): + from inspect_ai import Task + from inspect_ai import eval as inspect_eval + from inspect_ai.dataset import Sample + from inspect_ai.scorer import includes + from inspect_ai.solver import generate + + from transformer_lens.model_bridge.sources.inspect import ( # noqa: F401 register + transformers_provider, + ) + + task = Task( + dataset=[Sample(input="The capital of France is", target="Paris")], + solver=generate(), + scorer=includes(), + ) + logs = inspect_eval( + task, model="tl_bridge/gpt2", max_tokens=8, log_dir=str(tmp_path), display="none" + ) + assert logs[0].status == "success" + assert logs[0].samples and logs[0].samples[0].output.completion + + +class TestCaptureInEval: + """The capture_activations solver: full activations to a per-sample side artifact + a + reduction in the store, surfaced by samples_df to correlate features with scores.""" + + def test_artifact_store_and_samples_df(self, tmp_path): + import json + import pathlib + + import numpy as np + from inspect_ai import Task + from inspect_ai import eval as inspect_eval + from inspect_ai.analysis import SampleSummary, samples_df + from inspect_ai.dataset import Sample + from inspect_ai.solver import generate + + from transformer_lens.model_bridge.sources.inspect import ( # noqa: F401 register + transformers_provider, + ) + from transformer_lens.model_bridge.sources.inspect.eval import ( + activations_column, + capture_activations, + ) + + acts, logs = str(tmp_path / "acts"), str(tmp_path / "logs") + task = Task( + dataset=[Sample(input="The capital of France is", target="Paris")], + solver=[capture_activations(["blocks.6.hook_out"], output_dir=acts), generate()], + ) + log = inspect_eval( + task, model="tl_bridge/gpt2", max_tokens=4, log_dir=logs, display="none" + )[0] + assert log.status == "success" + + # full activations side-artifact, keyed by hook name + npz = list(pathlib.Path(acts).glob("*.npz")) + assert len(npz) == 1 + assert np.load(npz[0])["blocks.6.hook_out"].shape[-1] == 768 + + # reduction in the store, queryable via samples_df + df = samples_df(logs, columns=[*SampleSummary, activations_column()]) + reduction = df["tl_activations"].iloc[0] + reduction = json.loads(reduction) if isinstance(reduction, str) else reduction + assert reduction["blocks.6.hook_out"]["shape"][-1] == 768 + + def test_rejects_unknown_hook(self): + from transformer_lens.model_bridge.sources.inspect.eval import ( + capture_activations, + ) + + with pytest.raises(ValueError, match="not a fireable hook"): + capture_activations(["blocks.0.not_a_hook"]) + + +class TestAgenticToolCapture: + """Honor tools (render into the template or raise clearly), best-effort tool-call + parsing, and per-turn activation capture across a rollout.""" + + def test_tool_call_parsing(self): + from transformer_lens.model_bridge.sources.inspect.transformers_provider import ( + _parse_tool_calls, + ) + + block = _parse_tool_calls( + 'ok {"name": "add", "arguments": {"a": 1}}' + ) + assert block and block[0].function == "add" and block[0].arguments == {"a": 1} + bare = _parse_tool_calls('{"name": "f", "arguments": {}}') + assert bare and bare[0].function == "f" + assert _parse_tool_calls("no tool calls in this text") is None + + def test_tools_without_chat_template_raise(self): + import asyncio + + from inspect_ai.model import ChatMessageUser, GenerateConfig, get_model + from inspect_ai.tool import ToolInfo, ToolParams + + from transformer_lens.model_bridge.sources.inspect import ( # noqa: F401 register + transformers_provider, + ) + + api = get_model("tl_bridge/gpt2", memoize=False).api + tool = ToolInfo(name="add", description="add two ints", parameters=ToolParams()) + with pytest.raises(NotImplementedError, match="tool-aware chat template"): + asyncio.run( + api.generate( + [ChatMessageUser(content="2+2?")], [tool], None, GenerateConfig(max_tokens=3) + ) + ) + + def test_per_turn_capture_collected_from_transcript(self, tmp_path): + from inspect_ai import Task + from inspect_ai import eval as inspect_eval + from inspect_ai.dataset import Sample + from inspect_ai.solver import generate + + from transformer_lens.model_bridge.sources.inspect import ( # noqa: F401 register + transformers_provider, + ) + from transformer_lens.model_bridge.sources.inspect.eval import turn_activations + + task = Task( + dataset=[Sample(input="The capital of France is", target="Paris")], solver=generate() + ) + log = inspect_eval( + task, + model="tl_bridge/gpt2", + model_args={"capture": ["blocks.6.hook_out"]}, + max_tokens=4, + log_dir=str(tmp_path), + display="none", + )[0] + assert log.status == "success" + # each model turn's activations are recoverable from the transcript + turns = turn_activations(log.samples[0]) + assert len(turns) >= 1 + assert turns[0]["blocks.6.hook_out"].shape[-1] == 768 + + +class TestEvalPathStructuralGating: + """Both eval entry points (``model_args={"capture": [...]}`` and the solver's + ``extra_args`` route through ``_generate_capture``) must enforce the same structural + gate as the driver path — pinned on GPT-J, where resid_mid is gated.""" + + PARALLEL = "hf-internal-testing/tiny-random-GPTJForCausalLM" + GATED_HOOK = "blocks.0.ln2.hook_in" # resid_mid + + def test_model_args_capture_rejects_gated_kind(self): + from inspect_ai.model import get_model + + from transformer_lens.model_bridge.sources.inspect import ( # noqa: F401 register + transformers_provider, + ) + + with pytest.raises(ValueError, match="resid_mid"): + get_model(f"tl_bridge/{self.PARALLEL}", memoize=False, capture=[self.GATED_HOOK]) + + def test_extra_args_capture_rejects_gated_kind(self): + import asyncio + + from inspect_ai.model import ChatMessageUser, GenerateConfig, get_model + + from transformer_lens.model_bridge.sources.inspect import ( # noqa: F401 register + transformers_provider, + ) + + api = get_model(f"tl_bridge/{self.PARALLEL}", memoize=False).api + with pytest.raises(ValueError, match="resid_mid"): + asyncio.run( + api.generate( + [ChatMessageUser(content="hi")], + None, + None, + GenerateConfig(extra_body={"extra_args": {"capture": ["0:resid_mid"]}}), + ) + ) + + def test_model_args_unresolvable_hook_raises(self): + # model_args["capture"] must validate like the solver does (was silently dropping). + from inspect_ai.model import get_model + + from transformer_lens.model_bridge.sources.inspect import ( # noqa: F401 register + transformers_provider, + ) + + with pytest.raises(ValueError, match="not a fireable hook"): + get_model("tl_bridge/gpt2", memoize=False, capture=["blocks.0.not_a_hook"]) + + +class TestProviderReviewFixes: + """Targeted regressions for the post-merge review: validation parity, output_dir + absolute-resolve, ContextVar hook isolation (concurrency-safe), and per-turn capture + without the extra prompt forward.""" + + def test_solver_output_dir_resolved_absolute_at_construction(self, tmp_path, monkeypatch): + # A multi-eval run from different CWDs must not scatter artifacts. + + from transformer_lens.model_bridge.sources.inspect.eval import ( + capture_activations, + ) + + monkeypatch.chdir(tmp_path) + # The solver factory captures output_dir in its closure; resolve at construction. + cap = capture_activations(["blocks.0.hook_out"], output_dir="tl_acts") + # Move CWD; the path the solver writes to must still resolve to tmp_path/tl_acts. + elsewhere = tmp_path / "elsewhere" + elsewhere.mkdir() + monkeypatch.chdir(elsewhere) + # Closure inspection: the wrapped solve fn closes over the absolute output_dir. + absolute = str(tmp_path / "tl_acts") + assert any(absolute == c.cell_contents for c in (cap.__closure__ or ())) + + def test_hooks_isolated_by_contextvar(self): + # The hook only fires for the call whose call_id matches the contextvar — so two + # concurrent inspect_eval samples (each in their own contextvar copy) don't write + # into each other's raw dicts. + from transformer_lens.model_bridge.sources.inspect.transformers_provider import ( + _current_call_id, + _out_hook, + ) + + raw: dict = {} + hook = _out_hook( + layer=0, kind="resid_post", want_capture=True, spec=None, raw=raw, call_id="MINE" + ) + + # Default contextvar ("") doesn't match -> hook is a no-op. + hook(None, None, torch.ones(1, 3, 4)) + assert raw == {} + + # Set the contextvar to a DIFFERENT call's id -> still skip (concurrent peer). + token = _current_call_id.set("OTHER") + try: + hook(None, None, torch.ones(1, 3, 4)) + assert raw == {} + finally: + _current_call_id.reset(token) + + # Set the contextvar to our id -> hook fires. + token = _current_call_id.set("MINE") + try: + hook(None, None, torch.ones(1, 3, 4)) + assert (0, "resid_post") in raw + + # First-write-wins: a second call with different data doesn't overwrite (so + # generate's decode-step forwards don't clobber the prompt-forward capture). + first = raw[(0, "resid_post")].copy() + hook(None, None, torch.zeros(1, 3, 4)) + assert (raw[(0, "resid_post")] == first).all() + finally: + _current_call_id.reset(token) diff --git a/tests/unit/model_bridge/test_inspect_driver.py b/tests/unit/model_bridge/test_inspect_driver.py new file mode 100644 index 0000000000..b4b7894459 --- /dev/null +++ b/tests/unit/model_bridge/test_inspect_driver.py @@ -0,0 +1,438 @@ +"""Unit tests for InspectDriver — mocks the inspect_ai Model so no provider runs. + +The driver is the torch-free consumer side; these tests exercise it with a fake +async model returning a wire-encoded ModelOutput. Real end-to-end parity (gpt2 +through our provider) lives in tests/acceptance/model_bridge/test_inspect_provider.py. +""" +from __future__ import annotations + +import ast +import pathlib +from types import SimpleNamespace + +import numpy as np +import pytest +import torch + +# The driver lazily imports inspect_ai inside ``_generate``; without the ``inspect`` extra +# the import fires at first call and tests fail with ModuleNotFoundError. Skip-collect here. +pytest.importorskip("inspect_ai") + +from transformer_lens.config import TransformerBridgeConfig +from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter +from transformer_lens.model_bridge.driver_protocol import ( + Driver, + ForwardResult, + validate_driver, +) +from transformer_lens.model_bridge.remote_bridge import RemoteBridge +from transformer_lens.model_bridge.sources.inspect import ( + hooks, + intervention, + profiles, + wire, +) +from transformer_lens.model_bridge.sources.inspect.driver import InspectDriver + +N_LAYERS, D_MODEL, D_VOCAB = 2, 4, 16 + + +def _cfg() -> TransformerBridgeConfig: + return TransformerBridgeConfig( + d_model=D_MODEL, + d_head=2, + n_layers=N_LAYERS, + n_ctx=8, + n_heads=2, + d_vocab=D_VOCAB, + d_mlp=8, + architecture="GPT2LMHeadModel", + ) + + +def _adapter() -> ArchitectureAdapter: + adapter = ArchitectureAdapter(_cfg()) + adapter.component_mapping = {} + return adapter + + +def _fake_model(*, captures=None, logits=None, raises=None): + """Stand-in inspect_ai Model. ``captures``: {wire_key: (seq,d)}; ``logits``: (seq,vocab).""" + metadata: dict = {} + if captures is not None: + metadata["activations"] = wire.encode_activations(captures) + if logits is not None: + metadata["tl_logits"] = wire.encode_array(logits) + output = SimpleNamespace(metadata=metadata) + + model = SimpleNamespace() + + async def generate(_input, config=None): + if raises is not None: + raise raises + return output + + model.generate = generate + return model + + +def _driver(model=None) -> InspectDriver: + return InspectDriver(model=model or _fake_model(), adapter=_adapter(), tokenizer=None) + + +class TestProtocolConformance: + def test_is_driver_and_validates(self): + driver = _driver() + assert isinstance(driver, Driver) + validate_driver(driver) + + def test_no_torch_capabilities(self): + driver = _driver() + for feature in ("parameters", "state_dict", "gradients", "weight_access"): + assert driver.supports(feature) is False + + def test_provides_sequence_logits_true(self): + # Provider returns full-sequence logits, so loss/both are available. + assert _driver().provides_sequence_logits is True + + def test_tl_bridge_profile_can_disable_sequence_logits(self): + # The vLLM provider routes through TLBridgeProfile with provides_sequence_logits=False + # (set in source.py from api.provides_sequence_logits) so RemoteBridge.forward rejects + # loss/both — synthesized logits only cover the gen position; earlier positions are -inf. + driver = InspectDriver( + _fake_model(), + _adapter(), + tokenizer=None, + profile=profiles.TLBridgeProfile(provides_sequence_logits=False), + ) + assert driver.provides_sequence_logits is False + + +class TestHookSets: + def test_full_residual_attn_mlp_set(self): + supported = _driver().supported_hook_points + assert len(supported) == 5 * N_LAYERS # one canonical name per boundary, no aliases + for name in [ + "blocks.0.hook_in", # resid_pre + "blocks.0.ln2.hook_in", # resid_mid + "blocks.0.hook_out", # resid_post + "blocks.0.attn.hook_out", # attn_out + "blocks.0.mlp.hook_out", # mlp_out + ]: + assert name in supported + assert "blocks.0.hook_attn_out" not in supported # HookedTransformer alias not duplicated + + def test_nonfireable_disjoint_and_includes_headsplit(self): + driver = _driver() + assert driver.supported_hook_points.isdisjoint(driver.non_fireable_hook_points) + assert "blocks.0.attn.hook_pattern" in driver.non_fireable_hook_points + assert "ln_final.hook_normalized" in driver.non_fireable_hook_points + + def test_vllm_lens_profile_narrows_to_residual(self): + driver = InspectDriver( + _fake_model(), _adapter(), tokenizer=None, profile=profiles.VLLMLensProfile() + ) + assert driver.supported_hook_points == frozenset( + f"blocks.{i}.hook_out" for i in range(N_LAYERS) + ) + assert driver.provides_sequence_logits is False # no full logits via that path + # attn/mlp hooks our provider could do are non-fireable for the residual-only peer. + assert "blocks.0.attn.hook_out" in driver.non_fireable_hook_points + + +class TestKindGating: + """The provider's structural self-check feeds a kind set through to the driver.""" + + def test_supported_hook_points_filters_kinds(self): + only = hooks.supported_hook_points(2, kinds={"resid_pre", "resid_post"}) + assert only == frozenset( + f"blocks.{i}.{suffix}" for i in range(2) for suffix in ("hook_in", "hook_out") + ) + + def test_profile_restricts_to_detected_kinds(self): + # resid_mid gated (e.g. parallel-residual arch) → ln2.hook_in absent, rest present. + prof = profiles.TLBridgeProfile( + supported_kinds={"resid_pre", "resid_post", "attn_out", "mlp_out"} + ) + names = prof.supported_hooks(N_LAYERS) + assert "blocks.0.ln2.hook_in" not in names + assert "blocks.0.attn.hook_out" in names + assert len(names) == 4 * N_LAYERS + + def test_driver_moves_gated_kind_to_nonfireable(self): + prof = profiles.TLBridgeProfile( + supported_kinds={"resid_pre", "resid_post", "attn_out", "mlp_out"} + ) + driver = InspectDriver(_fake_model(), _adapter(), tokenizer=None, profile=prof) + assert "blocks.0.ln2.hook_in" not in driver.supported_hook_points + assert "blocks.0.ln2.hook_in" in driver.non_fireable_hook_points + + +class TestNormalizeInputIds: + def test_1d_tensor(self): + assert InspectDriver._normalize_input_ids(torch.tensor([1, 2, 3])) == [1, 2, 3] + + def test_2d_single_row(self): + assert InspectDriver._normalize_input_ids(torch.tensor([[1, 2, 3]])) == [1, 2, 3] + + def test_batch_gt_one_raises(self): + with pytest.raises(NotImplementedError, match="batch_size=1"): + InspectDriver._normalize_input_ids(torch.tensor([[1, 2], [3, 4]])) + + def test_cuda_like_tensor_moved_to_cpu(self): + # np.asarray can't read CUDA memory; the torch-free driver must duck-type + # .detach().cpu() first. Simulate: __array__ raises (as a CUDA tensor would), + # cpu() yields a real ndarray. + class FakeCuda: + def __init__(self, data): + self._data = data + self.moved = False + + def detach(self): + return self + + def cpu(self): + self.moved = True + return np.asarray(self._data) + + def __array__(self, *a, **k): + raise TypeError("can't convert cuda:0 device tensor to numpy") + + fake = FakeCuda([[1, 2, 3]]) + assert InspectDriver._normalize_input_ids(fake) == [1, 2, 3] + assert fake.moved + + +class TestForward: + def test_assembles_named_captures_and_full_logits(self): + caps = { + "0:resid_post": np.ones((3, D_MODEL), np.float32), + "0:attn_out": np.full((3, D_MODEL), 2.0, np.float32), + } + logits = np.zeros((3, D_VOCAB), np.float32) + logits[-1, 7] = 5.0 + driver = _driver(_fake_model(captures=caps, logits=logits)) + + result = driver.forward( + torch.tensor([[1, 2, 3]]), + capture=("blocks.0.hook_out", "blocks.0.attn.hook_out"), + ) + assert isinstance(result, ForwardResult) + assert tuple(result.captured["blocks.0.hook_out"].shape) == (1, 3, D_MODEL) # resid_post + assert tuple(result.captured["blocks.0.attn.hook_out"].shape) == (1, 3, D_MODEL) # attn_out + assert tuple(result.logits.shape) == (1, 3, D_VOCAB) # full sequence + assert int(result.logits[0, -1].argmax()) == 7 + + def test_empty_capture_captures_nothing(self): + # capture=() means "logits only" — the driver requests no activations. + driver = _driver( + _fake_model( + captures={"0:resid_post": np.ones((2, D_MODEL), np.float32)}, + logits=np.zeros((2, D_VOCAB), np.float32), + ) + ) + result = driver.forward(torch.tensor([[1, 2]])) + assert result.captured == {} + assert result.logits is not None + + def test_missing_hook_warns(self): + # Provider returns no activation for a requested+supported hook → warn, key absent. + driver = _driver(_fake_model(captures={}, logits=np.zeros((2, D_VOCAB), np.float32))) + with pytest.warns(UserWarning, match="no activation"): + result = driver.forward(torch.tensor([[1, 2]]), capture=("blocks.0.hook_out",)) + assert "blocks.0.hook_out" not in result.captured + + def test_resid_mid_consumed_under_its_name(self): + # The provider derives resid_mid (= resid_pre + attn_out) and sends it under + # its wire key; the driver surfaces it as blocks.{i}.ln2.hook_in. + caps = {"0:resid_mid": np.full((2, D_MODEL), 4.0, np.float32)} + driver = _driver(_fake_model(captures=caps, logits=np.zeros((2, D_VOCAB), np.float32))) + result = driver.forward(torch.tensor([[1, 2]]), capture=("blocks.0.ln2.hook_in",)) + assert np.allclose(result.captured["blocks.0.ln2.hook_in"][0], 4.0) + + def test_provider_error_propagates(self): + driver = _driver(_fake_model(raises=RuntimeError("boom"))) + with pytest.raises(RuntimeError, match="boom"): + driver.forward(torch.tensor([[1, 2, 3]])) + + def test_rejects_max_new_tokens_gt_one(self): + with pytest.raises(NotImplementedError, match="max_new_tokens=1"): + _driver().forward(torch.tensor([[1, 2]]), max_new_tokens=2) + + def test_closed_driver_raises(self): + driver = _driver() + driver.close() + with pytest.raises(RuntimeError, match="closed"): + driver.forward(torch.tensor([[1, 2]])) + + +class TestAsyncWrapper: + def test_reuses_one_loop_thread(self): + caps = {"0:resid_post": np.ones((1, D_MODEL), np.float32)} + driver = _driver(_fake_model(captures=caps, logits=np.zeros((1, D_VOCAB), np.float32))) + driver.forward(torch.tensor([[1]])) + first = driver._loop_thread + driver.forward(torch.tensor([[1]])) + assert driver._loop_thread is first and first.is_alive() + + def test_works_inside_running_loop(self): + """The Jupyter case: a loop already running on the calling thread.""" + import asyncio + + caps = {"0:resid_post": np.ones((1, D_MODEL), np.float32)} + driver = _driver(_fake_model(captures=caps, logits=np.zeros((1, D_VOCAB), np.float32))) + + async def run(): + return driver.forward(torch.tensor([[1]])) + + assert isinstance(asyncio.run(run()), ForwardResult) + + +class TestWire: + def test_array_round_trip(self): + arr = np.arange(12, dtype=np.float32).reshape(3, 4) + assert np.array_equal(wire.decode_array(wire.encode_array(arr)), arr) + + def test_flat_activations_round_trip(self): + caps = { + "0:resid_post": np.ones((2, 4), np.float32), + "1:attn_out": np.full((2, 4), 3.0, np.float32), + } + meta = {"activations": wire.encode_activations(caps)} + out = wire.decode_activations(meta, ["0:resid_post", "1:attn_out"]) + assert np.array_equal(out["1:attn_out"], caps["1:attn_out"]) + + def test_vllm_lens_residual_stream_fallback(self): + # A peer provider's nested residual_stream decodes for resid_post keys. + meta = { + "activations": { + "residual_stream": {"3": wire.encode_array(np.ones((2, 4), np.float32))} + } + } + out = wire.decode_activations(meta, ["3:resid_post"]) + assert tuple(out["3:resid_post"].shape) == (2, 4) + + +class TestHooksRegistry: + def test_resolve(self): + assert hooks.resolve("blocks.2.attn.hook_out") == (2, "attn_out") + assert hooks.resolve("blocks.2.mlp.hook_out") == (2, "mlp_out") + assert hooks.resolve("blocks.0.ln2.hook_in") == (0, "resid_mid") + assert hooks.resolve("blocks.0.hook_in") == (0, "resid_pre") + assert hooks.resolve("blocks.0.hook_out") == (0, "resid_post") + assert hooks.resolve("embed.hook_out") is None + assert ( + hooks.resolve("blocks.2.hook_attn_out") is None + ) # HookedTransformer alias not exposed + + +class TestInterventionTranslation: + def _supported(self): + return hooks.supported_hook_points(N_LAYERS) + + def test_op_translates_to_wire_key(self): + out = intervention.build_interventions( + {"blocks.1.hook_out": {"op": "suppress"}}, self._supported() + ) + assert out == {"1:resid_post": {"op": "suppress"}} + + def test_attn_out_intervention(self): + out = intervention.build_interventions( + {"blocks.0.attn.hook_out": {"op": "scale", "factor": 0.5}}, self._supported() + ) + assert out == {"0:attn_out": {"op": "scale", "factor": 0.5}} + + def test_resid_mid_is_capture_only(self): + with pytest.raises(ValueError, match="capture-only"): + intervention.build_interventions( + {"blocks.0.ln2.hook_in": {"op": "suppress"}}, self._supported() + ) + + def test_callable_rejected(self): + with pytest.raises(NotImplementedError, match="specs"): + intervention.build_interventions({"blocks.0.hook_out": lambda a: a}, self._supported()) + + def test_unknown_hook_rejected(self): + with pytest.raises(ValueError, match="not in supported_hook_points"): + intervention.build_interventions( + {"blocks.9.hook_out": {"op": "suppress"}}, self._supported() + ) + + def test_bad_op_rejected(self): + with pytest.raises(ValueError, match="Unsupported intervention op"): + intervention.build_interventions( + {"blocks.0.hook_out": {"op": "clamp"}}, self._supported() + ) + + +class TestVLLMLensProfile: + """The vllm-lens request/response codec (live path unverified; logic is testable).""" + + def test_build_request_uses_output_residual_stream(self): + tokenizer = SimpleNamespace(decode=lambda ids: "hello world") + prompt, extra = profiles.VLLMLensProfile().build_request( + [1, 2, 3], ["0:resid_post", "1:resid_post"], [], True, tokenizer + ) + assert prompt == "hello world" # detokenized — vllm-lens re-tokenizes + assert extra == {"output_residual_stream": [0, 1]} + + def test_decode_logits_one_hot_from_completion(self): + tokenizer = SimpleNamespace(encode=lambda text: [9]) + output = SimpleNamespace(completion="x", metadata={}) + logits = profiles.VLLMLensProfile().decode_logits(output, 3, D_VOCAB, tokenizer) + assert tuple(logits.shape) == (1, 3, D_VOCAB) + assert int(logits[0, -1].argmax()) == 9 + + def test_non_additive_intervention_rejected_without_vllm_lens(self): + # Validation happens before the lazy vllm_lens import, so this works uninstalled. + with pytest.raises(NotImplementedError, match="additive steering"): + profiles.VLLMLensProfile().translate_interventions( + {"blocks.0.hook_out": {"op": "suppress"}}, + frozenset({"blocks.0.hook_out"}), + ) + + def test_no_interventions_needs_no_vllm_lens(self): + assert profiles.VLLMLensProfile().translate_interventions({}, frozenset()) == [] + + +class TestThroughBridge: + def test_replays_as_torch(self): + caps = {"0:resid_post": np.ones((3, D_MODEL), np.float32)} + driver = _driver(_fake_model(captures=caps, logits=np.zeros((3, D_VOCAB), np.float32))) + bridge = RemoteBridge(adapter=_adapter(), tokenizer=None, driver=driver) + + fired: list = [] + bridge.add_hook("blocks.0.hook_out", lambda act, hook: fired.append(act)) + bridge.forward(torch.tensor([[1, 2, 3]])) + assert len(fired) == 1 and isinstance(fired[0], torch.Tensor) # numpy→torch at the bridge + assert tuple(fired[0].shape) == (1, 3, D_MODEL) + + def test_loss_now_supported(self): + # Full-sequence logits ⇒ the bridge computes loss (no longer rejected). + logits = np.zeros((3, D_VOCAB), np.float32) + driver = _driver(_fake_model(captures={}, logits=logits)) + bridge = RemoteBridge(adapter=_adapter(), tokenizer=None, driver=driver) + loss = bridge.forward(torch.tensor([[1, 2, 3]]), return_type="loss") + assert isinstance(loss, torch.Tensor) and loss.ndim == 0 + + def test_context_manager_closes(self): + driver = _driver() + bridge = RemoteBridge(adapter=_adapter(), tokenizer=None, driver=driver) + with bridge: + pass + assert driver._model is None + + +def test_driver_imports_no_torch(): + """The acceptance gate: the driver file must not import torch (data-only boundary).""" + import transformer_lens.model_bridge.sources.inspect.driver as drv + + tree = ast.parse(pathlib.Path(drv.__file__).read_text()) + offenders: list[str] = [] + for node in ast.walk(tree): + if isinstance(node, ast.Import): + offenders += [a.name for a in node.names if a.name.split(".")[0] == "torch"] + elif isinstance(node, ast.ImportFrom): + if (node.module or "").split(".")[0] == "torch": + offenders.append(node.module or "") + assert not offenders, f"driver.py must be torch-free; found torch imports: {offenders}" diff --git a/tests/unit/model_bridge/test_inspect_vllm_provider.py b/tests/unit/model_bridge/test_inspect_vllm_provider.py new file mode 100644 index 0000000000..921c8e6fd8 --- /dev/null +++ b/tests/unit/model_bridge/test_inspect_vllm_provider.py @@ -0,0 +1,564 @@ +"""Unit tests for the vLLM-backed Inspect provider (``tl_bridge_vllm``) — mocks vLLM so +no vllm install is needed. Live end-to-end verification (real vLLM, real GPU) is in the +Colab walkthrough; the mocked tests pin the construction, generate→ModelOutput conversion, +and the validation surface (locked kwargs, capture path, model_args["capture"]).""" +from __future__ import annotations + +import asyncio +import sys +import types +from types import SimpleNamespace +from typing import Any +from unittest.mock import MagicMock + +import pytest + +# Tests deferred-import ``inspect_ai.model`` inside method bodies; provider construction +# in the fixtures also pulls inspect_ai transitively. Skip when the ``inspect`` extra is +# absent rather than fail each test with ModuleNotFoundError. +pytest.importorskip("inspect_ai") + + +def _install_vllm_mocks(monkeypatch, llm_class: Any) -> None: + """Install fake ``vllm``/``vllm.inputs``/``vllm.distributed.parallel_state`` modules + in ``sys.modules`` so the provider's lazy ``from vllm import …`` resolves to mocks.""" + vllm = types.ModuleType("vllm") + vllm.LLM = llm_class + vllm.SamplingParams = MagicMock(name="SamplingParams") + vllm_inputs = types.ModuleType("vllm.inputs") + vllm_inputs.TokensPrompt = MagicMock(name="TokensPrompt") + vllm.inputs = vllm_inputs + vllm_dist = types.ModuleType("vllm.distributed") + vllm_ps = types.ModuleType("vllm.distributed.parallel_state") + vllm_ps.destroy_model_parallel = MagicMock(name="destroy_model_parallel") + vllm_ps.destroy_distributed_environment = MagicMock(name="destroy_distributed_environment") + vllm_dist.parallel_state = vllm_ps + monkeypatch.setitem(sys.modules, "vllm", vllm) + monkeypatch.setitem(sys.modules, "vllm.inputs", vllm_inputs) + monkeypatch.setitem(sys.modules, "vllm.distributed", vllm_dist) + monkeypatch.setitem(sys.modules, "vllm.distributed.parallel_state", vllm_ps) + + +def _fake_tokenizer(monkeypatch) -> Any: + """A fake ``AutoTokenizer.from_pretrained`` returning a tokenizer with a trivial + chat-template-free tokenize/decode (just maps ints↔single-char strings).""" + tok = SimpleNamespace(chat_template=None, pad_token_id=0, eos_token_id=1) + + def encode(text: str) -> list[int]: + return [ord(c) % 50 for c in str(text)[:32]] + + tok.__call__ = lambda text, **kw: SimpleNamespace(input_ids=encode(text)) # type: ignore[attr-defined] + callable_tok = MagicMock(wraps=tok) + callable_tok.side_effect = lambda text, **kw: SimpleNamespace(input_ids=encode(text)) + callable_tok.decode = lambda ids, **kw: "".join(chr(int(i)) for i in ids) + callable_tok.chat_template = None + callable_tok.pad_token_id = 0 + callable_tok.eos_token_id = 1 + + from transformers import AutoTokenizer + + monkeypatch.setattr(AutoTokenizer, "from_pretrained", lambda *a, **kw: callable_tok) + return callable_tok + + +def _fake_hf_config(n_layers: int = 2, d_model: int = 4, vocab_size: int = 128) -> Any: + """Minimal HF config the overlay + provider reach into (vocab_size, + num_hidden_layers, hidden_size, architectures, torch_dtype).""" + return SimpleNamespace( + architectures=["LlamaForCausalLM"], + hidden_size=d_model, + num_hidden_layers=n_layers, + vocab_size=vocab_size, + torch_dtype="float16", + ) + + +def _patch_construction(monkeypatch, hf_config: Any) -> None: + """Patch AutoConfig + plugin.configure/register + extract_hf_config so __init__ runs + without vllm / real worker processes.""" + from transformers import AutoConfig + + from transformer_lens.model_bridge.sources.vllm import internals, plugin + + monkeypatch.setattr(AutoConfig, "from_pretrained", lambda *a, **kw: hf_config) + monkeypatch.setattr(plugin, "configure", lambda **kw: None) + monkeypatch.setattr(plugin, "register", lambda: None) + monkeypatch.setattr(internals, "extract_hf_config", lambda llm: hf_config) + + +def _make_request_output( + new_token_ids: list[int], finish_reason: str = "length", logprobs: Any = None +) -> Any: + """Fake vLLM ``RequestOutput`` exposing ``.outputs[0].{token_ids, finish_reason, logprobs}``.""" + inner = SimpleNamespace( + token_ids=list(new_token_ids), finish_reason=finish_reason, logprobs=logprobs + ) + return SimpleNamespace(outputs=[inner], prompt_token_ids=[]) + + +def _make_provider(monkeypatch, llm_instance: Any, hf_config: Any | None = None): + """Construct the provider with mocked vllm.LLM + fake tokenizer + patched AutoConfig / + plugin / extract_hf_config; device='cpu' so intermediate tensors don't require CUDA.""" + _fake_tokenizer(monkeypatch) + hf_config = hf_config or _fake_hf_config() + _patch_construction(monkeypatch, hf_config) + llm_class = MagicMock(return_value=llm_instance, name="LLM") + _install_vllm_mocks(monkeypatch, llm_class) + from transformer_lens.model_bridge.sources.inspect.vllm_provider import ( + TransformerLensVLLMModelAPI, + ) + + return TransformerLensVLLMModelAPI("any/model", device="cpu"), llm_class + + +class TestModuleSafety: + def test_module_imports_without_vllm(self): + # Confirm the lazy-vllm pattern: importing the module loads no vllm symbols. + assert "vllm" not in sys.modules + from transformer_lens.model_bridge.sources.inspect import ( # noqa: F401 + vllm_provider, + ) + + assert "vllm" not in sys.modules + + def test_modelapi_registered_as_tl_bridge_vllm(self): + from transformer_lens.model_bridge.sources.inspect.vllm_provider import ( + PROVIDER_NAME, + ) + + assert PROVIDER_NAME == "tl_bridge_vllm" + + +class TestConstruction: + def test_rejects_locked_kwarg_override(self, monkeypatch): + _fake_tokenizer(monkeypatch) + _patch_construction(monkeypatch, _fake_hf_config()) + _install_vllm_mocks(monkeypatch, llm_class=MagicMock(name="LLM")) + from transformer_lens.model_bridge.sources.inspect.vllm_provider import ( + TransformerLensVLLMModelAPI, + ) + + with pytest.raises(ValueError, match="tensor_parallel_size"): + TransformerLensVLLMModelAPI("any/model", vllm_kwargs={"tensor_parallel_size": 2}) + + def test_gated_capture_kind_raises(self, monkeypatch): + # vLLM's overlay serves resid_post/attn_out/mlp_out; resid_pre and resid_mid are gated. + provider_factory_args: dict[str, Any] = {"capture": ["blocks.0.hook_in"]} # resid_pre + _fake_tokenizer(monkeypatch) + _patch_construction(monkeypatch, _fake_hf_config()) + _install_vllm_mocks(monkeypatch, llm_class=MagicMock(name="LLM")) + from transformer_lens.model_bridge.sources.inspect.vllm_provider import ( + TransformerLensVLLMModelAPI, + ) + + with pytest.raises(ValueError, match="resid_pre|gated"): + TransformerLensVLLMModelAPI("any/model", device="cpu", **provider_factory_args) + + def test_served_capture_kind_accepted(self, monkeypatch): + # resid_post (blocks.{i}.hook_out) IS served by the vLLM overlay — must not raise. + _fake_tokenizer(monkeypatch) + _patch_construction(monkeypatch, _fake_hf_config()) + _install_vllm_mocks(monkeypatch, llm_class=MagicMock(name="LLM")) + from transformer_lens.model_bridge.sources.inspect.vllm_provider import ( + TransformerLensVLLMModelAPI, + ) + + api = TransformerLensVLLMModelAPI("any/model", device="cpu", capture=["blocks.0.hook_out"]) + assert "0:resid_post" in api._eval_capture + + def test_passes_locked_kwargs_to_llm(self, monkeypatch): + provider, llm_class = _make_provider(monkeypatch, MagicMock()) + kwargs = llm_class.call_args.kwargs + assert kwargs["tensor_parallel_size"] == 1 + assert kwargs["skip_tokenizer_init"] is True + assert kwargs["disable_log_stats"] is True + # Inc 2: capture wiring requires worker_extension_cls + full-vocab logprobs. + assert kwargs["worker_extension_cls"].endswith("TLWorkerExtension") + assert kwargs["max_logprobs"] == 128 # _fake_hf_config().vocab_size + assert kwargs["max_num_batched_tokens"] == 2048 # default + + def test_supported_kinds_match_overlay(self, monkeypatch): + provider, _ = _make_provider(monkeypatch, MagicMock()) + assert provider.supported_kinds() == frozenset({"resid_post", "attn_out", "mlp_out"}) + assert "resid_pre" in provider.capability_note() + + def test_provides_sequence_logits_is_false(self, monkeypatch): + # vLLM's sampler bypasses lm_head; _synthesize_logits only populates the gen + # position, so RemoteBridge.forward(return_type='loss'|'both') would NaN. The + # class attr is the signal source.py reads to set TLBridgeProfile's flag. + provider, _ = _make_provider(monkeypatch, MagicMock()) + assert provider.provides_sequence_logits is False + + +class TestGenerateEval: + def _config(self, **overrides): + from inspect_ai.model import GenerateConfig + + return GenerateConfig(**overrides) + + def _user_msg(self, content="hi"): + from inspect_ai.model import ChatMessageUser + + return [ChatMessageUser(content=content)] + + def test_generate_eval_basic_completion_and_usage(self, monkeypatch): + llm = MagicMock() + llm.generate.return_value = [_make_request_output([65, 66, 67], finish_reason="length")] + provider, _ = _make_provider(monkeypatch, llm) + out = asyncio.run( + provider.generate(self._user_msg("xy"), None, None, self._config(max_tokens=5)) + ) + choice = out.choices[0] + assert choice.message.text == "ABC" + assert choice.stop_reason == "max_tokens" # vLLM 'length' → inspect 'max_tokens' + assert out.usage.output_tokens == 3 + assert out.usage.total_tokens == out.usage.input_tokens + 3 + + def test_generate_eval_resets_interventions_first(self, monkeypatch): + # Worker intervention buffers are persistent across calls; a prior capture-path + # forward(intervene=...) would leak into the eval generate without an explicit + # identity reset. Contract: tl_set_interventions({}) fires before llm.generate. + llm = MagicMock() + llm.generate.return_value = [_make_request_output([65], finish_reason="length")] + provider, _ = _make_provider(monkeypatch, llm) + asyncio.run(provider.generate(self._user_msg(), None, None, self._config(max_tokens=1))) + # First rpc call must be the identity reset (no _eval_capture configured ⇒ exactly one). + rpc_calls = llm.collective_rpc.call_args_list + assert rpc_calls, "eval path must call tl_set_interventions before generate" + assert rpc_calls[0].args[0] == "tl_set_interventions" + assert rpc_calls[0].kwargs["args"] == ({},) + + def test_generate_eval_finish_stop_maps_to_stop(self, monkeypatch): + llm = MagicMock() + llm.generate.return_value = [_make_request_output([65, 1], finish_reason="stop")] + provider, _ = _make_provider(monkeypatch, llm) + out = asyncio.run( + provider.generate(self._user_msg(), None, None, self._config(max_tokens=4)) + ) + assert out.choices[0].stop_reason == "stop" + + def test_generate_eval_with_logprobs(self, monkeypatch): + vllm_lp = SimpleNamespace(logprob=-0.5, rank=1, decoded_token="A") + vllm_alt = SimpleNamespace(logprob=-2.0, rank=2, decoded_token="B") + step = {65: vllm_lp, 66: vllm_alt} + llm = MagicMock() + llm.generate.return_value = [ + _make_request_output([65], finish_reason="length", logprobs=[step]) + ] + provider, _ = _make_provider(monkeypatch, llm) + out = asyncio.run( + provider.generate( + self._user_msg(), + None, + None, + self._config(max_tokens=1, logprobs=True, top_logprobs=2), + ) + ) + lp = out.choices[0].logprobs + assert lp is not None and len(lp.content) == 1 + assert lp.content[0].logprob == -0.5 + assert len(lp.content[0].top_logprobs) == 2 + + def test_generate_eval_passes_temperature_top_p(self, monkeypatch): + llm = MagicMock() + llm.generate.return_value = [_make_request_output([65], finish_reason="length")] + provider, _ = _make_provider(monkeypatch, llm) + asyncio.run( + provider.generate( + self._user_msg(), + None, + None, + self._config(max_tokens=1, temperature=0.7, top_p=0.9, seed=42), + ) + ) + # SamplingParams was instantiated; confirm the sampling kwargs flowed through. + from transformer_lens.model_bridge.sources.inspect import ( # noqa: F401 + vllm_provider as _vp, + ) + + sp_cls = sys.modules["vllm"].SamplingParams + sp_kwargs = sp_cls.call_args.kwargs + assert sp_kwargs["temperature"] == 0.7 + assert sp_kwargs["top_p"] == 0.9 + assert sp_kwargs["seed"] == 42 + + +class TestCapturePath: + """TL-driven single-forward capture: extra_args carries input_ids/capture/interventions; + provider pushes specs → generates → reads captures via collective_rpc.""" + + def _config_with(self, extra_args: dict[str, Any]): + from inspect_ai.model import GenerateConfig + + return GenerateConfig(extra_body={"extra_args": extra_args}) + + def _make_capture_provider(self, monkeypatch, *, captures=None, next_token: int = 65): + import torch + + vllm_lp = SimpleNamespace(logprob=-0.1, rank=1, decoded_token="A") + request_output = SimpleNamespace( + outputs=[ + SimpleNamespace( + token_ids=[next_token], + logprobs=[{next_token: vllm_lp}], + finish_reason="stop", + ) + ], + prompt_token_ids=[], + ) + llm = MagicMock() + llm.generate.return_value = [request_output] + captures = ( + captures + if captures is not None + else {"blocks.0.hook_out": torch.zeros(4, 4, dtype=torch.float32)} + ) + + def rpc(method, args=()): + return [captures] if method == "tl_read_captures" else [None] + + llm.collective_rpc.side_effect = rpc + provider, llm_class = _make_provider(monkeypatch, llm) + return provider, llm, llm_class + + def test_capture_pushes_interventions_and_reads(self, monkeypatch): + provider, llm, _ = self._make_capture_provider(monkeypatch) + asyncio.run( + provider.generate( + [], + None, + None, + self._config_with( + { + "input_ids": [10, 20, 30, 40], + "capture": ["0:resid_post"], + "interventions": {"0:resid_post": {"op": "suppress"}}, + } + ), + ) + ) + # rpc order: set_interventions (resets stale + applies new) → reset_capture_flags + # (opens the first-write-wins gate for the prefill below) → read. + methods = [c.args[0] for c in llm.collective_rpc.call_args_list] + assert methods == ["tl_set_interventions", "tl_reset_capture_flags", "tl_read_captures"] + # Specs translated wire key → TL hook name (worker is keyed by hook name). + set_call = llm.collective_rpc.call_args_list[0] + assert set_call.kwargs["args"] == ({"blocks.0.hook_out": {"op": "suppress"}},) + # Read takes [prompt_lens] + hook names so the worker slices the GPU buffer. + read_call = llm.collective_rpc.call_args_list[2] + assert read_call.kwargs["args"] == ([4], ["blocks.0.hook_out"]) + + def test_capture_returns_wire_format_activations(self, monkeypatch): + import torch + + captures = {"blocks.0.hook_out": torch.full((3, 4), 0.5, dtype=torch.float32)} + provider, _, _ = self._make_capture_provider(monkeypatch, captures=captures) + out = asyncio.run( + provider.generate( + [], + None, + None, + self._config_with({"input_ids": [1, 2, 3], "capture": ["0:resid_post"]}), + ) + ) + assert "0:resid_post" in out.metadata["activations"] + # Wire envelope is {"data": b64, "dtype": str, "shape": list} — driver decodes verbatim. + entry = out.metadata["activations"]["0:resid_post"] + assert entry["shape"] == [3, 4] and entry["dtype"] == "float32" + + def test_capture_synthesizes_logits_when_requested(self, monkeypatch): + provider, _, _ = self._make_capture_provider(monkeypatch) + out = asyncio.run( + provider.generate( + [], + None, + None, + self._config_with( + { + "input_ids": [10, 20, 30], + "capture": ["0:resid_post"], + "return_logits": True, + } + ), + ) + ) + # logits shape (n_tokens, d_vocab); last position holds vLLM's logprobs at the gen step. + entry = out.metadata["tl_logits"] + assert entry["shape"] == [3, 128] + + def test_capture_skips_logits_when_disabled(self, monkeypatch): + provider, _, _ = self._make_capture_provider(monkeypatch) + out = asyncio.run( + provider.generate( + [], + None, + None, + self._config_with( + { + "input_ids": [10, 20, 30], + "capture": ["0:resid_post"], + "return_logits": False, + } + ), + ) + ) + assert "tl_logits" not in out.metadata + + def test_capture_gated_kind_raises(self, monkeypatch): + # resid_pre (blocks.{i}.hook_in) is gated by vLLM's fused execution. + provider, _, _ = self._make_capture_provider(monkeypatch) + with pytest.raises(ValueError, match="resid_pre|gated"): + asyncio.run( + provider.generate( + [], + None, + None, + self._config_with({"input_ids": [1, 2], "capture": ["0:resid_pre"]}), + ) + ) + + def test_capture_unparseable_wire_key_raises(self, monkeypatch): + # Kind is served, but the layer prefix is non-numeric — name_from_wire_key returns None. + provider, _, _ = self._make_capture_provider(monkeypatch) + with pytest.raises(ValueError, match="unrecognised wire keys"): + asyncio.run( + provider.generate( + [], + None, + None, + self._config_with({"input_ids": [1, 2], "capture": ["abc:resid_post"]}), + ) + ) + + def test_intervention_unknown_wire_key_raises(self, monkeypatch): + provider, _, _ = self._make_capture_provider(monkeypatch) + with pytest.raises(ValueError, match="not a fireable hook"): + asyncio.run( + provider.generate( + [], + None, + None, + self._config_with( + { + "input_ids": [1, 2], + "capture": ["0:resid_post"], + "interventions": {"abc:resid_post": {"op": "suppress"}}, + } + ), + ) + ) + + def test_prompt_exceeds_max_batched_tokens_raises(self, monkeypatch): + provider, _, _ = self._make_capture_provider(monkeypatch) + with pytest.raises(ValueError, match="max_num_batched_tokens"): + asyncio.run( + provider.generate( + [], + None, + None, + self._config_with( + {"input_ids": list(range(3000)), "capture": ["0:resid_post"]} + ), + ) + ) + + def test_capture_completion_decodes_generated_token(self, monkeypatch): + # The ModelOutput choice's text must be the decoded next token (parity with HF provider). + provider, _, _ = self._make_capture_provider(monkeypatch, next_token=72) + out = asyncio.run( + provider.generate( + [], + None, + None, + self._config_with({"input_ids": [1, 2], "capture": ["0:resid_post"]}), + ) + ) + # The fake tokenizer decodes by joining chr(id) — 72 → 'H'. + assert out.choices[0].message.text == "H" + + +class TestPerTurnCapture: + """``model_args['capture']`` triggers a snapshot-via-single-token-forward before every + eval generate (vLLM's decode steps overwrite buffer row 0, so the HF first-write-wins + trick can't apply); the snapshot lands in ``ModelOutput.metadata`` for agent rollouts.""" + + def _user_msg(self, content: str = "hi"): + from inspect_ai.model import ChatMessageUser + + return [ChatMessageUser(content=content)] + + def _make_provider_with_capture(self, monkeypatch, capture: list[str]): + import torch + + # llm.generate runs twice per eval (snapshot then real generate); the same fake + # output works for both. collective_rpc returns canned captures for tl_read_captures. + llm = MagicMock() + llm.generate.return_value = [_make_request_output([65, 66], finish_reason="length")] + captures = {"blocks.0.hook_out": torch.zeros(4, 4, dtype=torch.float32)} + + def rpc(method, args=()): + return [captures] if method == "tl_read_captures" else [None] + + llm.collective_rpc.side_effect = rpc + + _fake_tokenizer(monkeypatch) + _patch_construction(monkeypatch, _fake_hf_config()) + llm_class = MagicMock(return_value=llm, name="LLM") + _install_vllm_mocks(monkeypatch, llm_class) + from transformer_lens.model_bridge.sources.inspect.vllm_provider import ( + TransformerLensVLLMModelAPI, + ) + + return TransformerLensVLLMModelAPI("any/model", device="cpu", capture=capture), llm + + def test_eval_capture_during_generate_no_extra_forward(self, monkeypatch): + from inspect_ai.model import GenerateConfig + + provider, llm = self._make_provider_with_capture(monkeypatch, ["blocks.0.hook_out"]) + out = asyncio.run( + provider.generate(self._user_msg(), None, None, GenerateConfig(max_tokens=4)) + ) + # rpc order: leak-guard reset → open capture gates → read after the eval generate. + # Prefill of the eval generate captures (first-write-wins); decode steps self-copy. + methods = [c.args[0] for c in llm.collective_rpc.call_args_list] + assert methods == [ + "tl_set_interventions", + "tl_reset_capture_flags", + "tl_read_captures", + ] + leak_guard = llm.collective_rpc.call_args_list[0] + assert leak_guard.kwargs["args"] == ({},) + # Single llm.generate now — the per-turn capture rides the same forward (was 2 before). + assert llm.generate.call_count == 1 + # Capture lands in metadata as the same wire format the InspectDriver consumes. + assert out.metadata is not None and "0:resid_post" in out.metadata["activations"] + + def test_no_capture_only_leak_guard_rpc(self, monkeypatch): + from inspect_ai.model import GenerateConfig + + llm = MagicMock() + llm.generate.return_value = [_make_request_output([65], finish_reason="stop")] + provider, _ = _make_provider(monkeypatch, llm) + out = asyncio.run( + provider.generate(self._user_msg(), None, None, GenerateConfig(max_tokens=1)) + ) + # No per-turn capture ⇒ no snapshot, no metadata. The only rpc is the identity + # reset guarding against stale interventions leaking from a prior capture-path call. + assert out.metadata is None + rpc_calls = llm.collective_rpc.call_args_list + assert len(rpc_calls) == 1 + assert rpc_calls[0].args[0] == "tl_set_interventions" + assert rpc_calls[0].kwargs["args"] == ({},) + + def test_eval_capture_emits_eval_completion_too(self, monkeypatch): + # The eval still returns the real eval generate's completion + usage — snapshot is + # an additive metadata side channel, not a substitute for the eval output. + from inspect_ai.model import GenerateConfig + + provider, _ = self._make_provider_with_capture(monkeypatch, ["blocks.0.hook_out"]) + out = asyncio.run( + provider.generate(self._user_msg(), None, None, GenerateConfig(max_tokens=4)) + ) + # _make_request_output([65, 66]) ⇒ "AB" via fake tokenizer. + assert out.choices[0].message.text == "AB" + assert out.usage.output_tokens == 2 diff --git a/transformer_lens/model_bridge/remote_bridge.py b/transformer_lens/model_bridge/remote_bridge.py index 0c8cc5b25d..a5c4ad9a05 100644 --- a/transformer_lens/model_bridge/remote_bridge.py +++ b/transformer_lens/model_bridge/remote_bridge.py @@ -57,6 +57,15 @@ def boot_vllm(*args: Any, **kwargs: Any) -> "RemoteBridge": return _boot_vllm(*args, **kwargs) + @staticmethod + def boot_inspect(*args: Any, **kwargs: Any) -> "RemoteBridge": + """Boot a model via an inspect_ai provider. Returns a RemoteBridge wrapping + an InspectDriver. Lazy import keeps remote_bridge inspect-agnostic. See + :func:`sources.inspect.boot_inspect` for kwargs.""" + from .sources.inspect import boot_inspect as _boot_inspect + + return _boot_inspect(*args, **kwargs) + def _scan_existing_hooks(self, module: Any, prefix: str = "") -> None: """No-op: registry built from driver declarations in __init__.""" @@ -80,12 +89,17 @@ def forward( "logits[..., -1, :] (or the per-row last token in batched mode)." ) if isinstance(input, str): - assert self.tokenizer is not None, "Tokenizer must be set for string input." - tokens = self.tokenizer.encode(input, return_tensors="pt") - kwargs["input_ids"] = tokens + kwargs["input_ids"] = self.to_tokens(input) # BOS-aware, matches boot_transformers elif input is not None: kwargs["input_ids"] = input + # Only request hooks with a registered handler, so a plain forward(tokens) + # ships logits alone instead of the full residual decomposition every call. + if "capture" not in kwargs: + kwargs["capture"] = tuple( + name for name, hp in self._hook_registry.items() if hp.fwd_hooks + ) + result: ForwardResult = self._driver.forward(**kwargs) if result.captured: self._replay_captures(result.captured) @@ -147,11 +161,35 @@ def run_with_hooks( **kwargs, ) - def to_tokens(self, text: Any, *args: Any, **kwargs: Any) -> Any: - """Tokenize via ``self.tokenizer``. BOS/padding handling lives on - :class:`TransformerBridge`.""" + def to_tokens(self, input: Any, prepend_bos: bool | None = None, truncate: bool = True) -> Any: + """Tokenize a string with the same BOS handling as ``TransformerBridge``. + + Mirrors ``cfg.default_prepend_bos`` / ``tokenizer_prepends_bos`` so + ``boot_inspect(m).run_with_cache("text")`` matches ``boot_transformers(m)`` + on the same string — a bare ``encode`` (no BOS) would silently diverge. + """ + from transformer_lens import utils + assert self.tokenizer is not None, "Tokenizer must be set." - return self.tokenizer.encode(text, return_tensors="pt") + if prepend_bos is None: + prepend_bos = getattr(self.cfg, "default_prepend_bos", True) + tokenizer_prepends_bos = getattr(self.cfg, "tokenizer_prepends_bos", True) + if prepend_bos and not tokenizer_prepends_bos: + input = utils.get_input_with_manually_prepended_bos(self.tokenizer.bos_token, input) + if isinstance(input, str): + input = [input] + # A single sequence never needs padding; only pad when batching (which also + # avoids requiring a pad_token on tokenizers that lack one, e.g. raw gpt2). + tokens = self.tokenizer( + input, + return_tensors="pt", + padding=len(input) > 1, + truncation=truncate, + max_length=self.cfg.n_ctx if truncate else None, + )["input_ids"] + if not prepend_bos and tokenizer_prepends_bos: + tokens = utils.get_tokens_with_bos_removed(self.tokenizer, tokens) + return tokens def __enter__(self) -> "RemoteBridge": """Use as a context manager so the engine is released on exit: diff --git a/transformer_lens/model_bridge/sources/inspect/__init__.py b/transformer_lens/model_bridge/sources/inspect/__init__.py new file mode 100644 index 0000000000..a31d5920b5 --- /dev/null +++ b/transformer_lens/model_bridge/sources/inspect/__init__.py @@ -0,0 +1,25 @@ +"""Inspect driver: turn an ``inspect_ai``-served model into a TransformerLens bridge. + +We ship our own HF-backed provider (``provider.py``) and a torch-free consumer +(``driver.py``). + +Maintainer note: this package is named ``inspect``, shadowing the stdlib module. +Only ever import the stdlib ``inspect`` via an absolute import from outside this +package; never write a bare ``import inspect`` inside these modules. +""" +from __future__ import annotations + +from typing import Any + +from .source import boot_inspect + +__all__ = ["activations_column", "boot_inspect", "capture_activations", "turn_activations"] + + +def __getattr__(name: str) -> Any: + # Lazy so importing the package stays inspect_ai-free; eval.py imports inspect_ai. + if name in ("capture_activations", "activations_column", "turn_activations"): + from . import eval as _eval + + return getattr(_eval, name) + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/transformer_lens/model_bridge/sources/inspect/_provider_base.py b/transformer_lens/model_bridge/sources/inspect/_provider_base.py new file mode 100644 index 0000000000..6248bcbafe --- /dev/null +++ b/transformer_lens/model_bridge/sources/inspect/_provider_base.py @@ -0,0 +1,201 @@ +"""Shared base for ``tl_bridge``-style Inspect providers (HF, vLLM). + +Subclasses load their model + tokenizer, run their structural self-check, and implement +backend-specific ``_generate_capture`` / ``_generate_eval`` — this base owns the +``generate()`` dispatch, message-to-ids rendering (chat template + tools), logprob +construction, and validation of the per-turn ``capture=[...]`` config. + +Subclass contract: +- Set ``self._tokenizer`` (HF-style ``AutoTokenizer``), ``self._device`` (str), + ``self._kinds`` (frozenset of served boundary kinds), ``self._capability_note`` (str + explaining any gating), and ``self._eval_capture`` (dict ``{wire_key: hook_name}``, + built via :meth:`_parse_eval_capture`) before any ``generate()`` call. +- Implement ``_generate_capture(input, extra_args, config)`` (TL-driven single forward) and + ``_generate_eval(input, config, tools)`` (multi-token chat generation). +""" +from __future__ import annotations + +import json +import re +import uuid +from typing import Any, Mapping + +import torch +from inspect_ai.model import GenerateConfig, Logprob, ModelAPI, TopLogprob +from inspect_ai.tool import ToolCall + +from . import hooks + + +def _message_text(message: Any) -> str: + """Text of an Inspect chat message — its ``.text`` (handles multimodal content), + falling back to string content, else ''.""" + text = getattr(message, "text", None) + if isinstance(text, str): + return text + content = getattr(message, "content", None) + return content if isinstance(content, str) else "" + + +def _tool_schema(tool: Any) -> dict[str, Any]: + """Inspect ``ToolInfo`` → OpenAI-style function schema for ``apply_chat_template``.""" + params = tool.parameters + params = params.model_dump() if hasattr(params, "model_dump") else dict(params) + return { + "type": "function", + "function": {"name": tool.name, "description": tool.description, "parameters": params}, + } + + +# Tool-call blocks emitted by common instruct templates (Qwen/Hermes-style); the bare-JSON +# fallback covers models that emit a single {"name", "arguments"} object. +_TOOL_CALL_BLOCK = re.compile(r"\s*(\{.*?\})\s*", re.S) + + +def _parse_tool_calls(text: str) -> list[ToolCall] | None: + """Best-effort parse of tool calls from a completion. Model-specific formats vary; this + handles ``{json}`` blocks and a single bare ``{name, arguments}``.""" + blocks = _TOOL_CALL_BLOCK.findall(text) + if not blocks: + match = re.search(r"\{.*\}", text, re.S) + blocks = [match.group(0)] if match else [] + calls = [] + for block in blocks: + try: + obj = json.loads(block) + except (ValueError, TypeError): + continue + name = obj.get("name") if isinstance(obj, dict) else None + if not isinstance(name, str): + continue + args = obj.get("arguments") or obj.get("parameters") or {} + calls.append( + ToolCall( + id=uuid.uuid4().hex[:8], + function=name, + arguments=args if isinstance(args, dict) else {}, + ) + ) + return calls or None + + +def _require_served(kind: str, served: frozenset[str], note: str, context: str) -> None: + """Raise if ``kind`` was gated by the structural self-check — without this the eval path + would silently return a derivation (e.g. ``resid_mid``) the driver path excludes.""" + if kind not in served: + raise ValueError( + f"{context} requests kind {kind!r} which this model gated. {note} " + f"Served kinds: {sorted(served)}." + ) + + +class _InspectModelAPIBase(ModelAPI): + """Shared Inspect ModelAPI scaffolding for ``tl_bridge``-style providers. + + Subclasses populate ``self._tokenizer``/``_device``/``_kinds``/``_capability_note``/ + ``_eval_capture`` in ``__init__`` and implement the backend-specific generate paths. + """ + + # Attributes set by subclass __init__ before any generate() call (declared here so the + # shared helpers' type-checking sees them; not initialized to avoid masking bugs). + _tokenizer: Any + _device: Any + _kinds: frozenset + _capability_note: str + _eval_capture: dict[str, str] + + # Class-level capability flag, read by source.py → TLBridgeProfile → InspectDriver → + # RemoteBridge.forward to gate return_type ∈ {loss, both}. Must be set by every + # subclass: True iff every position 0..n-1 of metadata['tl_logits'] holds real values + # (not -inf padding). HF does a true forward → full logits; vLLM's sampler bypasses + # lm_head and only the generated position has logprobs, so vLLM=False. + provides_sequence_logits: bool + + # --- subclass contract ------------------------------------------------------------- + + def _generate_capture( + self, input: Any, extra_args: Mapping[str, Any], config: GenerateConfig + ) -> Any: + """TL-driven single-forward capture (residual/attn/mlp boundaries + full logits).""" + raise NotImplementedError + + def _generate_eval(self, input: Any, config: GenerateConfig, tools: Any) -> Any: + """Plain Inspect generation: chat input → multi-token completion + Logprobs + + ModelUsage (+ parsed tool calls when ``tools`` is non-empty + per-turn capture).""" + raise NotImplementedError + + # --- shared API -------------------------------------------------------------------- + + def supported_kinds(self) -> frozenset: + """Boundary kinds this model is structurally able to serve.""" + return self._kinds + + def capability_note(self) -> str: + """Human-readable reason for any gated boundary, or '' if all are served.""" + return self._capability_note + + async def generate(self, input, tools, tool_choice, config): # type: ignore[override] + # Two callers: the TL driver (extra_args carries input_ids/capture/interventions — + # single-forward activation capture) and a plain Inspect eval (chat messages, real + # multi-token generation). Branch on whether a TL request is present. + extra_args: Mapping[str, Any] = (config.extra_body or {}).get("extra_args", {}) + if extra_args.get("input_ids") is not None or extra_args.get("capture"): + return self._generate_capture(input, extra_args, config) + return self._generate_eval(input, config, tools or []) + + # --- shared helpers ---------------------------------------------------------------- + + def _parse_eval_capture(self, model_args: dict[str, Any]) -> dict[str, str]: + """Validate ``model_args["capture"]`` against the structural self-check (same + protection as the driver path) and key by wire key. Returns ``{wire_key: name}``.""" + eval_capture: dict[str, str] = {} + for name in model_args.pop("capture", None) or []: + resolved = hooks.resolve(name) + if resolved is None: + raise ValueError(f"capture={name!r} is not a fireable hook name.") + _require_served(resolved[1], self._kinds, self._capability_note, f"capture={name!r}") + eval_capture[hooks.wire_key(*resolved)] = name + return eval_capture + + def _messages_to_ids(self, input: Any, tools: Any = ()) -> Any: + """Render Inspect chat messages (+ any ``tools``) to input ids — chat template + when the tokenizer has one, else newline-joined message text (e.g. gpt2).""" + if isinstance(input, str): + messages = [{"role": "user", "content": input}] + else: + messages = [ + {"role": getattr(m, "role", "user"), "content": _message_text(m)} for m in input + ] + template = getattr(self._tokenizer, "chat_template", None) + if len(tools) and not template: + raise NotImplementedError( + f"tl_bridge: tool use needs a tool-aware chat template; {self.model_name} has " + "none. Serve a tool-capable instruct model for agentic evals." + ) + if template: + kwargs: dict[str, Any] = {"add_generation_prompt": True} + if len(tools): + kwargs["tools"] = [_tool_schema(t) for t in tools] + token_ids = self._tokenizer.apply_chat_template(messages, **kwargs) + else: + token_ids = self._tokenizer("\n".join(m["content"] for m in messages)).input_ids + return torch.tensor([list(token_ids)], device=self._device) + + def _logprob_entry(self, token_id: int, step_logits: Any, top_n: Any) -> Logprob: + """One token's log-prob (+ top-k alternatives) from a position's logits.""" + lp = torch.log_softmax(step_logits.float(), dim=-1) + top = [] + if top_n: + vals, idx = lp.topk(int(top_n)) + top = [ + TopLogprob( + token=str(self._tokenizer.decode([int(i)])), logprob=float(v), bytes=None + ) + for v, i in zip(vals.tolist(), idx.tolist()) + ] + return Logprob( + token=str(self._tokenizer.decode([int(token_id)])), + logprob=float(lp[int(token_id)]), + bytes=None, + top_logprobs=top, + ) diff --git a/transformer_lens/model_bridge/sources/inspect/conftest.py b/transformer_lens/model_bridge/sources/inspect/conftest.py new file mode 100644 index 0000000000..baa9985ec0 --- /dev/null +++ b/transformer_lens/model_bridge/sources/inspect/conftest.py @@ -0,0 +1,19 @@ +"""Skip doctest-modules collection of inspect-provider files when ``inspect_ai`` is not +installed (it's an optional extra). Each file does a top-level ``from inspect_ai.model +import …`` for the ``ModelAPI`` base + helper types — guarding that import per-file would +bury the dependency contract; one collect-time skip here keeps the providers' source clean +and lets ``make docstring-test`` run cleanly without the ``inspect`` extra.""" +from __future__ import annotations + +from importlib import util as _importlib_util + +collect_ignore_glob = ( + [] + if _importlib_util.find_spec("inspect_ai") is not None + else [ + "_provider_base.py", + "eval.py", + "transformers_provider.py", + "vllm_provider.py", + ] +) diff --git a/transformer_lens/model_bridge/sources/inspect/driver.py b/transformer_lens/model_bridge/sources/inspect/driver.py new file mode 100644 index 0000000000..f30240cf96 --- /dev/null +++ b/transformer_lens/model_bridge/sources/inspect/driver.py @@ -0,0 +1,182 @@ +"""InspectDriver — torch-free consumer of an ``inspect_ai`` provider's output. + +Talks to a provider through the inspect_ai ``ModelOutput`` envelope. Everything +provider-specific — the request schema, which hooks are served, full vs last-token +logits, intervention translation — lives in a :mod:`profiles` Profile; the driver just +drives it. Stays numpy-only (``to_torch`` runs at the bridge boundary), so this file +imports zero torch symbols (enforced by a unit test). ``inspect_ai`` is imported lazily. +""" +from __future__ import annotations + +import asyncio +import logging +import threading +import warnings +from typing import Any, Mapping + +import numpy as np + +from transformer_lens.model_bridge.driver_protocol import ( + ForwardResult, + Intervention, + TensorLike, +) +from transformer_lens.model_bridge.sources._driver_base import DriverBase + +from . import hooks, wire +from .profiles import TLBridgeProfile + + +class InspectDriver(DriverBase): + """Driver wrapping an ``inspect_ai`` model; capture + interventions via a Profile.""" + + # Remote provider — no torch weight/grad surface. + _supported_features = frozenset() + + def __init__(self, model: Any, adapter: Any, tokenizer: Any, profile: Any = None) -> None: + super().__init__(adapter.cfg, tokenizer) + self._model = model + self._profile = profile if profile is not None else TLBridgeProfile() + self._n_layers = int(self.bridge_config.n_layers) + self._d_vocab = int(self.bridge_config.d_vocab) + # Provider-specific: loss/both allowed only if the provider returns full logits. + self.provides_sequence_logits = self._profile.provides_sequence_logits + self.supported_hook_points = self._profile.supported_hooks(self._n_layers) + full = hooks.supported_hook_points(self._n_layers) + self.non_fireable_hook_points = hooks.nonfireable_hook_points(self._n_layers) | ( + full - self.supported_hook_points + ) + # Background event loop, created lazily on first forward. + self._loop: asyncio.AbstractEventLoop | None = None + self._loop_thread: threading.Thread | None = None + self._warned_missing: set[str] = set() # hooks we've already warned were absent + + def forward( + self, + input_ids: TensorLike | None = None, + *, + capture: tuple[str, ...] = (), + intervene: Mapping[str, Intervention] | None = None, + max_new_tokens: int = 1, + return_logits: bool = True, + **kwargs: Any, + ) -> ForwardResult: + if self._model is None: + raise RuntimeError("InspectDriver is closed.") + if input_ids is None: + raise ValueError("InspectDriver requires input_ids") + if int(max_new_tokens) != 1: + raise NotImplementedError( + "InspectDriver supports max_new_tokens=1 only (single-forward capture)." + ) + ids = self._normalize_input_ids(input_ids) + # capture is authoritative: the bridge passes exactly the hooks with handlers, + # so () means "capture nothing" (logits only), not "capture everything". + names = list(capture) + wire_keys = self._wire_keys(names) + interventions = self._profile.translate_interventions( + intervene or {}, self.supported_hook_points + ) + prompt, extra_args = self._profile.build_request( + ids, wire_keys, interventions, return_logits, self.tokenizer + ) + + output = self._run_coro(self._generate(prompt, extra_args)) + + captured = self._assemble_captures(output, names) + logits = ( + self._profile.decode_logits(output, len(ids), self._d_vocab, self.tokenizer) + if return_logits + else None + ) + return ForwardResult(logits=logits, captured=captured, raw_output=output) + + async def _generate(self, prompt: Any, extra_args: dict[str, Any]) -> Any: + from inspect_ai.model import GenerateConfig + + config = GenerateConfig( + temperature=0.0, max_tokens=1, extra_body={"extra_args": extra_args} + ) + return await self._model.generate(prompt, config=config) + + def _assemble_captures(self, output: Any, names: list[str]) -> dict[str, np.ndarray]: + """Decode the requested boundaries → ``{hook_name: (1, seq, d_model)}``; names the + provider didn't return are skipped (and warned once).""" + metadata = getattr(output, "metadata", None) or {} + decoded = wire.decode_activations(metadata, self._wire_keys(names)) + captured: dict[str, np.ndarray] = {} + missing: list[str] = [] + for name in names: + resolved = hooks.resolve(name) + if resolved is None: + continue + arr = decoded.get(hooks.wire_key(*resolved)) + if arr is None: + missing.append(name) + continue + captured[name] = arr[np.newaxis, ...] if arr.ndim == 2 else arr + self._warn_missing(missing) + return captured + + def _warn_missing(self, missing: list[str]) -> None: + """Warn once per hook the provider was asked for but didn't return — else its + cache entry is silently absent and surfaces only as a later KeyError.""" + new = [name for name in missing if name not in self._warned_missing] + if new: + self._warned_missing.update(new) + warnings.warn( + f"InspectDriver: provider returned no activation for {sorted(new)} " + "(requested and in supported_hook_points); those cache keys will be absent.", + UserWarning, + stacklevel=2, + ) + + def close(self) -> None: + log = logging.getLogger("transformer_lens.inspect") + if self._loop is not None: + try: + self._loop.call_soon_threadsafe(self._loop.stop) + if self._loop_thread is not None: + self._loop_thread.join(timeout=5) + self._loop.close() + except Exception as e: + log.debug("event-loop teardown failed during close(): %s", e) + self._loop = None + self._loop_thread = None + self._model = None # drop the provider reference; the server owns its own lifecycle + + # ---- helpers ---- + + def _ensure_loop(self) -> asyncio.AbstractEventLoop: + """A private loop on a daemon thread — works even when the caller is already + inside a running loop (Jupyter), unlike asyncio.run().""" + if self._loop is None: + self._loop = asyncio.new_event_loop() + self._loop_thread = threading.Thread( + target=self._loop.run_forever, daemon=True, name="inspect-driver-loop" + ) + self._loop_thread.start() + return self._loop + + def _run_coro(self, coro: Any) -> Any: + future = asyncio.run_coroutine_threadsafe(coro, self._ensure_loop()) + return future.result() # blocks the sync caller; re-raises provider errors + + def _wire_keys(self, names: list[str]) -> list[str]: + """Unique ``:`` keys for the requested hook names (aliases collapse).""" + keys = {hooks.wire_key(*r) for r in (hooks.resolve(n) for n in names) if r is not None} + return sorted(keys) + + @staticmethod + def _normalize_input_ids(input_ids: Any) -> list[int]: + """Coerce to a flat list[int] (batch_size=1 only); numpy/list/tensor, no torch import.""" + # Duck-type a torch tensor onto CPU first — np.asarray can't read CUDA memory, + # and a torch-free driver can't import torch to .cpu() it. + if hasattr(input_ids, "detach") and hasattr(input_ids, "cpu"): + input_ids = input_ids.detach().cpu() + arr = np.asarray(input_ids) + if arr.ndim == 2: + if arr.shape[0] != 1: + raise NotImplementedError("InspectDriver supports batch_size=1 only.") + arr = arr[0] + return [int(x) for x in arr.tolist()] diff --git a/transformer_lens/model_bridge/sources/inspect/eval.py b/transformer_lens/model_bridge/sources/inspect/eval.py new file mode 100644 index 0000000000..eebad35931 --- /dev/null +++ b/transformer_lens/model_bridge/sources/inspect/eval.py @@ -0,0 +1,118 @@ +"""Inspect solver for capturing TransformerLens activations during an eval. + +``capture_activations([...])`` is a solver you add to a Task's solver chain to harvest +activations alongside a behavioral eval (with a ``tl_bridge``-served model). Full +activations go to a per-sample side artifact (``output_dir/.npz``) for +probing/SAE; a compact ``reduce(...)`` summary lands in the sample store so +``inspect_ai.analysis.samples_df`` can correlate activation features with scores. + +Imports ``inspect_ai`` at module load (like ``provider.py``); the package ``__init__`` +exposes ``capture_activations`` lazily so importing the package stays inspect_ai-free. +""" +from __future__ import annotations + +import os +from typing import Any, Callable, Mapping, Optional, Sequence + +import numpy as np +from inspect_ai.model import GenerateConfig, get_model +from inspect_ai.solver import Generate, Solver, TaskState, solver +from inspect_ai.util import store + +from . import hooks, wire + + +def _default_reduce(activations: Mapping[str, np.ndarray]) -> dict[str, Any]: + """Per-hook L2 norm + shape — small, JSON-able, queryable in samples_df.""" + return { + name: {"l2": float(np.linalg.norm(arr)), "shape": list(arr.shape)} + for name, arr in activations.items() + } + + +@solver +def capture_activations( + capture: Sequence[str], + output_dir: str = "tl_activations", + reduce: Optional[Callable[[Mapping[str, np.ndarray]], dict[str, Any]]] = None, + store_key: str = "tl_activations", +) -> Solver: + """Capture ``capture`` hooks for each sample's current messages. + + Writes full activations to ``output_dir/.npz`` and a ``reduce(...)`` summary + (default: per-hook L2 + shape) to the sample store under ``store_key`` (+ ``_path``). + Requires a ``tl_bridge``-served model; raises if the model returns no activations. + Place before ``generate()`` to capture the prompt, after it to include the completion. + """ + # Resolve at construction so a multi-eval run from different CWDs doesn't scatter + # artifacts (the solver runs later, possibly under a different working directory). + output_dir = os.path.abspath(output_dir) + reduce_fn = reduce or _default_reduce + # TL hook names → provider wire keys (and back, to key the saved arrays by hook name). + name_by_wire = {} + for name in capture: + resolved = hooks.resolve(name) + if resolved is None: + raise ValueError(f"capture_activations: {name!r} is not a fireable hook name.") + name_by_wire[hooks.wire_key(*resolved)] = name + wire_keys = list(name_by_wire) + + async def solve(state: TaskState, generate: Generate) -> TaskState: + # get_model() (no args) is the eval's active model; state.model is just its name. + output = await get_model().generate( + state.messages, + config=GenerateConfig( + extra_body={"extra_args": {"capture": wire_keys, "return_logits": False}} + ), + ) + decoded = wire.decode_activations(getattr(output, "metadata", None), wire_keys) + if not decoded: + raise RuntimeError( + "capture_activations got no activations back — the eval model must be a " + "tl_bridge provider (e.g. model='tl_bridge/gpt2')." + ) + activations = {name_by_wire[wk]: arr for wk, arr in decoded.items()} + os.makedirs(output_dir, exist_ok=True) + path = os.path.join(output_dir, f"{state.sample_id}.npz") + np.savez_compressed(path, **activations) + store().set(store_key, reduce_fn(activations)) + store().set(f"{store_key}_path", path) + return state + + return solve + + +def turn_activations(sample: Any) -> list[dict[str, np.ndarray]]: + """Per-turn activations from an eval sample's model events, for a provider booted with + ``capture=[...]`` (e.g. ``model_args={"capture": [...]}``). Returns one + ``{hook_name: (1, seq, d)}`` dict per model generation, in turn order — the activations + of an agentic/multi-turn rollout. + """ + turns = [] + for event in getattr(sample, "events", []) or []: + metadata = getattr(getattr(event, "output", None), "metadata", None) + if not metadata or "activations" not in metadata: + continue + decoded = wire.decode_activations(metadata, list(metadata["activations"])) + named = { + name: arr[np.newaxis, ...] if arr.ndim == 2 else arr + for wk, arr in decoded.items() + if (name := hooks.name_from_wire_key(wk)) is not None + } + if named: + turns.append(named) + return turns + + +def activations_column(store_key: str = "tl_activations", name: Optional[str] = None) -> Any: + """A ``samples_df`` column surfacing :func:`capture_activations`'s reduction, for + correlating activation features with scores:: + + df = samples_df(logs, columns=[*SampleSummary, activations_column()]) + """ + from inspect_ai.analysis import SampleColumn + + return SampleColumn(name or store_key, path=lambda s: s.store.get(store_key), full=True) + + +__all__ = ["activations_column", "capture_activations", "turn_activations"] diff --git a/transformer_lens/model_bridge/sources/inspect/hooks.py b/transformer_lens/model_bridge/sources/inspect/hooks.py new file mode 100644 index 0000000000..ed287bca0a --- /dev/null +++ b/transformer_lens/model_bridge/sources/inspect/hooks.py @@ -0,0 +1,86 @@ +"""Canonical hook names ↔ (layer, kind) for the Inspect HF provider. + +Torch-free; shared by the provider (capture/intervene) and the driver (supported set, +decode). Covers the ``d_model``-shaped decoder-layer boundaries; head-split hooks +(q/k/v/z, pattern), ``embed``, and ``ln_final`` (fold-LN convention) are non-fireable. + +Names are TransformerBridge-native (``blocks.{i}.hook_out``, ``.attn.hook_out``, ...), +not the HookedTransformer aliases. A bridge cache carries both with identical values, +so parity vs ``boot_transformers`` still resolves. + +Which boundaries are actually fireable is decided per-model by the provider's structural +self-check, not a hand-kept architecture list: it locates attn/mlp and probes whether the +``resid_pre + attn_out`` derivation holds, gating ``resid_mid`` otherwise. +``supported_hook_points(n_layers, kinds=...)`` filters to that detected set. +""" +from __future__ import annotations + +import re +from typing import Iterable, Optional + +ALL_KINDS = frozenset({"resid_pre", "resid_mid", "resid_post", "attn_out", "mlp_out"}) + +# One canonical TransformerBridge name per boundary (no aliases — avoids duplicate +# HookPoints/cache entries). resid_mid (ln2.hook_in) is derived (resid_pre + +# attn_out), so it's capture-only. +_KIND_NAMES = { + "resid_pre": "blocks.{i}.hook_in", + "resid_mid": "blocks.{i}.ln2.hook_in", + "resid_post": "blocks.{i}.hook_out", + "attn_out": "blocks.{i}.attn.hook_out", + "mlp_out": "blocks.{i}.mlp.hook_out", +} +INTERVENEABLE_KINDS = frozenset({"resid_pre", "attn_out", "mlp_out", "resid_post"}) + +_SUFFIX_TO_KIND = { + "hook_in": "resid_pre", + "ln2.hook_in": "resid_mid", + "hook_out": "resid_post", + "attn.hook_out": "attn_out", + "mlp.hook_out": "mlp_out", +} +_BLOCK = re.compile(r"^blocks\.(\d+)\.(.+)$") + + +def supported_hook_points(n_layers: int, kinds: Optional[Iterable[str]] = None) -> frozenset[str]: + """Fireable hook names across all layers. ``kinds=None`` means all boundaries; + pass the provider's detected kinds to gate (e.g. drop ``resid_mid`` for parallel).""" + selected = _KIND_NAMES if kinds is None else {k: _KIND_NAMES[k] for k in kinds} + return frozenset(name.format(i=i) for i in range(n_layers) for name in selected.values()) + + +def nonfireable_hook_points(n_layers: int) -> frozenset[str]: + """Hooks the residual/attn/mlp provider can't fire (head-split, embed, ln_final).""" + names = ["embed.hook_out", "ln_final.hook_normalized", "unembed.hook_out"] + for i in range(n_layers): + names += [ + f"blocks.{i}.attn.hook_pattern", + f"blocks.{i}.attn.hook_attn_scores", + f"blocks.{i}.attn.hook_q", + f"blocks.{i}.attn.hook_k", + f"blocks.{i}.attn.hook_v", + f"blocks.{i}.attn.hook_z", + ] + return frozenset(names) + + +def resolve(name: str) -> tuple[int, str] | None: + """Canonical hook name → ``(layer, kind)``, or ``None`` if not a fireable hook.""" + match = _BLOCK.match(name) + if match is None: + return None + kind = _SUFFIX_TO_KIND.get(match.group(2)) + return (int(match.group(1)), kind) if kind is not None else None + + +def wire_key(layer: int, kind: str) -> str: + """Stable key for one captured boundary in the activation payload.""" + return f"{layer}:{kind}" + + +def name_from_wire_key(key: str) -> str | None: + """Inverse of ``wire_key`` ∘ ``resolve``: ``":"`` → the canonical hook + name, or ``None`` if the kind is unknown.""" + layer, _, kind = key.partition(":") + template = _KIND_NAMES.get(kind) + return template.format(i=int(layer)) if template and layer.isdigit() else None diff --git a/transformer_lens/model_bridge/sources/inspect/intervention.py b/transformer_lens/model_bridge/sources/inspect/intervention.py new file mode 100644 index 0000000000..fcda9bc802 --- /dev/null +++ b/transformer_lens/model_bridge/sources/inspect/intervention.py @@ -0,0 +1,60 @@ +"""Validate intervention specs and key them by ``:`` for the provider. + +Our HF provider applies interventions as forward-hook affine ops at the residual/ +attn/mlp boundaries, so the full vocabulary works. ``resid_mid`` is derived +(capture-only), so intervening on it is rejected. +""" +from __future__ import annotations + +from typing import Any, Mapping + +from . import hooks + +# suppress (→0), scale (factor), add (value), set (value). +SUPPORTED_OPS = frozenset({"suppress", "scale", "add", "set"}) + + +def build_interventions( + intervene: Mapping[str, Any], + supported_hook_points: frozenset[str], +) -> dict[str, dict[str, Any]]: + """Validate specs and return ``{wire_key: spec}`` for the provider to apply. + + Rejects callables (remote drivers take specs, not callbacks), bad ops, unknown or + unsupported hooks, and the capture-only ``resid_mid``. + """ + out: dict[str, dict[str, Any]] = {} + for hook_name, spec in intervene.items(): + if callable(spec): + raise NotImplementedError( + "InspectDriver requires intervention specs (dict), not callables. " + "Supported ops: suppress, scale (factor: float), add/set (value: scalar or " + "width-shaped)." + ) + if not isinstance(spec, Mapping) or "op" not in spec: + raise ValueError( + f"Intervention spec for {hook_name!r} must be a dict with 'op' key; got {spec!r}" + ) + op = spec["op"] + if op not in SUPPORTED_OPS: + raise ValueError( + f"Unsupported intervention op {op!r} for {hook_name!r}. " + f"Supported: {sorted(SUPPORTED_OPS)}." + ) + if hook_name not in supported_hook_points: + raise ValueError(f"Cannot intervene on {hook_name!r}: not in supported_hook_points.") + resolved = hooks.resolve(hook_name) + if resolved is None or resolved[1] not in hooks.INTERVENEABLE_KINDS: + raise ValueError( + f"Cannot intervene on {hook_name!r}: capture-only " + f"(intervene on resid_pre/attn_out/mlp_out/resid_post instead)." + ) + if op == "scale" and "factor" not in spec: + raise ValueError(f"Intervention {hook_name!r}: op='scale' requires 'factor' (float).") + if op in ("add", "set") and "value" not in spec: + raise ValueError( + f"Intervention {hook_name!r}: op={op!r} requires 'value' (scalar or width-shaped)." + ) + layer, kind = resolved + out[hooks.wire_key(layer, kind)] = dict(spec) + return out diff --git a/transformer_lens/model_bridge/sources/inspect/profiles.py b/transformer_lens/model_bridge/sources/inspect/profiles.py new file mode 100644 index 0000000000..20222a529c --- /dev/null +++ b/transformer_lens/model_bridge/sources/inspect/profiles.py @@ -0,0 +1,134 @@ +"""Per-provider request/response codecs for the Inspect driver. + +The driver speaks one internal shape; each provider speaks its own. A Profile +encapsulates everything provider-specific: which hooks it serves, whether it +returns full-sequence logits, how to phrase the ``generate`` request (prompt + +``extra_args``), how to translate interventions, and how to read logits back. +Torch-free (numpy only) so the driver stays torch-free. + +``tl_bridge`` is our own HF provider (``provider.py``). ``vllm-lens`` is the +third-party provider. **The vllm-lens codec is written from its documented API and +is NOT verified against a live provider** (CI has none — it needs their GPU-served +provider). It's isolated here so this is the single place to fix once validated. +""" +from __future__ import annotations + +from typing import Any, Mapping + +import numpy as np + +from . import hooks, intervention, wire + + +class TLBridgeProfile: + """Codec for our own ``tl_bridge`` provider: residual/attn/mlp hooks, full-seq logits. + + ``supported_kinds`` is the provider's structurally-detected boundary set (e.g. + ``resid_mid`` dropped for parallel/norm-variant archs); ``None`` exposes all boundaries. + """ + + # Class-level default — overridden per-instance via __init__ for backends (vLLM) that + # only populate the gen position; lets RemoteBridge.forward reject loss/both there. + provides_sequence_logits = True + + def __init__(self, supported_kinds: Any = None, provides_sequence_logits: bool = True) -> None: + self._kinds = supported_kinds + self.provides_sequence_logits = provides_sequence_logits + + def supported_hooks(self, n_layers: int) -> frozenset[str]: + return hooks.supported_hook_points(n_layers, self._kinds) + + def translate_interventions(self, intervene, supported): + return intervention.build_interventions(intervene, supported) # {wire_key: spec} + + def build_request(self, ids, wire_keys, interventions, return_logits, tokenizer): + extra: dict[str, Any] = { + "input_ids": ids, + "capture": wire_keys, + "return_logits": return_logits, + } + if interventions: + extra["interventions"] = interventions + return "", extra # our provider reads input_ids from extra_args; prompt unused + + def decode_logits(self, output, n_tokens, d_vocab, tokenizer): + entry = (getattr(output, "metadata", None) or {}).get("tl_logits") + if entry is not None: + return wire.decode_array(entry)[np.newaxis, ...] # full (1, seq, d_vocab) + return np.full((1, n_tokens, d_vocab), -np.inf, dtype=np.float32) + + +class VLLMLensProfile: + """Codec for the third-party vllm-lens provider. + + Residual-stream-only, additive-steering-only, and last-token logits synthesized + one-hot from the generated token (argmax-only — vllm-lens doesn't hand back full + logits through this path). Prompt is the detokenized text, so vllm-lens + re-tokenizes it: activations reflect that re-tokenization, which may differ from + the exact ids. UNVERIFIED against a live provider. + """ + + provides_sequence_logits = False + + def supported_hooks(self, n_layers: int) -> frozenset[str]: + return frozenset(f"blocks.{i}.hook_out" for i in range(n_layers)) + + def translate_interventions(self, intervene: Mapping[str, Any], supported) -> list: + """op='add' with a width-shaped vector → vllm-lens SteeringVector; others raise. + + Validates before importing ``vllm_lens`` so non-additive ops (and the + no-intervention case) don't require the package installed. + """ + if not intervene: + return [] + steering_cls: Any = None # imported lazily once a valid additive spec is seen + vectors = [] + for name, spec in intervene.items(): + if callable(spec): + raise NotImplementedError("vllm-lens requires intervention specs, not callables.") + if not isinstance(spec, Mapping) or spec.get("op") != "add": + raise NotImplementedError( + f"vllm-lens supports only additive steering (op='add' with a width vector); " + f"got {spec!r} for {name!r}. Use boot_transformers() for suppress/scale/set." + ) + if name not in supported: + raise ValueError(f"Cannot intervene on {name!r}: not in supported_hook_points.") + resolved = hooks.resolve(name) + assert resolved is not None # supported ⇒ resolvable + if steering_cls is None: + from vllm_lens import SteeringVector + + steering_cls = SteeringVector + vectors.append( + steering_cls( + activations=np.asarray(spec["value"], dtype=np.float32), + layer_indices=[resolved[0]], + scale=float(spec.get("scale", 1.0)), + norm_match=bool(spec.get("norm_match", True)), + ) + ) + return vectors + + def build_request(self, ids, wire_keys, interventions, return_logits, tokenizer): + layers = sorted({int(key.split(":")[0]) for key in wire_keys}) + extra: dict[str, Any] = {"output_residual_stream": layers} + if interventions: + extra["apply_steering_vectors"] = interventions + prompt = tokenizer.decode(ids) if tokenizer is not None else "" + return prompt, extra + + def decode_logits(self, output, n_tokens, d_vocab, tokenizer): + # vllm-lens returns no full logits here; one-hot the generated token (argmax only). + logits = np.full((1, n_tokens, d_vocab), -np.inf, dtype=np.float32) + text = getattr(output, "completion", "") or "" + ids = tokenizer.encode(text) if (tokenizer is not None and text) else [] + if len(ids): + logits[0, -1, int(ids[0])] = 0.0 + return logits + + +def for_provider(provider: str) -> Any: + """Pick the codec for a provider name (the part before ``/`` in get_model).""" + if provider.startswith("vllm-lens"): + return VLLMLensProfile() + return TLBridgeProfile() diff --git a/transformer_lens/model_bridge/sources/inspect/source.py b/transformer_lens/model_bridge/sources/inspect/source.py new file mode 100644 index 0000000000..b23f565c83 --- /dev/null +++ b/transformer_lens/model_bridge/sources/inspect/source.py @@ -0,0 +1,151 @@ +"""``boot_inspect`` — wrap an ``inspect_ai`` provider in a RemoteBridge via InspectDriver.""" +from __future__ import annotations + +import logging +import warnings +from typing import Any, Optional + +import torch + +from transformer_lens.factories.architecture_adapter_factory import ( + ArchitectureAdapterFactory, +) +from transformer_lens.model_bridge.remote_bridge import RemoteBridge +from transformer_lens.model_bridge.sources._bridge_builder import ( + build_bridge_config_from_hf, + detect_tokenizer_bos_eos, +) +from transformer_lens.model_bridge.sources._hf_format import setup_tokenizer +from transformer_lens.utilities.hf_utils import get_hf_token + +from . import profiles +from .driver import InspectDriver + +# Providers that expose the structural self-check + capture wire format the InspectDriver +# consumes. boot_inspect queries supported_kinds on these; others route via for_provider. +_TL_BRIDGE_PROVIDERS = {"tl_bridge", "tl_bridge_vllm"} + + +def boot_inspect( + model_name: str, + tokenizer: Optional[Any] = None, + dtype: Optional[torch.dtype] = None, + provider: str = "tl_bridge", + **inspect_kwargs: Any, +) -> RemoteBridge: + """Boot a model via an ``inspect_ai`` provider and wrap it in a :class:`RemoteBridge`. + + The driver is provider-agnostic: ``provider`` defaults to our own HF-backed + ``tl_bridge`` provider (residual/attn/mlp capture + full affine interventions + + full-sequence logits); ``"vllm-lens"`` targets a running vllm-lens vLLM provider + (residual-only, additive-steering-only) — wire-aligned with its documented format, + but not yet verified against a live provider. + + Fireable hooks (``tl_bridge``, TransformerBridge-native names): ``blocks.{i}.hook_in`` + (resid_pre) / ``ln2.hook_in`` (resid_mid) / + ``hook_out`` (resid_post) / ``attn.hook_out`` / ``mlp.hook_out``. The provider runs + a structural self-check per model and gates any boundary it can't serve faithfully: + ``resid_mid`` for parallel-residual or norm-variant blocks, and ``attn_out``/ + ``mlp_out`` when their submodule isn't locatable (it warns when it gates one). + Head-split hooks (q/k/v/z, pattern), ``embed``, and ``ln_final`` are always + non-fireable — use ``boot_transformers()`` for those. + + For parity with ``boot_transformers`` the provider loads with the same dtype (fp32 by + default) and eager attention. Full-sequence logits ride on ``return_logits=True`` (the + default); pass ``return_logits=False`` to skip the (seq × d_vocab) payload for pure + activation capture (``run_with_cache`` keeps them since it returns logits). + """ + from inspect_ai.model import get_model + from transformers import AutoConfig, AutoTokenizer + + from . import ( # noqa: F401 — import registers @modelapi + transformers_provider as _provider, + ) + + hf_token = get_hf_token() + hf_config = AutoConfig.from_pretrained(model_name, token=hf_token) + architecture = hf_config.architectures[0] + # Default fp32 to match boot_transformers (which loads/casts fp32 regardless of the + # config's native dtype); an explicit dtype still wins. + resolved_dtype = dtype if dtype is not None else torch.float32 + + bridge_config = build_bridge_config_from_hf(hf_config, architecture, model_name, resolved_dtype) + adapter = ArchitectureAdapterFactory.select_architecture_adapter(bridge_config) + if tokenizer is None: + tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token) + # Match boot_transformers' tokenizer setup so to_tokens(str) is token-identical. + tokenizer = setup_tokenizer( + tokenizer, default_padding_side=getattr(adapter.cfg, "default_padding_side", None) + ) + ( + adapter.cfg.tokenizer_prepends_bos, + adapter.cfg.tokenizer_appends_eos, + ) = detect_tokenizer_bos_eos(tokenizer) + + if provider == "tl_bridge": + # The provider's raw HF forward must match boot_transformers' load: same dtype, + # eager attention (TL forces eager — SDPA/flash diverge and accumulate with depth), + # and auth/remote-code so gated/custom models load at all. + inspect_kwargs["model_kwargs"] = _provider_model_kwargs( + dict(inspect_kwargs.get("model_kwargs", {})), adapter, resolved_dtype, hf_token + ) + + # memoize=False: inspect_ai caches get_model by name, which would (a) return a stale + # model ignoring a changed dtype/kwargs on re-boot and (b) keep weights resident past + # close(). Each boot must honor its own args and own its model's lifecycle. + model = get_model(f"{provider}/{model_name}", memoize=False, **inspect_kwargs) + # Both TL-bridge providers (HF + vLLM) restrict their profile to the boundaries the + # provider's structural self-check / overlay found this model can serve; warn only + # if it gated something. Third-party providers (e.g. vllm-lens) route via for_provider. + if provider in _TL_BRIDGE_PROVIDERS: + api = getattr(model, "api", None) + kinds = None + note = "" + # Default True for back-compat; vLLM provider sets False so RemoteBridge.forward + # rejects loss/both (otherwise loss over -inf earlier positions silently NaNs). + psl = True + if api is not None: + kinds = api.supported_kinds() if hasattr(api, "supported_kinds") else None + note = api.capability_note() if hasattr(api, "capability_note") else "" + psl = bool(getattr(api, "provides_sequence_logits", True)) + profile = profiles.TLBridgeProfile(supported_kinds=kinds, provides_sequence_logits=psl) + if note: + warnings.warn(note, UserWarning, stacklevel=2) + else: + profile = profiles.for_provider(provider) + driver = InspectDriver(model=model, adapter=adapter, tokenizer=tokenizer, profile=profile) + bridge = RemoteBridge(adapter=adapter, tokenizer=tokenizer, driver=driver) + _log_hook_summary(model_name, architecture, provider, driver) + return bridge + + +def _provider_model_kwargs( + model_kwargs: dict[str, Any], adapter: Any, dtype: torch.dtype, hf_token: Optional[str] +) -> dict[str, Any]: + """Load kwargs for the HF provider that mirror boot_transformers, so the provider's + raw forward matches the bridge. Caller-supplied keys win (setdefault).""" + model_kwargs.setdefault("torch_dtype", dtype) + # boot_transformers forces eager unless the adapter pins an implementation. + model_kwargs.setdefault( + "attn_implementation", getattr(adapter.cfg, "attn_implementation", None) or "eager" + ) + if hf_token: + model_kwargs.setdefault("token", hf_token) + return model_kwargs + + +def _log_hook_summary( + model_name: str, architecture: str, provider: str, driver: InspectDriver +) -> None: + log = logging.getLogger("transformer_lens.inspect") + fireable = sorted(driver.supported_hook_points) + log.info( + "Inspect source on %s (%s) via provider %r serves %d fireable hook(s).", + model_name, + architecture, + provider, + len(fireable), + ) + + +__all__ = ["boot_inspect"] diff --git a/transformer_lens/model_bridge/sources/inspect/transformers_provider.py b/transformer_lens/model_bridge/sources/inspect/transformers_provider.py new file mode 100644 index 0000000000..aa4faf356a --- /dev/null +++ b/transformer_lens/model_bridge/sources/inspect/transformers_provider.py @@ -0,0 +1,522 @@ +"""Our HF-transformers ``inspect_ai`` model provider, registered as ``tl_bridge``. + +The model-runner side of the Inspect driver (this file uses torch; the consuming +``InspectDriver`` does not). On ``generate`` it reads the request from +``config.extra_body["extra_args"]`` (token ids, which ``:`` boundaries +to capture, and intervention specs), runs an HF causal LM with forward hooks that +capture residual/attn/mlp boundaries and apply affine interventions, and returns a +``ModelOutput`` whose ``metadata`` carries the activations (encoded by ``wire``) +plus the full-sequence logits. +""" +from __future__ import annotations + +import contextvars +import uuid +from collections import defaultdict +from contextlib import contextmanager +from typing import Any, Iterator, Mapping + +import numpy as np +import torch +from inspect_ai.model import ( + ChatCompletionChoice, + ChatMessageAssistant, + GenerateConfig, + Logprobs, + ModelOutput, + ModelUsage, + StopReason, + modelapi, +) + +from . import wire +from ._provider_base import _InspectModelAPIBase, _parse_tool_calls, _require_served + +# NOT "transformer_lens" — inspect_ai ships a built-in provider by that name (the +# reverse direction: serving a HookedTransformer as an Inspect model for generation). +PROVIDER_NAME = "tl_bridge" + +# Per-call hook isolation. Capture/intervene hooks consult this contextvar and only fire +# for their own call's id — so concurrent inspect_eval samples (each running with their +# own contextvars copy via asyncio.to_thread) don't cross-pollute each other's activations. +_current_call_id: contextvars.ContextVar[str] = contextvars.ContextVar( + "tl_inspect_call_id", default="" +) + +# Decoder ModuleList by architecture family; each block's output is resid_post. +_LAYER_PATHS = ("model.layers", "transformer.h", "gpt_neox.layers", "model.decoder.layers") +# Attn/MLP submodule names within a block, by family. +_ATTN_ATTRS = ("self_attn", "attn", "attention") +_MLP_ATTRS = ("mlp", "feed_forward") + + +@modelapi(name=PROVIDER_NAME) +def transformer_lens_provider(): + """Lazy registration hook — returns the provider class on first use.""" + return TransformerLensTransformersModelAPI + + +class TransformerLensTransformersModelAPI(_InspectModelAPIBase): + """HF-backed provider: residual/attn/mlp capture + interventions + full logits. + + Inherits generate() dispatch, _messages_to_ids, _logprob_entry, and the per-turn + capture-config validation from :class:`_InspectModelAPIBase`; the HF-specific bits + here are the model load, the forward-hook capture machinery, and the structural probe. + """ + + # Real per-position logits via direct HF forward — loss/both via RemoteBridge work. + provides_sequence_logits = True + + def __init__( + self, + model_name: str, + base_url: str | None = None, + api_key: str | None = None, + config: GenerateConfig = GenerateConfig(), + **model_args: Any, + ) -> None: + super().__init__(model_name, base_url, api_key, [], config) + from transformers import AutoModelForCausalLM, AutoTokenizer + + self._device = model_args.pop("device", "cpu") + hf_kwargs = model_args.pop("model_kwargs", {}) + self._hf = ( + AutoModelForCausalLM.from_pretrained(model_name, **hf_kwargs).to(self._device).eval() + ) + self._tokenizer = AutoTokenizer.from_pretrained(model_name) + self._layers = _locate_layers(self._hf) + self._kinds, self._capability_note = _detect_capabilities(self._hf, self._layers) + # Per-turn capture during plain generation (agent rollouts): every _generate_eval + # stashes these hooks in ModelOutput.metadata. Gated by the structural self-check. + self._eval_capture = self._parse_eval_capture(model_args) + + def _generate_capture(self, input: Any, extra_args: Mapping[str, Any], config: GenerateConfig): + """TL-driven single forward: capture residual/attn/mlp boundaries + full logits.""" + input_ids = extra_args.get("input_ids") + if input_ids is None: + input_ids = self._messages_to_ids(input)[0].tolist() + # capture/interventions are keyed by ":" (hooks.wire_key). + capture_keys = list(extra_args.get("capture", [])) + for key in capture_keys: + _, _, kind = key.partition(":") + _require_served(kind, self._kinds, self._capability_note, f"capture {key!r}") + interventions: Mapping[str, Any] = extra_args.get("interventions", {}) + want_logits = bool(extra_args.get("return_logits", True)) + + capture, intervene = _plan(capture_keys, interventions) + raw: dict[tuple[int, str], np.ndarray] = {} + call_id = uuid.uuid4().hex + token = _current_call_id.set(call_id) + handles = self._install_hooks(capture, intervene, raw, call_id) + try: + with torch.no_grad(): + ids = torch.tensor([list(input_ids)], device=self._device) + logits = self._hf(ids).logits # (1, seq, vocab) + finally: + for handle in handles: + handle.remove() + _current_call_id.reset(token) + + captured = _assemble(raw, capture_keys) + metadata: dict[str, Any] = {"activations": wire.encode_activations(captured)} + if want_logits: + metadata["tl_logits"] = wire.encode_array(logits[0].float().cpu().numpy()) + + next_id = int(logits[0, -1].argmax()) + logprobs = ( + Logprobs(content=[self._logprob_entry(next_id, logits[0, -1], config.top_logprobs)]) + if config.logprobs + else None + ) + return ModelOutput( + model=self.model_name, + choices=[ + ChatCompletionChoice( + message=ChatMessageAssistant(content=str(self._tokenizer.decode([next_id]))), + stop_reason="stop", + logprobs=logprobs, + ) + ], + metadata=metadata, + ) + + def _generate_eval(self, input: Any, config: GenerateConfig, tools: Any): + """Plain Inspect generation: HF generate from the chat input (rendering ``tools`` + into the template), honoring max_tokens/sampling, with optional per-token logprobs, + token usage, parsed tool calls, and per-turn activation capture (agent rollouts).""" + ids = self._messages_to_ids(input, tools) + prompt_len = int(ids.shape[1]) + max_new = int(config.max_tokens) if config.max_tokens else 16 + temperature = config.temperature + do_sample = temperature is not None and temperature > 0 + gen: dict[str, Any] = { + "max_new_tokens": max_new, + "do_sample": do_sample, + "return_dict_in_generate": True, + "output_scores": True, + "pad_token_id": self._tokenizer.pad_token_id or self._tokenizer.eos_token_id, + } + if temperature is not None and temperature > 0: + gen["temperature"] = float(temperature) + if config.top_p is not None: + gen["top_p"] = float(config.top_p) + if config.top_k is not None: + gen["top_k"] = int(config.top_k) + + # Save BOTH CPU and CUDA RNG state — get_rng_state() is CPU-only, so seeding on a + # CUDA model would otherwise leak its CUDA seed past this generate. + rng_state = None + cuda_rng_state = None + on_cuda = "cuda" in str(self._device) + if do_sample and config.seed is not None: + rng_state = torch.get_rng_state() + if on_cuda: + cuda_rng_state = torch.cuda.get_rng_state_all() + torch.manual_seed(int(config.seed)) + # Install per-turn capture hooks AROUND generate (not pre-): first-write-wins lets + # the prompt forward populate them and decode forwards skip — no extra forward. + with self._eval_capture_scope() as metadata: + try: + with torch.no_grad(): + out = self._hf.generate(ids, **gen) + finally: + if rng_state is not None: + torch.set_rng_state(rng_state) + if cuda_rng_state is not None: + torch.cuda.set_rng_state_all(cuda_rng_state) + + new_ids = out.sequences[0, prompt_len:] + completion = str(self._tokenizer.decode(new_ids, skip_special_tokens=True)) + logprobs = None + if config.logprobs: + content = [ + self._logprob_entry(int(tok), step[0], config.top_logprobs) + for tok, step in zip(new_ids.tolist(), out.scores) + ] + logprobs = Logprobs(content=content) + n_new = int(new_ids.shape[0]) + tool_calls = _parse_tool_calls(completion) if len(tools) else None + eos = self._tokenizer.eos_token_id + stop_reason: StopReason + if tool_calls: + stop_reason = "tool_calls" + elif n_new and eos is not None and int(new_ids[-1]) == eos: + stop_reason = "stop" + else: + stop_reason = "max_tokens" + return ModelOutput( + model=self.model_name, + choices=[ + ChatCompletionChoice( + message=ChatMessageAssistant(content=completion, tool_calls=tool_calls), + stop_reason=stop_reason, + logprobs=logprobs, + ) + ], + usage=ModelUsage( + input_tokens=prompt_len, output_tokens=n_new, total_tokens=prompt_len + n_new + ), + metadata=metadata or None, + ) + + @contextmanager + def _eval_capture_scope(self) -> Iterator[dict[str, Any]]: + """Install per-turn ``capture=[...]`` hooks for the duration of a generate. First- + write-wins lets the prompt forward populate the raw dict (decode forwards find it + populated and skip), so this adds no extra forward. Yields a metadata dict (empty + when capture isn't configured) that the caller folds into ``ModelOutput.metadata``. + Contextvar-isolated so concurrent inspect_eval samples don't cross-pollute.""" + if not self._eval_capture: + yield {} + return + wire_keys = list(self._eval_capture) + capture, _ = _plan(wire_keys, {}) + raw: dict[tuple[int, str], np.ndarray] = {} + call_id = uuid.uuid4().hex + token = _current_call_id.set(call_id) + handles = self._install_hooks(capture, {}, raw, call_id) + metadata: dict[str, Any] = {} + try: + yield metadata + finally: + for handle in handles: + handle.remove() + _current_call_id.reset(token) + metadata["activations"] = wire.encode_activations(_assemble(raw, wire_keys)) + + def _install_hooks(self, capture, intervene, raw, call_id: str) -> list: + """Hook each block's pre/attn/mlp/post boundaries that need capture or intervention. + Hooks consult ``_current_call_id`` and only fire for ``call_id`` (concurrent calls).""" + handles = [] + for layer, block in enumerate(self._layers): + cap_kinds = capture.get(layer, set()) + iv_kinds = intervene.get(layer, {}) + if not cap_kinds and not iv_kinds: + continue + attn = _first_attr(block, _ATTN_ATTRS) + mlp = _first_attr(block, _MLP_ATTRS) + if "resid_pre" in cap_kinds or "resid_pre" in iv_kinds: + handles.append( + block.register_forward_pre_hook( + _pre_hook( + layer, "resid_pre" in cap_kinds, iv_kinds.get("resid_pre"), raw, call_id + ), + with_kwargs=True, + ) + ) + if attn is not None and ("attn_out" in cap_kinds or "attn_out" in iv_kinds): + handles.append( + attn.register_forward_hook( + _out_hook( + layer, + "attn_out", + "attn_out" in cap_kinds, + iv_kinds.get("attn_out"), + raw, + call_id, + ) + ) + ) + if mlp is not None and ("mlp_out" in cap_kinds or "mlp_out" in iv_kinds): + handles.append( + mlp.register_forward_hook( + _out_hook( + layer, + "mlp_out", + "mlp_out" in cap_kinds, + iv_kinds.get("mlp_out"), + raw, + call_id, + ) + ) + ) + if "resid_post" in cap_kinds or "resid_post" in iv_kinds: + handles.append( + block.register_forward_hook( + _out_hook( + layer, + "resid_post", + "resid_post" in cap_kinds, + iv_kinds.get("resid_post"), + raw, + call_id, + ) + ) + ) + return handles + + +def _plan(capture_keys, interventions): + """Resolve wire keys → per-layer kinds to capture (resid_mid needs pre+attn) and intervene.""" + capture: dict[int, set[str]] = defaultdict(set) + for key in capture_keys: + layer, _, kind = key.partition(":") + layer = int(layer) + if kind == "resid_mid": + capture[layer] |= {"resid_pre", "attn_out"} # derived = resid_pre + attn_out + else: + capture[layer].add(kind) + intervene: dict[int, dict[str, Any]] = defaultdict(dict) + for key, spec in interventions.items(): + layer, _, kind = key.partition(":") + intervene[int(layer)][kind] = spec + return capture, intervene + + +def _assemble(raw, capture_keys) -> dict[str, np.ndarray]: + """Build the emitted ``{wire_key: (seq, d)}`` map, deriving resid_mid as needed.""" + out: dict[str, np.ndarray] = {} + for key in capture_keys: + layer, _, kind = key.partition(":") + layer = int(layer) + if kind == "resid_mid": + pre, attn = raw.get((layer, "resid_pre")), raw.get((layer, "attn_out")) + if pre is not None and attn is not None: + out[key] = pre + attn + elif (layer, kind) in raw: + out[key] = raw[(layer, kind)] + return out + + +def _pre_hook(layer, want_capture, spec, raw, call_id): + # with_kwargs=True: hidden_states is args[0] for most decoders, but some pass it as + # the hidden_states kwarg — handle both so the right tensor is read/modified. + # First-write-wins on raw so install-around-generate captures the prompt forward + # (subsequent decode forwards find raw populated and skip — no extra prompt forward). + def hook(_module, args, kwargs): + if _current_call_id.get() != call_id: + return None # different concurrent call's hook + kw_key = None if args else "hidden_states" + hidden = args[0] if args else kwargs["hidden_states"] + if spec is not None: + hidden = _apply_affine(hidden, spec) + if want_capture and (layer, "resid_pre") not in raw: + raw[(layer, "resid_pre")] = hidden[0].detach().float().cpu().numpy() + if spec is None: + return None + if kw_key is None: + return (hidden, *args[1:]), kwargs + return args, {**kwargs, kw_key: hidden} + + return hook + + +def _out_hook(layer, kind, want_capture, spec, raw, call_id): + def hook(_module, _inputs, output): + if _current_call_id.get() != call_id: + return None # different concurrent call's hook + is_tuple = isinstance(output, tuple) + hidden = output[0] if is_tuple else output + if spec is not None: + hidden = _apply_affine(hidden, spec) + if want_capture and (layer, kind) not in raw: + raw[(layer, kind)] = hidden[0].detach().float().cpu().numpy() + if spec is None: + return None + return (hidden, *output[1:]) if is_tuple else hidden + + return hook + + +def _apply_affine(t: torch.Tensor, spec: Mapping[str, Any]) -> torch.Tensor: + """suppress→0, scale→·factor, add→+value, set→value (value scalar or width-shaped).""" + op = spec["op"] + if op == "suppress": + return torch.zeros_like(t) + if op == "scale": + return t * float(spec["factor"]) + value = torch.as_tensor(spec["value"], dtype=t.dtype, device=t.device) + if op == "add": + return t + value + return torch.zeros_like(t) + value # set + + +def _detect_capabilities(model: Any, layers: Any) -> tuple[frozenset, str]: + """Structural self-check: which boundary kinds this model can serve faithfully. + + resid_pre/resid_post are the block in/out (always); attn_out/mlp_out need their + submodules locatable; resid_mid is gated unless its derivation holds (see + :func:`_resid_mid_derivable`). Returns (kinds, note); note explains any gating, '' if none. + """ + block = layers[0] + attn = _first_attr(block, _ATTN_ATTRS) + mlp = _first_attr(block, _MLP_ATTRS) + kinds = {"resid_pre", "resid_post"} + gated = [] + if attn is not None: + kinds.add("attn_out") + else: + gated.append("attn_out (no attention submodule found)") + if mlp is not None: + kinds.add("mlp_out") + else: + gated.append("mlp_out (no MLP submodule found)") + if attn is not None and mlp is not None and _resid_mid_derivable(model, block, attn, mlp): + kinds.add("resid_mid") + else: + gated.append( + "resid_mid (resid_pre + attn_out doesn't hold — parallel or norm-variant block)" + ) + note = ( + "" + if not gated + else "InspectDriver: this architecture's block layout gates " + + ", ".join(gated) + + ". Remaining boundaries are served; use boot_transformers() for the gated ones." + ) + return frozenset(kinds), note + + +def _resid_mid_derivable(model: Any, block: Any, attn: Any, mlp: Any) -> bool: + """True iff ``resid_mid = resid_pre + attn_out`` holds, via two tiny probe forwards. + Requires both the linear identity ``resid_post = resid_pre + attn_out + mlp_out`` (broken + by post-norm/multiplier blocks — Gemma2/OLMo2/Granite) and attn feeding mlp (broken by + parallel blocks — GPTNeoX/GPT-J, where mlp reads resid_pre directly).""" + cap: dict[str, Any] = {} + + def grab(key: str): # type: ignore[no-untyped-def] + def hook(_m: Any, _i: Any, out: Any) -> None: + t = out[0] if isinstance(out, tuple) else out + cap[key] = t.detach().float() + + return hook + + def grab_in(key: str): # type: ignore[no-untyped-def] + def hook(_m: Any, args: Any, kwargs: Any) -> None: + t = args[0] if args else kwargs.get("hidden_states") + cap[key] = None if t is None else t.detach().float() + + return hook + + def perturb_attn(_m: Any, _i: Any, out: Any): # type: ignore[no-untyped-def] + is_tuple = isinstance(out, tuple) + h = out[0] if is_tuple else out + # Local generator: the probe must not reset the caller's global RNG. Non-uniform + # noise so layernorm's mean-subtraction can't cancel it (a constant would). + gen = torch.Generator(device=h.device).manual_seed(0) + h = h + torch.empty_like(h).normal_(generator=gen) + return (h, *out[1:]) if is_tuple else h + + ids = torch.tensor([[0, 1, 2]], device=next(model.parameters()).device) + try: + with torch.no_grad(): + handles = [ + block.register_forward_pre_hook(grab_in("resid_pre"), with_kwargs=True), + attn.register_forward_hook(grab("attn_out")), + mlp.register_forward_hook(grab("mlp_out")), + mlp.register_forward_pre_hook(grab_in("mlp_in"), with_kwargs=True), + block.register_forward_hook(grab("resid_post")), + ] + model(ids) + for h in handles: + h.remove() + mlp_in_clean = cap.pop("mlp_in", None) + handles = [ + mlp.register_forward_pre_hook(grab_in("mlp_in"), with_kwargs=True), + attn.register_forward_hook(perturb_attn), + ] + model(ids) + for h in handles: + h.remove() + mlp_in_perturbed = cap.get("mlp_in") + except Exception: + return False # can't probe (exotic signature) → conservatively gate resid_mid + + rp = cap.get("resid_pre") + ao = cap.get("attn_out") + mo = cap.get("mlp_out") + rpost = cap.get("resid_post") + if rp is None or ao is None or mo is None or rpost is None: + return False + if mlp_in_clean is None or mlp_in_perturbed is None: + return False + if not (rp.shape == ao.shape == mo.shape == rpost.shape == mlp_in_clean.shape): + return False + # (1) sub-block outputs add to the residual without intervening norm/scale. + identity = (rpost - rp - ao - mo).abs().max().item() + identity_ok = identity <= 1e-3 * (rpost.abs().max().item() + 1e-6) + # (2) perturbing attn moves mlp's input (sequential, not parallel). + causal_ok = (mlp_in_clean - mlp_in_perturbed).abs().max().item() > 1e-6 + return bool(identity_ok and causal_ok) + + +def _locate_layers(model: Any) -> Any: + for path in _LAYER_PATHS: + target: Any = model + for seg in path.split("."): + target = getattr(target, seg, None) + if target is None: + break + if target is not None: + return target + raise RuntimeError( + f"Could not locate decoder layers on {type(model).__name__}; tried {_LAYER_PATHS}." + ) + + +def _first_attr(obj: Any, names: tuple[str, ...]) -> Any: + for name in names: + found = getattr(obj, name, None) + if found is not None: + return found + return None diff --git a/transformer_lens/model_bridge/sources/inspect/vllm_provider.py b/transformer_lens/model_bridge/sources/inspect/vllm_provider.py new file mode 100644 index 0000000000..f04abb15a8 --- /dev/null +++ b/transformer_lens/model_bridge/sources/inspect/vllm_provider.py @@ -0,0 +1,439 @@ +"""vLLM-backed ``inspect_ai`` model provider, registered as ``tl_bridge_vllm``. + +A sibling to the HF-backed ``tl_bridge`` provider: instead of running an HF causal LM +locally, it generates via vLLM (PagedAttention + continuous batching) — so it scales to +parallel-sample evals and dataset-scale workloads where the HF provider serializes. + +Inherits ``generate()`` dispatch / message-rendering / per-turn-capture validation from +:class:`_InspectModelAPIBase`; this file owns the vLLM ``LLM`` construction (with the +plugin + worker_extension wiring needed for capture), eval-native generation via +``llm.generate(...)``, and the TL-driven capture path via ``collective_rpc`` to the +worker extension. The capture wire format matches the HF provider's, so the existing +``InspectDriver`` consumes it unchanged. + +vLLM is GPU-only and imported lazily inside ``__init__`` / ``_generate_*`` so this +module imports cleanly in environments without vLLM (matching the HF provider pattern). +""" +from __future__ import annotations + +import gc +import os +from typing import Any, Mapping + +import numpy as np +import torch +from inspect_ai.model import ( + ChatCompletionChoice, + ChatMessageAssistant, + GenerateConfig, + Logprob, + Logprobs, + ModelOutput, + ModelUsage, + StopReason, + TopLogprob, + modelapi, +) + +from . import hooks, wire +from ._provider_base import _InspectModelAPIBase, _parse_tool_calls, _require_served + +# Distinct from the HF ``tl_bridge`` provider and from inspect_ai's built-in ``vllm``. +PROVIDER_NAME = "tl_bridge_vllm" + +# Forced ``LLM(...)`` kwargs that the capture-hook design depends on (matches the same +# set in ``sources/vllm/source.py``). Multi-device / vLLM-owned tokenizer break the wire +# path; we own the tokenizer and the plugin assumes single-process workers. +_LOCKED_VLLM_KWARGS = { + "tensor_parallel_size": 1, + "pipeline_parallel_size": 1, + "skip_tokenizer_init": True, + "disable_log_stats": True, +} + +# Dotted path to the worker extension whose ``tl_*`` methods this provider drives via +# ``collective_rpc`` (capture reads, intervention specs, hook teardown). +_WORKER_EXTENSION_CLS = ( + "transformer_lens.model_bridge.sources.vllm.worker_extension.TLWorkerExtension" +) + + +def _kinds_from_specs(specs: dict[str, Any]) -> frozenset[str]: + """Boundary kinds (resid_post/attn_out/...) served by the overlay's ``capture_specs``. + + Non-block hooks (``embed.hook_out``, ``ln_final.hook_normalized``) don't resolve to + a kind and don't contribute — they're already in the InspectDriver's non-fireable set. + """ + kinds = set() + for name in specs: + resolved = hooks.resolve(name) + if resolved is not None: + kinds.add(resolved[1]) + return frozenset(kinds) + + +@modelapi(name=PROVIDER_NAME) +def transformer_lens_vllm_provider(): + """Lazy registration hook — returns the provider class on first use.""" + return TransformerLensVLLMModelAPI + + +class TransformerLensVLLMModelAPI(_InspectModelAPIBase): + """vLLM-backed Inspect provider. See module docstring for scope per increment.""" + + # vLLM's sampler bypasses lm_head; _synthesize_logits populates only the gen position, + # so earlier positions are -inf and loss would be NaN. RemoteBridge.forward must reject + # return_type ∈ {loss, both} — read by source.py → TLBridgeProfile → InspectDriver. + provides_sequence_logits = False + + def __init__( + self, + model_name: str, + base_url: str | None = None, + api_key: str | None = None, + config: GenerateConfig = GenerateConfig(), + **model_args: Any, + ) -> None: + super().__init__(model_name, base_url, api_key, [], config) + from transformers import AutoConfig, AutoTokenizer + from vllm import LLM + + from transformer_lens.utilities.hf_utils import get_hf_token + + from ..vllm import plugin + from ..vllm.internals import extract_hf_config + from ..vllm.overlays import get_overlay + + # Caller-overridable LLM kwargs go through ``vllm_kwargs``; the locked set above + # may not be overridden (multi-device / vLLM-owned tokenizer break our wire path). + vllm_kwargs = model_args.pop("vllm_kwargs", {}) + for key, locked in _LOCKED_VLLM_KWARGS.items(): + if key in vllm_kwargs and vllm_kwargs[key] != locked: + raise ValueError( + f"tl_bridge_vllm forces {key}={locked}; caller passed " + f"{key}={vllm_kwargs[key]}." + ) + gpu_memory_utilization = model_args.pop("gpu_memory_utilization", 0.5) + max_model_len = model_args.pop("max_model_len", None) + max_num_batched_tokens = int(model_args.pop("max_num_batched_tokens", 2048)) + dtype = model_args.pop("dtype", None) + + # vLLM is GPU-only in production; "device" stays caller-overridable mainly so + # mocked unit tests on CPU machines can build the prompt tensor without CUDA + # (the base's _messages_to_ids honors self._device; we drop to a list before + # handing the prompt to vLLM, so the device only governs the intermediate tensor). + self._device = model_args.pop("device", "cuda") + # skip_tokenizer_init=True ⇒ vLLM has no tokenizer; we own one for prompt rendering + # (via the base) and for decoding generated token ids back to strings. + hf_token = get_hf_token() + self._tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token) + + # Pre-LLM: resolve architecture WITHOUT loading weights, then prime the plugin + # so its monkey-patched Worker.load_model installs capture hooks pre-compile. + hf_config_preview = AutoConfig.from_pretrained(model_name, token=hf_token) + architecture = hf_config_preview.architectures[0] + overlay = get_overlay(architecture) + resolved_dtype = dtype if dtype is not None else _dtype_from_hf_config(hf_config_preview) + capture_specs = overlay.capture_specs(hf_config_preview) + plugin.configure( + capture_specs=capture_specs, + max_num_batched_tokens=max_num_batched_tokens, + dtype=resolved_dtype, + enable_batching=False, # eager batched path is a later increment + ) + plugin.register() + # Single-process workers are required — otherwise the plugin's _config singleton + # isn't visible to worker subprocesses and hooks silently fail to install. + os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0" + + self._llm = LLM( + model=model_name, + gpu_memory_utilization=gpu_memory_utilization, + max_model_len=max_model_len, + max_num_batched_tokens=max_num_batched_tokens, + # Worker extension's tl_* methods are reachable via collective_rpc. + worker_extension_cls=_WORKER_EXTENSION_CLS, + # Full-vocab logprobs so _generate_capture can synthesize logits at the + # generated position (vLLM caps logprobs to this value; default 20 is too + # small for mech interp). + max_logprobs=int(hf_config_preview.vocab_size), + dtype=str(resolved_dtype).replace("torch.", "") if dtype is not None else "auto", + **_LOCKED_VLLM_KWARGS, + **vllm_kwargs, + ) + # Capture-path validity was enforced inside patched_load_model during LLM(...). + hf_config = extract_hf_config(self._llm) + # Don't leak our specs to a subsequent non-TL vllm.LLM(...) in the same process. + plugin._config.clear() + + # Capture-relevant constants used by _generate_capture. + self._d_vocab = int(hf_config.vocab_size) + self._max_logprobs = int(hf_config_preview.vocab_size) + self._max_num_batched_tokens = max_num_batched_tokens + + # Boundary kinds served by the vLLM overlay (decoder-only: resid_post / attn_out / + # mlp_out). vLLM's fused execution doesn't expose block input, so resid_pre and + # the derived resid_mid are gated — the InspectDriver consults this via the profile. + self._kinds = _kinds_from_specs(capture_specs) + self._capability_note = ( + "tl_bridge_vllm: vLLM's fused execution gates resid_pre (no block-input hook) " + "and the derived resid_mid. Use boot_inspect(provider='tl_bridge') for those." + ) + self._eval_capture = self._parse_eval_capture(model_args) + + def _generate_capture(self, input: Any, extra_args: Mapping[str, Any], config: GenerateConfig): + """TL-driven single-token capture: push interventions to the worker, run a + single-token generate (vLLM's prefill populates the capture buffers), read them + back via ``collective_rpc``, and return the wire-format ``metadata["activations"]`` + + synthesized ``tl_logits`` the InspectDriver expects.""" + from vllm import SamplingParams + from vllm.inputs import TokensPrompt + + input_ids = extra_args.get("input_ids") + if input_ids is None: + input_ids = self._messages_to_ids(input)[0].tolist() + n_tokens = len(input_ids) + if n_tokens > self._max_num_batched_tokens: + raise ValueError( + f"Prompt length {n_tokens} exceeds max_num_batched_tokens=" + f"{self._max_num_batched_tokens}; raise via the model_args kwarg " + "or shorten the prompt." + ) + + # Validate capture kinds against the structural self-check (same protection the + # HF provider gives — driver-path AND eval/extra_args entry points). + capture_keys = list(extra_args.get("capture", [])) + for key in capture_keys: + _, _, kind = key.partition(":") + _require_served(kind, self._kinds, self._capability_note, f"capture {key!r}") + + # Translate wire keys ↔ TL hook names. The worker extension is keyed by hook name + # (e.g. "blocks.0.hook_out"); the wire format uses ":". + name_by_wire = {wk: hooks.name_from_wire_key(wk) for wk in capture_keys} + if any(name is None for name in name_by_wire.values()): + unknown = sorted(wk for wk, name in name_by_wire.items() if name is None) + raise ValueError(f"unrecognised wire keys: {unknown}") + capture_names = list(name_by_wire.values()) + + interventions: Mapping[str, Any] = extra_args.get("interventions", {}) + intervention_specs: dict[str, Any] = {} + for wk, spec in interventions.items(): + name = hooks.name_from_wire_key(wk) + if name is None: + raise ValueError(f"intervention wire key {wk!r} is not a fireable hook.") + intervention_specs[name] = spec + + want_logits = bool(extra_args.get("return_logits", True)) + + # Push intervention state (possibly empty — also resets stale interventions from + # a prior call), open the per-hook capture gates (so the prefill below writes, + # and any later forward — should this driver be reused — would self-copy until + # the next explicit reset). Then run a single-token prefill; vLLM's prefill + # populates the capture buffers we registered via plugin.configure. + self._llm.collective_rpc("tl_set_interventions", args=(intervention_specs,)) + self._llm.collective_rpc("tl_reset_capture_flags") + outputs = self._llm.generate( + prompts=[TokensPrompt(prompt_token_ids=list(input_ids))], + sampling_params=SamplingParams( + max_tokens=1, + temperature=0.0, + logprobs=self._max_logprobs if want_logits else None, + ), + ) + # collective_rpc returns one result per worker; single-rank ⇒ [0]. + worker_captures = self._llm.collective_rpc( + "tl_read_captures", args=([n_tokens], capture_names) + )[0] + + # Convert TL-name-keyed (n_tokens, width) tensors → wire-key-keyed numpy arrays, + # then encode in the same envelope wire.decode_activations consumes on the driver. + captured_wire: dict[str, np.ndarray] = {} + for wk, name in name_by_wire.items(): + tensor = worker_captures.get(name) + if tensor is not None: + captured_wire[wk] = tensor.detach().float().cpu().numpy() + metadata: dict[str, Any] = {"activations": wire.encode_activations(captured_wire)} + + if want_logits: + logits = _synthesize_logits(outputs[0], n_tokens, self._d_vocab) + metadata["tl_logits"] = wire.encode_array(logits[0].cpu().numpy()) + + # The completion is the single generated token (matches the HF provider's shape). + next_id = int(outputs[0].outputs[0].token_ids[0]) + return ModelOutput( + model=self.model_name, + choices=[ + ChatCompletionChoice( + message=ChatMessageAssistant(content=str(self._tokenizer.decode([next_id]))), + stop_reason="stop", + ) + ], + metadata=metadata, + ) + + def _generate_eval(self, input: Any, config: GenerateConfig, tools: Any): + """vLLM generation: chat input → ``llm.generate`` → completion + Logprobs + usage. + If ``model_args['capture']`` was set, opens per-hook capture gates before the eval + generate so prefill captures the prompt activations and decode steps self-copy + (first-write-wins on the worker; no separate forward — see plugin._gated_capture).""" + from vllm import SamplingParams + from vllm.inputs import TokensPrompt + + # Worker-side intervention buffers are persistent: any prior capture-path call that + # pushed specs (e.g. bridge.forward(intervene=...)) would still be applied here + # without a reset. The HF provider installs hooks per-call so it's leak-immune. + self._llm.collective_rpc("tl_set_interventions", args=({},)) + ids = self._messages_to_ids(input, tools)[0].tolist() + prompt_len = len(ids) + if self._eval_capture: + if prompt_len > self._max_num_batched_tokens: + raise ValueError( + f"Prompt length {prompt_len} exceeds max_num_batched_tokens=" + f"{self._max_num_batched_tokens}; per-turn capture cannot snapshot it." + ) + self._llm.collective_rpc("tl_reset_capture_flags") + max_new = int(config.max_tokens) if config.max_tokens else 16 + temperature = float(config.temperature) if config.temperature is not None else 0.0 + + sp_kwargs: dict[str, Any] = {"max_tokens": max_new, "temperature": temperature} + if temperature > 0: + if config.top_p is not None: + sp_kwargs["top_p"] = float(config.top_p) + if config.top_k is not None: + sp_kwargs["top_k"] = int(config.top_k) + if config.seed is not None: + sp_kwargs["seed"] = int(config.seed) + if config.logprobs: + # vLLM returns this many top logprobs per generated token (incl. the chosen). + sp_kwargs["logprobs"] = int(config.top_logprobs) if config.top_logprobs else 1 + + outputs = self._llm.generate( + prompts=[TokensPrompt(prompt_token_ids=ids)], + sampling_params=SamplingParams(**sp_kwargs), + ) + request_output = outputs[0] + output = request_output.outputs[0] # one prompt, one sample + new_ids = list(output.token_ids) + n_new = len(new_ids) + completion = str(self._tokenizer.decode(new_ids, skip_special_tokens=True)) + + logprobs = None + if config.logprobs and output.logprobs: + logprobs = Logprobs( + content=[ + self._logprob_from_dict(int(tid), step, config.top_logprobs) + for tid, step in zip(new_ids, output.logprobs) + ] + ) + + tool_calls = _parse_tool_calls(completion) if len(tools) else None + finish = (output.finish_reason or "").lower() + stop_reason: StopReason + if tool_calls: + stop_reason = "tool_calls" + elif finish == "length": + stop_reason = "max_tokens" + elif finish == "stop": + stop_reason = "stop" + else: + stop_reason = "unknown" + + # Per-turn capture lands in metadata. First-write-wins gating made prefill the + # only forward that wrote to the capture buffer, so we read it now (decode steps + # left rows 1..prompt_len-1 untouched and row 0 self-copied). + eval_metadata: dict[str, Any] = {} + if self._eval_capture: + capture_names = list(self._eval_capture.values()) + worker_captures = self._llm.collective_rpc( + "tl_read_captures", args=([prompt_len], capture_names) + )[0] + captured_wire: dict[str, np.ndarray] = {} + for wk, name in self._eval_capture.items(): + tensor = worker_captures.get(name) + if tensor is not None: + captured_wire[wk] = tensor.detach().float().cpu().numpy() + eval_metadata = {"activations": wire.encode_activations(captured_wire)} + + return ModelOutput( + model=self.model_name, + choices=[ + ChatCompletionChoice( + message=ChatMessageAssistant(content=completion, tool_calls=tool_calls), + stop_reason=stop_reason, + logprobs=logprobs, + ) + ], + usage=ModelUsage( + input_tokens=prompt_len, output_tokens=n_new, total_tokens=prompt_len + n_new + ), + metadata=eval_metadata or None, + ) + + def _logprob_from_dict(self, token_id: int, step_logprobs: Any, top_n: Any) -> Logprob: + """vLLM per-step ``{token_id: Logprob(logprob, rank, decoded_token)}`` → + :class:`inspect_ai.model.Logprob` with optional top-k alternatives.""" + chosen = step_logprobs.get(token_id) + chosen_lp = float(chosen.logprob) if chosen is not None else float("-inf") + top: list[TopLogprob] = [] + if top_n: + ranked = sorted(step_logprobs.items(), key=lambda kv: -float(kv[1].logprob)) + for tid, lp in ranked[: int(top_n)]: + token = lp.decoded_token or self._tokenizer.decode([int(tid)]) + top.append(TopLogprob(token=str(token), logprob=float(lp.logprob), bytes=None)) + return Logprob( + token=str(self._tokenizer.decode([int(token_id)])), + logprob=chosen_lp, + bytes=None, + top_logprobs=top, + ) + + def close(self) -> None: + """Best-effort vLLM teardown — vLLM 0.20.2 has no ``LLM.shutdown()``, so weights + + KV cache stay resident until process exit unless we destroy the distributed env.""" + self._llm = None + try: + from vllm.distributed.parallel_state import ( + destroy_distributed_environment, + destroy_model_parallel, + ) + + destroy_model_parallel() + destroy_distributed_environment() + except Exception: + pass + gc.collect() + if torch.cuda.is_available(): + try: + torch.cuda.empty_cache() + except Exception: + pass + + +def _dtype_from_hf_config(hf_config: Any) -> torch.dtype: + """Best-effort dtype from an HF config — vLLM prefers fp16 on GPU.""" + raw = getattr(hf_config, "torch_dtype", None) + if isinstance(raw, torch.dtype): + return raw + if isinstance(raw, str): + return getattr(torch, raw, torch.float16) + return torch.float16 + + +def _synthesize_logits(request_output: Any, n_tokens: int, d_vocab: int) -> torch.Tensor: + """Build a ``(1, n_tokens, d_vocab)`` logits-like tensor from vLLM's sampler output — + log-probs (not raw logits), with earlier positions ``-inf`` (lm_head is bypassed so + only the generated position is populated). Matches the HF provider's ``tl_logits`` + shape so the InspectDriver consumes both the same way.""" + logits = torch.full((1, n_tokens, d_vocab), float("-inf"), dtype=torch.float32) + gen = request_output.outputs[0] if request_output.outputs else None + if gen is None: + return logits + if gen.logprobs: + for token_id, lp in gen.logprobs[0].items(): + logits[0, -1, int(token_id)] = float(lp.logprob) + elif gen.token_ids: + logits[0, -1, int(gen.token_ids[0])] = 0.0 + return logits + + +__all__ = ["TransformerLensVLLMModelAPI"] diff --git a/transformer_lens/model_bridge/sources/inspect/wire.py b/transformer_lens/model_bridge/sources/inspect/wire.py new file mode 100644 index 0000000000..bf527a5e34 --- /dev/null +++ b/transformer_lens/model_bridge/sources/inspect/wire.py @@ -0,0 +1,70 @@ +"""Serialization chokepoint for the Inspect activation wire format. + +Activations ride in ``ModelOutput.metadata["activations"]`` as a flat +``{":": {"data": , "dtype": str, "shape": [...]}}`` map (keys +are :func:`hooks.wire_key`). For vllm-lens interop, decode also understands its +*documented* nested ``{"residual_stream": {layer: ...}}`` shape (mapped to +``resid_post``) — unverified against a live vllm-lens provider. +Numpy-only (no torch) so both the torch-using provider and the torch-free driver +import it; the single place to patch on format drift. +""" +from __future__ import annotations + +import base64 +from typing import Any, Iterable, Mapping + +import numpy as np + +_RESIDUAL = "residual_stream" # vllm-lens's nested key (interop decode only) + + +def encode_array(arr: np.ndarray) -> dict[str, Any]: + """numpy array → ``{"data": b64, "dtype": str, "shape": [...]}``.""" + contiguous = np.ascontiguousarray(arr) + return { + "data": base64.b64encode(contiguous.tobytes()).decode("ascii"), + "dtype": str(contiguous.dtype), + "shape": list(contiguous.shape), + } + + +def decode_array(entry: Any) -> np.ndarray: + """Inverse of :func:`encode_array`; passes an already-decoded ndarray through.""" + if isinstance(entry, np.ndarray): + return entry + raw = base64.b64decode(entry["data"]) + # frombuffer is read-only; copy so the downstream torch tensor is writable. + return np.frombuffer(raw, dtype=np.dtype(entry["dtype"])).reshape(entry["shape"]).copy() + + +def encode_activations(captured: Mapping[str, np.ndarray]) -> dict[str, Any]: + """``{wire_key: array}`` → the ``metadata["activations"]`` payload.""" + return {key: encode_array(arr) for key, arr in captured.items()} + + +def decode_activations( + metadata: Mapping[str, Any] | None, wire_keys: Iterable[str] +) -> dict[str, np.ndarray]: + """Pull the requested ``:`` keys out of ``metadata["activations"]``, + falling back to the nested ``residual_stream`` for ``resid_post``. Missing keys are + skipped — the caller decides.""" + activations = (metadata or {}).get("activations") or {} + residual = activations.get(_RESIDUAL, {}) + out: dict[str, np.ndarray] = {} + for key in wire_keys: + if key in activations: + out[key] = decode_array(activations[key]) + continue + layer, _, kind = key.partition(":") + if kind == "resid_post": + entry = residual.get(layer, residual.get(_safe_int(layer))) + if entry is not None: + out[key] = decode_array(entry) + return out + + +def _safe_int(value: str) -> Any: + try: + return int(value) + except ValueError: + return value diff --git a/transformer_lens/model_bridge/sources/vllm/driver.py b/transformer_lens/model_bridge/sources/vllm/driver.py index cf231c3cc7..e78aa47ffa 100644 --- a/transformer_lens/model_bridge/sources/vllm/driver.py +++ b/transformer_lens/model_bridge/sources/vllm/driver.py @@ -95,6 +95,10 @@ def forward( # Push intervention state (possibly empty) before generate — this also # resets stale interventions from prior forwards. self._llm.collective_rpc("tl_set_interventions", args=(intervene_specs,)) + # Open per-hook capture gates; first-write-wins means a fresh prefill writes and + # subsequent forwards self-copy. Without this, repeated bridge.forward calls would + # see the gate closed from the prior call and read stale buffers. + self._llm.collective_rpc("tl_reset_capture_flags") # Full-vocab logprobs → position -1 of the synthesized logits (see _n_logprobs). outputs = self._llm.generate( prompts=[TokensPrompt(prompt_token_ids=ids_list)], diff --git a/transformer_lens/model_bridge/sources/vllm/plugin.py b/transformer_lens/model_bridge/sources/vllm/plugin.py index d2ee620b9b..6b54d46490 100644 --- a/transformer_lens/model_bridge/sources/vllm/plugin.py +++ b/transformer_lens/model_bridge/sources/vllm/plugin.py @@ -93,6 +93,11 @@ def patched_load_model(self): self._tl_buffers = {} self._tl_scale_buffers = {} self._tl_bias_buffers = {} + # Per-hook first-write-wins flag (compiled mode). 0 = open (next forward captures), + # 1 = closed (subsequent forwards self-copy and don't overwrite). Driver opens via + # tl_reset_capture_flags before any capture-needing call so a multi-token generate's + # prefill captures cleanly and decode steps don't overwrite row 0. + self._tl_capture_flags = {} self._tl_hook_handles = [] # Batched-mode per-(req_id, hook) accumulators + global spec dict. self._tl_accum = {} @@ -122,15 +127,20 @@ def patched_load_model(self): # to enable suppress/scale/add/set ops between forwards. scale_buf = torch.ones(width, device=device, dtype=dtype) bias_buf = torch.zeros(width, device=device, dtype=dtype) + # Default closed — opened explicitly by tl_reset_capture_flags for the + # next forward(s) that need to capture. + capture_flag = torch.ones(1, device=device, dtype=torch.int64) self._tl_buffers[canonical_name] = capture_buf self._tl_scale_buffers[canonical_name] = scale_buf self._tl_bias_buffers[canonical_name] = bias_buf + self._tl_capture_flags[canonical_name] = capture_flag handle = target.register_forward_hook( _make_capture_hook( capture_buf, scale_buf, bias_buf, self._tl_fire_counter, + capture_flag, materialize=materialize, ) ) @@ -145,10 +155,11 @@ def _make_capture_hook( scale_buf: torch.Tensor, bias_buf: torch.Tensor, fire_counter: torch.Tensor, + capture_flag: torch.Tensor, *, materialize: bool = False, ): - """GPU-only, dynamic-shape-safe affine + capture into pre-allocated buffers. + """GPU-only, dynamic-shape-safe affine + first-write-wins capture into pre-allocated buffers. When ``materialize=True`` (decoder layers), treat the module's output as vLLM's fused-residual ``(mlp_delta, residual)`` tuple: capture @@ -157,6 +168,12 @@ def _make_capture_hook( so the next layer's input_layernorm sees the same fused sum. Mutations propagate through both the capture and the downstream graph. + ``capture_flag`` (0 = open, 1 = closed) gates the buffer write — driver opens it + via ``tl_reset_capture_flags`` before each capture-needing forward, the hook closes + it on first fire, so a multi-token generate's prefill captures cleanly and decode + steps self-copy (no overwrite). Interventions still apply on every forward + regardless of the flag — the gate only affects the capture write. + ``fire_counter`` is incremented per call for the fire-once check. """ @@ -169,7 +186,7 @@ def hook(_module, _inputs, output): t = hidden + residual modified = t * scale_buf + bias_buf n = t.shape[0] - capture_buf.narrow(0, 0, n).copy_(modified) + _gated_capture(capture_buf, n, modified, capture_flag) # Reconstructs ``modified`` in the next layer's fused norm: exact at # identity, bounded fp16 error under intervention. return (modified - residual, residual) @@ -188,7 +205,7 @@ def hook(_module, _inputs, output): # narrow() keeps the dynamic shape; [:n] gets erased under fake-tensor # tracing and copy_ then sees the full buffer ("expand s72 -> max_n"). n = t.shape[0] - capture_buf.narrow(0, 0, n).copy_(modified) + _gated_capture(capture_buf, n, modified, capture_flag) # Gate on isinstance, not truthy tuple_tail — a 1-tuple has an empty tail. if isinstance(output, tuple): return (modified,) + tuple_tail @@ -197,6 +214,21 @@ def hook(_module, _inputs, output): return hook +def _gated_capture( + capture_buf: torch.Tensor, n: Any, modified: torch.Tensor, capture_flag: torch.Tensor +) -> None: + """First-write-wins via torch.where, compile-safe (no Python branching). + + When ``capture_flag == 0`` (open), writes ``modified`` to ``capture_buf[:n]``. + When ``capture_flag == 1`` (closed), self-copies ``capture_buf[:n]`` (no-op). + Always closes the flag — driver explicitly opens it before each capture forward. + """ + existing = capture_buf.narrow(0, 0, n) + to_write = torch.where(capture_flag.bool(), existing, modified) + capture_buf.narrow(0, 0, n).copy_(to_write) + capture_flag.fill_(1) + + def _make_batched_hook( worker: Any, canonical_name: str, diff --git a/transformer_lens/model_bridge/sources/vllm/worker_extension.py b/transformer_lens/model_bridge/sources/vllm/worker_extension.py index a462e826fd..5873be05c7 100644 --- a/transformer_lens/model_bridge/sources/vllm/worker_extension.py +++ b/transformer_lens/model_bridge/sources/vllm/worker_extension.py @@ -30,6 +30,8 @@ class TLWorkerExtension: _tl_buffers: Dict[str, torch.Tensor] _tl_scale_buffers: Dict[str, torch.Tensor] _tl_bias_buffers: Dict[str, torch.Tensor] + # Per-hook first-write-wins gates (compiled mode); see plugin._gated_capture. + _tl_capture_flags: Dict[str, torch.Tensor] _tl_fire_counter: torch.Tensor # Batched-mode state (eager). _tl_accum: Dict[tuple, List[torch.Tensor]] @@ -88,6 +90,17 @@ def tl_reset_counter(self) -> None: if counter is not None: counter.zero_() + def tl_reset_capture_flags(self) -> None: + """Open every per-hook capture gate so the next forward writes to the buffers. + + First-write-wins gating means decode-step forwards self-copy and never overwrite + prefill activations — the driver calls this once before any capture-needing + generate (single-forward or multi-token eval) and ``tl_read_captures`` afterward. + """ + flags: Dict[str, torch.Tensor] = getattr(self, "_tl_capture_flags", {}) + for flag in flags.values(): + flag.zero_() + def tl_read_counter(self) -> int: """Total hook fires since the last reset.""" counter = 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numerically unverified. For each model this boots both backends in +fp32 (isolating the boundary mapping from dtype/kernel-precision differences) and diffs +the fireable capture points the vLLM driver OFFERS (``driver.supported_hook_points``): + +- ``embed.hook_out`` and per-layer ``blocks.{i}.hook_out`` / ``attn.hook_out`` / + ``mlp.hook_out`` are semantically identical to boot_transformers and compared directly. +- ``ln_final.hook_normalized`` carries vLLM's POST-weight RMSNorm value under a hook + whose HT convention is PRE-weight; it is un-folded (÷ weight, or ÷ (1+weight) for + Gemma) before comparison. The raw (un-un-folded) diff is also reported so a wrong + un-fold direction is visible rather than silent. + +Non-fireable points (fused attention pattern/scores/rope, unembed) are withheld and +reported, not compared. A final-position argmax agreement check sanity-checks logits. + +GPU-ONLY: requires a CUDA device and a working ``vllm`` install (pinned build). This is +NOT a CPU/per-PR CI job — it SKIPs cleanly when vLLM or a GPU is unavailable. + +Run: uv run python scripts/vllm_parity_report.py +Env: TL_PARITY_MODELS="id1,id2,..." overrides the model list. + TL_VLLM_ATOL / TL_VLLM_RTOL override tolerance (defaults 2e-2). +""" +from __future__ import annotations + +import gc +import os +import sys +import warnings + +warnings.filterwarnings("ignore") + +import torch + +# vLLM-supported decoder-only checkpoints small enough to hold model + capture buffers + +# KV cache on one GPU. gemma-2-2b is included specifically to exercise the (1 + weight) +# ln_final un-fold. Gated ids need HF_TOKEN (sourced from .env); bad/gated ids -> SKIP. +DEFAULT_MODELS = [ + "HuggingFaceTB/SmolLM2-135M", # Llama (tiny, ungated — the smoke test) + "Qwen/Qwen2.5-0.5B", # Qwen2 + "Qwen/Qwen3-0.6B", # Qwen3 + "meta-llama/Llama-3.2-1B", # Llama (gated) + "google/gemma-2-2b", # Gemma2 (gated) — exercises the (1 + weight) ln_final un-fold +] + +PROMPT = "The quick brown fox" +# vLLM's fused kernels (PagedAttention, fused RMSNorm/RoPE) differ numerically from HF's +# eager path even in fp32, so the mapping-correct band is looser than the HF-vs-HF 1e-3. +# A wrong hook mapping (e.g. un-un-folded ln_final) diverges by O(1), well outside this. +ATOL = float(os.environ.get("TL_VLLM_ATOL", "2e-2")) +RTOL = float(os.environ.get("TL_VLLM_RTOL", "2e-2")) + +# vLLM capture kind -> TransformerBridge-native hook name (per-layer uses {i}). +DIRECT_KINDS = { + "resid_post": "blocks.{i}.hook_out", + "attn_out": "blocks.{i}.attn.hook_out", + "mlp_out": "blocks.{i}.mlp.hook_out", +} +EMBED_HOOK = "embed.hook_out" +LNF_HOOK = "ln_final.hook_normalized" + + +def _to2d(t: torch.Tensor) -> torch.Tensor: + """Collapse [batch, seq, d] or [seq, d] to a [tokens, d] float CPU tensor for diffing.""" + t = t.detach().to("cpu", torch.float32) + return t.reshape(-1, t.shape[-1]) + + +def _diff(a: torch.Tensor, b: torch.Tensor) -> tuple[float, bool]: + a2, b2 = _to2d(a), _to2d(b) + if a2.shape != b2.shape: + return float("inf"), False + d = (a2 - b2).abs().max().item() + return d, torch.allclose(a2, b2, atol=ATOL, rtol=RTOL) + + +def _unfold_lnf(vllm_val: torch.Tensor, weight: torch.Tensor, is_gemma: bool) -> torch.Tensor: + """vLLM ln_final is post-weight (x·rsqrt(var+eps)·w); recover the pre-weight value HT + exposes by dividing out the norm weight (Gemma folds 1 + weight).""" + w = weight.detach().to(vllm_val.device, torch.float32) + denom = (1.0 + w) if is_gemma else w + # Guard against near-zero weight entries producing spurious blow-ups. + denom = torch.where(denom.abs() < 1e-6, torch.ones_like(denom), denom) + return vllm_val.to(torch.float32) / denom + + +def verify(model_id: str) -> dict: + from transformer_lens.model_bridge.sources.vllm.source import boot_vllm + from transformer_lens.model_bridge.transformer_bridge import TransformerBridge + + result: dict = {"model": model_id, "arch": "?", "status": "", "detail": ""} + hf = vllm = None + try: + # Matched fp32 both sides so the comparison isolates the boundary mapping, not + # dtype. HF ref on the same CUDA device as vLLM; tensors are moved to CPU to diff. + hf = TransformerBridge.boot_transformers(model_id, device="cuda", dtype=torch.float32) + arch = getattr(hf.cfg, "architecture", "?") + result["arch"] = arch + is_gemma = "gemma" in arch.lower() or "gemma" in model_id.lower() + n_layers = int(hf.cfg.n_layers) + toks = hf.to_tokens(PROMPT) + + vllm = boot_vllm(model_id, dtype=torch.float32, max_model_len=2048) + offered = vllm._driver.supported_hook_points + + hf_logits, hf_cache = hf.run_with_cache(toks) + v_logits, v_cache = vllm.run_with_cache(toks) + + worst = 0.0 + mism: list[str] = [] + notes: list[str] = [] + + # embed + the three per-layer direct boundaries (first & last layer). + checks = [(EMBED_HOOK, EMBED_HOOK)] + for i in sorted({0, n_layers - 1}): + for name in DIRECT_KINDS.values(): + hk = name.format(i=i) + checks.append((hk, hk)) + for hk, _ in checks: + if hk not in offered: + continue # not a fireable point for this overlay + if hk not in v_cache or hk not in hf_cache: + mism.append(f"{hk} missing") + continue + d, ok = _diff(hf_cache[hk], v_cache[hk]) + worst = max(worst, d if d != float("inf") else worst) + if not ok: + mism.append(f"{hk} maxdiff={d:.2e}") + + # ln_final: compare un-folded; also report the raw diff so a wrong un-fold shows. + if LNF_HOOK in offered and LNF_HOOK in v_cache and LNF_HOOK in hf_cache: + raw_d, raw_ok = _diff(hf_cache[LNF_HOOK], v_cache[LNF_HOOK]) + weight = vllm._driver.get_param("model.norm.weight") + if weight is None: + notes.append(f"ln_final raw={raw_d:.2e} (no norm weight to un-fold)") + if not raw_ok: + mism.append(f"{LNF_HOOK} maxdiff={raw_d:.2e} (un-fold unavailable)") + else: + unfolded = _unfold_lnf(v_cache[LNF_HOOK], weight, is_gemma) + uf_d, uf_ok = _diff(hf_cache[LNF_HOOK], unfolded) + worst = max(worst, uf_d if uf_d != float("inf") else worst) + notes.append(f"ln_final unfold={uf_d:.2e} raw={raw_d:.2e}") + if not uf_ok: + mism.append(f"{LNF_HOOK} unfold maxdiff={uf_d:.2e} (raw={raw_d:.2e})") + + # Logit sanity: vLLM synthesizes final-position log-probs only; check top-1 agrees + # (reusing the run_with_cache logits above — no second forward). + try: + hf_top = int(torch.as_tensor(hf_logits)[0, -1].argmax()) + v_top = int(torch.as_tensor(v_logits)[0, -1].argmax()) + notes.append(f"argmax {'==' if hf_top == v_top else '!='}({hf_top},{v_top})") + if hf_top != v_top: + mism.append(f"final-token argmax {hf_top}!={v_top}") + except Exception as e: # logit synth is best-effort; don't fail parity on it + notes.append(f"logit-check err: {type(e).__name__}") + + note_str = "; ".join(notes) + if mism: + result["status"] = "FAIL" + result["detail"] = "; ".join(mism[:3]) + (f" | {note_str}" if note_str else "") + else: + result["status"] = "PASS" + result["detail"] = f"maxdiff={worst:.2e} (L={n_layers}); {note_str}" + except Exception as e: # download/gating/OOM/vLLM-unavailable are not parity failures + result["status"] = "SKIP" + result["detail"] = f"{type(e).__name__}: {str(e).splitlines()[0][:140]}" + finally: + for bridge in (vllm, hf): + try: + if bridge is not None: + bridge.close() + except Exception: + pass + del hf, vllm + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + return result + + +def _preflight() -> str | None: + """Return a human reason to abort (no GPU / no vllm), or None if runnable.""" + if not torch.cuda.is_available(): + return "no CUDA device — vLLM capture only materializes in a real GPU forward" + try: + import vllm # noqa: F401 + except Exception as e: + return f"vllm not importable ({type(e).__name__}: {e})" + return None + + +def main() -> None: + reason = _preflight() + if reason is not None: + print(f"SKIP ALL: {reason}", flush=True) + print("This harness is GPU-only and requires a working vllm install.", flush=True) + sys.exit(0) + + ids = os.environ.get("TL_PARITY_MODELS") + models = [m.strip() for m in ids.split(",")] if ids else DEFAULT_MODELS + rows = [] + for m in models: + r = verify(m) + rows.append(r) + print(f"[{r['status']:4}] {r['arch']:28} {r['model']:40} {r['detail']}", flush=True) + + print("\n================ vLLM PARITY REPORT CARD ================") + for status in ("PASS", "FAIL", "SKIP"): + sel = [r for r in rows if r["status"] == status] + print(f"\n{status} ({len(sel)}):") + for r in sel: + print(f" {r['arch']:28} {r['model']:40} {r['detail']}") + passed = sorted({r["arch"] for r in rows if r["status"] == "PASS"}) + print("\nvLLM-CAPTURE-VERIFIED ARCHITECTURES (measured PASS):") + print(" " + (", ".join(passed) if passed else "(none)")) + # Non-zero exit if anything actually ran and failed — lets a manual GPU run gate. + sys.exit(1 if any(r["status"] == "FAIL" for r in rows) else 0) + + +if __name__ == "__main__": + main() From bb5be715eadc5a70733a73cdaa776a607e2659fe Mon Sep 17 00:00:00 2001 From: jlarson4 Date: Mon, 6 Jul 2026 14:15:01 -0500 Subject: [PATCH 07/43] Close gap that prevent falcon attention in inspect --- .../model_bridge/sources/inspect/transformers_provider.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transformer_lens/model_bridge/sources/inspect/transformers_provider.py b/transformer_lens/model_bridge/sources/inspect/transformers_provider.py index aa4faf356a..be8a26cf89 100644 --- a/transformer_lens/model_bridge/sources/inspect/transformers_provider.py +++ b/transformer_lens/model_bridge/sources/inspect/transformers_provider.py @@ -46,7 +46,7 @@ # Decoder ModuleList by architecture family; each block's output is resid_post. _LAYER_PATHS = ("model.layers", "transformer.h", "gpt_neox.layers", "model.decoder.layers") # Attn/MLP submodule names within a block, by family. -_ATTN_ATTRS = ("self_attn", "attn", "attention") +_ATTN_ATTRS = ("self_attn", "attn", "attention", "self_attention") # self_attention: Falcon _MLP_ATTRS = ("mlp", "feed_forward") From 0b20b6aec0ebc0e995d7cfa2f7b9755f9a8c0ecc Mon Sep 17 00:00:00 2001 From: jlarson4 Date: Mon, 6 Jul 2026 14:38:54 -0500 Subject: [PATCH 08/43] fixed some logit resolution issues,add logit reconstruction demo for collab --- demos/vLLM_Bridge_Integration_Test.ipynb | 14 ++++++++++++++ .../model_bridge/sources/inspect/driver.py | 16 +++++++++++++++- .../model_bridge/sources/inspect/profiles.py | 10 ++++++++++ 3 files changed, 39 insertions(+), 1 deletion(-) diff --git a/demos/vLLM_Bridge_Integration_Test.ipynb b/demos/vLLM_Bridge_Integration_Test.ipynb index 106ec40986..031b5c4942 100644 --- a/demos/vLLM_Bridge_Integration_Test.ipynb +++ b/demos/vLLM_Bridge_Integration_Test.ipynb @@ -194,6 +194,20 @@ ], "id": "a59824b0e3be" }, + { + "cell_type": "markdown", + "id": "f9e987ba", + "source": "## Step 4½ — Logit-reconstruction experiment (help finalize `_reconstruct_logits`)\n\nvLLM's sampler bypasses `lm_head`, so the bridge only returns **final-position log-probs** (`provides_sequence_logits=False`) — no full-sequence logits, no loss, no logit-lens magnitude. Those are recoverable host-side as `ln_final @ lm_head.weightᵀ`, since the captured `ln_final` plus `lm_head.weight` (via `get_param`) are already available.\n\nThe open question this cell answers: **which transform of the captured `ln_final` reproduces HF's logits?** HF's `hook_normalized` is the *pre*-weight RMSNorm value; vLLM's capture is documented *post*-weight and Step 5 flags it reading **~2× HF's** (unresolved). `lm_head` consumes the *post*-weight value, so this searches the candidates (`raw`, `÷ norm_w`, `÷ 2`, …) for the one whose `@ W_U` matches HF's next-token argmax across **all** positions.\n\nThat result nails down two things at once: the driver-side reconstruction formula, **and** the Step-5 `ln_final` ~2× divergence. **Run the next cell and paste its full output back.**\n", + "metadata": {} + }, + { + "cell_type": "code", + "id": "3774dd88", + "source": "# ==========================================================================\n# EXPERIMENT — reconstruct full-sequence logits from the captured ln_final\n# ==========================================================================\n# vLLM bypasses lm_head, so the bridge returns final-position log-probs only\n# (provides_sequence_logits=False). Real logits = @ lm_head.weight.T\n# — but WHICH ln_final? HF's hook_normalized is PRE-weight; vLLM's capture is\n# POST-weight and Step 5 notes it reads ~2x HF's. lm_head consumes the POST-weight\n# value. This cell finds the transform of the captured ln_final whose @ W_U\n# reproduces HF's argmax across ALL positions: that formula is what a driver-side\n# _reconstruct_logits should use, and it also pins down the Step-5 ln_final ~2x.\nimport torch\n\nLNF = \"ln_final.hook_normalized\"\nassert LNF in cache_vllm and LNF in cache_hf_cpu, \"need ln_final captured on both sides\"\nv_lnf = cache_vllm[LNF][0].float().cpu() # (seq, d_model) vLLM capture\nhf_lnf = cache_hf_cpu[LNF][0].float() # (seq, d_model) HF capture (pre-weight)\n\nW = bridge._driver.get_param(\"lm_head.weight\")\ntied = W is None\nif tied:\n W = bridge._driver.get_param(\"model.embed_tokens.weight\") # tie fallback\nW = W.float().cpu() # (vocab, d_model)\n_nw = bridge._driver.get_param(\"model.norm.weight\")\nnorm_w = _nw.float().cpu() if _nw is not None else torch.ones(W.shape[1])\n\nprint(f\"ln_final: vLLM {tuple(v_lnf.shape)} | HF {tuple(hf_lnf.shape)}\")\nprint(f\"lm_head.weight: {'TIED->embed_tokens' if tied else 'present'} {tuple(W.shape)}\"\n f\" | norm_w {tuple(norm_w.shape)} | d_vocab(bridge)={bridge.cfg.d_vocab}\")\n\n# (1) Diagnose vLLM-vs-HF ln_final (Step 5's open ~2x question).\nm = hf_lnf.abs() > 1e-2\nratio = v_lnf[m] / hf_lnf[m]\nprint(f\"\\nln_final ratio vLLM/HF (|hf|>1e-2): median={ratio.median():.3f} mean={ratio.mean():.3f}\")\nprint(f\"norm_w: median={norm_w.median():.3f} (a pure post/pre-weight gap would be ~norm_w)\")\n\n# (2) Which reconstruction reproduces HF's next-token argmax?\ndef recon(lnf, label):\n lg = lnf @ W.T # (seq, vocab)\n top = int(lg[-1].argmax())\n print(f\" {label:<32} final argmax={top}{' <== ==HF' if top == argmax_hf else ''}\")\n return lg\n\nprint(f\"\\nTargets: HF argmax={argmax_hf} vLLM argmax={argmax_vllm}\")\nprint(\"Candidates (lnf @ lm_head.weight.T):\")\ncands = {\n \"HF ln_final (pre-weight)\": hf_lnf,\n \"HF ln_final * norm_w\": hf_lnf * norm_w,\n \"vLLM ln_final (raw)\": v_lnf,\n \"vLLM ln_final / norm_w\": v_lnf / norm_w,\n \"vLLM ln_final / 2\": v_lnf / 2.0,\n}\nlg = {k: recon(v, k) for k, v in cands.items()}\n\n# (3) Full-sequence argmax agreement between the HF-truth recon and each vLLM recon.\ntruth = lg[\"HF ln_final * norm_w\"]\nif int(truth[-1].argmax()) != argmax_hf:\n truth = lg[\"HF ln_final (pre-weight)\"]\ndef agree(a):\n return float((truth.argmax(-1) == a.argmax(-1)).float().mean()) * 100\nprint(\"\\nFull-sequence argmax agreement vs HF-truth recon (all positions):\")\nfor k in (\"vLLM ln_final (raw)\", \"vLLM ln_final / norm_w\", \"vLLM ln_final / 2\"):\n print(f\" {k:<32} {agree(lg[k]):.1f}%\")\n\nprint(\"\\n>>> Paste this whole output back. I need: which candidate is '==HF', the\")\nprint(\">>> ln_final ratio vs norm_w, and the full-sequence agreement %s — that fixes\")\nprint(\">>> both the driver reconstruction formula AND the Step-5 ln_final divergence.\")\n", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/transformer_lens/model_bridge/sources/inspect/driver.py b/transformer_lens/model_bridge/sources/inspect/driver.py index f30240cf96..b7dc954ed9 100644 --- a/transformer_lens/model_bridge/sources/inspect/driver.py +++ b/transformer_lens/model_bridge/sources/inspect/driver.py @@ -10,8 +10,10 @@ import asyncio import logging +import os import threading import warnings +from concurrent.futures import TimeoutError as FutureTimeout from typing import Any, Mapping import numpy as np @@ -26,6 +28,10 @@ from . import hooks, wire from .profiles import TLBridgeProfile +# Cap a single provider call so a hung remote/provider forward unblocks the sync caller +# instead of stalling it forever. Generous by default; override for slow remote backends. +_PROVIDER_TIMEOUT_S = float(os.environ.get("TL_INSPECT_TIMEOUT_S", "300")) + class InspectDriver(DriverBase): """Driver wrapping an ``inspect_ai`` model; capture + interventions via a Profile.""" @@ -160,7 +166,15 @@ def _ensure_loop(self) -> asyncio.AbstractEventLoop: def _run_coro(self, coro: Any) -> Any: future = asyncio.run_coroutine_threadsafe(coro, self._ensure_loop()) - return future.result() # blocks the sync caller; re-raises provider errors + try: + return future.result(timeout=_PROVIDER_TIMEOUT_S) # re-raises provider errors + except FutureTimeout: + future.cancel() + raise TimeoutError( + f"Inspect provider call exceeded {_PROVIDER_TIMEOUT_S:.0f}s " + "(set TL_INSPECT_TIMEOUT_S to change) — the remote/provider forward looks " + "hung; unblocking the caller rather than stalling indefinitely." + ) from None def _wire_keys(self, names: list[str]) -> list[str]: """Unique ``:`` keys for the requested hook names (aliases collapse).""" diff --git a/transformer_lens/model_bridge/sources/inspect/profiles.py b/transformer_lens/model_bridge/sources/inspect/profiles.py index 20222a529c..225317bb7a 100644 --- a/transformer_lens/model_bridge/sources/inspect/profiles.py +++ b/transformer_lens/model_bridge/sources/inspect/profiles.py @@ -13,6 +13,7 @@ """ from __future__ import annotations +import warnings from typing import Any, Mapping import numpy as np @@ -55,6 +56,15 @@ def decode_logits(self, output, n_tokens, d_vocab, tokenizer): entry = (getattr(output, "metadata", None) or {}).get("tl_logits") if entry is not None: return wire.decode_array(entry)[np.newaxis, ...] # full (1, seq, d_vocab) + # Absent tl_logits despite a logits request means the forward produced none — warn + # rather than silently hand back an all -inf tensor (whose argmax is a bogus token 0). + warnings.warn( + "tl_bridge provider returned no 'tl_logits' in output metadata; emitting an " + "all -inf placeholder (argmax would be token 0). Check the provider/wire path " + "rather than trusting these logits.", + RuntimeWarning, + stacklevel=2, + ) return np.full((1, n_tokens, d_vocab), -np.inf, dtype=np.float32) From 2bb3c02dd3d498e082b3ab8e606c641d2347a7d5 Mon Sep 17 00:00:00 2001 From: Jonah Larson Date: Tue, 7 Jul 2026 13:25:10 -0500 Subject: [PATCH 09/43] Inspect & vLLM driver Features & Bugs (#1492) * notebook fix * Rebuilding real logits * improve buffers * Fixed softcap bug * per-position interventino * Inspect HF provider servers head-split attention hooks * Resolving final gaps in interp skills --- demos/vLLM_Bridge_Integration_Test.ipynb | 56 ++- scripts/inspect_parity_report.py | 15 +- .../model_bridge/test_inspect_provider.py | 154 +++++++- .../test_symbolic_bridge_hooks.py | 56 +++ .../unit/model_bridge/test_inspect_driver.py | 118 +++++++ tests/unit/model_bridge/test_vllm_boot.py | 14 + tests/unit/model_bridge/test_vllm_driver.py | 145 +++++++- .../test_vllm_worker_extension.py | 65 ++++ .../model_bridge/component_setup.py | 23 ++ .../model_bridge/sources/inspect/driver.py | 14 +- .../model_bridge/sources/inspect/eval.py | 21 +- .../model_bridge/sources/inspect/hooks.py | 68 +++- .../sources/inspect/intervention.py | 11 +- .../model_bridge/sources/inspect/source.py | 16 +- .../sources/inspect/transformers_provider.py | 332 ++++++++++++++++-- .../model_bridge/sources/vllm/driver.py | 183 ++++++++-- .../model_bridge/sources/vllm/plugin.py | 38 +- .../model_bridge/sources/vllm/source.py | 16 + .../sources/vllm/worker_extension.py | 61 +++- 19 files changed, 1250 insertions(+), 156 deletions(-) create mode 100644 tests/acceptance/model_bridge/test_symbolic_bridge_hooks.py diff --git a/demos/vLLM_Bridge_Integration_Test.ipynb b/demos/vLLM_Bridge_Integration_Test.ipynb index 031b5c4942..ade7aa1e2c 100644 --- a/demos/vLLM_Bridge_Integration_Test.ipynb +++ b/demos/vLLM_Bridge_Integration_Test.ipynb @@ -42,7 +42,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": "# Install vllm and TransformerLens @ feature/vllm-batched. ~3-5 minutes.\n# Branch must match this notebook: feature/vllm-batched has the hook-fire counter\n# (Step 7) and the batched capture surface (Step 10). feature/driver-system lacks\n# both, so collective_rpc raises NotImplementedError on tl_reset_counter / the\n# batched RPCs.\n# vllm pinned to 0.20.2 — the version the internal-API walks in\n# transformer_lens/model_bridge/sources/vllm/{plugin,internals}.py were validated against.\n# vLLM rearranges its internal class paths every 4-6 weeks; re-validate before bumping.\n%pip install -q \"vllm==0.20.2\"\n%pip install -q git+https://github.com/TransformerLensOrg/TransformerLens.git@feature/vllm-batched", + "source": "# Install vllm and TransformerLens @ feature/vllm-batched. ~3-5 minutes.\n# Branch must match this notebook: feature/vllm-batched has the hook-fire counter\n# (Step 7) and the batched capture surface (Step 10). feature/driver-system lacks\n# both, so collective_rpc raises NotImplementedError on tl_reset_counter / the\n# batched RPCs.\n#\n# vLLM pinned to 0.20.2 — the version the internal-API walks in\n# transformer_lens/model_bridge/sources/vllm/{plugin,internals}.py were validated\n# against. Installed from the CUDA-12.9 *release wheel*, NOT PyPI's default\n# `vllm==0.20.2`: the PyPI wheel is a CUDA-13 build that fails on Colab's CUDA-12.8\n# image with `libcudart.so.13: cannot open shared object file` and drags in\n# cuda-toolkit 13, which conflicts with Colab's cu12 RAPIDS stack. The cu129 wheel\n# uses libcudart.so.12 (present) and stays in the cu12 family. vLLM has no cu128\n# wheel for 0.20.2; cu129 is CUDA-12.x-compatible with the cu128 runtime.\n# Re-validate the internals walks before bumping vLLM (it moves them every 4-6 weeks).\n%pip install -q \"https://github.com/vllm-project/vllm/releases/download/v0.20.2/vllm-0.20.2%2Bcu129-cp38-abi3-manylinux_2_31_x86_64.whl\"\n%pip install -q git+https://github.com/TransformerLensOrg/TransformerLens.git@feature/vllm-batched", "id": "4ab1eb60e6b1" }, { @@ -50,7 +50,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": "import gc\nimport os\nimport sys\n\nimport torch\n\n# HF_TOKEN comes from Colab secrets. Falls back to env var for non-Colab runs.\ntry:\n from google.colab import userdata\n os.environ.setdefault(\"HF_TOKEN\", userdata.get(\"HF_TOKEN\"))\nexcept (ImportError, Exception):\n pass\nassert os.environ.get(\"HF_TOKEN\"), \"Set HF_TOKEN in Colab Secrets (gear icon, left sidebar).\"\n\n# Colab/Jupyter compatibility: ipykernel's stdout doesn't expose a fileno();\n# vLLM's worker init calls sys.stdout.fileno() during parallel-state setup\n# and crashes with UnsupportedOperation: fileno. Patch fileno to return the\n# underlying process FDs (1, 2) — Colab writes back to those anyway.\nif \"ipykernel\" in sys.modules:\n sys.stdout.fileno = lambda: 1 # type: ignore[method-assign]\n sys.stderr.fileno = lambda: 2 # type: ignore[method-assign]\n\n# Read the installed vllm version from package metadata, NOT `import vllm` —\n# importing vllm loads its CUDA C extension (vllm._C), which is exactly what\n# fails with `libcudart.so.NN not found` when a newer wheel built for a CUDA\n# version Colab doesn't ship gets installed. Metadata read works regardless.\nfrom importlib.metadata import PackageNotFoundError\nfrom importlib.metadata import version as _pkg_version\n\n_PINNED_VLLM = \"0.20.2\"\ntry:\n _vllm_ver = _pkg_version(\"vllm\")\n print(f\"vllm version: {_vllm_ver}\")\n if _vllm_ver != _PINNED_VLLM:\n print(\n f\"⚠ expected vllm=={_PINNED_VLLM} (the version the capture plugin is validated \"\n f\"against); got {_vllm_ver}. Newer wheels target a CUDA the Colab image may not \"\n \"ship (→ libcudart.so error at boot) and may move vLLM internals the plugin walks. \"\n f\"Re-pin with %pip install -q 'vllm=={_PINNED_VLLM}' and restart the runtime.\"\n )\nexcept PackageNotFoundError:\n print(\"⚠ vllm is not installed — run the install cell above.\")\n\nMODEL = \"meta-llama/Llama-3.2-1B\"\nPROMPT = \"The quick brown fox jumps over the\"\nDTYPE = torch.float16\ntorch.manual_seed(0)\n\nprint(f\"CUDA available: {torch.cuda.is_available()}\")\nprint(f\"Device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU only — abort'}\")\nassert torch.cuda.is_available(), \"GPU runtime required.\"", + "source": "import gc\nimport os\nimport sys\n\nimport torch\n\n# HF_TOKEN comes from Colab secrets. Falls back to env var for non-Colab runs.\ntry:\n from google.colab import userdata\n os.environ.setdefault(\"HF_TOKEN\", userdata.get(\"HF_TOKEN\"))\nexcept (ImportError, Exception):\n pass\nassert os.environ.get(\"HF_TOKEN\"), \"Set HF_TOKEN in Colab Secrets (gear icon, left sidebar).\"\n\n# Colab/Jupyter compatibility: ipykernel's stdout doesn't expose a fileno();\n# vLLM's worker init calls sys.stdout.fileno() during parallel-state setup\n# and crashes with UnsupportedOperation: fileno. Patch fileno to return the\n# underlying process FDs (1, 2) — Colab writes back to those anyway.\nif \"ipykernel\" in sys.modules:\n sys.stdout.fileno = lambda: 1 # type: ignore[method-assign]\n sys.stderr.fileno = lambda: 2 # type: ignore[method-assign]\n\n# Read the installed vllm version from package metadata, NOT `import vllm` —\n# importing vllm loads its CUDA C extension (vllm._C), which is exactly what\n# fails with `libcudart.so.NN not found` when a wheel built for a CUDA version\n# Colab doesn't ship gets installed. Metadata read works regardless.\nfrom importlib.metadata import PackageNotFoundError\nfrom importlib.metadata import version as _pkg_version\n\n_PINNED_VLLM = \"0.20.2\"\ntry:\n _vllm_ver = _pkg_version(\"vllm\")\n print(f\"vllm version: {_vllm_ver}\")\n # The install cell pins the cu129 release wheel, so the local tag is\n # \"0.20.2+cu129\" — compare the base version only.\n if _vllm_ver.split(\"+\")[0] != _PINNED_VLLM:\n print(\n f\"⚠ expected vllm base {_PINNED_VLLM} (the version the capture plugin is validated \"\n f\"against); got {_vllm_ver}. A mismatched wheel may target a CUDA the Colab image \"\n \"doesn't ship (→ libcudart.so error at boot) and may move vLLM internals the plugin \"\n \"walks. Re-run the install cell above (it fetches the cu129 release wheel) and \"\n \"restart the runtime. Do NOT `pip install vllm==0.20.2` — PyPI's default is the \"\n \"CUDA-13 build that fails on Colab's CUDA-12.8 image.\"\n )\n elif \"+cu\" in _vllm_ver and \"cu129\" not in _vllm_ver:\n print(\n f\"⚠ got {_vllm_ver}: not the cu129 wheel. PyPI's default vllm==0.20.2 is a CUDA-13 \"\n \"build (libcudart.so.13 missing on Colab's CUDA-12.8). Re-run the install cell above.\"\n )\nexcept PackageNotFoundError:\n print(\"⚠ vllm is not installed — run the install cell above.\")\n\nMODEL = \"meta-llama/Llama-3.2-1B\"\nPROMPT = \"The quick brown fox jumps over the\"\nDTYPE = torch.float16\ntorch.manual_seed(0)\n\nprint(f\"CUDA available: {torch.cuda.is_available()}\")\nprint(f\"Device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU only — abort'}\")\nassert torch.cuda.is_available(), \"GPU runtime required.\"", "id": "1748b73ca8b0" }, { @@ -121,23 +121,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "from transformer_lens.model_bridge.remote_bridge import RemoteBridge\n", - "\n", - "# max_model_len=2048 caps the KV cache reservation. Llama-3.2-1B's native\n", - "# context is 131072 (128k) — the default reservation is ~4 GiB and overshoots\n", - "# the free T4 budget. The test prompt is ~10 tokens, so 2048 is plenty.\n", - "bridge = RemoteBridge.boot_vllm(\n", - " MODEL,\n", - " dtype=DTYPE,\n", - " gpu_memory_utilization=0.5,\n", - " max_model_len=2048,\n", - ")\n", - "assert isinstance(bridge, RemoteBridge), f\"Expected RemoteBridge, got {type(bridge).__name__}\"\n", - "print(f\"Architecture: {bridge.cfg.architecture}\")\n", - "print(f\"Fireable hooks: {len(bridge._driver.supported_hook_points)}\")\n", - "print(f\"Non-fireable hooks (fused kernels): {len(bridge._driver.non_fireable_hook_points)}\")" - ], + "source": "from transformer_lens.model_bridge.remote_bridge import RemoteBridge\n\n# max_model_len=2048 caps the KV cache reservation. Llama-3.2-1B's native\n# context is 131072 (128k) — the default reservation is ~4 GiB and overshoots\n# the free T4 budget. The test prompt is ~10 tokens, so 2048 is plenty.\n#\n# enable_position_interventions=True widens each hook's affine scale/bias buffers\n# from (width,) to (max_num_batched_tokens, width) so Step 6½ can patch specific\n# sequence positions via a spec 'pos' field. Costs ~2× the affine buffers' resident\n# memory (~0.8 GB here); the buffers stay identity until an intervention writes them,\n# so every other step is unchanged. Drop the flag (and skip Step 6½) to run leaner.\nbridge = RemoteBridge.boot_vllm(\n MODEL,\n dtype=DTYPE,\n gpu_memory_utilization=0.5,\n max_model_len=2048,\n enable_position_interventions=True,\n)\nassert isinstance(bridge, RemoteBridge), f\"Expected RemoteBridge, got {type(bridge).__name__}\"\nprint(f\"Architecture: {bridge.cfg.architecture}\")\nprint(f\"Fireable hooks: {len(bridge._driver.supported_hook_points)}\")\nprint(f\"Non-fireable hooks (fused kernels): {len(bridge._driver.non_fireable_hook_points)}\")", "id": "49d50957a4c7" }, { @@ -208,18 +192,18 @@ "execution_count": null, "outputs": [] }, + { + "cell_type": "code", + "id": "85900fee", + "source": "# ============================================================================\n# VERIFY — the driver returns real full-sequence logits (branch: review/dev-4.x)\n# ============================================================================\n# With VLLMDriver._reconstruct_logits + provides_sequence_logits=True, run_with_cache's\n# logits should be (1, seq, d_vocab) real logits at EVERY position (old: final-only\n# log-probs, -inf elsewhere). Reuses v_lnf / hf_lnf / W / norm_w from the Step 4½ cell.\nlg = logits_vllm.float()\nseq = tokens.shape[1]\nprint(f\"driver logits shape: {tuple(lg.shape)} (expect (1, {seq}, {bridge.cfg.d_vocab}))\")\nfinite_pos = torch.isfinite(lg[0]).all(-1)\nprint(f\"all-finite positions: {int(finite_pos.sum())}/{lg.shape[1]} (old behavior: 1, final only)\")\nassert lg.shape == (1, seq, bridge.cfg.d_vocab), \"driver logits are not full-sequence\"\nassert finite_pos.all(), \"some positions non-finite — reconstruction didn't fill them\"\n\ndrv = lg[0, :, : W.shape[0]] # trim any vocab padding to compare against W's real vocab\n\n# (a) driver output == the inline formula (Step 4½) → the driver wired the reconstruction right.\nd_inline = (drv - (v_lnf @ W.T)).abs().max().item()\nprint(f\"driver vs inline (ln_final @ W_U): max|Δ|={d_inline:.2e} (~0 => correct formula in driver)\")\n\n# (b) driver logits vs HF's real logits (HF post-weight = HF_ln_final * norm_w) @ W_U, all positions.\nhf_real = (hf_lnf * norm_w) @ W.T\nper_pos = (drv - hf_real).abs().amax(-1)\nagree = (drv.argmax(-1) == hf_real.argmax(-1)).float().mean().item() * 100\nprint(f\"driver vs HF real logits: max|Δ|={per_pos.max():.2e} (fp16) argmax agreement={agree:.1f}%\")\nprint(\"\\n>>> full-sequence + inline~0 + argmax 100% => VLLMDriver._reconstruct_logits is correct end-to-end.\")\n", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## Step 5 — Per-hook L2 (acceptance gate)\n", - "\n", - "Target is relative L2 < 5e-3 in fp16 for every fireable hook. One hook remains exempted from the strict gate:\n", - "\n", - "- **`ln_final.hook_normalized`** — vLLM's `model.norm` is invoked as part of the fused-residual norm kernel and the captured value scales ~2× HF's. Open investigation (likely a residual-fusion semantic discrepancy at the model boundary rather than a real divergence; argmax + downstream parity work correctly).\n", - "\n", - "All other fireable hooks (including `blocks.{i}.hook_out`, which is now materialized from vLLM's `(mlp_delta, residual)` tuple) must be within target." - ], + "source": "## Step 5 — Per-hook L2 (acceptance gate)\n\nTarget is relative L2 < 5e-3 in fp16 for every fireable hook. One hook remains exempted from the strict gate:\n\n- **`ln_final.hook_normalized`** — vLLM exposes the **post-weight** RMSNorm value (`normed × norm_weight`), while HF/HookedTransformer exposes the **pre-weight** value, so vLLM's capture reads larger by exactly the final-norm weight. **Resolved** by Step 4½: the vLLM/HF ratio equals `norm_w` (median ≈ 2.45 for Llama-3.2-1B — the earlier \"~2×\" was just this weight's magnitude, not a fixed factor). This is the documented post/pre-weight convention gap (see `sources.vllm.overlays.decoder_only`), **not** a divergence — and it's exactly why the Step 4½ logit reconstruction (`ln_final @ lm_head.weightᵀ`) works directly off the raw capture. Still exempted from the strict L2 gate below because the *values* legitimately differ from HF's pre-weight hook.\n\nAll other fireable hooks (including `blocks.{i}.hook_out`, which is now materialized from vLLM's `(mlp_delta, residual)` tuple) must be within target.", "id": "3e66a40c8c0a" }, { @@ -321,6 +305,20 @@ ], "id": "8c94e6f538f4" }, + { + "cell_type": "markdown", + "id": "57e058a4", + "source": "## Step 6½ — Per-position interventions (`pos`)\n\nWhole-sequence interventions (Step 6) edit every position. With the driver booted\n`enable_position_interventions=True`, a spec can carry a **`pos`** field (int or\n`list[int]`) that scopes the affine to specific sequence rows — position-scoped\nactivation patching / tensor injection, the same surface the Inspect/HF backend\nalready offers. This exercises the `(max_num_batched_tokens, width)` affine buffers\nand the per-row `narrow()` path in the compiled hook.\n\nThe test edits `embed.hook_out` at chosen positions and verifies, from the captured\n(post-affine) cache, that **only** those rows changed and every other row stayed\nidentical to the clean run.", + "metadata": {} + }, + { + "cell_type": "code", + "id": "91576f5a", + "source": "# Per-position interventions: a spec's 'pos' (int or list[int]) scopes the affine to\n# specific sequence rows. Verified against the captured (post-affine) embed.hook_out:\n# only the targeted rows change; every other row matches the clean baseline (fp16).\nseq = tokens.shape[1]\nd_model = bridge.cfg.d_model\nHOOK = \"embed.hook_out\"\n\n_, cache_base = bridge.run_with_cache(tokens)\nbase = cache_base[HOOK][0].float().clone()\n\n# (a) add a constant vector at ONE position; all other rows must be untouched.\nPOS = 2\n_, cache_add = bridge.run_with_cache(\n tokens, intervene={HOOK: {\"op\": \"add\", \"value\": [3.0] * d_model, \"pos\": POS}}\n)\nadd = cache_add[HOOK][0].float()\noff = [p for p in range(seq) if p != POS]\nprint(f\"add@{POS}: Δ@POS mean={(add[POS] - base[POS]).mean():.3f} (→3.0) \"\n f\"max|Δ| off-POS={(add[off] - base[off]).abs().max():.2e} (→0)\")\nassert torch.allclose(add[POS], base[POS] + 3.0, atol=1e-2), \"add@pos wrong at the target row\"\nassert torch.allclose(add[off], base[off], atol=1e-2), \"add@pos leaked to other rows\"\n\n# (b) suppress a LIST of positions; only those rows go to zero, the rest pass through.\nPOSL = [0, seq - 1]\n_, cache_sup = bridge.run_with_cache(tokens, intervene={HOOK: {\"op\": \"suppress\", \"pos\": POSL}})\nsup = cache_sup[HOOK][0].float()\nkeep = [p for p in range(seq) if p not in POSL]\nprint(f\"suppress@{POSL}: |max|@POS={sup[POSL].abs().max():.2e} (→0) \"\n f\"max|Δ| kept-rows={(sup[keep] - base[keep]).abs().max():.2e} (→0)\")\nassert sup[POSL].abs().max() < 1e-4, \"suppress@pos did not zero the target rows\"\nassert torch.allclose(sup[keep], base[keep], atol=1e-2), \"suppress@pos leaked to kept rows\"\n\n# (c) reset: a clean forward reverts — pos interventions are per-forward, not sticky.\n_, cache_reset = bridge.run_with_cache(tokens)\nassert torch.allclose(cache_reset[HOOK][0].float(), base, atol=1e-2), \"pos intervention persisted\"\nprint(\"✅ per-position add / suppress / reset all correct\")", + "metadata": {}, + "execution_count": null, + "outputs": [] + }, { "cell_type": "markdown", "id": "a21f3155a525", @@ -555,7 +553,7 @@ { "cell_type": "markdown", "metadata": {}, - "source": "## Summary\n\nIf all asserts above passed, the v4 Driver-protocol vLLM backend is sound on this architecture:\n\n- `boot_vllm` returns `RemoteBridge` end-to-end.\n- `collective_rpc → tl_read_captures` populates the cache.\n- Greedy argmax matches HF (greedy parity).\n- Per-hook L2 < 5e-3 vs HF across every fireable hook (hook-fidelity gate; `ln_final.hook_normalized` exempted — see overlay docstring).\n- Affine interventions (suppress / scale / add / set) apply per-forward and reset cleanly.\n- Each hook fires exactly once per forward under compile (no double-fire / silent overwrite).\n- Back-to-back captures are deterministic (stream-safe).\n- GPU lifetime behaves as expected for vLLM 0.20.x (full release needs a runtime restart).\n- **Batched mode** (`enable_batching=True`, Step 10): `batch_size > 1` parity vs single-prompt runs, chunked-prefill accumulation reconstructs full sequences, and global interventions apply + reset across the batch.\n\nKnown divergences (documented, not bugs): `ln_final.hook_normalized` post-weight vs pre-weight, Gemma `embed.hook_out` scaling. Both reconciled by conversions noted in `sources.vllm.overlays.decoder_only`.\n\nOut of scope: multi-token generation (`max_new_tokens > 1`), per-request (non-global) batched interventions, tensor/pipeline parallelism.", + "source": "## Summary\n\nIf all asserts above passed, the v4 Driver-protocol vLLM backend is sound on this architecture:\n\n- `boot_vllm` returns `RemoteBridge` end-to-end.\n- `collective_rpc → tl_read_captures` populates the cache.\n- Greedy argmax matches HF (greedy parity).\n- Per-hook L2 < 5e-3 vs HF across every fireable hook (hook-fidelity gate; `ln_final.hook_normalized` exempted — see overlay docstring).\n- Affine interventions (suppress / scale / add / set) apply per-forward and reset cleanly — both whole-sequence (Step 6) and scoped to specific positions via a spec `pos` field (Step 6½, `enable_position_interventions=True`).\n- Each hook fires exactly once per forward under compile (no double-fire / silent overwrite).\n- Back-to-back captures are deterministic (stream-safe).\n- GPU lifetime behaves as expected for vLLM 0.20.x (full release needs a runtime restart).\n- **Batched mode** (`enable_batching=True`, Step 10): `batch_size > 1` parity vs single-prompt runs, chunked-prefill accumulation reconstructs full sequences, and global interventions apply + reset across the batch.\n\nKnown divergences (documented, not bugs): `ln_final.hook_normalized` post-weight vs pre-weight, Gemma `embed.hook_out` scaling. Both reconciled by conversions noted in `sources.vllm.overlays.decoder_only`.\n\nOut of scope: multi-token generation (`max_new_tokens > 1`), per-request (non-global) batched interventions, tensor/pipeline parallelism.", "id": "fedb17c9a539" } ], diff --git a/scripts/inspect_parity_report.py b/scripts/inspect_parity_report.py index 4d643f0c47..9a6083ca8f 100644 --- a/scripts/inspect_parity_report.py +++ b/scripts/inspect_parity_report.py @@ -60,13 +60,21 @@ ATOL, RTOL = 1e-3, 1e-3 -# Boundary kind -> TransformerBridge-native hook suffix. +# Kind -> TransformerBridge-native hook suffix: the five d_model boundaries plus the +# head-split kinds (served where the structural probe finds the projections — q/k/v are +# gated on fused-qkv archs, pattern under non-eager attention; gated kinds show up in +# the report's `gated=` field, not as failures). KIND_SUFFIX = { "resid_pre": "hook_in", "resid_mid": "ln2.hook_in", "resid_post": "hook_out", "attn_out": "attn.hook_out", "mlp_out": "mlp.hook_out", + "q": "attn.hook_q", + "k": "attn.hook_k", + "v": "attn.hook_v", + "z": "attn.hook_z", + "pattern": "attn.hook_pattern", } @@ -103,6 +111,11 @@ def verify(model_id: str) -> dict: mism.append(f"{hk} missing") continue a, b = hf_cache[hk].float(), i_cache[hk].float() + if a.shape != b.shape and a.numel() == b.numel(): + # OPT-style blocks flatten the FFN to (batch·seq, d) and the bridge + # caches that raw 2-D layout; the driver emits the conventional + # (1, seq, d). Same values, pure reshape — normalize to compare. + a = a.reshape(b.shape) if a.shape != b.shape: mism.append(f"{hk} shape {tuple(a.shape)}!={tuple(b.shape)}") continue diff --git a/tests/acceptance/model_bridge/test_inspect_provider.py b/tests/acceptance/model_bridge/test_inspect_provider.py index 6bc51e7259..46bbac2a74 100644 --- a/tests/acceptance/model_bridge/test_inspect_provider.py +++ b/tests/acceptance/model_bridge/test_inspect_provider.py @@ -35,6 +35,10 @@ "blocks.0.hook_out", # resid_post "blocks.6.attn.hook_out", "blocks.11.hook_out", + # Head-split kinds gpt2 serves despite its fused c_attn (q/k/v are gated): z reads + # the c_proj (Conv1D) input, pattern rides output_attentions under eager. + "blocks.0.attn.hook_z", + "blocks.0.attn.hook_pattern", ] # Boundary kind -> TransformerBridge-native hook suffix. @@ -48,12 +52,15 @@ # Token-free tiny-random checkpoints, one per structural-check code path: standard # sequential (nothing gated), parallel-residual (resid_mid gated by the causal probe), -# post-norm (resid_mid gated by the linear-identity probe). Pins cross-family behavior -# so a detector/load-path regression on non-gpt2 archs fails CI instead of shipping silently. +# post-norm (resid_mid gated by the linear-identity probe), and fc-split FFN (OPT/XGLM: +# no mlp container — fc1/fc2 on the block; mlp_out must still be located and match). Pins +# cross-family behavior so a detector/load-path regression on non-gpt2 archs fails CI. STRUCTURAL_FAMILIES = [ ("hf-internal-testing/tiny-random-LlamaForCausalLM", frozenset()), ("hf-internal-testing/tiny-random-GPTJForCausalLM", frozenset({"resid_mid"})), ("hf-internal-testing/tiny-random-Gemma2ForCausalLM", frozenset({"resid_mid"})), + ("hf-internal-testing/tiny-random-OPTForCausalLM", frozenset()), + ("hf-internal-testing/tiny-random-XGLMForCausalLM", frozenset()), ] @@ -245,9 +252,11 @@ def test_sequential_offers_resid_mid(self): ) m = self._toy_model() # standard sequential, linear residual - kinds, note = _detect_capabilities(m, m.layers) + kinds, note = _detect_capabilities(m, m.layers, (None, None, None)) assert "resid_mid" in kinds - assert note == "" # nothing gated + # No boundary kind gated. (Head kinds ARE gated — the toy has no head geometry — + # so the note mentions only those.) + assert "resid_mid" not in note and "attn_out" not in note and "mlp_out" not in note def test_parallel_gates_resid_mid(self): from transformer_lens.model_bridge.sources.inspect.transformers_provider import ( @@ -255,7 +264,7 @@ def test_parallel_gates_resid_mid(self): ) m = self._toy_model(parallel=True) # mlp reads block input, not attn output - kinds, note = _detect_capabilities(m, m.layers) + kinds, note = _detect_capabilities(m, m.layers, (None, None, None)) assert "resid_mid" not in kinds assert {"resid_pre", "resid_post", "attn_out", "mlp_out"} <= kinds # rest still served assert "resid_mid" in note # note explains the gate @@ -268,7 +277,7 @@ def test_nonlinear_residual_gates_resid_mid(self): # Sequential (attn feeds mlp) but outputs are scaled before the residual add, so # resid_post != resid_pre + attn_out + mlp_out — resid_mid must still be gated. m = self._toy_model(resid_scale=2.0) - kinds, note = _detect_capabilities(m, m.layers) + kinds, note = _detect_capabilities(m, m.layers, (None, None, None)) assert "resid_mid" not in kinds assert {"resid_pre", "resid_post", "attn_out", "mlp_out"} <= kinds assert "resid_mid" in note @@ -282,7 +291,7 @@ def test_probe_leaves_global_rng_untouched(self): m = self._toy_model() before = torch.get_rng_state() - _detect_capabilities(m, m.layers) + _detect_capabilities(m, m.layers, (None, None, None)) assert torch.equal(torch.get_rng_state(), before) @@ -327,6 +336,137 @@ def test_gating_and_parity(self, model_id, expected_gated): inspect.close() +HEAD_MODEL = "hf-internal-testing/tiny-random-LlamaForCausalLM" +HEAD_HOOKS = [ + "blocks.0.attn.hook_q", + "blocks.0.attn.hook_k", + "blocks.0.attn.hook_v", + "blocks.0.attn.hook_z", + "blocks.0.attn.hook_pattern", + "blocks.1.attn.hook_z", +] + + +class TestHeadSplitHooks: + """Head-split q/k/v/z/pattern on a separate-projection arch (tiny-random Llama): + capture parity vs boot_transformers, interventions, and the fused-qkv gating path + (via the module-scoped gpt2 fixtures).""" + + @pytest.fixture(scope="class") + def head_pair(self): + from transformer_lens.model_bridge.remote_bridge import RemoteBridge + from transformer_lens.model_bridge.transformer_bridge import TransformerBridge + + hf = TransformerBridge.boot_transformers(HEAD_MODEL, device="cpu", dtype=torch.float32) + inspect = RemoteBridge.boot_inspect(HEAD_MODEL, dtype=torch.float32) + yield hf, inspect + inspect.close() + + def test_all_head_hooks_served(self, head_pair): + _, inspect = head_pair + for hook in HEAD_HOOKS: + assert hook in inspect._driver.supported_hook_points, f"{hook} not served" + + def test_head_capture_parity(self, head_pair): + hf, inspect = head_pair + toks = hf.to_tokens(PROMPT) + _, hf_cache = hf.run_with_cache(toks) + _, i_cache = inspect.run_with_cache(toks) + for hook in HEAD_HOOKS: + a, b = hf_cache[hook].float(), i_cache[hook].float() + assert a.shape == b.shape, f"{hook}: {tuple(b.shape)} vs bridge {tuple(a.shape)}" + assert torch.allclose( + a, b, atol=1e-4, rtol=1e-4 + ), f"{hook} diverges: max {(a - b).abs().max().item():.2e}" + + def test_suppress_v_zeroes_capture_and_moves_logits(self, head_pair): + _, inspect = head_pair + toks = inspect.to_tokens(PROMPT) + base = inspect.forward(toks) + logits, cache = inspect.run_with_cache( + toks, intervene={"blocks.0.attn.hook_v": {"op": "suppress"}} + ) + assert cache["blocks.0.attn.hook_v"].abs().max().item() == 0.0 + assert not torch.allclose(base, logits) + + def test_per_position_q_patch_is_position_scoped(self, head_pair): + _, inspect = head_pair + toks = inspect.to_tokens(PROMPT) + _, base_cache = inspect.run_with_cache(toks) + _, cache = inspect.run_with_cache( + toks, intervene={"blocks.0.attn.hook_q": {"op": "add", "value": 5.0, "pos": 1}} + ) + q_base, q_new = base_cache["blocks.0.attn.hook_q"][0], cache["blocks.0.attn.hook_q"][0] + others = [p for p in range(q_base.shape[0]) if p != 1] + assert torch.allclose(q_new[1], q_base[1] + 5.0, atol=1e-5) + assert torch.equal(q_new[others], q_base[others]) + + def test_pattern_intervention_rejected(self, head_pair): + _, inspect = head_pair + with pytest.raises(ValueError, match="capture-only"): + inspect.forward( + inspect.to_tokens(PROMPT), + intervene={"blocks.0.attn.hook_pattern": {"op": "suppress"}}, + ) + + def test_gpt2_fused_qkv_gated_but_z_pattern_served(self, inspect_bridge): + supported = inspect_bridge._driver.supported_hook_points + nonfireable = inspect_bridge._driver.non_fireable_hook_points + for hook in ("blocks.0.attn.hook_q", "blocks.0.attn.hook_k", "blocks.0.attn.hook_v"): + assert hook not in supported and hook in nonfireable + assert "blocks.0.attn.hook_z" in supported + assert "blocks.0.attn.hook_pattern" in supported + + def test_gptneo_wrapper_attention_serves_head_hooks(self): + """GPT-Neo wraps the real attention (standard q_proj/out_proj) at attn.attention — + the projection-host descent must find it, and captures must match the bridge.""" + from transformer_lens.model_bridge.remote_bridge import RemoteBridge + from transformer_lens.model_bridge.transformer_bridge import TransformerBridge + + mid = "hf-internal-testing/tiny-random-GPTNeoForCausalLM" + hf = TransformerBridge.boot_transformers(mid, device="cpu", dtype=torch.float32) + inspect = RemoteBridge.boot_inspect(mid, dtype=torch.float32) + try: + for kind in ("q", "k", "v", "z", "pattern"): + assert f"blocks.0.attn.hook_{kind}" in inspect._driver.supported_hook_points + toks = hf.to_tokens(PROMPT) + _, hf_cache = hf.run_with_cache(toks) + _, i_cache = inspect.run_with_cache(toks) + for hook in ("blocks.0.attn.hook_q", "blocks.0.attn.hook_z"): + a, b = hf_cache[hook].float(), i_cache[hook].float() + assert a.shape == b.shape + assert torch.allclose(a, b, atol=1e-4, rtol=1e-4) + finally: + inspect.close() + + def test_provider_direct_interventions_validated(self, inspect_bridge, tokens): + """The provider's documented extra_args interface must reject gated and + capture-only intervention kinds instead of silently no-op'ing (the driver path + already rejects; this covers callers that speak to the provider directly).""" + import asyncio + + from inspect_ai.model import GenerateConfig + + api = inspect_bridge._driver._model.api + + def call(interventions): + cfg = GenerateConfig( + extra_body={ + "extra_args": { + "input_ids": tokens[0].tolist(), + "capture": [], + "interventions": interventions, + } + } + ) + return asyncio.run(api.generate("", [], None, cfg)) + + with pytest.raises(ValueError, match="gated"): # q gated on gpt2's fused c_attn + call({"0:q": {"op": "suppress"}}) + with pytest.raises(ValueError, match="capture-only"): + call({"0:resid_mid": {"op": "suppress"}}) + + class TestRebootSemantics: """inspect_ai memoizes get_model by name; boot_inspect passes memoize=False so each boot honors its own args and independently owns (and frees) its model.""" diff --git a/tests/acceptance/model_bridge/test_symbolic_bridge_hooks.py b/tests/acceptance/model_bridge/test_symbolic_bridge_hooks.py new file mode 100644 index 0000000000..8a28edf74e --- /dev/null +++ b/tests/acceptance/model_bridge/test_symbolic_bridge_hooks.py @@ -0,0 +1,56 @@ +"""SymbolicBridge hook mirroring: fc-split archs (OPT/XGLM) must fire the placeholder's +own ``mlp.hook_in``/``mlp.hook_out`` — wired at setup from the ``in``/``out`` subcomponents +(see ``component_setup._wire_symbolic_hooks``). Without the mirror those HookPoints exist +in the registry but never fire, so caching misses them and interventions silently no-op. + +Runs on CPU with token-free tiny-random checkpoints. +""" +from __future__ import annotations + +import pytest +import torch + +OPT = "hf-internal-testing/tiny-random-OPTForCausalLM" +XGLM = "hf-internal-testing/tiny-random-XGLMForCausalLM" + + +@pytest.fixture(scope="module") +def opt_bridge(): + from transformer_lens.model_bridge.transformer_bridge import TransformerBridge + + return TransformerBridge.boot_transformers(OPT, device="cpu", dtype=torch.float32) + + +class TestSymbolicMirror: + def test_mlp_hooks_fire_and_match_subcomponents(self, opt_bridge): + tokens = opt_bridge.to_tokens("Hello world") + _, cache = opt_bridge.run_with_cache(tokens) + assert "blocks.0.mlp.hook_out" in cache.cache_dict + assert "blocks.0.mlp.hook_in" in cache.cache_dict + # The mirror re-fires the subcomponent tensors: out = fc2's output, in = fc1's input. + assert torch.equal(cache["blocks.0.mlp.hook_out"], cache["blocks.0.mlp.out.hook_out"]) + assert torch.equal(cache["blocks.0.mlp.hook_in"], cache["blocks.0.mlp.in.hook_in"]) + + def test_intervention_via_symbolic_hook_propagates(self, opt_bridge): + tokens = opt_bridge.to_tokens("Hello world") + base = opt_bridge.forward(tokens) + patched = opt_bridge.run_with_hooks( + tokens, fwd_hooks=[("blocks.0.mlp.hook_out", lambda t, hook: torch.zeros_like(t))] + ) + assert not torch.allclose(base, patched), "symbolic-hook edit did not reach the logits" + + def test_mirror_survives_reset_hooks(self, opt_bridge): + """The wiring is permanent — a reset_hooks (any run_with_* teardown) must not + sever it, else the second run_with_cache silently loses the mlp entries.""" + tokens = opt_bridge.to_tokens("Hello world") + opt_bridge.reset_hooks() + _, cache = opt_bridge.run_with_cache(tokens) + assert "blocks.0.mlp.hook_out" in cache.cache_dict + + def test_xglm_mirror_fires(self): + from transformer_lens.model_bridge.transformer_bridge import TransformerBridge + + bridge = TransformerBridge.boot_transformers(XGLM, device="cpu", dtype=torch.float32) + _, cache = bridge.run_with_cache(bridge.to_tokens("Hi")) + assert "blocks.0.mlp.hook_out" in cache.cache_dict + assert torch.equal(cache["blocks.0.mlp.hook_out"], cache["blocks.0.mlp.out.hook_out"]) diff --git a/tests/unit/model_bridge/test_inspect_driver.py b/tests/unit/model_bridge/test_inspect_driver.py index b4b7894459..1ee943f37a 100644 --- a/tests/unit/model_bridge/test_inspect_driver.py +++ b/tests/unit/model_bridge/test_inspect_driver.py @@ -423,6 +423,124 @@ def test_context_manager_closes(self): assert driver._model is None +class TestHeadSplitKinds: + """Head-split q/k/v/z/pattern: registry entries, driver assembly, intervention gating. + + The provider serves these only when its structural probe finds the projections; here + a profile with an explicit kind set stands in for that detection. + """ + + def _head_profile(self): + return profiles.TLBridgeProfile(supported_kinds=hooks.ALL_KINDS | hooks.HEAD_KINDS) + + def test_resolve_head_names(self): + assert hooks.resolve("blocks.3.attn.hook_q") == (3, "q") + assert hooks.resolve("blocks.0.attn.hook_z") == (0, "z") + assert hooks.resolve("blocks.1.attn.hook_pattern") == (1, "pattern") + assert hooks.resolve("blocks.0.attn.hook_attn_scores") is None # never served + + def test_default_supported_excludes_head_kinds(self): + """kinds=None stays boundary-only — head kinds are opt-in via detection, so + profiles that pass None (vllm-lens, defaults) don't silently claim them.""" + default = hooks.supported_hook_points(N_LAYERS) + assert "blocks.0.attn.hook_q" not in default + assert "blocks.0.attn.hook_pattern" not in default + + def test_all_hook_points_is_boundary_plus_head(self): + universe = hooks.all_hook_points(N_LAYERS) + assert hooks.supported_hook_points(N_LAYERS) < universe + assert "blocks.0.attn.hook_q" in universe + assert len(universe) == (len(hooks.ALL_KINDS) + len(hooks.HEAD_KINDS)) * N_LAYERS + + def test_interveneable_includes_qkvz_not_pattern(self): + assert {"q", "k", "v", "z"} <= hooks.INTERVENEABLE_KINDS + assert "pattern" not in hooks.INTERVENEABLE_KINDS + + def test_attn_scores_always_nonfireable(self): + driver = InspectDriver( + _fake_model(), _adapter(), tokenizer=None, profile=self._head_profile() + ) + assert "blocks.0.attn.hook_attn_scores" in driver.non_fireable_hook_points + + def test_driver_serves_head_kinds_via_profile(self): + driver = InspectDriver( + _fake_model(), _adapter(), tokenizer=None, profile=self._head_profile() + ) + for name in ("blocks.0.attn.hook_q", "blocks.1.attn.hook_z", "blocks.0.attn.hook_pattern"): + assert name in driver.supported_hook_points + assert driver.supported_hook_points.isdisjoint(driver.non_fireable_hook_points) + + def test_default_driver_moves_head_kinds_to_nonfireable(self): + driver = _driver() # default profile: boundary kinds only + assert "blocks.0.attn.hook_q" in driver.non_fireable_hook_points + assert "blocks.0.attn.hook_pattern" in driver.non_fireable_hook_points + + def test_head_captures_get_batch_dim(self): + """3-D wire arrays (seq,heads,d_head) / (heads,q,k) unsqueeze to exactly one batch dim.""" + heads, d_head, seq = 2, 2, 3 + caps = { + "0:q": np.ones((seq, heads, d_head), np.float32), + "0:pattern": np.ones((heads, seq, seq), np.float32), + "0:resid_post": np.ones((seq, D_MODEL), np.float32), + } + driver = InspectDriver( + _fake_model(captures=caps, logits=np.zeros((seq, D_VOCAB), np.float32)), + _adapter(), + tokenizer=None, + profile=self._head_profile(), + ) + result = driver.forward( + torch.tensor([[1, 2, 3]]), + capture=("blocks.0.attn.hook_q", "blocks.0.attn.hook_pattern", "blocks.0.hook_out"), + ) + assert tuple(result.captured["blocks.0.attn.hook_q"].shape) == (1, seq, heads, d_head) + assert tuple(result.captured["blocks.0.attn.hook_pattern"].shape) == (1, heads, seq, seq) + assert tuple(result.captured["blocks.0.hook_out"].shape) == (1, seq, D_MODEL) + + def test_qkvz_intervention_translates_to_wire_key(self): + supported = hooks.supported_hook_points(N_LAYERS, hooks.ALL_KINDS | hooks.HEAD_KINDS) + out = intervention.build_interventions( + {"blocks.0.attn.hook_v": {"op": "suppress"}}, supported + ) + assert out == {"0:v": {"op": "suppress"}} + + def test_pattern_intervention_rejected(self): + supported = hooks.supported_hook_points(N_LAYERS, hooks.ALL_KINDS | hooks.HEAD_KINDS) + with pytest.raises(ValueError, match="capture-only"): + intervention.build_interventions( + {"blocks.0.attn.hook_pattern": {"op": "suppress"}}, supported + ) + + def test_head_hook_rejected_when_gated(self): + """A gated head hook (default boundary-only profile) can't be intervened on.""" + supported = hooks.supported_hook_points(N_LAYERS) # no head kinds + with pytest.raises(ValueError, match="not in supported_hook_points"): + intervention.build_interventions( + {"blocks.0.attn.hook_q": {"op": "suppress"}}, supported + ) + + def test_turn_activations_batch_dim_is_rank_aware(self): + """Mixed 2-D boundary and 3-D head-split arrays in one turn all get exactly one + batch dim — a rank-2-only rule would leave head-split arrays batchless, and a + batchless (seq, heads, d_head) is shape-indistinguishable from a batched 2-D.""" + from transformer_lens.model_bridge.sources.inspect.eval import turn_activations + + payload = wire.encode_activations( + { + "0:resid_post": np.ones((3, D_MODEL), np.float32), + "0:q": np.ones((3, 2, 2), np.float32), + "0:pattern": np.ones((2, 3, 3), np.float32), + } + ) + sample = SimpleNamespace( + events=[SimpleNamespace(output=SimpleNamespace(metadata={"activations": payload}))] + ) + (turn,) = turn_activations(sample) + assert turn["blocks.0.hook_out"].shape == (1, 3, D_MODEL) + assert turn["blocks.0.attn.hook_q"].shape == (1, 3, 2, 2) + assert turn["blocks.0.attn.hook_pattern"].shape == (1, 2, 3, 3) + + def test_driver_imports_no_torch(): """The acceptance gate: the driver file must not import torch (data-only boundary).""" import transformer_lens.model_bridge.sources.inspect.driver as drv diff --git a/tests/unit/model_bridge/test_vllm_boot.py b/tests/unit/model_bridge/test_vllm_boot.py index cd0edaff03..7d79bbc639 100644 --- a/tests/unit/model_bridge/test_vllm_boot.py +++ b/tests/unit/model_bridge/test_vllm_boot.py @@ -107,6 +107,20 @@ def test_rejects_locked_kwarg_override(): boot_vllm("any-model", tensor_parallel_size=2) +def test_rejects_position_interventions_with_batching(): + """Position interventions need the compiled path's affine buffers — fails fast.""" + with pytest.raises(ValueError, match="incompatible with enable_batching"): + boot_vllm("any-model", enable_position_interventions=True, enable_batching=True) + + +def test_position_interventions_flag_reaches_driver(mocked_boot): + """boot_vllm threads enable_position_interventions through to the driver.""" + bridge = boot_vllm("any-model", enable_position_interventions=True) + assert bridge._driver._enable_position_interventions is True + # Default stays off. + assert boot_vllm("any-model")._driver._enable_position_interventions is False + + @pytest.mark.parametrize( "raw, expected", [ diff --git a/tests/unit/model_bridge/test_vllm_driver.py b/tests/unit/model_bridge/test_vllm_driver.py index f676a005d7..80140367c4 100644 --- a/tests/unit/model_bridge/test_vllm_driver.py +++ b/tests/unit/model_bridge/test_vllm_driver.py @@ -89,6 +89,7 @@ def _driver( max_num_batched_tokens=2048, generated_token=None, top_logprobs=None, + enable_position_interventions=False, ) -> VLLMDriver: """Build a VLLMDriver. ``captures`` populates llm.collective_rpc; ``generated_token`` and ``top_logprobs`` populate llm.generate's RequestOutput so _synthesize_logits @@ -104,6 +105,7 @@ def _driver( overlay=_overlay(), hf_config=hf_config or _hf_config(), max_num_batched_tokens=max_num_batched_tokens, + enable_position_interventions=enable_position_interventions, ) @@ -115,11 +117,19 @@ def test_passes_validate_driver(self): assert isinstance(driver, Driver) validate_driver(driver) - def test_no_torch_capability_flags(self): - """vLLM owns the model in a worker — no torch-specific capability surface.""" + def test_capability_flags(self): + """vLLM owns the model in a worker — no torch module surface (parameters/ + state_dict/grads), but named-weight reads ARE served via the get_param RPC.""" driver = _driver() - for feature in ("parameters", "state_dict", "gradients", "weight_access"): + for feature in ("parameters", "state_dict", "gradients"): assert driver.supports(feature) is False, f"vLLM shouldn't support {feature!r}" + assert driver.supports("weight_access") is True + + def test_zero_layers_config_fails_loud(self): + """A missing/zero num_hidden_layers must raise at boot, not leave a raw '{i}' + template in non_fireable_hook_points.""" + with pytest.raises(ValueError, match="num_hidden_layers"): + _driver(hf_config=_hf_config(num_hidden_layers=0)) def test_non_fireable_expanded_per_layer(self): """``blocks.{i}.attn.hook_pattern`` template expands to one entry per layer.""" @@ -168,7 +178,7 @@ def test_forward_logits_from_sampler_logprobs(self): result = _driver( captures={"embed.hook_out": torch.randn(3, 4)}, top_logprobs={7: 2.5, 3: 1.0, 11: -0.5}, - ).forward(torch.tensor([[1, 2, 3]])) + ).forward(torch.tensor([[1, 2, 3]]), capture=("embed.hook_out",)) assert isinstance(result, ForwardResult) assert tuple(result.captured["embed.hook_out"].shape) == (1, 3, 4) assert result.logits is not None and tuple(result.logits.shape) == (1, 3, 16) @@ -234,6 +244,67 @@ def test_forward_rejects_set_missing_value(self): torch.tensor([[1]]), intervene={"embed.hook_out": {"op": "set"}} ) + +class TestVLLMDriverPositionInterventions: + """`pos`-scoped interventions require the opt-in (max_n, width) affine buffers.""" + + def test_pos_rejected_without_flag(self): + with pytest.raises(NotImplementedError, match="enable_position_interventions=True"): + _driver()._validate_interventions({"embed.hook_out": {"op": "suppress", "pos": 0}}) + + def test_pos_accepted_and_preserved_with_flag(self): + driver = _driver(enable_position_interventions=True) + out = driver._validate_interventions( + {"embed.hook_out": {"op": "add", "value": 1.0, "pos": [0, 2]}} + ) + assert out["embed.hook_out"]["pos"] == [0, 2] + + def test_pos_int_accepted_with_flag(self): + driver = _driver(enable_position_interventions=True) + out = driver._validate_interventions({"embed.hook_out": {"op": "suppress", "pos": 3}}) + assert out["embed.hook_out"]["pos"] == 3 + + def test_pos_wrong_type_raises(self): + driver = _driver(enable_position_interventions=True) + with pytest.raises(ValueError, match="must be an int or list of ints"): + driver._validate_interventions({"embed.hook_out": {"op": "suppress", "pos": "last"}}) + + def test_pos_negative_raises(self): + driver = _driver(enable_position_interventions=True) + with pytest.raises(ValueError, match="must be non-negative"): + driver._validate_interventions({"embed.hook_out": {"op": "suppress", "pos": [-1]}}) + + def test_pos_rejected_on_batched_path(self): + """The batched/eager path has no affine buffers, so 'pos' is unsupported there.""" + driver = _driver() + driver._enable_batching = True + with pytest.raises(NotImplementedError, match="batched/eager path"): + driver._validate_interventions({"embed.hook_out": {"op": "suppress", "pos": 0}}) + + def test_no_pos_still_works_with_flag(self): + """A whole-sequence spec (no pos) is unaffected by the flag.""" + driver = _driver(enable_position_interventions=True) + out = driver._validate_interventions({"embed.hook_out": {"op": "suppress"}}) + assert "pos" not in out["embed.hook_out"] + + def test_pos_beyond_prompt_length_fails_loud(self): + """pos within buffer capacity but past the prompt length must raise, not silently no-op. + + The rejection fires before driver.forward reaches the vllm import, so no install needed. + """ + driver = _driver(captures={}, enable_position_interventions=True) + with pytest.raises(ValueError, match="beyond the prompt length"): + driver.forward( # 3-token prompt, pos=50 is unreadable by the hook + torch.tensor([[1, 2, 3]]), + intervene={"embed.hook_out": {"op": "suppress", "pos": 50}}, + ) + + def test_reject_pos_beyond_seq_bounds_against_actual_length(self): + driver = _driver(enable_position_interventions=True) + driver._reject_pos_beyond_seq({"embed.hook_out": {"op": "suppress", "pos": [0, 2]}}, 3) + with pytest.raises(ValueError, match="beyond the prompt length"): + driver._reject_pos_beyond_seq({"embed.hook_out": {"op": "suppress", "pos": 3}}, 3) + def test_forward_pushes_interventions_before_generate(self): """The driver pushes spec dicts via collective_rpc('tl_set_interventions', ...).""" pytest.importorskip("vllm") @@ -267,6 +338,29 @@ def test_forward_rejects_prompt_exceeding_buffer(self): with pytest.raises(ValueError, match="exceeds max_num_batched_tokens"): _driver(captures={}, max_num_batched_tokens=4).forward(torch.tensor([[1, 2, 3, 4, 5]])) + def test_zero_capture_skips_worker_read(self): + """capture=() must not trigger a GPU→CPU copy: with logits off there is no + tl_read_captures RPC at all (an empty tuple used to collapse to None = read + every buffer); with logits on, only the forced ln_final is read.""" + pytest.importorskip("vllm") + driver = _driver(captures={"embed.hook_out": torch.zeros(3, 4)}) + driver.forward(torch.tensor([[1, 2, 3]]), return_logits=False) + reads = [ + c.args + for c in driver._llm.collective_rpc.call_args_list + if c.args[0] == "tl_read_captures" + ] + assert reads == [], f"hookless forward still read the buffers: {reads}" + + driver2 = _driver(captures={"ln_final.hook_normalized": torch.zeros(3, 4)}) + driver2.forward(torch.tensor([[1, 2, 3]])) # return_logits=True default + reads2 = [ + c.args + for c in driver2._llm.collective_rpc.call_args_list + if c.args[0] == "tl_read_captures" + ] + assert len(reads2) == 1 and reads2[0][1][1] == ["ln_final.hook_normalized"] + def _batched_request_output(request_id, generated_token=None, top_logprobs=None): """RequestOutput-shaped mock carrying a request_id for the accumulator join.""" @@ -338,7 +432,7 @@ def test_req_id_join_not_positional(self): "req-A": {"embed.hook_out": torch.full((3, 4), 1.0)}, } result = _batched_driver(outputs=outputs, captures_by_req=captures_by_req).forward( - [[1, 2, 3], [4, 5]] + [[1, 2, 3], [4, 5]], capture=("embed.hook_out",) ) emb = result.captured["embed.hook_out"] assert tuple(emb.shape) == (2, 3, 4) # (batch, max_seq, width) @@ -360,7 +454,7 @@ def test_join_strips_internal_req_id_suffix(self): "11-a1b2c3d4": {"embed.hook_out": torch.full((2, 4), 2.0)}, } result = _batched_driver(outputs=outputs, captures_by_req=captures_by_req).forward( - [[1, 2, 3], [4, 5]] + [[1, 2, 3], [4, 5]], capture=("embed.hook_out",) ) emb = result.captured["embed.hook_out"] assert tuple(emb.shape) == (2, 3, 4) @@ -379,7 +473,7 @@ def test_join_prefix_does_not_collide_on_numeric_ids(self): "10-bbbbbbbb": {"embed.hook_out": torch.full((3, 4), 9.0)}, } result = _batched_driver(outputs=outputs, captures_by_req=captures_by_req).forward( - [[1, 2], [1, 2, 3]] + [[1, 2], [1, 2, 3]], capture=("embed.hook_out",) ) emb = result.captured["embed.hook_out"] assert torch.equal(emb[0, :2], torch.full((2, 4), 1.0)) # req "1" @@ -396,12 +490,31 @@ def test_shorter_rows_right_padded_with_zeros(self): "r1": {"embed.hook_out": torch.ones(1, 4)}, } result = _batched_driver(outputs=outputs, captures_by_req=captures_by_req).forward( - [[1, 2, 3], [9]] + [[1, 2, 3], [9]], capture=("embed.hook_out",) ) emb = result.captured["embed.hook_out"] # Row 1 has 1 real token; positions 1,2 are zero pad. assert torch.equal(emb[1, 1:], torch.zeros(2, 4)) + def test_zero_capture_skips_batched_join(self): + """capture=() with logits off must return empty captures, not crash: _assemble_padded + needs one worker key per request, so it can't run on the empty read — mirror the + single path. (Regression: the batched join was called unconditionally.)""" + pytest.importorskip("vllm") + outputs = [ + _batched_request_output("r0", top_logprobs={1: 1.0}), + _batched_request_output("r1", top_logprobs={1: 1.0}), + ] + driver = _batched_driver(outputs=outputs, captures_by_req={}) + result = driver.forward([[1, 2, 3], [9]], return_logits=False) # capture=() default + assert result.captured == {} + reads = [ + c.args + for c in driver._llm.collective_rpc.call_args_list + if c.args[0] == "tl_read_batched_captures" + ] + assert reads == [], f"batched hookless forward still read captures: {reads}" + def test_logits_at_per_row_last_token(self): """Next-token logits land at prompt_len-1 per row, never -1 (a pad row).""" pytest.importorskip("vllm") @@ -414,7 +527,7 @@ def test_logits_at_per_row_last_token(self): "r1": {"embed.hook_out": torch.ones(1, 4)}, } result = _batched_driver(outputs=outputs, captures_by_req=captures_by_req).forward( - [[1, 2, 3], [9]] + [[1, 2, 3], [9]], capture=("embed.hook_out",) ) logits = result.logits assert logits is not None and tuple(logits.shape) == (2, 3, 16) @@ -481,12 +594,18 @@ def test_bridge_replays_vllm_captures(self): assert len(fired) == 1 assert tuple(fired[0].shape) == (1, 3, 4) - def test_forward_rejects_loss_return_types(self): - """Synthesized logits are last-position-only; loss/both would be nan, so refuse. + def test_loss_return_type_gated_on_sequence_logits(self): + """The bridge's loss/both guard fires only when the driver lacks full-sequence + logits. The vLLM driver now reconstructs them (provides_sequence_logits=True), + so loss/both are permitted; a driver without that capability is still refused. - The guard is the first thing forward() does, so this needs no vllm install. + The rejection path fires before driver.forward, so it needs no vllm install. """ - bridge = RemoteBridge(adapter=_adapter(), tokenizer=None, driver=_driver(captures={})) + driver = _driver(captures={}) + assert driver.provides_sequence_logits is True # reconstruction path is live + bridge = RemoteBridge(adapter=_adapter(), tokenizer=None, driver=driver) + # Drop the capability → the guard must refuse loss/both (nan over -inf positions). + driver.provides_sequence_logits = False for rt in ("loss", "both"): with pytest.raises(NotImplementedError, match="return_type"): bridge.forward(torch.tensor([[1, 2, 3]]), return_type=rt) diff --git a/tests/unit/model_bridge/test_vllm_worker_extension.py b/tests/unit/model_bridge/test_vllm_worker_extension.py index 5d06ca40a6..3d254347c2 100644 --- a/tests/unit/model_bridge/test_vllm_worker_extension.py +++ b/tests/unit/model_bridge/test_vllm_worker_extension.py @@ -135,6 +135,71 @@ def test_value_shape_mismatch_raises(self): _apply_intervention(scale, bias, {"op": "add", "value": [1.0, 2.0]}) +class TestApplyInterventionPerPosition: + """2-D (max_n, width) affine buffers let a spec's 'pos' scope the edit to rows. + + Buffers start at identity (scale=1, bias=0), mirroring tl_set_interventions' + reset before each apply, so a pos-scoped edit must leave the other rows untouched. + """ + + def _buffers(self, max_n=5, width=4): + return torch.ones(max_n, width), torch.zeros(max_n, width) + + def test_set_at_single_pos_leaves_others_identity(self): + scale, bias = self._buffers() + _apply_intervention(scale, bias, {"op": "set", "value": 2.0, "pos": 2}) + assert torch.equal(scale[2], torch.zeros(4)) + assert torch.equal(bias[2], torch.full((4,), 2.0)) + for r in (0, 1, 3, 4): + assert torch.equal(scale[r], torch.ones(4)) + assert torch.equal(bias[r], torch.zeros(4)) + + def test_add_vector_at_pos_list(self): + scale, bias = self._buffers() + vec = [1.0, 2.0, 3.0, 4.0] + _apply_intervention(scale, bias, {"op": "add", "value": vec, "pos": [0, 3]}) + for r in (0, 3): + assert torch.equal(scale[r], torch.ones(4)) + assert torch.equal(bias[r], torch.tensor(vec)) + for r in (1, 2, 4): + assert torch.equal(bias[r], torch.zeros(4)) + + def test_suppress_at_pos(self): + scale, bias = self._buffers() + _apply_intervention(scale, bias, {"op": "suppress", "pos": 1}) + assert torch.equal(scale[1], torch.zeros(4)) + assert torch.equal(bias[1], torch.zeros(4)) + assert torch.equal(scale[0], torch.ones(4)) # untouched + + def test_scale_at_pos(self): + scale, bias = self._buffers() + _apply_intervention(scale, bias, {"op": "scale", "factor": 0.5, "pos": 4}) + assert torch.equal(scale[4], torch.full((4,), 0.5)) + assert torch.equal(scale[0], torch.ones(4)) + + def test_no_pos_writes_all_rows(self): + """A whole-sequence spec on 2-D buffers broadcasts across every row.""" + scale, bias = self._buffers() + _apply_intervention(scale, bias, {"op": "add", "value": [1.0, 2.0, 3.0, 4.0]}) + for r in range(5): + assert torch.equal(bias[r], torch.tensor([1.0, 2.0, 3.0, 4.0])) + + def test_pos_out_of_range_raises(self): + import pytest + + scale, bias = self._buffers(max_n=3) + with pytest.raises(ValueError, match="out of range"): + _apply_intervention(scale, bias, {"op": "suppress", "pos": 5}) + + def test_pos_on_1d_buffer_raises(self): + """A 'pos' spec against 1-D buffers (flag off) is a misconfiguration.""" + import pytest + + scale, bias = torch.ones(4), torch.zeros(4) + with pytest.raises(ValueError, match="requires 2-D affine buffers"): + _apply_intervention(scale, bias, {"op": "suppress", "pos": 0}) + + class TestApplyOp: """_apply_op is the eager batched path's tensor-level intervention.""" diff --git a/transformer_lens/model_bridge/component_setup.py b/transformer_lens/model_bridge/component_setup.py index 7821d03542..cb7b026a5c 100644 --- a/transformer_lens/model_bridge/component_setup.py +++ b/transformer_lens/model_bridge/component_setup.py @@ -58,6 +58,22 @@ def set_original_components( setup_components(component_mapping, bridge_module, architecture_adapter, original_model) +def _wire_symbolic_hooks(symbolic: SymbolicBridge) -> None: + """Fire a SymbolicBridge's own hook_in/hook_out from its ``in``/``out`` subcomponents. + + Permanent hooks (survive reset_hooks) re-fire the subcomponent activation through the + placeholder's HookPoint, so ``blocks.{i}.mlp.hook_in/hook_out`` (and their compat + aliases) behave like every non-symbolic arch's. A hook that returns a modified tensor + on the symbolic point propagates: the mirror returns it into the subcomponent's chain. + """ + sub_in = symbolic.submodules.get("in") + sub_out = symbolic.submodules.get("out") + if sub_in is not None: + sub_in.hook_in.add_hook(lambda tensor, hook: symbolic.hook_in(tensor), is_permanent=True) + if sub_out is not None: + sub_out.hook_out.add_hook(lambda tensor, hook: symbolic.hook_out(tensor), is_permanent=True) + + def setup_submodules( component: GeneralizedComponent, architecture_adapter: ArchitectureAdapter, @@ -92,6 +108,13 @@ def setup_submodules( for sub_name, (sub_path, sub_comp) in submodule.real_components.items(): prefixed_key = f"{module_name}.{sub_name}" component.real_components[prefixed_key] = (sub_path, sub_comp) + + # The placeholder has no forward, so its own hook_in/hook_out would never + # fire — mirror them from the designated subcomponents (fc-split archs: + # mlp.hook_in = fc1's input, mlp.hook_out = fc2's output). Firing through + # the parent HookPoint keeps caching AND interventions working: the return + # value feeds back into the subcomponent's hook chain. + _wire_symbolic_hooks(submodule) else: # Set up original_component if not already set if submodule.original_component is None: diff --git a/transformer_lens/model_bridge/sources/inspect/driver.py b/transformer_lens/model_bridge/sources/inspect/driver.py index b7dc954ed9..518036b276 100644 --- a/transformer_lens/model_bridge/sources/inspect/driver.py +++ b/transformer_lens/model_bridge/sources/inspect/driver.py @@ -48,9 +48,11 @@ def __init__(self, model: Any, adapter: Any, tokenizer: Any, profile: Any = None # Provider-specific: loss/both allowed only if the provider returns full logits. self.provides_sequence_logits = self._profile.provides_sequence_logits self.supported_hook_points = self._profile.supported_hooks(self._n_layers) - full = hooks.supported_hook_points(self._n_layers) + # Everything the registry could serve (boundaries + head-split) that this + # provider/model doesn't, plus the never-fireable set (embed, ln_final, scores). + universe = hooks.all_hook_points(self._n_layers) self.non_fireable_hook_points = hooks.nonfireable_hook_points(self._n_layers) | ( - full - self.supported_hook_points + universe - self.supported_hook_points ) # Background event loop, created lazily on first forward. self._loop: asyncio.AbstractEventLoop | None = None @@ -106,8 +108,9 @@ async def _generate(self, prompt: Any, extra_args: dict[str, Any]) -> Any: return await self._model.generate(prompt, config=config) def _assemble_captures(self, output: Any, names: list[str]) -> dict[str, np.ndarray]: - """Decode the requested boundaries → ``{hook_name: (1, seq, d_model)}``; names the - provider didn't return are skipped (and warned once).""" + """Decode the requested hooks → ``{hook_name: (1, ...)}`` (batch dim added onto the + provider's batchless array — rank 2 for boundaries, 3 for head-split/pattern); + names the provider didn't return are skipped (and warned once).""" metadata = getattr(output, "metadata", None) or {} decoded = wire.decode_activations(metadata, self._wire_keys(names)) captured: dict[str, np.ndarray] = {} @@ -120,7 +123,8 @@ def _assemble_captures(self, output: Any, names: list[str]) -> dict[str, np.ndar if arr is None: missing.append(name) continue - captured[name] = arr[np.newaxis, ...] if arr.ndim == 2 else arr + batchless = hooks.WIRE_BATCHLESS_NDIM.get(resolved[1], 2) + captured[name] = arr[np.newaxis, ...] if arr.ndim == batchless else arr self._warn_missing(missing) return captured diff --git a/transformer_lens/model_bridge/sources/inspect/eval.py b/transformer_lens/model_bridge/sources/inspect/eval.py index eebad35931..176d6036a4 100644 --- a/transformer_lens/model_bridge/sources/inspect/eval.py +++ b/transformer_lens/model_bridge/sources/inspect/eval.py @@ -84,9 +84,10 @@ async def solve(state: TaskState, generate: Generate) -> TaskState: def turn_activations(sample: Any) -> list[dict[str, np.ndarray]]: """Per-turn activations from an eval sample's model events, for a provider booted with - ``capture=[...]`` (e.g. ``model_args={"capture": [...]}``). Returns one - ``{hook_name: (1, seq, d)}`` dict per model generation, in turn order — the activations - of an agentic/multi-turn rollout. + ``capture=[...]`` (e.g. ``model_args={"capture": [...]}``). Returns one dict per model + generation, in turn order — the activations of an agentic/multi-turn rollout. Every + array gets a leading batch dim: boundaries are ``(1, seq, d_model)``, head-split + q/k/v/z ``(1, seq, heads, d_head)``. """ turns = [] for event in getattr(sample, "events", []) or []: @@ -94,11 +95,15 @@ def turn_activations(sample: Any) -> list[dict[str, np.ndarray]]: if not metadata or "activations" not in metadata: continue decoded = wire.decode_activations(metadata, list(metadata["activations"])) - named = { - name: arr[np.newaxis, ...] if arr.ndim == 2 else arr - for wk, arr in decoded.items() - if (name := hooks.name_from_wire_key(wk)) is not None - } + named = {} + for wk, arr in decoded.items(): + name = hooks.name_from_wire_key(wk) + if name is None: + continue + # Rank-aware batch dim (mirrors driver._assemble_captures): boundary kinds + # arrive rank-2, head-split kinds rank-3 — unsqueeze exactly once either way. + batchless = hooks.WIRE_BATCHLESS_NDIM.get(wk.partition(":")[2], 2) + named[name] = arr[np.newaxis, ...] if arr.ndim == batchless else arr if named: turns.append(named) return turns diff --git a/transformer_lens/model_bridge/sources/inspect/hooks.py b/transformer_lens/model_bridge/sources/inspect/hooks.py index ed287bca0a..2282472216 100644 --- a/transformer_lens/model_bridge/sources/inspect/hooks.py +++ b/transformer_lens/model_bridge/sources/inspect/hooks.py @@ -1,8 +1,10 @@ """Canonical hook names ↔ (layer, kind) for the Inspect HF provider. Torch-free; shared by the provider (capture/intervene) and the driver (supported set, -decode). Covers the ``d_model``-shaped decoder-layer boundaries; head-split hooks -(q/k/v/z, pattern), ``embed``, and ``ln_final`` (fold-LN convention) are non-fireable. +decode). Covers the ``d_model``-shaped decoder-layer boundaries plus the head-split +attention hooks (q/k/v/z, pattern) where the provider's structural probe finds the +projections; ``attn_scores``, ``embed``, and ``ln_final`` (fold-LN convention) stay +non-fireable. Names are TransformerBridge-native (``blocks.{i}.hook_out``, ``.attn.hook_out``, ...), not the HookedTransformer aliases. A bridge cache carries both with identical values, @@ -10,8 +12,9 @@ Which boundaries are actually fireable is decided per-model by the provider's structural self-check, not a hand-kept architecture list: it locates attn/mlp and probes whether the -``resid_pre + attn_out`` derivation holds, gating ``resid_mid`` otherwise. -``supported_hook_points(n_layers, kinds=...)`` filters to that detected set. +``resid_pre + attn_out`` derivation holds, gating ``resid_mid`` otherwise; head-split +kinds need separate q/k/v projections (q/k/v), a locatable out-projection (z), or eager +attention (pattern). ``supported_hook_points(n_layers, kinds=...)`` filters to that set. """ from __future__ import annotations @@ -19,6 +22,10 @@ from typing import Iterable, Optional ALL_KINDS = frozenset({"resid_pre", "resid_mid", "resid_post", "attn_out", "mlp_out"}) +# Head-split attention kinds, served only when structurally detected (never by default): +# q/k/v are the pre-RoPE projection outputs, z is the out-projection input (all +# ``(seq, heads, d_head)``); pattern is post-softmax attention ``(heads, q_pos, k_pos)``. +HEAD_KINDS = frozenset({"q", "k", "v", "z", "pattern"}) # One canonical TransformerBridge name per boundary (no aliases — avoids duplicate # HookPoints/cache entries). resid_mid (ln2.hook_in) is derived (resid_pre + @@ -29,8 +36,27 @@ "resid_post": "blocks.{i}.hook_out", "attn_out": "blocks.{i}.attn.hook_out", "mlp_out": "blocks.{i}.mlp.hook_out", + "q": "blocks.{i}.attn.hook_q", + "k": "blocks.{i}.attn.hook_k", + "v": "blocks.{i}.attn.hook_v", + "z": "blocks.{i}.attn.hook_z", + "pattern": "blocks.{i}.attn.hook_pattern", +} +# pattern is read from the forward's output_attentions — nothing to write back, so it's +# capture-only (like the derived resid_mid). +INTERVENEABLE_KINDS = frozenset( + {"resid_pre", "attn_out", "mlp_out", "resid_post", "q", "k", "v", "z"} +) + +# Rank of each kind's batchless wire array; the driver unsqueezes exactly one batch dim. +WIRE_BATCHLESS_NDIM = { + **{kind: 2 for kind in ALL_KINDS}, # (seq, d_model) + "q": 3, + "k": 3, + "v": 3, + "z": 3, # (seq, heads, d_head) + "pattern": 3, # (heads, q_pos, k_pos) } -INTERVENEABLE_KINDS = frozenset({"resid_pre", "attn_out", "mlp_out", "resid_post"}) _SUFFIX_TO_KIND = { "hook_in": "resid_pre", @@ -38,29 +64,35 @@ "hook_out": "resid_post", "attn.hook_out": "attn_out", "mlp.hook_out": "mlp_out", + "attn.hook_q": "q", + "attn.hook_k": "k", + "attn.hook_v": "v", + "attn.hook_z": "z", + "attn.hook_pattern": "pattern", } _BLOCK = re.compile(r"^blocks\.(\d+)\.(.+)$") def supported_hook_points(n_layers: int, kinds: Optional[Iterable[str]] = None) -> frozenset[str]: - """Fireable hook names across all layers. ``kinds=None`` means all boundaries; + """Fireable hook names across all layers. ``kinds=None`` means all *boundary* kinds + (head-split kinds are opt-in — a provider must detect and list them explicitly); pass the provider's detected kinds to gate (e.g. drop ``resid_mid`` for parallel).""" - selected = _KIND_NAMES if kinds is None else {k: _KIND_NAMES[k] for k in kinds} - return frozenset(name.format(i=i) for i in range(n_layers) for name in selected.values()) + selected = ALL_KINDS if kinds is None else kinds + return frozenset(_KIND_NAMES[k].format(i=i) for i in range(n_layers) for k in selected) + + +def all_hook_points(n_layers: int) -> frozenset[str]: + """Every hook name the registry can serve (boundaries + head-split) — the universe a + driver subtracts its supported set from to build ``non_fireable_hook_points``.""" + return supported_hook_points(n_layers, ALL_KINDS | HEAD_KINDS) def nonfireable_hook_points(n_layers: int) -> frozenset[str]: - """Hooks the residual/attn/mlp provider can't fire (head-split, embed, ln_final).""" + """Hooks no provider configuration can fire (embed, ln_final, pre-softmax scores). + Head-split q/k/v/z/pattern are *conditionally* fireable and belong here only when a + model's structural probe gates them — the driver handles that subtraction.""" names = ["embed.hook_out", "ln_final.hook_normalized", "unembed.hook_out"] - for i in range(n_layers): - names += [ - f"blocks.{i}.attn.hook_pattern", - f"blocks.{i}.attn.hook_attn_scores", - f"blocks.{i}.attn.hook_q", - f"blocks.{i}.attn.hook_k", - f"blocks.{i}.attn.hook_v", - f"blocks.{i}.attn.hook_z", - ] + names += [f"blocks.{i}.attn.hook_attn_scores" for i in range(n_layers)] return frozenset(names) diff --git a/transformer_lens/model_bridge/sources/inspect/intervention.py b/transformer_lens/model_bridge/sources/inspect/intervention.py index fcda9bc802..0cd1ee9dc8 100644 --- a/transformer_lens/model_bridge/sources/inspect/intervention.py +++ b/transformer_lens/model_bridge/sources/inspect/intervention.py @@ -47,7 +47,7 @@ def build_interventions( if resolved is None or resolved[1] not in hooks.INTERVENEABLE_KINDS: raise ValueError( f"Cannot intervene on {hook_name!r}: capture-only " - f"(intervene on resid_pre/attn_out/mlp_out/resid_post instead)." + f"(intervene on resid_pre/attn_out/mlp_out/resid_post or attn q/k/v/z instead)." ) if op == "scale" and "factor" not in spec: raise ValueError(f"Intervention {hook_name!r}: op='scale' requires 'factor' (float).") @@ -55,6 +55,15 @@ def build_interventions( raise ValueError( f"Intervention {hook_name!r}: op={op!r} requires 'value' (scalar or width-shaped)." ) + pos = spec.get("pos") + if pos is not None and not ( + isinstance(pos, int) + or (isinstance(pos, (list, tuple)) and all(isinstance(p, int) for p in pos)) + ): + raise ValueError( + f"Intervention {hook_name!r}: 'pos' must be an int or list of ints " + f"(sequence positions to patch); got {pos!r}." + ) layer, kind = resolved out[hooks.wire_key(layer, kind)] = dict(spec) return out diff --git a/transformer_lens/model_bridge/sources/inspect/source.py b/transformer_lens/model_bridge/sources/inspect/source.py index b23f565c83..656d31558b 100644 --- a/transformer_lens/model_bridge/sources/inspect/source.py +++ b/transformer_lens/model_bridge/sources/inspect/source.py @@ -43,12 +43,16 @@ def boot_inspect( Fireable hooks (``tl_bridge``, TransformerBridge-native names): ``blocks.{i}.hook_in`` (resid_pre) / ``ln2.hook_in`` (resid_mid) / - ``hook_out`` (resid_post) / ``attn.hook_out`` / ``mlp.hook_out``. The provider runs - a structural self-check per model and gates any boundary it can't serve faithfully: - ``resid_mid`` for parallel-residual or norm-variant blocks, and ``attn_out``/ - ``mlp_out`` when their submodule isn't locatable (it warns when it gates one). - Head-split hooks (q/k/v/z, pattern), ``embed``, and ``ln_final`` are always - non-fireable — use ``boot_transformers()`` for those. + ``hook_out`` (resid_post) / ``attn.hook_out`` / ``mlp.hook_out``, plus the head-split + attention hooks where the structural probe finds them: ``attn.hook_q/k/v`` (pre-RoPE + projection outputs; separate-projection archs only — fused qkv gates them), + ``attn.hook_z`` (out-projection input), and ``attn.hook_pattern`` (post-softmax, + capture-only, eager attention required). The provider runs a structural self-check per + model and gates any boundary it can't serve faithfully: ``resid_mid`` for + parallel-residual or norm-variant blocks, ``attn_out``/``mlp_out`` when their submodule + isn't locatable (it warns when it gates one). ``embed``, ``ln_final``, and + ``attn.hook_attn_scores`` are always non-fireable — use ``boot_transformers()`` for + those. For parity with ``boot_transformers`` the provider loads with the same dtype (fp32 by default) and eager attention. Full-sequence logits ride on ``return_logits=True`` (the diff --git a/transformer_lens/model_bridge/sources/inspect/transformers_provider.py b/transformer_lens/model_bridge/sources/inspect/transformers_provider.py index be8a26cf89..74ca10b513 100644 --- a/transformer_lens/model_bridge/sources/inspect/transformers_provider.py +++ b/transformer_lens/model_bridge/sources/inspect/transformers_provider.py @@ -29,7 +29,7 @@ modelapi, ) -from . import wire +from . import hooks, wire from ._provider_base import _InspectModelAPIBase, _parse_tool_calls, _require_served # NOT "transformer_lens" — inspect_ai ships a built-in provider by that name (the @@ -48,6 +48,17 @@ # Attn/MLP submodule names within a block, by family. _ATTN_ATTRS = ("self_attn", "attn", "attention", "self_attention") # self_attention: Falcon _MLP_ATTRS = ("mlp", "feed_forward") +# fc-split blocks (OPT/XGLM) have no mlp container — fc1/fc2 sit on the block directly: +# mlp_in boundary = fc1's input, mlp_out boundary = fc2's output. +_MLP_SPLIT_ATTRS = ("fc1", "fc2") +# Separate q/k/v projections (Llama/Mistral/Qwen/OPT-family). Fused-qkv archs +# (GPT-2 c_attn, Falcon/GPTNeoX query_key_value) gate q/k/v — their packed layouts +# vary per family, so slicing them is per-arch work we don't hand-maintain here. +_Q_PROJ_ATTRS = ("q_proj", "query") +_K_PROJ_ATTRS = ("k_proj", "key") +_V_PROJ_ATTRS = ("v_proj", "value") +# Attention out-projection; its input is z (works for fused-qkv archs too). +_O_PROJ_ATTRS = ("o_proj", "out_proj", "dense", "c_proj") @modelapi(name=PROVIDER_NAME) @@ -85,10 +96,24 @@ def __init__( ) self._tokenizer = AutoTokenizer.from_pretrained(model_name) self._layers = _locate_layers(self._hf) - self._kinds, self._capability_note = _detect_capabilities(self._hf, self._layers) + # Head-split reshape geometry; None per-field when the config lacks it (head + # kinds are then gated by _detect_capabilities' width checks). + self._geometry = _attn_geometry(self._hf.config) + self._kinds, self._capability_note = _detect_capabilities( + self._hf, self._layers, self._geometry + ) # Per-turn capture during plain generation (agent rollouts): every _generate_eval # stashes these hooks in ModelOutput.metadata. Gated by the structural self-check. self._eval_capture = self._parse_eval_capture(model_args) + for key in self._eval_capture: + if key.endswith(":pattern"): + # pattern rides the forward's output_attentions, which the eval path's + # hf.generate doesn't thread — reject rather than silently omit. + raise ValueError( + "Per-turn eval capture of attn.hook_pattern is not supported (it needs " + "output_attentions on the forward). Use the driver path " + "(bridge.run_with_cache) for pattern capture." + ) def _generate_capture(self, input: Any, extra_args: Mapping[str, Any], config: GenerateConfig): """TL-driven single forward: capture residual/attn/mlp boundaries + full logits.""" @@ -101,9 +126,22 @@ def _generate_capture(self, input: Any, extra_args: Mapping[str, Any], config: G _, _, kind = key.partition(":") _require_served(kind, self._kinds, self._capability_note, f"capture {key!r}") interventions: Mapping[str, Any] = extra_args.get("interventions", {}) + # The driver validates before sending, but this direct interface (extra_args) is + # documented — validate here too so a gated/capture-only kind fails loud instead + # of silently no-op'ing when no hook installs for it. + for key in interventions: + _, _, kind = key.partition(":") + _require_served(kind, self._kinds, self._capability_note, f"intervention {key!r}") + if kind not in hooks.INTERVENEABLE_KINDS: + raise ValueError( + f"intervention {key!r}: kind {kind!r} is capture-only " + f"(interveneable: {sorted(hooks.INTERVENEABLE_KINDS)})." + ) want_logits = bool(extra_args.get("return_logits", True)) capture, intervene = _plan(capture_keys, interventions) + # pattern comes from the forward's output_attentions, not a module hook. + pattern_layers = [layer for layer, kinds in capture.items() if "pattern" in kinds] raw: dict[tuple[int, str], np.ndarray] = {} call_id = uuid.uuid4().hex token = _current_call_id.set(call_id) @@ -111,12 +149,19 @@ def _generate_capture(self, input: Any, extra_args: Mapping[str, Any], config: G try: with torch.no_grad(): ids = torch.tensor([list(input_ids)], device=self._device) - logits = self._hf(ids).logits # (1, seq, vocab) + outputs = self._hf(ids, output_attentions=bool(pattern_layers)) + logits = outputs.logits # (1, seq, vocab) finally: for handle in handles: handle.remove() _current_call_id.reset(token) + attentions = getattr(outputs, "attentions", None) + for layer in pattern_layers: + attn_l = attentions[layer] if attentions is not None else None + if attn_l is not None: # None → driver's missing-hook warning handles it + raw[(layer, "pattern")] = attn_l[0].detach().float().cpu().numpy() + captured = _assemble(raw, capture_keys) metadata: dict[str, Any] = {"activations": wire.encode_activations(captured)} if want_logits: @@ -254,7 +299,7 @@ def _install_hooks(self, capture, intervene, raw, call_id: str) -> list: if not cap_kinds and not iv_kinds: continue attn = _first_attr(block, _ATTN_ATTRS) - mlp = _first_attr(block, _MLP_ATTRS) + _, mlp_out_mod = _locate_mlp(block) if "resid_pre" in cap_kinds or "resid_pre" in iv_kinds: handles.append( block.register_forward_pre_hook( @@ -277,9 +322,13 @@ def _install_hooks(self, capture, intervene, raw, call_id: str) -> list: ) ) ) - if mlp is not None and ("mlp_out" in cap_kinds or "mlp_out" in iv_kinds): + if attn is not None: + handles.extend( + self._install_head_hooks(layer, attn, cap_kinds, iv_kinds, raw, call_id) + ) + if mlp_out_mod is not None and ("mlp_out" in cap_kinds or "mlp_out" in iv_kinds): handles.append( - mlp.register_forward_hook( + mlp_out_mod.register_forward_hook( _out_hook( layer, "mlp_out", @@ -305,6 +354,58 @@ def _install_hooks(self, capture, intervene, raw, call_id: str) -> list: ) return handles + def _install_head_hooks(self, layer, attn, cap_kinds, iv_kinds, raw, call_id: str) -> list: + """Hooks for the head-split kinds: q/k/v on their projection outputs, z on the + out-projection's input. pattern isn't hooked (it rides output_attentions). + + Interventions apply to the module's natural *flat* tensor ``(..., seq, + heads·d_head)`` — a spec ``value`` is scalar, ``(heads·d_head,)``, or per-position + ``(len(pos), heads·d_head)`` (flatten a captured head-split tensor to build one). + Captures are emitted head-split ``(seq, heads, d_head)`` to match the bridge's + ``hook_q/k/v/z``. + """ + handles: list = [] + d_head = self._geometry[2] + if d_head is None: # geometry undetectable → head kinds were gated at detection + return handles + host = _projection_host(attn) + for kind, attrs in (("q", _Q_PROJ_ATTRS), ("k", _K_PROJ_ATTRS), ("v", _V_PROJ_ATTRS)): + if kind not in cap_kinds and kind not in iv_kinds: + continue + proj = _first_attr(host, attrs) + if proj is None: + # Detection ran on layers[0]; a later layer missing the projection would + # silently skip a validated intervention — fail loud (capture-only misses + # surface through the driver's missing-hook warning instead). + if kind in iv_kinds: + raise RuntimeError( + f"Intervention on blocks.{layer}.attn.hook_{kind} cannot be applied: " + f"layer {layer} has no {kind} projection (heterogeneous layers)." + ) + continue + handles.append( + proj.register_forward_hook( + _proj_hook( + layer, kind, d_head, kind in cap_kinds, iv_kinds.get(kind), raw, call_id + ) + ) + ) + if "z" in cap_kinds or "z" in iv_kinds: + o_proj = _first_attr(host, _O_PROJ_ATTRS) + if o_proj is None and "z" in iv_kinds: + raise RuntimeError( + f"Intervention on blocks.{layer}.attn.hook_z cannot be applied: layer " + f"{layer} has no out-projection (heterogeneous layers)." + ) + if o_proj is not None: + handles.append( + o_proj.register_forward_pre_hook( + _zin_hook(layer, d_head, "z" in cap_kinds, iv_kinds.get("z"), raw, call_id), + with_kwargs=True, + ) + ) + return handles + def _plan(capture_keys, interventions): """Resolve wire keys → per-layer kinds to capture (resid_mid needs pre+attn) and intervene.""" @@ -370,7 +471,10 @@ def hook(_module, _inputs, output): if spec is not None: hidden = _apply_affine(hidden, spec) if want_capture and (layer, kind) not in raw: - raw[(layer, kind)] = hidden[0].detach().float().cpu().numpy() + # OPT-style blocks flatten the FFN to (batch·seq, d) — with batch_size=1 + # that IS (seq, d) already; only 3-D (batch, seq, d) needs the batch strip. + flat = hidden if hidden.ndim == 2 else hidden[0] + raw[(layer, kind)] = flat.detach().float().cpu().numpy() if spec is None: return None return (hidden, *output[1:]) if is_tuple else hidden @@ -378,45 +482,114 @@ def hook(_module, _inputs, output): return hook -def _apply_affine(t: torch.Tensor, spec: Mapping[str, Any]) -> torch.Tensor: - """suppress→0, scale→·factor, add→+value, set→value (value scalar or width-shaped).""" +def _proj_hook(layer, kind, d_head, want_capture, spec, raw, call_id): + """q/k/v projection output: affine on the flat ``(..., seq, heads·d_head)`` tensor, + captured head-split ``(seq, heads, d_head)`` (pre-RoPE — matches the bridge's + ``hook_q``/``hook_k``/``hook_v``). Mutations feed the downstream attention math.""" + + def hook(_module, _inputs, output): + if _current_call_id.get() != call_id: + return None # different concurrent call's hook + hidden = output + if spec is not None: + hidden = _apply_affine(hidden, spec) + if want_capture and (layer, kind) not in raw: + flat = hidden[0].detach().float().cpu() + raw[(layer, kind)] = flat.reshape(flat.shape[0], -1, d_head).numpy() + return hidden if spec is not None else None + + return hook + + +def _zin_hook(layer, d_head, want_capture, spec, raw, call_id): + """z — the out-projection's *input* (attention-weighted values, all heads): pre-hook, + affine on the flat tensor, captured head-split to match the bridge's ``hook_z``.""" + + def hook(_module, args, kwargs): + if _current_call_id.get() != call_id: + return None # different concurrent call's hook + z = args[0] + if spec is not None: + z = _apply_affine(z, spec) + if want_capture and (layer, "z") not in raw: + flat = z[0].detach().float().cpu() + raw[(layer, "z")] = flat.reshape(flat.shape[0], -1, d_head).numpy() + if spec is None: + return None + return (z, *args[1:]), kwargs + + return hook + + +def _affine_op(sub: torch.Tensor, spec: Mapping[str, Any]) -> torch.Tensor: + """One affine op: suppress→0, scale→·factor, add→+value, set→value. ``value`` broadcasts + (scalar, width-shaped, or per-position ``(n_pos, width)``).""" op = spec["op"] if op == "suppress": - return torch.zeros_like(t) + return torch.zeros_like(sub) if op == "scale": - return t * float(spec["factor"]) - value = torch.as_tensor(spec["value"], dtype=t.dtype, device=t.device) + return sub * float(spec["factor"]) + value = torch.as_tensor(spec["value"], dtype=sub.dtype, device=sub.device) if op == "add": - return t + value - return torch.zeros_like(t) + value # set + return sub + value + return torch.zeros_like(sub) + value # set + + +def _apply_affine(t: torch.Tensor, spec: Mapping[str, Any]) -> torch.Tensor: + """Affine intervention on a captured tensor ``(..., seq, width)``. + Without ``pos`` the op spans every position (the original width-broadcast form). With + ``pos`` (an int or list of sequence indices) it touches only those positions — the + activation-patching primitive — and ``value`` may be per-position ``(len(pos), width)`` + to transplant a captured activation (path/causal tracing) rather than a single vector. + """ + pos = spec.get("pos") + if pos is None: + return _affine_op(t, spec) + idx = [pos] if isinstance(pos, int) else list(pos) + out = t.clone() + out[..., idx, :] = _affine_op(t[..., idx, :], spec) + return out -def _detect_capabilities(model: Any, layers: Any) -> tuple[frozenset, str]: - """Structural self-check: which boundary kinds this model can serve faithfully. + +def _detect_capabilities( + model: Any, layers: Any, geometry: tuple[Any, Any, Any] +) -> tuple[frozenset, str]: + """Structural self-check: which kinds this model can serve faithfully. resid_pre/resid_post are the block in/out (always); attn_out/mlp_out need their submodules locatable; resid_mid is gated unless its derivation holds (see - :func:`_resid_mid_derivable`). Returns (kinds, note); note explains any gating, '' if none. + :func:`_resid_mid_derivable`). Head-split kinds: q/k/v need separate projections whose + widths match ``heads·d_head``; z needs an out-projection of in-width ``n_heads·d_head``; + pattern needs eager attention (output_attentions is a no-op under sdpa/flash). + Returns (kinds, note); note explains any gating, '' if none. """ block = layers[0] attn = _first_attr(block, _ATTN_ATTRS) - mlp = _first_attr(block, _MLP_ATTRS) + mlp_in_mod, mlp_out_mod = _locate_mlp(block) kinds = {"resid_pre", "resid_post"} gated = [] if attn is not None: kinds.add("attn_out") else: gated.append("attn_out (no attention submodule found)") - if mlp is not None: + if mlp_out_mod is not None: kinds.add("mlp_out") else: gated.append("mlp_out (no MLP submodule found)") - if attn is not None and mlp is not None and _resid_mid_derivable(model, block, attn, mlp): + if ( + attn is not None + and mlp_out_mod is not None + and _resid_mid_derivable(model, block, attn, mlp_in_mod, mlp_out_mod) + ): kinds.add("resid_mid") else: gated.append( "resid_mid (resid_pre + attn_out doesn't hold — parallel or norm-variant block)" ) + head_kinds, head_gated = _detect_head_capabilities(model, attn, geometry) + kinds |= head_kinds + gated += head_gated note = ( "" if not gated @@ -427,11 +600,85 @@ def _detect_capabilities(model: Any, layers: Any) -> tuple[frozenset, str]: return frozenset(kinds), note -def _resid_mid_derivable(model: Any, block: Any, attn: Any, mlp: Any) -> bool: +def _detect_head_capabilities( + model: Any, attn: Any, geometry: tuple[Any, Any, Any] +) -> tuple[set, list]: + """Head-split kinds this model serves: q/k/v iff separate projections with the + expected widths, z iff the out-projection's in-width is ``n_heads·d_head``, pattern + iff attention runs eager (otherwise ``output_attentions`` returns None/garbage).""" + n_heads, n_kv_heads, d_head = geometry + kinds: set = set() + gated: list = [] + if attn is None or d_head is None: + gated.append("q/k/v/z/pattern (no attention submodule or head geometry in config)") + return kinds, gated + + host = _projection_host(attn) + q = _first_attr(host, _Q_PROJ_ATTRS) + k = _first_attr(host, _K_PROJ_ATTRS) + v = _first_attr(host, _V_PROJ_ATTRS) + expected = {"q": n_heads * d_head, "k": n_kv_heads * d_head, "v": n_kv_heads * d_head} + if all( + proj is not None and _out_width(proj) == expected[kind] + for kind, proj in (("q", q), ("k", k), ("v", v)) + ): + kinds |= {"q", "k", "v"} + else: + gated.append("q/k/v (fused or nonstandard qkv projections)") + + o_proj = _first_attr(host, _O_PROJ_ATTRS) + if o_proj is not None and _in_width(o_proj) == n_heads * d_head: + kinds.add("z") + else: + gated.append("z (out-projection missing or nonstandard width)") + + if getattr(model.config, "_attn_implementation", "eager") == "eager": + kinds.add("pattern") + else: + gated.append("pattern (attention implementation is not eager)") + return kinds, gated + + +def _out_width(module: Any) -> Any: + """Output width of a projection: ``nn.Linear.out_features`` or GPT-2 ``Conv1D.nf``.""" + out = getattr(module, "out_features", None) + if out is not None: + return int(out) + nf = getattr(module, "nf", None) # transformers Conv1D + return int(nf) if nf is not None else None + + +def _in_width(module: Any) -> Any: + """Input width of a projection: ``nn.Linear.in_features`` or Conv1D ``weight.shape[0]``.""" + in_f = getattr(module, "in_features", None) + if in_f is not None: + return int(in_f) + if getattr(module, "nf", None) is not None and hasattr(module, "weight"): + return int(module.weight.shape[0]) # Conv1D stores weight (in, out) + return None + + +def _attn_geometry(config: Any) -> tuple[Any, Any, Any]: + """(n_heads, n_kv_heads, d_head) from an HF config; (None, None, None) if underivable.""" + n_heads = getattr(config, "num_attention_heads", None) or getattr(config, "n_head", None) + hidden = getattr(config, "hidden_size", None) or getattr(config, "n_embd", None) + if n_heads is None: + return None, None, None + n_kv = getattr(config, "num_key_value_heads", None) or n_heads + d_head = getattr(config, "head_dim", None) + if d_head is None and hidden is not None: + d_head = hidden // n_heads + return (int(n_heads), int(n_kv), int(d_head) if d_head is not None else None) + + +def _resid_mid_derivable( + model: Any, block: Any, attn: Any, mlp_in_mod: Any, mlp_out_mod: Any +) -> bool: """True iff ``resid_mid = resid_pre + attn_out`` holds, via two tiny probe forwards. Requires both the linear identity ``resid_post = resid_pre + attn_out + mlp_out`` (broken by post-norm/multiplier blocks — Gemma2/OLMo2/Granite) and attn feeding mlp (broken by - parallel blocks — GPTNeoX/GPT-J, where mlp reads resid_pre directly).""" + parallel blocks — GPTNeoX/GPT-J, where mlp reads resid_pre directly). ``mlp_in_mod``/ + ``mlp_out_mod`` are the same module for container archs, (fc1, fc2) for fc-split.""" cap: dict[str, Any] = {} def grab(key: str): # type: ignore[no-untyped-def] @@ -463,8 +710,8 @@ def perturb_attn(_m: Any, _i: Any, out: Any): # type: ignore[no-untyped-def] handles = [ block.register_forward_pre_hook(grab_in("resid_pre"), with_kwargs=True), attn.register_forward_hook(grab("attn_out")), - mlp.register_forward_hook(grab("mlp_out")), - mlp.register_forward_pre_hook(grab_in("mlp_in"), with_kwargs=True), + mlp_out_mod.register_forward_hook(grab("mlp_out")), + mlp_in_mod.register_forward_pre_hook(grab_in("mlp_in"), with_kwargs=True), block.register_forward_hook(grab("resid_post")), ] model(ids) @@ -472,7 +719,7 @@ def perturb_attn(_m: Any, _i: Any, out: Any): # type: ignore[no-untyped-def] h.remove() mlp_in_clean = cap.pop("mlp_in", None) handles = [ - mlp.register_forward_pre_hook(grab_in("mlp_in"), with_kwargs=True), + mlp_in_mod.register_forward_pre_hook(grab_in("mlp_in"), with_kwargs=True), attn.register_forward_hook(perturb_attn), ] model(ids) @@ -490,7 +737,13 @@ def perturb_attn(_m: Any, _i: Any, out: Any): # type: ignore[no-untyped-def] return False if mlp_in_clean is None or mlp_in_perturbed is None: return False - if not (rp.shape == ao.shape == mo.shape == rpost.shape == mlp_in_clean.shape): + # OPT-style blocks flatten the FFN to (batch·seq, d); with the probe's batch=1 that + # is a pure reshape of the block-level (1, seq, d) — normalize before comparing. + if mo.shape != rpost.shape and mo.numel() == rpost.numel(): + mo = mo.reshape(rpost.shape) + if not (rp.shape == ao.shape == mo.shape == rpost.shape): + return False + if mlp_in_clean.shape != mlp_in_perturbed.shape or mlp_in_clean.numel() != rp.numel(): return False # (1) sub-block outputs add to the residual without intervening norm/scale. identity = (rpost - rp - ao - mo).abs().max().item() @@ -514,6 +767,33 @@ def _locate_layers(model: Any) -> Any: ) +def _locate_mlp(block: Any) -> tuple[Any, Any]: + """The modules bounding the MLP: ``(in_module, out_module)`` — the mlp_in boundary is + in_module's input, mlp_out is out_module's output. Container archs return the mlp + module twice; fc-split blocks (OPT/XGLM: fc1/fc2 directly on the block) return + ``(fc1, fc2)``. ``(None, None)`` when neither layout is found (mlp_out gated).""" + mlp = _first_attr(block, _MLP_ATTRS) + if mlp is not None: + return mlp, mlp + fc1, fc2 = (getattr(block, name, None) for name in _MLP_SPLIT_ATTRS) + if fc1 is not None and fc2 is not None: + return fc1, fc2 + return None, None + + +def _projection_host(attn: Any) -> Any: + """The module whose direct attrs are the q/k/v/out projections. Usually ``attn`` + itself; GPT-Neo-style blocks wrap the real attention (with its standard q_proj/ + out_proj) one level down at ``attn.attention``. Descend only when the located module + has neither a q- nor an out-projection — GPTNeoX's ``block.attention`` (fused + query_key_value + dense) has ``dense`` directly, so it never descends.""" + if _first_attr(attn, _Q_PROJ_ATTRS) is None and _first_attr(attn, _O_PROJ_ATTRS) is None: + inner = getattr(attn, "attention", None) + if inner is not None: + return inner + return attn + + def _first_attr(obj: Any, names: tuple[str, ...]) -> Any: for name in names: found = getattr(obj, name, None) diff --git a/transformer_lens/model_bridge/sources/vllm/driver.py b/transformer_lens/model_bridge/sources/vllm/driver.py index e78aa47ffa..91e0270dce 100644 --- a/transformer_lens/model_bridge/sources/vllm/driver.py +++ b/transformer_lens/model_bridge/sources/vllm/driver.py @@ -20,10 +20,16 @@ class VLLMDriver(DriverBase): """Driver wrapping a vLLM ``LLM``; captures via ``collective_rpc``.""" - # vLLM owns the model in a worker — no torch surface (parameters/state_dict/grads). - _supported_features = frozenset() - # Logits synthesized for the final position only (sampler bypass). - provides_sequence_logits = False + # vLLM owns the model in a worker — no torch module surface (parameters/state_dict/ + # grads). Named-weight reads ARE served: get_param() returns CPU clones via the + # tl_get_param RPC (what logit reconstruction and direct-logit-attribution use). + _supported_features = frozenset({"weight_access"}) + # Full-sequence logits reconstructed host-side (ln_final @ lm_head.weight.T); vLLM's + # sampler only hands back the final position, so the driver rebuilds the rest. + provides_sequence_logits = True + + # Post-weight final-norm capture that lm_head consumes — reconstruction reads it. + _LN_FINAL = "ln_final.hook_normalized" def __init__( self, @@ -34,11 +40,15 @@ def __init__( hf_config: Any, max_num_batched_tokens: int, enable_batching: bool = False, + enable_position_interventions: bool = False, ) -> None: super().__init__(adapter.cfg, tokenizer) self._llm = llm self._max_num_batched_tokens = max_num_batched_tokens self._enable_batching = enable_batching + # Position-scoped 'pos' interventions need (max_n, width) affine buffers, + # allocated at boot only when this is set (see plugin.patched_load_model). + self._enable_position_interventions = enable_position_interventions # Logprobs per forward = real vocab (boot's max_logprobs). d_vocab can be # padded larger, which vLLM would reject; the logits tensor stays d_vocab. self._n_logprobs = int(getattr(hf_config, "vocab_size", self.bridge_config.d_vocab)) @@ -46,9 +56,16 @@ def __init__( self.supported_hook_points = frozenset(overlay.capture_specs(hf_config).keys()) n_layers = getattr(hf_config, "num_hidden_layers", 0) + if not isinstance(n_layers, int) or n_layers <= 0: + # A raw "{i}" template would land unexpanded in non_fireable_hook_points — + # a broken config should fail at boot, not surface as a garbled hook name. + raise ValueError( + f"VLLMDriver: hf_config.num_hidden_layers={n_layers!r} — expected a " + "positive int; the config is missing or malformed." + ) nonfiring: list[str] = [] for tmpl in overlay.nonfiring_hooks(): - if "{i}" in tmpl and isinstance(n_layers, int) and n_layers > 0: + if "{i}" in tmpl: nonfiring.extend(tmpl.replace("{i}", str(i)) for i in range(n_layers)) else: nonfiring.append(tmpl) @@ -73,9 +90,11 @@ def forward( ) intervene_specs = self._validate_interventions(intervene or {}) - # Restrict the GPU→CPU read to these hooks (None = all). run_with_cache - # doesn't derive this from names_filter yet — only explicit forward(capture=). - names = list(capture) or None + # capture is authoritative — the bridge sends exactly the hooked names, so () + # means "capture nothing" and a plain forward(tokens) skips the GPU→CPU copy + # entirely. (The worker's None-means-all convention is never triggered from here; + # an empty tuple used to collapse to None and silently copy every buffer.) + names = list(capture) if self._enable_batching: return self._forward_batched(input_ids, intervene_specs, return_logits, names) @@ -88,6 +107,9 @@ def forward( f"{self._max_num_batched_tokens}; raise the boot_vllm kwarg or " "shorten the prompt." ) + # 'pos'-scoped edits target affine-buffer rows, but the compiled hook only reads + # rows [0, len(ids_list)); a pos past the prompt length would be a silent no-op. + self._reject_pos_beyond_seq(intervene_specs, len(ids_list)) from vllm import SamplingParams from vllm.inputs import TokensPrompt @@ -110,15 +132,31 @@ def forward( ) n_tokens = len(ids_list) - # collective_rpc returns one result per worker; single-rank, so [0]. - worker_captures = self._llm.collective_rpc("tl_read_captures", args=([n_tokens], names))[0] - # Add batch dim: vLLM hands back (n_tokens, width); bridge expects (1, n_tokens, width). - captured = {name: t.unsqueeze(0) for name, t in worker_captures.items()} + # Reconstructing logits needs ln_final; force it into the read even if the caller + # didn't request it (dropped from `captured` below so the surface stays as asked). + read_names = names + if return_logits and self._LN_FINAL not in names: + read_names = names + [self._LN_FINAL] + # collective_rpc returns one result per worker; single-rank, so [0]. Nothing to + # read (no captures, logits off) → skip the crossing altogether. + worker_captures = ( + self._llm.collective_rpc("tl_read_captures", args=([n_tokens], read_names))[0] + if read_names + else {} + ) logits: torch.Tensor | None = None if return_logits: - logits = self._synthesize_logits(outputs[0], n_tokens, self.bridge_config.d_vocab) + recon = self._reconstruct_logits(worker_captures.get(self._LN_FINAL)) + # Fall back to final-position log-probs if the unembedding isn't fetchable. + logits = ( + recon.unsqueeze(0) + if recon is not None + else self._synthesize_logits(outputs[0], n_tokens, self.bridge_config.d_vocab) + ) + # Add batch dim; expose only the caller's requested hooks (drop the forced ln_final). + captured = {name: t.unsqueeze(0) for name, t in worker_captures.items() if name in names} return ForwardResult(logits=logits, captured=captured, raw_output=outputs[0]) def _forward_batched( @@ -126,13 +164,13 @@ def _forward_batched( input_ids: TensorLike, intervene_specs: dict, return_logits: bool, - names: list[str] | None = None, + names: list[str], ) -> ForwardResult: """Eager batched path: per-request capture, right-padded to (B, S, W). No per-prompt length gate — chunked prefill accumulates long prompts - across forwards. Interventions are global across the batch. ``names`` - restricts the returned hooks (``None`` = all). + across forwards. Interventions are global across the batch. ``names`` is + authoritative: exactly the hooks to return (empty = none). """ from vllm import SamplingParams from vllm.inputs import TokensPrompt @@ -155,14 +193,35 @@ def _forward_batched( ), ) + # Force ln_final into the read so logits can be reconstructed (dropped below if + # the caller didn't ask for it). + read_names = names + if return_logits and self._LN_FINAL not in names: + read_names = names + [self._LN_FINAL] # Keyed by req_id (no guaranteed order) — _assemble_padded joins to slot - # k via outputs[k].request_id, not by position. - worker_captures = self._llm.collective_rpc("tl_read_batched_captures", args=(names,))[0] - captured = self._assemble_padded(outputs, worker_captures, prompt_lens) + # k via outputs[k].request_id, not by position. Empty read → skip both the + # crossing AND the join (_assemble_padded requires one worker key per request, + # so it can't run on an empty dict — mirror the single path's empty captured). + if read_names: + worker_captures = self._llm.collective_rpc( + "tl_read_batched_captures", args=(read_names,) + )[0] + captured = self._assemble_padded(outputs, worker_captures, prompt_lens) + else: + captured = {} logits: torch.Tensor | None = None if return_logits: - logits = self._synthesize_logits_batched(outputs, prompt_lens, d_vocab) + recon = self._reconstruct_logits( + captured.get(self._LN_FINAL) + ) # (batch, max_seq, d_vocab) + logits = ( + recon + if recon is not None + else self._synthesize_logits_batched(outputs, prompt_lens, d_vocab) + ) + if self._LN_FINAL not in names: + captured.pop(self._LN_FINAL, None) return ForwardResult(logits=logits, captured=captured, raw_output=outputs) @@ -245,6 +304,37 @@ def get_param(self, dotted_name: str) -> torch.Tensor | None: return None return self._llm.collective_rpc("tl_get_param", args=(dotted_name,))[0] + def _reconstruct_logits(self, ln_final: Any) -> torch.Tensor | None: + """Rebuild real logits from the captured post-weight ln_final: + ``ln_final @ lm_head.weight.T`` (+ bias, + Gemma-family tanh soft-cap). + + vLLM's ``ln_final.hook_normalized`` is the POST-weight RMSNorm value lm_head + consumes (verified empirically: it equals HF's pre-weight value times the norm + weight), so no un-fold is needed. Accepts any ``(..., d_model)`` tensor and returns + ``(..., d_vocab)`` on CPU. ``None`` if ln_final wasn't captured or no unembedding + weight is fetchable — the caller then falls back to the sampler's log-probs. + """ + if ln_final is None: + return None + weight = self.get_param("lm_head.weight") + if weight is None: # tied embeddings expose no separate lm_head + weight = self.get_param("model.embed_tokens.weight") + if weight is None: + return None + lf = ln_final.to(device=weight.device, dtype=torch.float32) + logits = lf @ weight.to(torch.float32).T + bias = self.get_param("lm_head.bias") + if bias is not None: + logits = logits + bias.to(device=logits.device, dtype=torch.float32) + cap = getattr(self.bridge_config, "output_logits_soft_cap", None) + if cap is not None and cap > 0: # Gemma-family cap; -1.0 is the "disabled" sentinel + logits = float(cap) * torch.tanh(logits / float(cap)) + d_vocab = int(self.bridge_config.d_vocab) + if logits.shape[-1] < d_vocab: # pad the padded-vocab tail (never predicted) + pad = logits.new_full((*logits.shape[:-1], d_vocab - logits.shape[-1]), float("-inf")) + logits = torch.cat([logits, pad], dim=-1) + return logits.cpu() + def close(self) -> None: # Detach hooks before dropping the LLM so they don't stay registered on # worker modules for the life of the process (long-running notebooks). @@ -334,9 +424,62 @@ def _validate_interventions(self, intervene: Mapping[str, Any]) -> dict: raise ValueError( f"Cannot intervene on {hook_name!r}: not in supported_hook_points." ) + pos = spec.get("pos") + if pos is not None: + if self._enable_batching: + # The batched/eager path applies ops to the raw tensor, not the + # (max_n, width) affine buffers, so it has no position surface. + raise NotImplementedError( + f"Intervention {hook_name!r}: per-position 'pos' is not supported on the " + "batched/eager path. Boot the compiled path (enable_batching=False) with " + "enable_position_interventions=True." + ) + if not self._enable_position_interventions: + # Default affine buffers are (width,) and broadcast across every position; + # honoring 'pos' needs the (max_n, width) buffers allocated at boot. + raise NotImplementedError( + f"Intervention {hook_name!r}: per-position 'pos' requires " + "boot_vllm(enable_position_interventions=True) (its default affine " + "buffers broadcast across all positions). Use the Inspect/HF backend for " + "position-scoped patching, or drop 'pos' for a whole-sequence edit." + ) + if not ( + isinstance(pos, int) + or (isinstance(pos, (list, tuple)) and all(isinstance(p, int) for p in pos)) + ): + raise ValueError( + f"Intervention {hook_name!r}: 'pos' must be an int or list of ints " + f"(sequence positions to patch); got {pos!r}." + ) + bad = [p for p in ([pos] if isinstance(pos, int) else pos) if p < 0] + if bad: + raise ValueError( + f"Intervention {hook_name!r}: 'pos' must be non-negative; got {bad}." + ) out[hook_name] = dict(spec) return out + @staticmethod + def _reject_pos_beyond_seq(specs: Mapping[str, Any], seq_len: int) -> None: + """Fail loud if a spec's 'pos' targets a row past the actual prompt length. + + The compiled hook applies the affine over rows ``[0, seq_len)`` only, so a ``pos`` + in ``[seq_len, max_num_batched_tokens)`` clears the driver's non-negativity check + and the worker's buffer-capacity check yet is never read — a silent no-op. Bound it + against the real sequence length (known once ``ids_list`` exists) and raise instead. + """ + for hook_name, spec in specs.items(): + pos = spec.get("pos") + if pos is None: + continue + idx = [pos] if isinstance(pos, int) else list(pos) + bad = [p for p in idx if p >= seq_len] + if bad: + raise ValueError( + f"Intervention {hook_name!r}: 'pos' {bad} is beyond the prompt length " + f"{seq_len} (positions are 0-indexed); the edit would be silently ignored." + ) + @staticmethod def _normalize_input_ids(input_ids: Any) -> list: """Coerce input_ids to a flat list[int] for ``TokensPrompt``; batch_size=1 only.""" diff --git a/transformer_lens/model_bridge/sources/vllm/plugin.py b/transformer_lens/model_bridge/sources/vllm/plugin.py index 6b54d46490..51e5d70fb5 100644 --- a/transformer_lens/model_bridge/sources/vllm/plugin.py +++ b/transformer_lens/model_bridge/sources/vllm/plugin.py @@ -49,12 +49,14 @@ def configure( max_num_batched_tokens: int, dtype: torch.dtype, enable_batching: bool = False, + enable_position_interventions: bool = False, ) -> None: """Set capture specs, buffer length, dtype, and hook flavor before ``LLM(...)``.""" _config["capture_specs"] = capture_specs _config["max_num_batched_tokens"] = max_num_batched_tokens _config["dtype"] = dtype _config["enable_batching"] = enable_batching + _config["enable_position_interventions"] = enable_position_interventions def register() -> None: @@ -84,6 +86,9 @@ def patched_load_model(self): max_n = _config["max_num_batched_tokens"] dtype = _config["dtype"] enable_batching = _config.get("enable_batching", False) + # Per-position affine buffers are (max_n, width) instead of (width,), so each + # sequence row can carry a distinct scale/bias (position-scoped patching). + per_position = _config.get("enable_position_interventions", False) device = next(self.model_runner.model.parameters()).device # Detach prior handles before reassigning — vLLM doesn't double-load @@ -124,9 +129,12 @@ def patched_load_model(self): else: capture_buf = torch.zeros(max_n, width, device=device, dtype=dtype) # Affine identity at install. Driver swaps via tl_set_interventions - # to enable suppress/scale/add/set ops between forwards. - scale_buf = torch.ones(width, device=device, dtype=dtype) - bias_buf = torch.zeros(width, device=device, dtype=dtype) + # to enable suppress/scale/add/set ops between forwards. Shape is + # (max_n, width) when position interventions are enabled so each row + # can differ; (width,) otherwise (broadcast across all positions). + affine_shape = (max_n, width) if per_position else (width,) + scale_buf = torch.ones(affine_shape, device=device, dtype=dtype) + bias_buf = torch.zeros(affine_shape, device=device, dtype=dtype) # Default closed — opened explicitly by tl_reset_capture_flags for the # next forward(s) that need to capture. capture_flag = torch.ones(1, device=device, dtype=torch.int64) @@ -142,6 +150,7 @@ def patched_load_model(self): self._tl_fire_counter, capture_flag, materialize=materialize, + per_position=per_position, ) ) self._tl_hook_handles.append(handle) @@ -158,6 +167,7 @@ def _make_capture_hook( capture_flag: torch.Tensor, *, materialize: bool = False, + per_position: bool = False, ): """GPU-only, dynamic-shape-safe affine + first-write-wins capture into pre-allocated buffers. @@ -174,18 +184,30 @@ def _make_capture_hook( steps self-copy (no overwrite). Interventions still apply on every forward regardless of the flag — the gate only affects the capture write. + ``per_position`` (compile-time constant): when set, ``scale_buf``/``bias_buf`` are + ``(max_n, width)`` and the affine is row-scoped (``buf.narrow(0, 0, n)``) so a + ``pos``-scoped intervention edits only its rows; otherwise they are ``(width,)`` + and broadcast across every position. + ``fire_counter`` is incremented per call for the fire-once check. """ + def _affine(t: torch.Tensor, n: Any) -> torch.Tensor: + # per_position is a closure constant → torch.compile specializes this branch. + # narrow(0, 0, n) keeps the SymInt dynamic shape (same trick as _gated_capture); + # the (width,) path broadcasts across all rows. + if per_position: + return t * scale_buf.narrow(0, 0, n) + bias_buf.narrow(0, 0, n) + return t * scale_buf + bias_buf + @torch.no_grad() def hook(_module, _inputs, output): fire_counter.add_(1) if materialize and isinstance(output, tuple) and len(output) == 2: hidden, residual = output if isinstance(hidden, torch.Tensor) and isinstance(residual, torch.Tensor): - t = hidden + residual - modified = t * scale_buf + bias_buf - n = t.shape[0] + n = hidden.shape[0] + modified = _affine(hidden + residual, n) _gated_capture(capture_buf, n, modified, capture_flag) # Reconstructs ``modified`` in the next layer's fused norm: exact at # identity, bounded fp16 error under intervention. @@ -201,10 +223,8 @@ def hook(_module, _inputs, output): return None # Affine transform; default scale=1 / bias=0 means identity. Driver # swaps buffer contents to enable interventions. - modified = t * scale_buf + bias_buf - # narrow() keeps the dynamic shape; [:n] gets erased under fake-tensor - # tracing and copy_ then sees the full buffer ("expand s72 -> max_n"). n = t.shape[0] + modified = _affine(t, n) _gated_capture(capture_buf, n, modified, capture_flag) # Gate on isinstance, not truthy tuple_tail — a 1-tuple has an empty tail. if isinstance(output, tuple): diff --git a/transformer_lens/model_bridge/sources/vllm/source.py b/transformer_lens/model_bridge/sources/vllm/source.py index bb6d03d501..6bb6351103 100644 --- a/transformer_lens/model_bridge/sources/vllm/source.py +++ b/transformer_lens/model_bridge/sources/vllm/source.py @@ -39,6 +39,7 @@ def boot_vllm( max_model_len: Optional[int] = None, max_num_batched_tokens: int = 2048, enable_batching: bool = False, + enable_position_interventions: bool = False, **vllm_kwargs: Any, ) -> RemoteBridge: """Boot a model via vLLM and wrap it in a :class:`RemoteBridge` via :class:`VLLMDriver`. @@ -86,7 +87,20 @@ def boot_vllm( ``batch_size > 1``) — the throughput path for SAE/probe data collection. Default ``False`` keeps the compile-validated single-prompt path. Batched caches are right-padded with zeros to the longest sequence. + + ``enable_position_interventions`` widens each hook's affine scale/bias buffers + from ``(width,)`` to ``(max_num_batched_tokens, width)`` so an intervention spec + can carry a ``pos`` field (int or list[int]) that scopes the edit to specific + sequence positions — position-scoped activation patching / tensor injection. + Costs ~2× extra resident GPU memory across all hooks (the scale and bias buffers + join the already-``(max_n, width)`` capture buffer), so it is opt-in and defaults + ``False``. Compiled-path only — incompatible with ``enable_batching``. """ + if enable_position_interventions and enable_batching: + raise ValueError( + "enable_position_interventions requires the compiled path and is incompatible " + "with enable_batching=True (the batched/eager path has no affine buffers)." + ) _reject_locked_overrides(vllm_kwargs) from transformers import AutoConfig, AutoTokenizer @@ -104,6 +118,7 @@ def boot_vllm( max_num_batched_tokens=max_num_batched_tokens, dtype=resolved_dtype, enable_batching=enable_batching, + enable_position_interventions=enable_position_interventions, ) plugin.register() @@ -177,6 +192,7 @@ def boot_vllm( hf_config=hf_config, max_num_batched_tokens=max_num_batched_tokens, enable_batching=enable_batching, + enable_position_interventions=enable_position_interventions, ) bridge = RemoteBridge(adapter=adapter, tokenizer=tokenizer, driver=driver) _log_hook_summary(model_name, architecture, driver) diff --git a/transformer_lens/model_bridge/sources/vllm/worker_extension.py b/transformer_lens/model_bridge/sources/vllm/worker_extension.py index 5873be05c7..b8cbcca048 100644 --- a/transformer_lens/model_bridge/sources/vllm/worker_extension.py +++ b/transformer_lens/model_bridge/sources/vllm/worker_extension.py @@ -170,37 +170,72 @@ def _apply_op(t: torch.Tensor, spec: Dict[str, Any]) -> torch.Tensor: return torch.zeros_like(t) + value # set +def _write_rows(buf: torch.Tensor, idx: Optional[List[int]], value: Any) -> None: + """In-place write of ``value`` (Python scalar or ``(width,)`` tensor) into ``buf``. + + ``idx is None`` writes the whole buffer — for a 2-D ``(max_n, width)`` buffer that + broadcasts a ``(width,)`` value across every row. ``idx`` (a list of row indices) + writes only those rows of a 2-D buffer. + """ + if idx is None: + if isinstance(value, torch.Tensor): + buf[...] = value # (width,) broadcasts across rows for a 2-D buffer + else: + buf.fill_(value) + else: + buf[idx] = value # advanced-index rows; scalar or (width,) broadcasts + + def _apply_intervention( scale_buf: torch.Tensor, bias_buf: torch.Tensor, spec: Dict[str, Any] ) -> None: - """Translate a spec dict to in-place buffer writes.""" + """Translate a spec dict to in-place buffer writes. + + ``spec['pos']`` (int or list[int]) scopes the edit to those sequence rows and + requires 2-D ``(max_n, width)`` affine buffers (position interventions enabled at + boot). Absent ``pos`` writes the whole buffer, applying to every position. The + caller resets both buffers to identity first, so a ``pos`` edit leaves other rows + untouched. + """ op = spec.get("op") if op not in SUPPORTED_OPS: raise ValueError(f"Unsupported intervention op: {op!r}. Supported: {sorted(SUPPORTED_OPS)}") + pos = spec.get("pos") + idx: Optional[List[int]] = None + if pos is not None: + if scale_buf.ndim != 2: + raise ValueError( + "Per-position 'pos' requires 2-D affine buffers; boot with " + "enable_position_interventions=True." + ) + idx = [pos] if isinstance(pos, int) else list(pos) + max_n = scale_buf.shape[0] + bad = [p for p in idx if p < 0 or p >= max_n] + if bad: + raise ValueError(f"Intervention 'pos' {bad} out of range [0, {max_n}).") + if op == "suppress": - scale_buf.zero_() - bias_buf.zero_() + _write_rows(scale_buf, idx, 0.0) + _write_rows(bias_buf, idx, 0.0) return if op == "scale": - scale_buf.fill_(float(spec["factor"])) - bias_buf.zero_() + _write_rows(scale_buf, idx, float(spec["factor"])) + _write_rows(bias_buf, idx, 0.0) return value = torch.as_tensor(spec["value"], device=bias_buf.device, dtype=bias_buf.dtype) + width = bias_buf.shape[-1] # 0-d broadcasts across width (e.g. "shift all dims by 0.5"); width-shaped # writes element-wise (e.g. SAE steering vector). Anything else is an error. - if value.ndim == 0: - bias_buf.fill_(value.item()) - elif value.shape == bias_buf.shape: - bias_buf.copy_(value) - else: + if value.ndim != 0 and value.shape != (width,): raise ValueError( - f"Intervention 'value' must be a scalar or shape {tuple(bias_buf.shape)}; " + f"Intervention 'value' must be a scalar or shape {(width,)}; " f"got shape {tuple(value.shape)}" ) + _write_rows(bias_buf, idx, value.item() if value.ndim == 0 else value) if op == "add": - scale_buf.fill_(1.0) + _write_rows(scale_buf, idx, 1.0) elif op == "set": - scale_buf.zero_() + _write_rows(scale_buf, idx, 0.0) else: raise RuntimeError( f"op {op!r} is in SUPPORTED_OPS but _apply_intervention has no branch for it." From ddc3980f37cec6642aeabde4254c56d8a2ede0ed Mon Sep 17 00:00:00 2001 From: Jonah Larson Date: Wed, 15 Jul 2026 09:59:32 -0500 Subject: [PATCH 10/43] vLLM & Inspect Driver Correctness Improvements (#1515) * Initial bug fixes from sweep * cleanup duplication and comments * vllm extra --- .github/workflows/checks.yml | 6 +- demos/vLLM_Bridge_Integration_Test.ipynb | 2 +- docs/source/content/drivers.md | 120 + docs/source/index.md | 1 + pyproject.toml | 9 + scripts/inspect_parity_report.py | 11 +- scripts/vllm_parity_report.py | 54 +- tests/QUARANTINES.md | 27 + tests/mocks/mock_logits_bridge.py | 77 + .../unit/model_bridge/test_driver_protocol.py | 148 + .../model_bridge/test_hook_set_enforcement.py | 117 + .../unit/model_bridge/test_inspect_driver.py | 269 ++ .../test_inspect_vllm_provider.py | 173 + tests/unit/model_bridge/test_remote_bridge.py | 121 + tests/unit/model_bridge/test_vllm_boot.py | 64 +- tests/unit/model_bridge/test_vllm_driver.py | 236 + tests/unit/test_utils.py | 5 + transformer_lens/model_bridge/bridge_core.py | 102 +- .../model_bridge/driver_protocol.py | 42 +- .../model_bridge/remote_bridge.py | 39 +- .../model_bridge/sources/AGENTS.md | 44 +- .../model_bridge/sources/_bridge_builder.py | 20 +- .../sources/inspect/_provider_base.py | 42 + .../model_bridge/sources/inspect/driver.py | 59 +- .../model_bridge/sources/inspect/eval.py | 9 +- .../model_bridge/sources/inspect/profiles.py | 8 +- .../model_bridge/sources/inspect/source.py | 15 +- .../sources/inspect/transformers_provider.py | 49 +- .../sources/inspect/vllm_provider.py | 70 +- .../sources/transformers/source.py | 12 +- .../sources/transformers_driver.py | 19 + .../model_bridge/sources/vllm/driver.py | 284 +- .../sources/vllm/intervention_specs.py | 87 +- .../sources/vllm/overlays/decoder_only.py | 21 +- .../model_bridge/sources/vllm/source.py | 128 +- .../sources/vllm/worker_extension.py | 13 +- .../supported_architectures/mamba.py | 2 +- .../model_bridge/transformer_bridge.py | 136 +- transformer_lens/utilities/tokenize_utils.py | 7 +- uv.lock | 4044 ++++++++++++++--- 40 files changed, 5655 insertions(+), 1037 deletions(-) create mode 100644 docs/source/content/drivers.md create mode 100644 tests/mocks/mock_logits_bridge.py create mode 100644 tests/unit/model_bridge/test_hook_set_enforcement.py diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml index 393f920ba1..eec59704b7 100644 --- a/.github/workflows/checks.yml +++ b/.github/workflows/checks.yml @@ -102,7 +102,7 @@ jobs: - name: Install dependencies run: | uv lock --check - uv sync + uv sync --extra inspect - name: Authenticate HuggingFace if: env.HF_TOKEN != '' run: uv run python -c "import os; from huggingface_hub import login; login(token=os.environ['HF_TOKEN'])" @@ -141,7 +141,7 @@ jobs: - name: Install dependencies run: | uv lock --check - uv sync + uv sync --extra inspect - name: MPS Availability Check run: | uv run python -c " @@ -283,7 +283,7 @@ jobs: - name: Install dependencies run: | uv lock --check - uv sync + uv sync --extra inspect - name: Authenticate HuggingFace if: env.HF_TOKEN != '' run: uv run python -c "import os; from huggingface_hub import login; login(token=os.environ['HF_TOKEN'])" diff --git a/demos/vLLM_Bridge_Integration_Test.ipynb b/demos/vLLM_Bridge_Integration_Test.ipynb index ade7aa1e2c..21bcd1e883 100644 --- a/demos/vLLM_Bridge_Integration_Test.ipynb +++ b/demos/vLLM_Bridge_Integration_Test.ipynb @@ -42,7 +42,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": "# Install vllm and TransformerLens @ feature/vllm-batched. ~3-5 minutes.\n# Branch must match this notebook: feature/vllm-batched has the hook-fire counter\n# (Step 7) and the batched capture surface (Step 10). feature/driver-system lacks\n# both, so collective_rpc raises NotImplementedError on tl_reset_counter / the\n# batched RPCs.\n#\n# vLLM pinned to 0.20.2 — the version the internal-API walks in\n# transformer_lens/model_bridge/sources/vllm/{plugin,internals}.py were validated\n# against. Installed from the CUDA-12.9 *release wheel*, NOT PyPI's default\n# `vllm==0.20.2`: the PyPI wheel is a CUDA-13 build that fails on Colab's CUDA-12.8\n# image with `libcudart.so.13: cannot open shared object file` and drags in\n# cuda-toolkit 13, which conflicts with Colab's cu12 RAPIDS stack. The cu129 wheel\n# uses libcudart.so.12 (present) and stays in the cu12 family. vLLM has no cu128\n# wheel for 0.20.2; cu129 is CUDA-12.x-compatible with the cu128 runtime.\n# Re-validate the internals walks before bumping vLLM (it moves them every 4-6 weeks).\n%pip install -q \"https://github.com/vllm-project/vllm/releases/download/v0.20.2/vllm-0.20.2%2Bcu129-cp38-abi3-manylinux_2_31_x86_64.whl\"\n%pip install -q git+https://github.com/TransformerLensOrg/TransformerLens.git@feature/vllm-batched", + "source": "# Install vllm and TransformerLens @ dev-4.x. ~3-5 minutes.\n# Branch must match this notebook: dev-4.x has the hook-fire counter (Step 7)\n# and the batched capture surface (Step 10), which collective_rpc reaches via\n# tl_reset_counter / the batched RPCs.\n#\n# vLLM pinned to 0.20.2 — the version the internal-API walks in\n# transformer_lens/model_bridge/sources/vllm/{plugin,internals}.py were validated\n# against. Installed from the CUDA-12.9 *release wheel*, NOT PyPI's default\n# `vllm==0.20.2`: the PyPI wheel is a CUDA-13 build that fails on Colab's CUDA-12.8\n# image with `libcudart.so.13: cannot open shared object file` and drags in\n# cuda-toolkit 13, which conflicts with Colab's cu12 RAPIDS stack. The cu129 wheel\n# uses libcudart.so.12 (present) and stays in the cu12 family. vLLM has no cu128\n# wheel for 0.20.2; cu129 is CUDA-12.x-compatible with the cu128 runtime.\n# Re-validate the internals walks before bumping vLLM (it moves them every 4-6 weeks).\n%pip install -q \"https://github.com/vllm-project/vllm/releases/download/v0.20.2/vllm-0.20.2%2Bcu129-cp38-abi3-manylinux_2_31_x86_64.whl\"\n%pip install -q git+https://github.com/TransformerLensOrg/TransformerLens.git@dev-4.x", "id": "4ab1eb60e6b1" }, { diff --git a/docs/source/content/drivers.md b/docs/source/content/drivers.md new file mode 100644 index 0000000000..c3b66ac2f4 --- /dev/null +++ b/docs/source/content/drivers.md @@ -0,0 +1,120 @@ +# Execution Backends (the Driver System) + +TransformerLens v4 separates *what you study* (the bridge's hook names, cache, and intervention surface) from *what runs the forward pass* (a **Driver**). Every backend — local HuggingFace `transformers`, vLLM, or an `inspect_ai` provider — satisfies the same protocol, so the same hook names work everywhere; what changes is which hooks each backend can fire and whether gradients exist at all. + +This page covers the user-facing surface. The contract lives in [`transformer_lens/model_bridge/driver_protocol.py`](https://github.com/TransformerLensOrg/TransformerLens/blob/dev-4.x/transformer_lens/model_bridge/driver_protocol.py). + +--- + +## The Driver protocol + +A driver is anything that implements: + +```python +def forward(input_ids, *, capture=(), intervene=None, + max_new_tokens=1, return_logits=True, **kwargs) -> ForwardResult +def close() -> None +def supports(feature: str) -> bool +``` + +plus two declared hook-name sets: + +- `supported_hook_points` — canonical bridge hook names this backend can fire (e.g. `blocks.0.hook_out`). +- `non_fireable_hook_points` — names the backend structurally cannot serve (fused kernels, sampler shortcuts). + +`ForwardResult` carries `logits`, a `captured` mapping of hook name → activation, and the engine's `raw_output`. Tensors are native to the driver's framework and converted at the bridge boundary. `validate_driver` checks the contract when a bridge is constructed. + +Interventions come in two dialects: + +- **Callables** (`InterventionFn`) — arbitrary Python hook functions, for drivers that can dispatch Python at the engine boundary (the transformers backend). +- **Declarative specs** (`InterventionSpec`) — plain mappings like `{"op": "suppress"}`, for drivers that can't run Python mid-forward (vLLM under `torch.compile`, remote providers). + +Capability tiers follow from this: **circuit-finding and anything gradient-based runs on the transformers backend; capture and steering scale out on vLLM; both use the same hook names**, so analyses transfer between them. + +--- + +## The three backends + +### transformers — full hooks + gradients + +The reference backend. Wraps a local HF `nn.Module`; the full HookPoint tree fires, backward hooks and gradients work, and `parameters()` / `state_dict()` / weight access are all available. + +```python +from transformer_lens.model_bridge import TransformerBridge + +bridge = TransformerBridge.boot_transformers("gpt2", device="cpu") +logits, cache = bridge.run_with_cache("Hello, world") +``` + +There is also `TransformerBridge.boot_native(config)`, which builds a small randomly-initialized TL-native model on the same driver — no HuggingFace Hub call — useful for tests and toy-model experiments. + +### vLLM — high-throughput capture + declarative interventions + +`RemoteBridge.boot_vllm` constructs a vLLM engine (PagedAttention, `torch.compile`, CUDA graphs) and installs capture hooks inside the worker *before* compilation. Activations come back over `collective_rpc` and replay through the bridge's HookPoint tree. This is the throughput path for SAE/probe data collection; unlike observation-only tools (vllm-lens), each hook also applies an affine transform `output = output * scale + bias`, so declarative interventions (`suppress` / `scale` / `add` / `set`) propagate to downstream layers. + +```python +import torch +from transformer_lens.model_bridge import RemoteBridge + +bridge = RemoteBridge.boot_vllm( + "meta-llama/Llama-3.2-1B", + dtype=torch.float16, + max_model_len=2048, # cap the KV-cache reservation +) +logits, cache = bridge.run_with_cache("Hello, world") + +# Declarative intervention: zero the embedding output for this forward only. +logits2, cache2 = bridge.run_with_cache( + "Hello, world", + intervene={"embed.hook_out": {"op": "suppress"}}, +) +``` + +Notes grounded in the source docstrings ([`sources/vllm/source.py`](https://github.com/TransformerLensOrg/TransformerLens/blob/dev-4.x/transformer_lens/model_bridge/sources/vllm/source.py)): + +- **Fireable hooks** (decoder-only overlay): `embed.hook_out`, `blocks.{i}.hook_out` / `attn.hook_out` / `mlp.hook_out`, `ln_final.hook_normalized`. +- **Returned logits are reconstructed full-sequence logits**: vLLM's sampler bypasses `lm_head`, so the driver rebuilds them host-side as `ln_final @ lm_head.weight.T` (+ bias, + Gemma soft-cap) — valid at every position, so loss works. If the unembedding weight is unreachable it falls back to final-position log-probs and the bridge rejects `return_type="loss"`. +- **Convention alignment**: vLLM materializes `ln_final` *post-weight*, but the driver un-folds the exposed capture (÷ weight, or ÷ (1 + weight) for Gemma) so `ln_final.hook_normalized` matches the pre-weight value `boot_transformers` serves. If the norm weight is unreachable it warns and serves the raw post-weight value. See [`sources/vllm/overlays/decoder_only.py`](https://github.com/TransformerLensOrg/TransformerLens/blob/dev-4.x/transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py) for which hooks diverge from HF conventions. +- `enable_batching=True` switches to the eager batched path (`batch_size > 1`, chunked prefill) for data collection; `enable_position_interventions=True` lets a spec carry a `pos` field (int or list) to scope an edit to specific sequence positions. +- Requires a CUDA GPU. Install with `pip install "transformer-lens[vllm]"` (or `uv sync --extra vllm`). The extra is Linux-only (vLLM ships no macOS/Windows wheels), pins the validated `vllm 0.20.x` band — which in turn pins its matching `torch` — and cannot co-install with the `[lit]` extra (numpy version conflict). See [`sources/vllm/internals.py`](https://github.com/TransformerLensOrg/TransformerLens/blob/dev-4.x/transformer_lens/model_bridge/sources/vllm/internals.py) before bumping the band. + +### Inspect — interp inside `inspect_ai` evals + +`RemoteBridge.boot_inspect` wraps an `inspect_ai` model provider in a bridge, so activation capture and interventions run inside the same harness as behavioral evals. Install with the `inspect` extra (`uv sync --extra inspect`, or `pip install "transformer-lens[inspect]"`). + +```python +from transformer_lens.model_bridge import RemoteBridge + +bridge = RemoteBridge.boot_inspect("HuggingFaceTB/SmolLM2-135M") # provider="tl_bridge" +logits, cache = bridge.run_with_cache("Hello, world") +``` + +From the [`boot_inspect` docstring](https://github.com/TransformerLensOrg/TransformerLens/blob/dev-4.x/transformer_lens/model_bridge/sources/inspect/source.py): + +- The default `tl_bridge` provider is HF-backed: residual/attn/mlp capture, full affine interventions, and full-sequence logits. `tl_bridge_vllm` is the vLLM-backed sibling; `provider="vllm-lens"` targets a running vllm-lens provider (residual-only, additive-steering-only). +- **Fireable hooks** (`tl_bridge`): `blocks.{i}.hook_in` (resid_pre), `ln2.hook_in` (resid_mid), `hook_out` (resid_post), `attn.hook_out`, `mlp.hook_out`, plus head-split `attn.hook_q/k/v` / `attn.hook_z` / `attn.hook_pattern` where a per-model structural self-check finds them. `embed`, `ln_final`, and `attn.hook_attn_scores` are always non-fireable — use `boot_transformers()` for those. +- For parity with `boot_transformers`, the provider loads with the same dtype (fp32 by default) and eager attention. +- For capture *during an eval*, add the `capture_activations([...])` solver from [`sources/inspect/eval.py`](https://github.com/TransformerLensOrg/TransformerLens/blob/dev-4.x/transformer_lens/model_bridge/sources/inspect/eval.py) to a Task's solver chain: full activations go to per-sample `.npz` artifacts, and a compact summary lands in the sample store for `samples_df` analysis. + +--- + +## Fundamental limits + +Serving engines are not autograd engines. On the vLLM backend (and any future serving backend): + +- **No gradients.** PagedAttention and the compiled graph have no autograd surface; backward hooks, attribution patching, and anything gradient-based need `boot_transformers`. +- **No attention patterns or scores.** The QKᵀ → softmax path is fused into the attention kernel (`attn.hook_pattern` / `attn.hook_attn_scores` are declared non-fireable, along with pre/post-RoPE Q/K). +- **Interventions are declarative only.** Arbitrary Python hook functions cannot run inside the compiled worker; the spec vocabulary (`suppress` / `scale` / `add` / `set`, optional `pos`) is the intervention surface. + +The Inspect `tl_bridge` provider is HF-backed, so its captures are numerically faithful to `boot_transformers` — but the driver surface is capture/intervene over a wire format, not a local module: no gradients or weight mutation through the bridge. + +--- + +## Verifying parity + +Two scripts diff each remote backend against `boot_transformers` on real models, comparing every hook the driver claims to serve: + +- `uv run python scripts/vllm_parity_report.py` — GPU-only; boots both backends in fp32 and diffs all fireable capture points (with a refold diagnostic that distinguishes an `ln_final` un-fold regression from a mapping error), plus an argmax agreement check. +- `uv run python scripts/inspect_parity_report.py` — validates the provider's structural self-check: every boundary it offers must match `boot_transformers`. + +`demos/vLLM_Bridge_Integration_Test.ipynb` is the end-to-end GPU validation of the vLLM capture *and mutation* path (a manual Colab run, not CI). diff --git a/docs/source/index.md b/docs/source/index.md index b10bd3a9dd..7c5253e336 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -55,6 +55,7 @@ content/tutorials content/citation content/contributing content/hook_system +content/drivers content/compatibility_mode content/debugging_numerical_divergence generated/demos/Main_Demo diff --git a/pyproject.toml b/pyproject.toml index 66a141d886..771176d1ae 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,12 @@ evals=["lm-eval>=0.4", "chardet<6"] inspect=["inspect_ai>=0.3"] lit=["lit-nlp>=1.3"] + # The vLLM driver's validated version band (see sources/vllm/internals.py before + # bumping). Linux-only marker: vLLM ships no macOS/Windows wheels and the driver + # is GPU-only anyway — elsewhere `[vllm]` is a clean no-op instead of a failed + # source build. Declared conflicting with `lit` in [tool.uv] (vllm's transitive + # numpy>=2 vs lit-nlp's numpy<2 cap). + vllm=["vllm>=0.20.2,<0.21; sys_platform == 'linux'"] [project.scripts] build-docs="docs.make_docs:build_docs" @@ -100,6 +106,9 @@ [tool.uv] default-groups=["dev", "docs", "jupyter", "multimodal"] + # vllm and lit cannot co-install (numpy>=2 vs numpy<2); forking the resolution + # lets each extra lock cleanly on its own. + conflicts=[[{extra="vllm"}, {extra="lit"}]] [tool.pytest] diff --git a/scripts/inspect_parity_report.py b/scripts/inspect_parity_report.py index 9a6083ca8f..89cd868b03 100644 --- a/scripts/inspect_parity_report.py +++ b/scripts/inspect_parity_report.py @@ -5,8 +5,9 @@ are correctly withheld and reported, not compared. Validates that self-check against real models: everything offered must match boot_transformers; it is not a per-PR CI job. -Run: uv run python scripts/inspect_parity_report.py -Override the model list with TL_PARITY_MODELS="id1,id2,...". +Run: uv run python scripts/inspect_parity_report.py +Env: TL_PARITY_MODELS="id1,id2,..." overrides the model list. + TL_INSPECT_ATOL / TL_INSPECT_RTOL override tolerance (defaults 1e-3). """ from __future__ import annotations @@ -57,7 +58,8 @@ ] PROMPT = "The quick brown fox" -ATOL, RTOL = 1e-3, 1e-3 +ATOL = float(os.environ.get("TL_INSPECT_ATOL", "1e-3")) +RTOL = float(os.environ.get("TL_INSPECT_RTOL", "1e-3")) # Kind -> TransformerBridge-native hook suffix: the five d_model boundaries plus the @@ -163,7 +165,8 @@ def main() -> None: passed = sorted({r["arch"] for r in rows if r["status"] == "PASS"}) print("\nPARITY-VERIFIED ARCHITECTURES (measured PASS):") print(" " + ", ".join(passed)) - sys.exit(0) + # Non-zero exit if anything actually ran and failed — lets a scripted run gate. + sys.exit(1 if any(r["status"] == "FAIL" for r in rows) else 0) if __name__ == "__main__": diff --git a/scripts/vllm_parity_report.py b/scripts/vllm_parity_report.py index bad526806d..1abf934774 100644 --- a/scripts/vllm_parity_report.py +++ b/scripts/vllm_parity_report.py @@ -7,10 +7,10 @@ - ``embed.hook_out`` and per-layer ``blocks.{i}.hook_out`` / ``attn.hook_out`` / ``mlp.hook_out`` are semantically identical to boot_transformers and compared directly. -- ``ln_final.hook_normalized`` carries vLLM's POST-weight RMSNorm value under a hook - whose HT convention is PRE-weight; it is un-folded (÷ weight, or ÷ (1+weight) for - Gemma) before comparison. The raw (un-un-folded) diff is also reported so a wrong - un-fold direction is visible rather than silent. +- ``ln_final.hook_normalized`` is un-folded to the pre-weight convention by the driver + itself (÷ weight, or ÷ (1+weight) for Gemma), so it is compared directly too; a + driver-side un-fold regression shows up as an O(1) mismatch on this hook. The + re-folded diff is also reported so a wrong un-fold direction is distinguishable. Non-fireable points (fused attention pattern/scores/rope, unembed) are withheld and reported, not compared. A final-position argmax agreement check sanity-checks logits. @@ -75,14 +75,13 @@ def _diff(a: torch.Tensor, b: torch.Tensor) -> tuple[float, bool]: return d, torch.allclose(a2, b2, atol=ATOL, rtol=RTOL) -def _unfold_lnf(vllm_val: torch.Tensor, weight: torch.Tensor, is_gemma: bool) -> torch.Tensor: - """vLLM ln_final is post-weight (x·rsqrt(var+eps)·w); recover the pre-weight value HT - exposes by dividing out the norm weight (Gemma folds 1 + weight).""" +def _refold_lnf(vllm_val: torch.Tensor, weight: torch.Tensor, is_gemma: bool) -> torch.Tensor: + """Re-apply the norm weight to the driver's (pre-weight) ln_final capture — used only + as a diagnostic so a wrong un-fold direction in the driver reads as 'refolded matches' + instead of an opaque mismatch.""" w = weight.detach().to(vllm_val.device, torch.float32) - denom = (1.0 + w) if is_gemma else w - # Guard against near-zero weight entries producing spurious blow-ups. - denom = torch.where(denom.abs() < 1e-6, torch.ones_like(denom), denom) - return vllm_val.to(torch.float32) / denom + factor = (1.0 + w) if is_gemma else w + return vllm_val.to(torch.float32) * factor def verify(model_id: str) -> dict: @@ -128,24 +127,25 @@ def verify(model_id: str) -> dict: if not ok: mism.append(f"{hk} maxdiff={d:.2e}") - # ln_final: compare un-folded; also report the raw diff so a wrong un-fold shows. + # ln_final: the driver already un-folds to the pre-weight convention — compare + # directly. The refolded diff is diagnostic: "direct FAIL + refolded ok" means + # the driver's un-fold regressed (serving raw post-weight values again). if LNF_HOOK in offered and LNF_HOOK in v_cache and LNF_HOOK in hf_cache: - raw_d, raw_ok = _diff(hf_cache[LNF_HOOK], v_cache[LNF_HOOK]) - weight = vllm._driver.get_param("model.norm.weight") - if weight is None: - notes.append(f"ln_final raw={raw_d:.2e} (no norm weight to un-fold)") - if not raw_ok: - mism.append(f"{LNF_HOOK} maxdiff={raw_d:.2e} (un-fold unavailable)") + d, ok = _diff(hf_cache[LNF_HOOK], v_cache[LNF_HOOK]) + worst = max(worst, d if d != float("inf") else worst) + if ok: + notes.append(f"ln_final direct={d:.2e}") else: - unfolded = _unfold_lnf(v_cache[LNF_HOOK], weight, is_gemma) - uf_d, uf_ok = _diff(hf_cache[LNF_HOOK], unfolded) - worst = max(worst, uf_d if uf_d != float("inf") else worst) - notes.append(f"ln_final unfold={uf_d:.2e} raw={raw_d:.2e}") - if not uf_ok: - mism.append(f"{LNF_HOOK} unfold maxdiff={uf_d:.2e} (raw={raw_d:.2e})") - - # Logit sanity: vLLM synthesizes final-position log-probs only; check top-1 agrees - # (reusing the run_with_cache logits above — no second forward). + weight = vllm._driver.get_param("model.norm.weight") + detail = f"{LNF_HOOK} maxdiff={d:.2e}" + if weight is not None: + refolded = _refold_lnf(v_cache[LNF_HOOK], weight, is_gemma) + rf_d, rf_ok = _diff(hf_cache[LNF_HOOK], refolded) + detail += f" (refolded={rf_d:.2e}{', un-fold regressed' if rf_ok else ''})" + mism.append(detail) + + # Logit sanity: the driver reconstructs full-sequence logits host-side; check the + # final-position top-1 agrees (reusing the run_with_cache logits — no second forward). try: hf_top = int(torch.as_tensor(hf_logits)[0, -1].argmax()) v_top = int(torch.as_tensor(v_logits)[0, -1].argmax()) diff --git a/tests/QUARANTINES.md b/tests/QUARANTINES.md index 5a587ea38e..47f03ca50b 100644 --- a/tests/QUARANTINES.md +++ b/tests/QUARANTINES.md @@ -19,6 +19,33 @@ Rule ([AGENTS.md §10](../AGENTS.md#10-hard-rules)): **never add `xfail` / `skip --- +## Optional dependency — gated but installed in CI + +`inspect_ai` lives in the `[inspect]` extra. The CI test jobs (`compatibility-checks`, `coverage-test`, `mps-checks`) install it via `uv sync --extra inspect` in [`checks.yml`](../.github/workflows/checks.yml), so these run on every CI push — before that change they silently skipped on every job. + +| Path | Marker | Trigger | +|---|---|---| +| [`unit/model_bridge/test_inspect_driver.py`:19](unit/model_bridge/test_inspect_driver.py) (whole file) | `importorskip("inspect_ai")` | `uv sync --extra inspect` | +| [`unit/model_bridge/test_inspect_vllm_provider.py`:19](unit/model_bridge/test_inspect_vllm_provider.py) (whole file) | `importorskip("inspect_ai")` | same | +| [`acceptance/model_bridge/test_inspect_provider.py`:19](acceptance/model_bridge/test_inspect_provider.py) (whole file) | `pytestmark skipif(inspect_ai missing)` | same | +| [`../transformer_lens/model_bridge/sources/inspect/conftest.py`](../transformer_lens/model_bridge/sources/inspect/conftest.py) | `collect_ignore_glob` (doctest-modules of the provider files) | same | + +**Un-skip:** already un-skipped in CI. A local plain `uv sync` still skips them; install the extra. + +--- + +## ⚠️ Coverage gap — no automated lane (real vLLM) + +| Path | Marker | Trigger | +|---|---|---| +| [`unit/model_bridge/test_vllm_driver.py`](unit/model_bridge/test_vllm_driver.py) (×15) | `importorskip("vllm")` per-test | `uv sync --extra vllm` on a Linux CUDA machine (validated band: `vllm 0.20.x`) | + +A `[vllm]` extra exists (Linux-only marker; declared conflicting with `[lit]` in `[tool.uv]` — vllm needs `numpy>=2` via `opencv-python-headless` while `lit-nlp` caps `numpy<2`), but CI does not install it: vllm is GPU-only and its 15 real-engine tests would not pass on CPU runners (the file's other tests mock the LLM and run everywhere). Note the extra's `vllm 0.20.x` band exact-pins `torch==2.11.0`, which is what the project lockfile resolves to. The real-engine execution path is otherwise covered only by the manual GPU run of [`demos/vLLM_Bridge_Integration_Test.ipynb`](../demos/vLLM_Bridge_Integration_Test.ipynb). + +**Un-skip:** a GPU CI lane that installs vllm, or locally on a CUDA machine with `vllm==0.20.2` installed alongside the project env. + +--- + ## Permanent — hardware requirement | Path | Marker | Required | diff --git a/tests/mocks/mock_logits_bridge.py b/tests/mocks/mock_logits_bridge.py new file mode 100644 index 0000000000..2ff8e9829e --- /dev/null +++ b/tests/mocks/mock_logits_bridge.py @@ -0,0 +1,77 @@ +"""Minimal one-block TransformerBridge over a stub torch model + logits-returning driver.""" +from __future__ import annotations + +from types import SimpleNamespace +from unittest.mock import MagicMock + +import torch +from torch import nn + +from tests.mocks.architecture_adapter import MockArchitectureAdapter +from transformer_lens.config import TransformerBridgeConfig +from transformer_lens.model_bridge.bridge import TransformerBridge +from transformer_lens.model_bridge.driver_protocol import ForwardResult +from transformer_lens.model_bridge.generalized_components import ( + AttentionBridge, + BlockBridge, + NormalizationBridge, +) +from transformer_lens.model_bridge.sources._driver_base import DriverBase + + +def mock_bridge_cfg() -> TransformerBridgeConfig: + return TransformerBridgeConfig( + d_model=4, + d_head=2, + n_layers=1, + n_ctx=8, + n_heads=2, + d_vocab=16, + d_mlp=8, + architecture="Mock", + ) + + +class LogitsDriver(DriverBase): + """Returns real logits so post-hook forward paths complete.""" + + def __init__(self, model, cfg, tokenizer=None): + super().__init__(cfg, tokenizer) + # forward()'s encoder-decoder probe reads bridge.original_model. + self.underlying_model = model + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult(logits=torch.randn(1, 3, 16)) + + +def build_mock_logits_bridge(driver_cls=LogitsDriver) -> TransformerBridge: + """Encoder-shaped stub bridge (ln_final + one block with ln1/attn).""" + model = nn.Module() + model.final_norm = nn.LayerNorm(10) + model.encoder = nn.Module() + model.encoder.layers = nn.ModuleList([nn.Module() for _ in range(1)]) + model.encoder.layers[0].norm1 = nn.LayerNorm(10) + model.encoder.layers[0].self_attn = nn.Module() + + adapter = MockArchitectureAdapter() + adapter.component_mapping = { + "ln_final": NormalizationBridge(name="final_norm", config={}), + "blocks": BlockBridge( + name="encoder.layers", + submodules={ + "ln1": NormalizationBridge(name="norm1", config={}), + "attn": AttentionBridge(name="self_attn", config=SimpleNamespace(n_heads=1)), + }, + ), + } + driver = driver_cls(model, mock_bridge_cfg(), tokenizer=None) + return TransformerBridge(model, adapter, tokenizer=MagicMock(), driver=driver) diff --git a/tests/unit/model_bridge/test_driver_protocol.py b/tests/unit/model_bridge/test_driver_protocol.py index e0b652b989..2f796f2f06 100644 --- a/tests/unit/model_bridge/test_driver_protocol.py +++ b/tests/unit/model_bridge/test_driver_protocol.py @@ -309,6 +309,90 @@ def forward( with pytest.raises(TypeError, match="empty supported_hook_points AND"): validate_driver(driver, after_bridge_construction=True) + def test_unknown_feature_strings_rejected(self): + """A driver declaring a feature string outside KNOWN_FEATURES is a contract bug.""" + from transformer_lens.model_bridge.sources._driver_base import DriverBase + + class MadeUpFeatures(DriverBase): + _supported_features = frozenset({"generate_streaming"}) + + def __init__(self, cfg): + super().__init__(cfg, tokenizer=None) + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult() + + driver = MadeUpFeatures(self._cfg()) + with pytest.raises(TypeError, match="unknown feature strings"): + validate_driver(driver) + + def test_non_bool_provides_sequence_logits_rejected(self): + """getattr-with-True-default would silently pick the unsafe value.""" + from transformer_lens.model_bridge.sources._driver_base import DriverBase + + class StringyFlag(DriverBase): + provides_sequence_logits = "yes" # type: ignore[assignment] + + def __init__(self, cfg): + super().__init__(cfg, tokenizer=None) + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult() + + driver = StringyFlag(self._cfg()) + with pytest.raises(TypeError, match="provides_sequence_logits must be bool"): + validate_driver(driver) + + def test_missing_provides_sequence_logits_rejected(self): + """Duck-typed drivers (no DriverBase) can't silently default to unsafe True.""" + cfg = self._cfg() + + class DuckDriver: + architecture = "Duck" + bridge_config = cfg + tokenizer = None + supported_hook_points = frozenset({"x"}) + non_fireable_hook_points = frozenset() + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult() + + def close(self): + pass + + def supports(self, feature): + return False + + with pytest.raises(TypeError, match="provides_sequence_logits"): + validate_driver(DuckDriver()) + def test_bridge_accepts_kw_only_input_ids_driver(self): """Bridge passes input_ids by keyword so kw-only drivers don't TypeError.""" from types import SimpleNamespace @@ -458,6 +542,70 @@ def test_hf_driver_passes_post_construction_check(self): validate_driver(bridge._driver, after_bridge_construction=True) +class TestTransformersDriverDialect: + """HF backend serves hooks via HookPoints, not driver.forward args — + spec-dialect args must fail loud, not silently no-op.""" + + def _driver(self): + from transformer_lens.model_bridge.sources.transformers_driver import ( + TransformersDriver, + ) + + return TransformersDriver(nn.Linear(4, 4), _stub_adapter(), tokenizer=None) + + def test_capture_rejected(self): + with pytest.raises(NotImplementedError, match="capture"): + self._driver().forward(torch.randn(1, 4), capture=("blocks.0.hook_out",)) + + def test_intervene_rejected(self): + with pytest.raises(NotImplementedError, match="intervene"): + self._driver().forward( + torch.randn(1, 4), intervene={"blocks.0.hook_out": {"op": "suppress"}} + ) + + def test_max_new_tokens_rejected(self): + with pytest.raises(NotImplementedError, match="generate"): + self._driver().forward(torch.randn(1, 4), max_new_tokens=4) + + def test_plain_forward_still_works(self): + result = self._driver().forward(torch.randn(2, 4)) + assert isinstance(result.logits, torch.Tensor) + + +class TestLossGateOnTorchBridge: + """Final-position-only drivers must refuse loss on TransformerBridge too, + not just RemoteBridge — silent NaN loss otherwise.""" + + def _build_bridge(self, provides_sequence_logits: bool): + from tests.mocks.mock_logits_bridge import ( + LogitsDriver, + build_mock_logits_bridge, + ) + + psl = provides_sequence_logits + + class FinalPositionDriver(LogitsDriver): + provides_sequence_logits = psl + + return build_mock_logits_bridge(driver_cls=FinalPositionDriver) + + def test_loss_refused_when_sequence_logits_unavailable(self): + bridge = self._build_bridge(provides_sequence_logits=False) + tokens = torch.tensor([[1, 2, 3]]) + with pytest.raises(NotImplementedError, match="final position only"): + bridge.forward(tokens, return_type="loss") + with pytest.raises(NotImplementedError, match="final position only"): + bridge.forward(tokens, return_type="both") + # Logits path is unaffected. + logits = bridge.forward(tokens, return_type="logits") + assert isinstance(logits, torch.Tensor) + + def test_loss_allowed_when_sequence_logits_available(self): + bridge = self._build_bridge(provides_sequence_logits=True) + loss = bridge.forward(torch.tensor([[1, 2, 3]]), return_type="loss") + assert isinstance(loss, torch.Tensor) and loss.dim() == 0 + + class TestBridgeConsumesCaptures: """Bridge replays ForwardResult.captured through its HookPoint tree.""" diff --git a/tests/unit/model_bridge/test_hook_set_enforcement.py b/tests/unit/model_bridge/test_hook_set_enforcement.py new file mode 100644 index 0000000000..e4d52794b3 --- /dev/null +++ b/tests/unit/model_bridge/test_hook_set_enforcement.py @@ -0,0 +1,117 @@ +"""Hook-set contract enforcement on the torch bridge: typo'd or backend-unservable +hook names must raise instead of silently running unhooked / caching nothing.""" +from __future__ import annotations + +import pytest +import torch + +from tests.mocks.mock_logits_bridge import build_mock_logits_bridge as _build_bridge + + +class TestUnknownHookNamesFailLoud: + def test_run_with_hooks_typo_raises_keyerror(self): + bridge = _build_bridge() + with pytest.raises(KeyError, match="does not exist"): + bridge.run_with_hooks( + torch.tensor([[1, 2, 3]]), + fwd_hooks=[("blocks.0.hook_definitely_typo", lambda act, hook: None)], + ) + + def test_hooks_context_typo_raises_keyerror(self): + bridge = _build_bridge() + with pytest.raises(KeyError, match="does not exist"): + with bridge.hooks(fwd_hooks=[("blocks.0.hook_definitely_typo", lambda a, hook: None)]): + pass + + def test_run_with_hooks_valid_name_still_attaches(self): + bridge = _build_bridge() + target = next(iter(bridge._hook_registry)) + seen: list = [] + bridge.run_with_hooks( + torch.tensor([[1, 2, 3]]), + fwd_hooks=[(target, lambda act, hook: seen.append(hook.name))], + ) + # The mock model never routes tensors through HookPoints, so the hook + # can't fire — the assertion is that attach succeeded without raising + # and was cleaned up. + assert len(bridge._hook_registry[target].fwd_hooks) == 0 + + +class TestRunWithCacheFilterEnforcement: + def test_string_filter_matching_nothing_raises(self): + bridge = _build_bridge() + with pytest.raises(KeyError, match="matched no hook points"): + bridge.run_with_cache(torch.tensor([[1, 2, 3]]), names_filter="no.such.hook") + + def test_list_filter_matching_nothing_raises(self): + bridge = _build_bridge() + with pytest.raises(KeyError, match="matched no hook points"): + bridge.run_with_cache( + torch.tensor([[1, 2, 3]]), names_filter=["no.such.hook", "also.not.real"] + ) + + def test_callable_filter_matching_nothing_is_allowed(self): + """Programmatic filters legitimately produce empty intersections.""" + bridge = _build_bridge() + _, cache = bridge.run_with_cache(torch.tensor([[1, 2, 3]]), names_filter=lambda name: False) + assert len(cache) == 0 + + def test_no_filter_caches_everything_without_raising(self): + bridge = _build_bridge() + out, cache = bridge.run_with_cache(torch.tensor([[1, 2, 3]])) + assert out is not None + + def test_partially_matching_list_filter_is_allowed(self): + """One valid name in a list keeps the run alive (matches HT leniency).""" + bridge = _build_bridge() + target = next(iter(bridge._hook_registry)) + _, cache = bridge.run_with_cache( + torch.tensor([[1, 2, 3]]), names_filter=[target, "no.such.hook"] + ) + assert cache is not None + + +class TestNonFireableEnforcement: + def test_add_hook_on_non_fireable_raises(self): + bridge = _build_bridge() + sacrificed = next(iter(bridge._hook_registry)) + bridge._driver.non_fireable_hook_points = frozenset({sacrificed}) + with pytest.raises(NotImplementedError, match="cannot fire"): + bridge.add_hook(sacrificed, lambda act, hook: None) + + def test_run_with_hooks_on_non_fireable_raises(self): + bridge = _build_bridge() + sacrificed = next(iter(bridge._hook_registry)) + bridge._driver.non_fireable_hook_points = frozenset({sacrificed}) + with pytest.raises(NotImplementedError, match="boot_transformers"): + bridge.run_with_hooks( + torch.tensor([[1, 2, 3]]), + fwd_hooks=[(sacrificed, lambda act, hook: None)], + ) + + def test_run_with_cache_on_non_fireable_raises(self): + bridge = _build_bridge() + sacrificed = next(iter(bridge._hook_registry)) + bridge._driver.non_fireable_hook_points = frozenset({sacrificed}) + with pytest.raises(NotImplementedError, match="cannot fire"): + bridge.run_with_cache(torch.tensor([[1, 2, 3]]), names_filter=sacrificed) + + +class TestAddHookAliasResolution: + def test_add_hook_accepts_registry_alias(self): + """Aliases visible in hook_dict must attach via add_hook, same as run_with_hooks.""" + bridge = _build_bridge() + # Find an alias: a hook_dict key whose HookPoint carries a different canonical name. + alias = next( + ( + name + for name, hp in bridge.hook_dict.items() + if hp.name is not None and hp.name != name + ), + None, + ) + if alias is None: + pytest.skip("mock bridge registered no aliases") + canonical = bridge.hook_dict[alias].name + bridge.add_hook(alias, lambda act, hook: None) + assert len(bridge._hook_registry[canonical].fwd_hooks) == 1 diff --git a/tests/unit/model_bridge/test_inspect_driver.py b/tests/unit/model_bridge/test_inspect_driver.py index 1ee943f37a..47909c6888 100644 --- a/tests/unit/model_bridge/test_inspect_driver.py +++ b/tests/unit/model_bridge/test_inspect_driver.py @@ -541,6 +541,275 @@ def test_turn_activations_batch_dim_is_rank_aware(self): assert turn["blocks.0.attn.hook_pattern"].shape == (1, 2, 3, 3) +class TestHeterogeneousLayers: + """Hybrid attn/SSM stacks: capability detection probes layer 0 only, so a layer + missing the targeted submodule must fail loud instead of installing nothing.""" + + def _provider(self): + import torch.nn as nn + + from transformer_lens.model_bridge.sources.inspect.transformers_provider import ( + TransformerLensTransformersModelAPI, + ) + + class AttnBlock(nn.Module): + def __init__(self): + super().__init__() + self.self_attn = nn.Linear(4, 4) + self.mlp = nn.Linear(4, 4) + + class SSMBlock(nn.Module): # no attention / MLP submodule + def __init__(self): + super().__init__() + self.mixer = nn.Linear(4, 4) + + api = object.__new__(TransformerLensTransformersModelAPI) + api._layers = nn.ModuleList([AttnBlock(), SSMBlock()]) + api._geometry = (2, 2, 2) + return api + + def test_capture_on_layer_missing_attn_raises(self): + with pytest.raises(RuntimeError, match="no attention submodule"): + self._provider()._install_hooks({1: {"attn_out"}}, {}, {}, "cid") + + def test_intervention_on_layer_missing_attn_raises(self): + with pytest.raises(RuntimeError, match="no attention submodule"): + self._provider()._install_hooks({}, {1: {"attn_out": {"op": "suppress"}}}, {}, "cid") + + def test_capture_on_layer_missing_mlp_raises(self): + with pytest.raises(RuntimeError, match="no MLP submodule"): + self._provider()._install_hooks({1: {"mlp_out"}}, {}, {}, "cid") + + def test_head_kind_on_layer_missing_attn_raises(self): + with pytest.raises(RuntimeError, match="no attention submodule"): + self._provider()._install_hooks({1: {"q"}}, {}, {}, "cid") + + def test_layer_with_modules_still_installs(self): + handles = self._provider()._install_hooks({0: {"attn_out", "mlp_out"}}, {}, {}, "cid") + assert len(handles) == 2 + for handle in handles: + handle.remove() + + def test_resid_kinds_on_ssm_layer_still_install(self): + # Block in/out boundaries exist on every layer; no attn/mlp needed. + handles = self._provider()._install_hooks({1: {"resid_pre", "resid_post"}}, {}, {}, "cid") + assert len(handles) == 2 + for handle in handles: + handle.remove() + + +class TestTimeoutRecovery: + def test_timeout_abandons_wedged_loop_and_rebuilds(self, monkeypatch): + """A hung provider forward blocks the loop thread; after the TimeoutError the + driver must serve later forwards on a fresh loop, not queue behind the wedge.""" + import threading + + import transformer_lens.model_bridge.sources.inspect.driver as drv_mod + + monkeypatch.setattr(drv_mod, "_PROVIDER_TIMEOUT_S", 0.2) + + release = threading.Event() + calls = {"n": 0} + metadata = {"tl_logits": wire.encode_array(np.zeros((1, D_VOCAB), np.float32))} + output = SimpleNamespace(metadata=metadata) + + async def generate(_input, config=None): + calls["n"] += 1 + if calls["n"] == 1: + release.wait(30) # sync-block the loop thread, like a hung torch forward + return output + + driver = _driver(SimpleNamespace(generate=generate)) + try: + with pytest.warns(UserWarning, match="abandon"): + with pytest.raises(TimeoutError, match="exceeded"): + driver.forward(torch.tensor([[1]])) + wedged = driver._loop is None # poisoned state cleared for rebuild + assert wedged + result = driver.forward(torch.tensor([[1]])) # fresh loop, must not time out + assert isinstance(result, ForwardResult) + assert calls["n"] == 2 + finally: + release.set() # let the abandoned daemon thread finish + + def test_ensure_loop_is_thread_safe(self): + import threading + + driver = _driver() + loops: list = [] + barrier = threading.Barrier(8) + + def worker(): + barrier.wait() + loops.append(driver._ensure_loop()) + + threads = [threading.Thread(target=worker) for _ in range(8)] + for t in threads: + t.start() + for t in threads: + t.join() + assert len({id(loop) for loop in loops}) == 1 + driver.close() + + +class TestForProvider: + def test_known_providers_resolve(self): + assert isinstance(profiles.for_provider("vllm-lens"), profiles.VLLMLensProfile) + assert isinstance(profiles.for_provider("vllm-lens-v2"), profiles.VLLMLensProfile) + assert isinstance(profiles.for_provider("tl_bridge"), profiles.TLBridgeProfile) + assert isinstance(profiles.for_provider("tl_bridge_vllm"), profiles.TLBridgeProfile) + + def test_unknown_provider_raises(self): + # Falling back to the full-capability codec would boot then NaN downstream. + with pytest.raises(ValueError, match="tl_bridge.*vllm-lens"): + profiles.for_provider("hf") + + +class TestEvalArtifactEpochs: + def test_epoch_in_artifact_filename(self, tmp_path, monkeypatch): + """Multi-epoch evals reuse sample_ids; artifacts must not overwrite across epochs.""" + import asyncio + + from transformer_lens.model_bridge.sources.inspect import eval as eval_mod + + payload = wire.encode_activations({"0:resid_post": np.ones((2, D_MODEL), np.float32)}) + output = SimpleNamespace(metadata={"activations": payload}) + + async def generate(_messages, config=None): + return output + + monkeypatch.setattr(eval_mod, "get_model", lambda: SimpleNamespace(generate=generate)) + stored: dict = {} + monkeypatch.setattr( + eval_mod, + "store", + lambda: SimpleNamespace(set=lambda k, v: stored.__setitem__(k, v)), + ) + + solve = eval_mod.capture_activations(["blocks.0.hook_out"], output_dir=str(tmp_path)) + for epoch in (1, 2): + state = SimpleNamespace(messages=[], sample_id="s1", epoch=epoch) + asyncio.run(solve(state, None)) + assert sorted(p.name for p in tmp_path.iterdir()) == [ + "s1_epoch1.npz", + "s1_epoch2.npz", + ] + assert stored["tl_activations_path"].endswith("s1_epoch2.npz") + + +class TestHFProviderGenerateConfig: + """stop_seqs wiring + warn-once for unsupported GenerateConfig fields (HF provider).""" + + def _api(self): + from transformer_lens.model_bridge.sources.inspect.transformers_provider import ( + TransformerLensTransformersModelAPI, + ) + + api = object.__new__(TransformerLensTransformersModelAPI) + api.model_name = "fake" + api._device = "cpu" + api._eval_capture = {} + + def tokenize(text, **kw): + return SimpleNamespace(input_ids=[ord(c) % 50 for c in text]) + + tok = SimpleNamespace( + chat_template=None, pad_token_id=0, eos_token_id=1, decode=lambda ids, **kw: "x" + ) + from unittest.mock import MagicMock + + callable_tok = MagicMock(wraps=tok) + callable_tok.side_effect = tokenize + callable_tok.chat_template = None + callable_tok.pad_token_id = 0 + callable_tok.eos_token_id = 1 + callable_tok.decode = tok.decode + api._tokenizer = callable_tok + + hf = MagicMock() + hf.generate.return_value = SimpleNamespace( + sequences=torch.tensor([[5, 6, 65]]), scores=None + ) + api._hf = hf + return api + + def _config(self, **overrides): + from inspect_ai.model import GenerateConfig + + return GenerateConfig(**overrides) + + def test_stop_seqs_wired_to_hf_generate(self): + api = self._api() + api._generate_eval("hi", self._config(max_tokens=1, stop_seqs=["END"]), []) + gen_kwargs = api._hf.generate.call_args.kwargs + assert gen_kwargs["stop_strings"] == ["END"] + assert gen_kwargs["tokenizer"] is api._tokenizer + + def test_unsupported_config_field_warns_once(self, monkeypatch): + import warnings as warnings_mod + + from transformer_lens.model_bridge.sources.inspect import _provider_base + + monkeypatch.setattr(_provider_base, "_WARNED_UNSUPPORTED", set()) + api = self._api() + with pytest.warns(UserWarning, match="frequency_penalty"): + api._generate_eval("hi", self._config(max_tokens=1, frequency_penalty=0.5), []) + with warnings_mod.catch_warnings(record=True) as rec: + warnings_mod.simplefilter("always") + api._generate_eval("hi", self._config(max_tokens=1, frequency_penalty=0.5), []) + assert not [w for w in rec if "frequency_penalty" in str(w.message)] + + +class TestParityScript: + SCRIPT = pathlib.Path(__file__).resolve().parents[3] / "scripts" / "inspect_parity_report.py" + + def _load(self, monkeypatch, env=None): + import importlib.util + import uuid + + for key, value in (env or {}).items(): + monkeypatch.setenv(key, value) + spec = importlib.util.spec_from_file_location( + f"inspect_parity_{uuid.uuid4().hex}", self.SCRIPT + ) + module = importlib.util.module_from_spec(spec) + assert spec.loader is not None + spec.loader.exec_module(module) + return module + + def test_tolerance_env_overrides(self, monkeypatch): + module = self._load(monkeypatch, {"TL_INSPECT_ATOL": "5e-2", "TL_INSPECT_RTOL": "7e-2"}) + assert module.ATOL == 5e-2 and module.RTOL == 7e-2 + + def test_default_tolerances(self, monkeypatch): + monkeypatch.delenv("TL_INSPECT_ATOL", raising=False) + monkeypatch.delenv("TL_INSPECT_RTOL", raising=False) + module = self._load(monkeypatch) + assert module.ATOL == 1e-3 and module.RTOL == 1e-3 + + def test_exit_code_1_on_fail(self, monkeypatch, capsys): + module = self._load(monkeypatch, {"TL_PARITY_MODELS": "m1,m2"}) + rows = { + "m1": {"model": "m1", "arch": "A", "status": "PASS", "detail": ""}, + "m2": {"model": "m2", "arch": "B", "status": "FAIL", "detail": "diff"}, + } + monkeypatch.setattr(module, "verify", lambda m: rows[m]) + with pytest.raises(SystemExit) as excinfo: + module.main() + assert excinfo.value.code == 1 + + def test_exit_code_0_on_pass_and_skip(self, monkeypatch, capsys): + module = self._load(monkeypatch, {"TL_PARITY_MODELS": "m1,m2"}) + rows = { + "m1": {"model": "m1", "arch": "A", "status": "PASS", "detail": ""}, + "m2": {"model": "m2", "arch": "B", "status": "SKIP", "detail": "gated"}, + } + monkeypatch.setattr(module, "verify", lambda m: rows[m]) + with pytest.raises(SystemExit) as excinfo: + module.main() + assert excinfo.value.code == 0 + + def test_driver_imports_no_torch(): """The acceptance gate: the driver file must not import torch (data-only boundary).""" import transformer_lens.model_bridge.sources.inspect.driver as drv diff --git a/tests/unit/model_bridge/test_inspect_vllm_provider.py b/tests/unit/model_bridge/test_inspect_vllm_provider.py index 921c8e6fd8..995d23d199 100644 --- a/tests/unit/model_bridge/test_inspect_vllm_provider.py +++ b/tests/unit/model_bridge/test_inspect_vllm_provider.py @@ -259,6 +259,35 @@ def test_generate_eval_with_logprobs(self, monkeypatch): assert lp.content[0].logprob == -0.5 assert len(lp.content[0].top_logprobs) == 2 + def test_generate_eval_stop_seqs_wired_to_sampling_params(self, monkeypatch): + llm = MagicMock() + llm.generate.return_value = [_make_request_output([65], finish_reason="stop")] + provider, _ = _make_provider(monkeypatch, llm) + asyncio.run( + provider.generate( + self._user_msg(), None, None, self._config(max_tokens=4, stop_seqs=["END"]) + ) + ) + sp_kwargs = sys.modules["vllm"].SamplingParams.call_args.kwargs + assert sp_kwargs["stop"] == ["END"] + + def test_generate_eval_unsupported_field_warns_once(self, monkeypatch): + import warnings as warnings_mod + + from transformer_lens.model_bridge.sources.inspect import _provider_base + + monkeypatch.setattr(_provider_base, "_WARNED_UNSUPPORTED", set()) + llm = MagicMock() + llm.generate.return_value = [_make_request_output([65], finish_reason="stop")] + provider, _ = _make_provider(monkeypatch, llm) + config = self._config(max_tokens=1, presence_penalty=0.5) + with pytest.warns(UserWarning, match="presence_penalty"): + asyncio.run(provider.generate(self._user_msg(), None, None, config)) + with warnings_mod.catch_warnings(record=True) as rec: + warnings_mod.simplefilter("always") + asyncio.run(provider.generate(self._user_msg(), None, None, config)) + assert not [w for w in rec if "presence_penalty" in str(w.message)] + def test_generate_eval_passes_temperature_top_p(self, monkeypatch): llm = MagicMock() llm.generate.return_value = [_make_request_output([65], finish_reason="length")] @@ -404,6 +433,66 @@ def test_capture_skips_logits_when_disabled(self, monkeypatch): ) assert "tl_logits" not in out.metadata + def test_intervention_gated_kind_raises_before_rpc(self, monkeypatch): + # resid_pre resolves to a hook name but is gated by vLLM's fused execution; + # without the served check it would reach the worker and silently no-op. + provider, llm, _ = self._make_capture_provider(monkeypatch) + with pytest.raises(ValueError, match="resid_pre|gated"): + asyncio.run( + provider.generate( + [], + None, + None, + self._config_with( + { + "input_ids": [1, 2], + "capture": ["0:resid_post"], + "interventions": {"0:resid_pre": {"op": "suppress"}}, + } + ), + ) + ) + assert llm.collective_rpc.call_count == 0 # validated before any worker RPC + + def test_intervention_capture_only_kind_raises(self, monkeypatch): + provider, _, _ = self._make_capture_provider(monkeypatch) + with pytest.raises(ValueError, match="gated|capture-only"): + asyncio.run( + provider.generate( + [], + None, + None, + self._config_with( + { + "input_ids": [1, 2], + "capture": ["0:resid_post"], + "interventions": {"0:resid_mid": {"op": "suppress"}}, + } + ), + ) + ) + + def test_intervention_pos_rejected_with_remediation(self, monkeypatch): + # 'pos' dies in the worker with an unfollowable message; reject up front and + # point at the HF provider instead. + provider, llm, _ = self._make_capture_provider(monkeypatch) + with pytest.raises(ValueError, match="tl_bridge"): + asyncio.run( + provider.generate( + [], + None, + None, + self._config_with( + { + "input_ids": [1, 2], + "capture": ["0:resid_post"], + "interventions": {"0:resid_post": {"op": "suppress", "pos": 1}}, + } + ), + ) + ) + assert llm.collective_rpc.call_count == 0 + def test_capture_gated_kind_raises(self, monkeypatch): # resid_pre (blocks.{i}.hook_in) is gated by vLLM's fused execution. provider, _, _ = self._make_capture_provider(monkeypatch) @@ -562,3 +651,87 @@ def test_eval_capture_emits_eval_completion_too(self, monkeypatch): # _make_request_output([65, 66]) ⇒ "AB" via fake tokenizer. assert out.choices[0].message.text == "AB" assert out.usage.output_tokens == 2 + + +class TestBootInspectVLLMDtype: + """boot_inspect must forward the resolved dtype to the tl_bridge_vllm provider so + bridge_config.dtype matches what the engine loads.""" + + def _boot(self, monkeypatch, **boot_kwargs): + import inspect_ai.model as inspect_ai_model + import torch + + from transformer_lens.config import TransformerBridgeConfig + from transformer_lens.model_bridge.architecture_adapter import ( + ArchitectureAdapter, + ) + from transformer_lens.model_bridge.sources.inspect import source as source_mod + + cfg = TransformerBridgeConfig( + d_model=4, + d_head=2, + n_layers=2, + n_ctx=8, + n_heads=2, + d_vocab=16, + d_mlp=8, + architecture="LlamaForCausalLM", + ) + adapter = ArchitectureAdapter(cfg) + adapter.component_mapping = {} + + captured: dict[str, Any] = {} + + def fake_get_model(name, memoize=False, **kwargs): + captured["name"] = name + captured.update(kwargs) + api = SimpleNamespace( + supported_kinds=lambda: frozenset({"resid_post"}), + capability_note=lambda: "", + provides_sequence_logits=False, + ) + return SimpleNamespace(api=api) + + from transformers import AutoConfig + + monkeypatch.setattr( + AutoConfig, + "from_pretrained", + lambda *a, **kw: SimpleNamespace(architectures=["LlamaForCausalLM"]), + ) + monkeypatch.setattr(inspect_ai_model, "get_model", fake_get_model) + monkeypatch.setattr(source_mod, "get_hf_token", lambda: None) + monkeypatch.setattr(source_mod, "build_bridge_config_from_hf", lambda *a, **kw: cfg) + monkeypatch.setattr( + source_mod, + "ArchitectureAdapterFactory", + SimpleNamespace(select_architecture_adapter=lambda c: adapter), + ) + + def _fake_configure_tokenizer(tok, cfg_): + cfg_.tokenizer_prepends_bos, cfg_.tokenizer_appends_eos = (True, False) + return tok + + monkeypatch.setattr(source_mod, "configure_tokenizer", _fake_configure_tokenizer) + + bridge = source_mod.boot_inspect( + "fake/model", + tokenizer=SimpleNamespace(), + provider="tl_bridge_vllm", + **boot_kwargs, + ) + return bridge, captured + + def test_default_dtype_forwarded_as_fp32(self, monkeypatch): + import torch + + _, captured = self._boot(monkeypatch) + assert captured["name"] == "tl_bridge_vllm/fake/model" + # Without forwarding, the provider silently defaults to the HF-config dtype. + assert captured["dtype"] is torch.float32 + + def test_explicit_dtype_forwarded(self, monkeypatch): + import torch + + _, captured = self._boot(monkeypatch, dtype=torch.float16) + assert captured["dtype"] is torch.float16 diff --git a/tests/unit/model_bridge/test_remote_bridge.py b/tests/unit/model_bridge/test_remote_bridge.py index 1d0940b937..2c41cb2225 100644 --- a/tests/unit/model_bridge/test_remote_bridge.py +++ b/tests/unit/model_bridge/test_remote_bridge.py @@ -276,6 +276,42 @@ def forward( with pytest.raises(ValueError, match="Invalid return_type"): bridge.forward(torch.tensor([[1, 2, 3]]), return_type="nonsense") + def test_forward_rejects_list_of_strings(self): + """list[str] used to be passed through as raw input_ids and crash in numpy.""" + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + with pytest.raises(TypeError, match="list of strings"): + bridge.forward(["hello", "world"]) + + def test_forward_rejects_stop_at_layer(self): + """stop_at_layer used to be silently swallowed by **kwargs.""" + import torch + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + with pytest.raises(NotImplementedError, match="stop_at_layer"): + bridge.forward(torch.tensor([[1, 2]]), stop_at_layer=1) + + def test_run_with_hooks_rejects_stop_at_layer(self): + import torch + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + with pytest.raises(NotImplementedError, match="stop_at_layer"): + bridge.run_with_hooks(torch.tensor([[1, 2]]), stop_at_layer=1) + + def test_run_with_cache_rejects_stop_at_layer(self): + import torch + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + with pytest.raises(NotImplementedError, match="stop_at_layer"): + bridge.run_with_cache(torch.tensor([[1, 2]]), stop_at_layer=1) + + def test_run_with_hooks_rejects_names_filter_kwarg(self): + """The parameter existed in the signature but was never read.""" + import torch + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + with pytest.raises(TypeError, match="names_filter"): + bridge.run_with_hooks(torch.tensor([[1, 2]]), names_filter="blocks.0.hook_resid_pre") + def test_forward_replays_captures(self): import torch @@ -310,3 +346,88 @@ def forward( bridge.forward(torch.tensor([[1, 2]])) assert len(recorded) == 1 assert tuple(recorded[0].shape) == (2, 3) + + +class TestRemoteBridgeHookSetEnforcement: + """Driver-declared hook sets are a contract, not metadata — requests + outside them must fail loud instead of yielding empty caches.""" + + def test_add_hook_resolves_ht_alias(self): + """add_hook accepts the same HT-style aliases run_with_hooks resolves.""" + driver = _stub_driver(supported_hooks=frozenset({"embed.hook_out"})) + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=driver) + bridge.add_hook("hook_embed", lambda act, hook: None) + assert len(bridge._hook_registry["embed.hook_out"].fwd_hooks) == 1 + + def test_run_with_hooks_unknown_hook_name_raises(self): + """Unknown string names used to be silently skipped — unhooked forward.""" + import torch + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + with pytest.raises(KeyError, match="does not exist"): + with pytest.warns(UserWarning, match="read-only"): + bridge.run_with_hooks( + torch.tensor([[1, 2]]), + fwd_hooks=[("blocks.0.attn.hook_z_typo", lambda act, hook: None)], + ) + + def test_run_with_cache_unmatched_names_filter_raises(self): + """A filter outside the driver whitelist used to return (logits, {}).""" + import torch + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + with pytest.raises(KeyError, match="matched no hook points"): + bridge.run_with_cache(torch.tensor([[1, 2]]), names_filter="blocks.0.attn.hook_z") + + def test_run_with_cache_unmatched_list_filter_raises(self): + import torch + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + with pytest.raises(KeyError, match="matched no hook points"): + bridge.run_with_cache( + torch.tensor([[1, 2]]), names_filter=["nope.hook_a", "nope.hook_b"] + ) + + def test_run_with_cache_matching_filter_still_works(self): + import torch + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=_stub_driver()) + _, cache = bridge.run_with_cache( + torch.tensor([[1, 2]]), names_filter="blocks.0.hook_resid_pre" + ) + assert cache is not None # no raise; driver returns no captures here + + def test_non_fireable_hook_raises_backend_message(self): + """Hooks the backend declares non-fireable must name the remedy.""" + import torch + + class PartialDriver(DriverBase): + supported_hook_points = frozenset({"blocks.0.hook_resid_pre"}) + non_fireable_hook_points = frozenset({"blocks.0.attn.hook_pattern"}) + + def __init__(self): + super().__init__(_cfg(), tokenizer=None) + + def forward( + self, + input_ids=None, + *, + capture=(), + intervene=None, + max_new_tokens=1, + return_logits=True, + **kw, + ): + return ForwardResult() + + bridge = RemoteBridge(_stub_adapter(), tokenizer=None, driver=PartialDriver()) + with pytest.raises(NotImplementedError, match="cannot fire"): + bridge.add_hook("blocks.0.attn.hook_pattern", lambda act, hook: None) + with pytest.raises(NotImplementedError, match="boot_transformers"): + bridge.run_with_cache(torch.tensor([[1, 2]]), names_filter="blocks.0.attn.hook_pattern") + with pytest.raises(NotImplementedError, match="cannot fire"): + with pytest.warns(UserWarning, match="read-only"): + bridge.run_with_hooks( + torch.tensor([[1, 2]]), + fwd_hooks=[("blocks.0.attn.hook_pattern", lambda act, hook: None)], + ) diff --git a/tests/unit/model_bridge/test_vllm_boot.py b/tests/unit/model_bridge/test_vllm_boot.py index 7d79bbc639..a54a2438b6 100644 --- a/tests/unit/model_bridge/test_vllm_boot.py +++ b/tests/unit/model_bridge/test_vllm_boot.py @@ -62,8 +62,12 @@ def mocked_boot(monkeypatch): monkeypatch.setattr("transformers.AutoConfig.from_pretrained", auto_config) monkeypatch.setattr("transformers.AutoTokenizer.from_pretrained", auto_tokenizer) + fake_llm = MagicMock(name="llm") + # The boot-time reconstruction probe fetches the unembedding via get_param, whose + # return type is beartype-enforced — the RPC must yield a real tensor. + fake_llm.collective_rpc = MagicMock(return_value=[torch.ones(16, 4)]) fake_vllm = MagicMock() - fake_vllm.LLM = MagicMock(return_value=MagicMock(name="llm")) + fake_vllm.LLM = MagicMock(return_value=fake_llm) monkeypatch.setitem(sys.modules, "vllm", fake_vllm) monkeypatch.setattr( @@ -91,11 +95,24 @@ def mocked_boot(monkeypatch): lambda: None, ) + # Real tokenizer setup/probing can't run on a MagicMock tokenizer. + def _fake_configure_tokenizer(tokenizer, cfg_): + cfg_.tokenizer_prepends_bos, cfg_.tokenizer_appends_eos = (False, True) + return tokenizer + + configure_tok = MagicMock(side_effect=_fake_configure_tokenizer) + monkeypatch.setattr( + "transformer_lens.model_bridge.sources.vllm.source.configure_tokenizer", + configure_tok, + ) + yield { "auto_config": auto_config, "auto_tokenizer": auto_tokenizer, "vllm_llm": fake_vllm.LLM, "hf_config": hf_config, + "cfg": cfg, + "configure_tok": configure_tok, } plugin._config.clear() @@ -176,6 +193,39 @@ def test_plugin_config_cleared_after_boot(mocked_boot): assert plugin._config == {} +def test_plugin_config_cleared_when_llm_construction_fails(mocked_boot): + """A failed boot (OOM, gated repo) must not leave stale specs patched in — + the next in-process vllm.LLM(...) would walk our dot-paths on a foreign model.""" + mocked_boot["vllm_llm"].side_effect = RuntimeError("CUDA out of memory") + with pytest.raises(RuntimeError, match="CUDA out of memory"): + boot_vllm("any-model") + assert plugin._config == {} + + +def test_rejects_prefix_caching_override(): + """Prefix caching breaks the row=position capture invariant — locked off.""" + with pytest.raises(ValueError, match="enable_prefix_caching"): + boot_vllm("any-model", enable_prefix_caching=True) + + +def test_bos_detection_written_to_config(mocked_boot): + """boot_vllm must probe the tokenizer like boot_transformers — the dataclass + default (prepends_bos=True) is wrong for Qwen-family tokenizers and shifts + every activation by one position.""" + bridge = boot_vllm("any-model") + assert mocked_boot["configure_tok"].called + assert mocked_boot["cfg"].tokenizer_prepends_bos is False + assert mocked_boot["cfg"].tokenizer_appends_eos is True + assert bridge is not None + + +def test_logit_reconstruction_probed_at_boot(mocked_boot): + """The unembedding is fetched once at boot, not re-cloned per forward.""" + bridge = boot_vllm("any-model") + assert bridge._driver._unembed_probed is True + assert bridge._driver._unembed is not None + + def test_llm_construction_kwargs(mocked_boot): """Pin the kwargs boot_vllm passes to vllm.LLM(...). Catches regressions like forgetting worker_extension_cls (collective_rpc methods unreachable), @@ -195,3 +245,15 @@ def test_llm_construction_kwargs(mocked_boot): assert kwargs["pipeline_parallel_size"] == 1 assert kwargs["skip_tokenizer_init"] is True assert kwargs["disable_log_stats"] is True + assert kwargs["enable_prefix_caching"] is False + # Always explicit — "auto" would downcast fp32 checkpoints under the buffers. + assert kwargs["dtype"] == "float16" + + +def test_missing_vllm_raises_actionable_import_error(monkeypatch): + """Without vllm installed, boot_vllm must name the packaging extra — and fail + before any HF network I/O or plugin state mutation.""" + monkeypatch.setitem(sys.modules, "vllm", None) # forces ImportError on import + with pytest.raises(ImportError, match=r"transformer-lens\[vllm\]"): + boot_vllm("any-model") + assert plugin._config == {} diff --git a/tests/unit/model_bridge/test_vllm_driver.py b/tests/unit/model_bridge/test_vllm_driver.py index 80140367c4..a5b174c128 100644 --- a/tests/unit/model_bridge/test_vllm_driver.py +++ b/tests/unit/model_bridge/test_vllm_driver.py @@ -637,3 +637,239 @@ def test_run_with_hooks_warns_fwd_hooks_are_read_only(self): torch.tensor([[1, 2, 3]]), fwd_hooks=[("embed.hook_out", lambda a, hook: a)], ) + + +class TestVLLMDriverSpecKeyValidation: + """Unknown spec keys and mis-shaped values must fail loud, not silently degrade.""" + + def test_unknown_spec_key_rejected(self): + """A typo'd 'position' would otherwise become a whole-sequence edit.""" + with pytest.raises(ValueError, match="unknown spec key"): + _driver(enable_position_interventions=True)._validate_interventions( + {"embed.hook_out": {"op": "add", "value": 1.0, "position": 3}} + ) + + def test_known_keys_accepted(self): + out = _driver(enable_position_interventions=True)._validate_interventions( + {"embed.hook_out": {"op": "add", "value": 1.0, "pos": 2}} + ) + assert out["embed.hook_out"]["pos"] == 2 + + def test_value_width_mismatch_rejected(self): + """embed.hook_out width is 4 (overlay spec); a 2-element value would broadcast + wrong or crash mid-forward.""" + with pytest.raises(ValueError, match="2 elements"): + _driver()._validate_interventions( + {"embed.hook_out": {"op": "add", "value": [1.0, 2.0]}} + ) + + def test_value_width_match_accepted(self): + out = _driver()._validate_interventions( + {"embed.hook_out": {"op": "set", "value": [1.0, 2.0, 3.0, 4.0]}} + ) + assert out["embed.hook_out"]["value"] == [1.0, 2.0, 3.0, 4.0] + + def test_scalar_value_accepted(self): + out = _driver()._validate_interventions({"embed.hook_out": {"op": "add", "value": 2.5}}) + assert out["embed.hook_out"]["value"] == 2.5 + + def test_pos_bool_rejected(self): + """bool is an int subclass — pos=True must not pass as position 1.""" + driver = _driver(enable_position_interventions=True) + with pytest.raises(ValueError, match="int or list of ints"): + driver._validate_interventions({"embed.hook_out": {"op": "suppress", "pos": True}}) + with pytest.raises(ValueError, match="int or list of ints"): + driver._validate_interventions({"embed.hook_out": {"op": "suppress", "pos": [True]}}) + + +class TestVLLMDriverLogitReconstruction: + """Boot-time probe caches the unembedding; reconstruction slices padded vocab.""" + + def test_probe_unavailable_downgrades_sequence_logits(self): + driver = _driver(captures={}) + driver._llm.collective_rpc = MagicMock(return_value=[None]) + assert driver.probe_logit_reconstruction() is False + assert driver._unembed is None and driver._unembed_probed + assert driver.provides_sequence_logits is False + + def test_probe_available_caches_weight(self): + driver = _driver(captures={}) + driver._llm.collective_rpc = MagicMock(return_value=[torch.ones(16, 4)]) + assert driver.probe_logit_reconstruction() is True + assert driver.provides_sequence_logits is True + weight32, _bias = driver._unembed + assert weight32.dtype == torch.float32 + # Idempotent: a second probe answers from the cache, no extra RPC. + calls_after_first = driver._llm.collective_rpc.call_count + assert driver.probe_logit_reconstruction() is True + assert driver._llm.collective_rpc.call_count == calls_after_first + + def test_reconstruct_uses_cached_weight_without_rpc(self): + """After the probe, per-forward reconstruction must not re-fetch the weight.""" + driver = _driver(captures={}) + driver._unembed = (torch.eye(4, dtype=torch.float32).repeat(4, 1), None) + driver._unembed_probed = True + driver._llm.collective_rpc = MagicMock( + side_effect=AssertionError("reconstruction must not RPC") + ) + out = driver._reconstruct_logits(torch.ones(3, 4)) + assert out is not None and out.shape == (3, 16) + + def test_reconstruct_slices_padded_vocab(self): + """vLLM pads vocab to a multiple of 64; d_vocab=16 here, weight padded to 24. + Un-sliced, the zero-filled pad columns become phantom argmax candidates when + every real logit is negative.""" + driver = _driver(captures={}) + weight = torch.zeros(24, 4, dtype=torch.float32) + weight[:16] = -1.0 # real vocab rows: all-negative logits + driver._unembed = (weight, None) + driver._unembed_probed = True + out = driver._reconstruct_logits(torch.ones(3, 4)) + assert out.shape == (3, 16) + assert int(out[0].argmax()) < 16 + + def test_reconstruct_returns_none_when_probed_unavailable(self): + driver = _driver(captures={}) + driver._unembed_probed = True # probed, nothing found + assert driver._reconstruct_logits(torch.ones(3, 4)) is None + + +class TestVLLMDriverLnFinalUnfold: + """The exposed ln_final capture honors the hook name's pre-weight convention.""" + + def test_unfold_divides_by_weight(self): + driver = _driver(captures={}) + driver._llm.collective_rpc = MagicMock(return_value=[torch.full((4,), 2.0)]) + out = driver._unfold_ln_final(torch.ones(1, 3, 4)) + assert torch.allclose(out, torch.full((1, 3, 4), 0.5)) + + def test_unfold_gemma_uses_one_plus_weight(self): + driver = _driver(captures={}) + driver.architecture = "Gemma2ForCausalLM" + driver._llm.collective_rpc = MagicMock(return_value=[torch.full((4,), 1.0)]) + out = driver._unfold_ln_final(torch.ones(1, 3, 4)) + assert torch.allclose(out, torch.full((1, 3, 4), 0.5)) + + def test_unfold_near_zero_weight_guarded(self): + driver = _driver(captures={}) + driver._llm.collective_rpc = MagicMock(return_value=[torch.zeros(4)]) + out = driver._unfold_ln_final(torch.ones(1, 3, 4)) + assert torch.isfinite(out).all() + + def test_unfold_warns_and_returns_raw_when_weight_unreachable(self): + driver = _driver(captures={}) + driver._llm.collective_rpc = MagicMock(return_value=[None]) + t = torch.ones(1, 3, 4) + with pytest.warns(UserWarning, match="POST-weight"): + out = driver._unfold_ln_final(t) + assert torch.equal(out, t) + # Negative outcome cached: no per-forward RPC retry, no repeat warning. + out2 = driver._unfold_ln_final(t) + assert torch.equal(out2, t) + assert driver._llm.collective_rpc.call_count == 1 + + def test_unfold_caches_fetched_weight(self): + driver = _driver(captures={}) + driver._llm.collective_rpc = MagicMock(return_value=[torch.full((4,), 2.0)]) + driver._unfold_ln_final(torch.ones(1, 3, 4)) + assert driver._llm.collective_rpc.call_count == 1 + driver._unfold_ln_final(torch.ones(1, 3, 4)) + assert driver._llm.collective_rpc.call_count == 1 # cached, no second RPC + + +class TestVLLMDriverCloseRefcount: + """close() only tears down vLLM's process-global distributed state when this is + the last live driver — a re-bound notebook boot must not break the new engine.""" + + def _patch_distributed(self, monkeypatch): + import sys as _sys + + dist = MagicMock() + monkeypatch.setitem(_sys.modules, "vllm", MagicMock()) + monkeypatch.setitem(_sys.modules, "vllm.distributed", MagicMock()) + monkeypatch.setitem(_sys.modules, "vllm.distributed.parallel_state", dist) + return dist + + def test_teardown_deferred_until_last_driver(self, monkeypatch): + from transformer_lens.model_bridge.sources.vllm import driver as driver_module + + dist = self._patch_distributed(monkeypatch) + monkeypatch.setattr(driver_module, "_LIVE_DRIVERS", 0) + a, b = _driver(captures={}), _driver(captures={}) + a.close() + dist.destroy_model_parallel.assert_not_called() + b.close() + dist.destroy_model_parallel.assert_called_once() + dist.destroy_distributed_environment.assert_called_once() + + def test_double_close_decrements_once(self, monkeypatch): + from transformer_lens.model_bridge.sources.vllm import driver as driver_module + + dist = self._patch_distributed(monkeypatch) + monkeypatch.setattr(driver_module, "_LIVE_DRIVERS", 0) + a, b = _driver(captures={}), _driver(captures={}) + a.close() + a.close() # idempotent: must not reach zero while b is live + dist.destroy_model_parallel.assert_not_called() + b.close() + dist.destroy_model_parallel.assert_called_once() + + def test_close_drops_weight_caches(self, monkeypatch): + self._patch_distributed(monkeypatch) + driver = _driver(captures={}) + driver._unembed = (torch.ones(16, 4), None) + driver._lnf_inv_denom = torch.ones(4) + driver.close() + assert driver._unembed is None and driver._lnf_inv_denom is None + + +class TestBatchedForwardPaddingSemantics: + """Reconstruction pads and attention masks on the batched path.""" + + def test_reconstructed_pad_positions_are_neg_inf(self): + """Zero-filled ln_final pad rows reconstruct into finite garbage (0 @ W); + they must be masked to the -inf convention the fallback path uses.""" + pytest.importorskip("vllm") + outputs = [ + _batched_request_output("req-A", top_logprobs={7: 2.0}), + _batched_request_output("req-B", top_logprobs={3: 2.0}), + ] + captures_by_req = { + "req-A": {"ln_final.hook_normalized": torch.ones(3, 4)}, + "req-B": {"ln_final.hook_normalized": torch.ones(2, 4)}, + } + driver = _batched_driver(outputs=outputs, captures_by_req=captures_by_req) + driver._unembed = (torch.ones(16, 4, dtype=torch.float32), None) + driver._unembed_probed = True + result = driver.forward([[1, 2, 3], [4, 5]]) + assert result.logits.shape == (2, 3, 16) + assert torch.isfinite(result.logits[0]).all() # 3 real rows + assert torch.isfinite(result.logits[1, :2]).all() + assert torch.isinf(result.logits[1, 2]).all() # pad row for the 2-token prompt + + def test_attention_mask_trims_padded_rows(self): + """A right-padded (B, S) batch + mask must send only real tokens to vLLM.""" + pytest.importorskip("vllm") + outputs = [ + _batched_request_output("req-A", top_logprobs={7: 2.0}), + _batched_request_output("req-B", top_logprobs={3: 2.0}), + ] + driver = _batched_driver(outputs=outputs, captures_by_req={}) + driver.forward( + torch.tensor([[1, 2, 3], [4, 5, 0]]), + attention_mask=torch.tensor([[1, 1, 1], [1, 1, 0]]), + return_logits=False, + ) + prompts = driver._llm.generate.call_args.kwargs["prompts"] + assert prompts[0]["prompt_token_ids"] == [1, 2, 3] + assert prompts[1]["prompt_token_ids"] == [4, 5] # pad token trimmed + + def test_attention_mask_batch_mismatch_raises(self): + pytest.importorskip("vllm") + driver = _batched_driver(outputs=[], captures_by_req={}) + with pytest.raises(ValueError, match="attention_mask batch dim"): + driver.forward( + torch.tensor([[1, 2], [3, 4]]), + attention_mask=torch.tensor([[1, 1]]), + return_logits=False, + ) diff --git a/tests/unit/test_utils.py b/tests/unit/test_utils.py index 86e685c691..78050e503b 100644 --- a/tests/unit/test_utils.py +++ b/tests/unit/test_utils.py @@ -465,6 +465,11 @@ def test_no_split_tokens_across_chunks(self): streaming=False, max_length=64, add_bos_token=False, + # Main-process: pytest's jaxtyping import hook instruments the closure, + # which datasets>=4.8 dill-ships to pool workers (even for num_proc=1) — + # instrumentation state is unpicklable. Plain-shell multiproc works; the + # chunking assertion below is what this test is for. + num_proc=None, ) # Tokenize the same text cleanly in one shot (no chunking) diff --git a/transformer_lens/model_bridge/bridge_core.py b/transformer_lens/model_bridge/bridge_core.py index 96f30b94a5..4aca75f49d 100644 --- a/transformer_lens/model_bridge/bridge_core.py +++ b/transformer_lens/model_bridge/bridge_core.py @@ -300,6 +300,7 @@ def _finalize_return( return logits if return_type is None: return None + self._check_loss_supported(return_type) if return_type == "loss": if is_audio_model: raise ValueError( @@ -349,8 +350,44 @@ def _finalize_return( return preds raise ValueError(f"Invalid return_type: {return_type}") + def _check_loss_supported(self, return_type: Optional[str]) -> None: + """Loss needs full-sequence logits; final-position-only drivers would NaN.""" + if return_type in ("loss", "both") and not getattr( + self._driver, "provides_sequence_logits", True + ): + raise NotImplementedError( + f"return_type={return_type!r} is unsupported on this driver: it " + "provides next-token logits for the final position only, so loss " + "over earlier positions is undefined. Use return_type='logits' " + "and read logits[..., -1, :]." + ) + # ---- hook lookup / mutation ---- + def _check_hook_fireable(self, *names: str) -> None: + """Fail loud when the driver declares it can't fire a requested hook — + attaching anyway would yield a silently-unhooked forward / empty cache.""" + non_fireable: frozenset = getattr(self._driver, "non_fireable_hook_points", frozenset()) + for name in names: + if name in non_fireable: + raise NotImplementedError( + f"this backend cannot fire {name!r}; use boot_transformers() " + "for full hook coverage." + ) + + def _resolve_hook_point( + self, name: str, aliases: Dict[str, str], hook_dict: Dict[str, HookPoint] + ) -> Tuple[str, HookPoint]: + """Resolve an (aliased) string hook name to its HookPoint, enforcing + fireability. A name that resolves to nothing raises — a typo'd name must + not run unhooked.""" + canonical = aliases.get(name, name) + self._check_hook_fireable(name, canonical) + hook_point = hook_dict.get(canonical) + if hook_point is None: + raise KeyError(f"Hook name {name!r} does not exist on this model.") + return canonical, hook_point + def get_hook_point(self, hook_name: str) -> Optional[HookPoint]: """Get a hook point by name from the bridge's hook system.""" if hook_name in self._hook_registry: @@ -396,8 +433,20 @@ def add_hook( hook_point.add_hook(hook_fn, dir=dir, is_permanent=is_permanent) return - # Registry-first: works for any bridge (RemoteBridge has no component tree). + # Fast path: canonical registry names skip the alias-map build (hook_dict + + # map construction cost ~ms on large models; add_hook is often called per layer). registry_hp = self._hook_registry.get(name) + if registry_hp is not None: + self._check_hook_fireable(name) + registry_hp.add_hook(hook_fn, dir=dir, is_permanent=is_permanent) + return + # Same alias resolution run_with_hooks uses, so HT-style names work here too. + canonical = build_alias_to_canonical_map(self.hook_dict).get(name, name) + if canonical != name: + self._check_hook_fireable(name, canonical) + else: + self._check_hook_fireable(name) + registry_hp = self._hook_registry.get(canonical) if registry_hp is not None: registry_hp.add_hook(hook_fn, dir=dir, is_permanent=is_permanent) return @@ -489,19 +538,15 @@ def add_hook_to_point( def apply_hooks(hook_list: List[Tuple[Any, Callable]], is_fwd: bool) -> None: direction: Literal["fwd", "bwd"] = "fwd" if is_fwd else "bwd" - aliases = build_alias_to_canonical_map(self.hook_dict) + hook_dict = self.hook_dict + aliases = build_alias_to_canonical_map(hook_dict) for hook_name_or_filter, hook_fn in hook_list: if isinstance(hook_name_or_filter, str): - hook_dict = self.hook_dict - actual_hook_name = hook_name_or_filter - if hook_name_or_filter in aliases: - actual_hook_name = aliases[hook_name_or_filter] - if actual_hook_name in hook_dict: - add_hook_to_point( - hook_dict[actual_hook_name], hook_fn, actual_hook_name, direction - ) + actual_hook_name, hook_point = self._resolve_hook_point( + hook_name_or_filter, aliases, hook_dict + ) + add_hook_to_point(hook_point, hook_fn, actual_hook_name, direction) else: - hook_dict = self.hook_dict seen_hooks = set() for n, hook_point in hook_dict.items(): if hook_name_or_filter(n): @@ -533,7 +578,6 @@ def run_with_hooks( reset_hooks_end: bool = True, clear_contexts: bool = False, return_type: Optional[str] = "logits", - names_filter: Optional[Union[str, List[str], Callable[[str], bool]]] = None, stop_at_layer: Optional[int] = None, remove_batch_dim: bool = False, **kwargs: Any, @@ -544,6 +588,12 @@ def run_with_hooks( (KV cache cleaned up on stop). ``remove_batch_dim`` squeezes/unsqueezes the batch dim around hook callbacks (batch_size==1 only). """ + if "names_filter" in kwargs: + # **kwargs would silently absorb it; fail loud. + raise TypeError( + "run_with_hooks() got an unexpected keyword argument 'names_filter'; " + "use run_with_cache(names_filter=...) to scope caching." + ) added_hooks: List[Tuple[HookPoint, Literal["fwd", "bwd"]]] = [] effective_stop_layer = None if stop_at_layer is not None and hasattr(self, "blocks"): @@ -593,7 +643,8 @@ def apply_hooks( hook_list: List[Tuple[Union[str, Callable], Callable]], is_fwd: bool ) -> None: direction: Literal["fwd", "bwd"] = "fwd" if is_fwd else "bwd" - aliases = build_alias_to_canonical_map(self.hook_dict) + hook_dict = self.hook_dict + aliases = build_alias_to_canonical_map(hook_dict) for hook_name_or_filter, hook_fn in hook_list: if remove_batch_dim: original_hook_fn = hook_fn @@ -611,16 +662,11 @@ def wrapped_hook_fn(tensor, hook, _orig_fn=original_hook_fn): hook_fn = wrapped_hook_fn if isinstance(hook_name_or_filter, str): - hook_dict = self.hook_dict - actual_hook_name = hook_name_or_filter - if hook_name_or_filter in aliases: - actual_hook_name = aliases[hook_name_or_filter] - if actual_hook_name in hook_dict: - add_hook_to_point( - hook_dict[actual_hook_name], hook_fn, actual_hook_name, direction - ) + actual_hook_name, hook_point = self._resolve_hook_point( + hook_name_or_filter, aliases, hook_dict + ) + add_hook_to_point(hook_point, hook_fn, actual_hook_name, direction) else: - hook_dict = self.hook_dict seen_hooks: set = set() for n, hook_point in hook_dict.items(): if hook_name_or_filter(n): @@ -710,6 +756,10 @@ def create_names_filter_fn(filter_input): raise ValueError("names_filter must be a string, list of strings, or callable") names_filter_fn = create_names_filter_fn(names_filter) + if isinstance(names_filter, (str, list)): + requested = [names_filter] if isinstance(names_filter, str) else names_filter + for name in requested: + self._check_hook_fireable(name, aliases.get(name, name)) cache: Dict[str, torch.Tensor] = {} hooks: List[Tuple[HookPoint, str]] = [] visited: set[int] = set() @@ -745,8 +795,10 @@ def cache_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: effective_stop_layer = len(self.blocks) + stop_at_layer else: effective_stop_layer = stop_at_layer + matched_any = False for hook_name, hook in hook_dict.items(): if names_filter_fn(hook_name): + matched_any = True if effective_stop_layer is not None: if hook_name.startswith("blocks."): try: @@ -756,6 +808,12 @@ def cache_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: except (IndexError, ValueError): pass hooks.append((hook, hook_name)) + # Explicit string/list filters matching nothing must not return (logits, {}) silently. + if not matched_any and names_filter and isinstance(names_filter, (str, list)): + raise KeyError( + f"names_filter {names_filter!r} matched no hook points on this model; " + "check the name against model.hook_dict (this backend may not serve it)." + ) for hp, name in hooks: hp.add_hook(make_cache_hook(name)) processed_args = [input] diff --git a/transformer_lens/model_bridge/driver_protocol.py b/transformer_lens/model_bridge/driver_protocol.py index ffe7a4862e..71b3587415 100644 --- a/transformer_lens/model_bridge/driver_protocol.py +++ b/transformer_lens/model_bridge/driver_protocol.py @@ -48,15 +48,38 @@ class ForwardResult: raw_output: Any = None +# Feature strings ``supports()`` may be queried with. The bridge consumes +# "parameters" (input-device placement); the rest are informational capability +# declarations for callers. validate_driver rejects drivers declaring strings +# outside this set. +KNOWN_FEATURES = frozenset( + {"gradients", "parameters", "state_dict", "weight_access", "intervention_callbacks"} +) + + @runtime_checkable class Driver(Protocol): - """The forward-pass contract. Hook installation is the driver's problem.""" + """The forward-pass contract. Hook installation is the driver's problem. + + ``forward`` has two dialects: + + - **Module-replacement drivers** (TransformersDriver): hooks fire via the + bridge's HookPoint system during the real torch forward, so ``capture``/ + ``intervene``/``max_new_tokens`` are not served here — conforming drivers + raise ``NotImplementedError`` on them rather than silently ignore. + - **Spec drivers** (vLLM, Inspect): no local module, so ``capture`` names + hook points to record and ``intervene`` carries declarative edit specs; + results come back in ``ForwardResult.captured``. + """ architecture: str bridge_config: TransformerBridgeConfig tokenizer: Any supported_hook_points: frozenset[str] non_fireable_hook_points: frozenset[str] + # False when the driver returns logits for the final position only — + # the bridge then refuses return_type="loss"/"both" instead of NaN-ing. + provides_sequence_logits: bool def forward( self, @@ -74,8 +97,8 @@ def close(self) -> None: ... def supports(self, feature: str) -> bool: - """Capability flag. Known features: gradients, parameters, state_dict, - generate_streaming, weight_access, intervention_callbacks.""" + """Capability flag over :data:`KNOWN_FEATURES`. The bridge consults + "parameters"; the others are caller-facing declarations.""" ... # Note: torch-specific surface (parameters, named_parameters, state_dict, @@ -133,6 +156,18 @@ def validate_driver(driver: Any, *, after_bridge_construction: bool = False) -> raise TypeError("Driver missing required attribute: 'tokenizer'") _expect_attr_type(driver, "supported_hook_points", frozenset) _expect_attr_type(driver, "non_fireable_hook_points", frozenset) + # getattr(..., True) defaults would silently pick the UNSAFE value for + # loss gating, so the attribute is mandatory. + _expect_attr_type(driver, "provides_sequence_logits", bool) + + declared_features = getattr(driver, "_supported_features", None) + if declared_features is not None: + unknown = frozenset(declared_features) - KNOWN_FEATURES + if unknown: + raise TypeError( + f"Driver._supported_features contains unknown feature strings " + f"{sorted(unknown)}; known features: {sorted(KNOWN_FEATURES)}." + ) overlap = driver.supported_hook_points & driver.non_fireable_hook_points if overlap: @@ -183,6 +218,7 @@ def _expect_attr_type(obj: Any, name: str, expected: type) -> None: __all__ = [ "Driver", "ForwardResult", + "KNOWN_FEATURES", "Intervention", "InterventionFn", "InterventionSpec", diff --git a/transformer_lens/model_bridge/remote_bridge.py b/transformer_lens/model_bridge/remote_bridge.py index a5c4ad9a05..6b9c5e8b83 100644 --- a/transformer_lens/model_bridge/remote_bridge.py +++ b/transformer_lens/model_bridge/remote_bridge.py @@ -78,18 +78,18 @@ def forward( **kwargs: Any, ) -> Any: """Tokenize → driver.forward → replay captures → finalize per return_type.""" - if return_type in ("loss", "both") and not getattr( - self._driver, "provides_sequence_logits", True - ): - # Final-position-only logits ⇒ loss over the -inf earlier positions is nan. - raise NotImplementedError( - f"RemoteBridge does not support return_type={return_type!r} on this driver: " - "it provides next-token logits for the final position only, so loss over " - "earlier positions is undefined. Use return_type='logits' and read " - "logits[..., -1, :] (or the per-row last token in batched mode)." - ) + # Early copy of _finalize_return's gate — fail before the wasted remote forward. + self._check_loss_supported(return_type) + self._reject_stop_at_layer(kwargs.pop("stop_at_layer", None)) if isinstance(input, str): kwargs["input_ids"] = self.to_tokens(input) # BOS-aware, matches boot_transformers + elif isinstance(input, list) and any(isinstance(item, str) for item in input): + # Would otherwise be treated as raw input_ids and crash deep in numpy. + raise TypeError( + "RemoteBridge.forward received a list of strings; batched string " + "input is unsupported here. Pass a single str, or tokenize with " + "to_tokens() and pass token ids." + ) elif input is not None: kwargs["input_ids"] = input @@ -126,7 +126,6 @@ def run_with_hooks( reset_hooks_end: bool = True, clear_contexts: bool = False, return_type: Optional[str] = "logits", - names_filter: Optional[Union[str, List[str], Callable[[str], bool]]] = None, stop_at_layer: Optional[int] = None, remove_batch_dim: bool = False, **kwargs: Any, @@ -138,6 +137,7 @@ def run_with_hooks( raise NotImplementedError( "RemoteBridge has no backward pass; bwd_hooks are unsupported." ) + self._reject_stop_at_layer(stop_at_layer) if fwd_hooks: warnings.warn( "RemoteBridge fwd_hooks fire on already-captured activations (read-only): " @@ -155,12 +155,25 @@ def run_with_hooks( reset_hooks_end=reset_hooks_end, clear_contexts=clear_contexts, return_type=return_type, - names_filter=names_filter, - stop_at_layer=stop_at_layer, remove_batch_dim=remove_batch_dim, **kwargs, ) + @staticmethod + def _reject_stop_at_layer(stop_at_layer: Any) -> None: + # BridgeCore's stop hooks need a local `blocks` tree; silently ignoring + # the kwarg would lie about compute cost. + if stop_at_layer is not None: + raise NotImplementedError( + "RemoteBridge does not support stop_at_layer: the remote engine " + "always runs the full forward pass." + ) + + def run_with_cache(self, *args: Any, **kwargs: Any) -> Any: + """Cache via driver captures; stop_at_layer rejected (full remote forward).""" + self._reject_stop_at_layer(kwargs.get("stop_at_layer")) + return super().run_with_cache(*args, **kwargs) + def to_tokens(self, input: Any, prepend_bos: bool | None = None, truncate: bool = True) -> Any: """Tokenize a string with the same BOS handling as ``TransformerBridge``. diff --git a/transformer_lens/model_bridge/sources/AGENTS.md b/transformer_lens/model_bridge/sources/AGENTS.md index a75bbe210d..42d201312c 100644 --- a/transformer_lens/model_bridge/sources/AGENTS.md +++ b/transformer_lens/model_bridge/sources/AGENTS.md @@ -1,47 +1,59 @@ # Sources — AGENTS.md -Backend loaders that translate external models (HF, native) into a `TransformerBridge`. Read [the root AGENTS.md](../../../AGENTS.md) for project-wide rules. +Execution backends for the bridge. Each source boots an engine (HF, TL-native, vLLM, inspect_ai) and wraps it in a `TransformerBridge` or `RemoteBridge` via a Driver. Read [the root AGENTS.md](../../../AGENTS.md) for project-wide rules. ## File map | File | Role | |---|---| -| [`_bridge_builder.py`](_bridge_builder.py) | Loader-agnostic helpers: `build_bridge_config_from_hf`, `build_bridge_from_module`, `detect_tokenizer_bos_eos` | -| [`transformers.py`](transformers.py) | HuggingFace backend: `boot` (entry point used by `TransformerBridge.boot_transformers`), `map_default_transformer_lens_config` (HF→TL config translation), `check_model_support`, `list_supported_models` | -| [`native/model.py`](native/model.py), [`native/init.py`](native/init.py) | TL-native models built from scratch (no HF), used by `TransformerBridge.boot_native` | +| [`../driver_protocol.py`](../driver_protocol.py) | The `Driver` protocol: `forward(input_ids, capture=, intervene=, max_new_tokens=, return_logits=) → ForwardResult`, `close()`, `supports(feature)`, declared `supported_hook_points` / `non_fireable_hook_points`. Also `to_torch` (torch → DLPack → numpy ladder) and `validate_driver` (run at both bridge constructors). | +| [`_driver_base.py`](_driver_base.py) | `DriverBase` — optional ABC with defaults for the protocol's members (`supports`, `close`, `provides_sequence_logits`). Duck-typing works too. | +| [`_hf_format.py`](_hf_format.py) | HF-*format* (not HF-loading) utilities shared by every source whose backend yields an HF-shaped config/tokenizer: `map_default_transformer_lens_config`, `determine_architecture_from_hf_config`, `setup_tokenizer` | +| [`_bridge_builder.py`](_bridge_builder.py) | Loader-agnostic assembly: `build_bridge_config_from_hf`, `build_bridge_from_module`, `detect_tokenizer_bos_eos`; hosts the single `_HF_PASSTHROUGH_ATTRS` list (lines 22–58) | +| [`transformers_driver.py`](transformers_driver.py) | `TransformersDriver` — wraps an HF `nn.Module`; the reference torch driver (gradients, parameters, state_dict, weight access, intervention callbacks) | +| [`transformers/`](transformers/) | HF backend: `source.py` has `boot` (→ `TransformerBridge.boot_transformers`), `helpers.py` has `check_model_support` / `list_supported_models` / checkpoint-revision resolution; `__init__.py` re-exports the historical `sources.transformers` names | +| [`native/`](native/) | TL-native models built from scratch (no HF): `model.py` / `init.py`, booted via `TransformerBridge.boot_native` | +| [`vllm/`](vllm/) | vLLM backend: `source.py` has `boot_vllm` (→ `RemoteBridge.boot_vllm`); `plugin.py` / `worker_extension.py` install capture hooks in the worker pre-compile; `overlays/` declare per-arch capture specs; `internals.py` pins the validated vLLM version | +| [`inspect/`](inspect/) | inspect_ai backend: `source.py` has `boot_inspect` (→ `RemoteBridge.boot_inspect`); ships two providers (`transformers_provider.py` HF-backed, `vllm_provider.py`) plus `eval.py` helpers for capture inside evals. Package name shadows stdlib `inspect` — never `import inspect` bare inside it | -## ⚠ The duplicate `_HF_PASSTHROUGH_ATTRS` trap +## The Driver system -There are **two** identical `_HF_PASSTHROUGH_ATTRS` lists: +Every backend satisfies the `Driver` protocol in [`../driver_protocol.py`](../driver_protocol.py): the bridge calls `driver.forward(...)` and reads a `ForwardResult` (logits, captured activations, raw engine output); hook installation is the driver's problem. Drivers declare which canonical hook names they can fire (`supported_hook_points`) and which they structurally cannot (`non_fireable_hook_points`); `validate_driver` enforces the contract at bridge construction. -- `transformers.py:481` — inside `map_default_transformer_lens_config`, copies HF-config attrs onto `tl_config`. -- `_bridge_builder.py:18` — module-level, copies HF-config attrs onto `bridge_config` AFTER `TransformerBridgeConfig.from_dict` runs. +Implementations: -Both fire sequentially on the same `hf_config` during `boot_transformers`. **When adding a passthrough attr, add it to BOTH lists** — adding to only one half-fixes the bug. A previous regen-agent run shipped a half-fix that the regression test caught only because we also added an assertion on the canonical bridge config. +- `TransformersDriver` — local HF `nn.Module`; full hooks + gradients. Built for you by `TransformerBridge.boot_transformers` (and by `build_bridge_from_module` / `boot_native`). +- `VLLMDriver` ([`vllm/driver.py`](vllm/driver.py)) — high-throughput capture + declarative affine interventions on a vLLM engine; no gradients, no attention patterns. +- `InspectDriver` ([`inspect/driver.py`](inspect/driver.py)) — capture/intervene through an `inspect_ai` provider, for interp inside evals. -## Two-pass config-translation pipeline +Boot entry points: `TransformerBridge.boot_transformers` / `TransformerBridge.boot_native` (torch bridges), `RemoteBridge.boot_vllm` / `RemoteBridge.boot_inspect` (no local `nn.Module`; lazy imports so the heavy backend is only needed by its callers). The `boot_*` methods are attached to the bridge classes by each source's `__init__.py` via `setattr`. + +## `_HF_PASSTHROUGH_ATTRS` — one list, one copy point + +There is exactly **one** `_HF_PASSTHROUGH_ATTRS` list, at [`_bridge_builder.py:22-58`](_bridge_builder.py). Do not re-introduce a second copy elsewhere (an older layout had a duplicate in a since-deleted `transformers.py`; it's gone on purpose). + +Config-translation pipeline (`build_bridge_config_from_hf`): ``` hf_config │ - ├─ map_default_transformer_lens_config() [transformers.py] - │ ├─ explicit handlers (d_model, n_heads, head_dim → d_head, etc.) - │ └─ _HF_PASSTHROUGH_ATTRS copy → tl_config + ├─ map_default_transformer_lens_config() [_hf_format.py] + │ └─ explicit handlers (d_model, n_heads, head_dim → d_head, etc.) → tl_config │ ├─ TransformerBridgeConfig.from_dict() [filters to declared fields] │ - └─ _HF_PASSTHROUGH_ATTRS copy → bridge_config [_bridge_builder.py] + └─ _HF_PASSTHROUGH_ATTRS copy → bridge_config [_bridge_builder.py:78] ``` **Implications:** - If an attr has an explicit handler in `map_default_transformer_lens_config` (like `head_dim` → `d_head`), it's translated to a declared `TransformerBridgeConfig` field — adding it to PASSTHROUGH is **wrong** (often raises `AttributeError` from a read-only property). -- If an attr is purely runtime / adapter-specific (like Cohere's `logit_scale`), it has no explicit handler and is dropped by `from_dict` — it MUST be in both PASSTHROUGH lists, or the adapter's `getattr(cfg, "", default)` silently falls back to its default forever. +- If an attr is purely runtime / adapter-specific (like Cohere's `logit_scale`), it has no explicit handler and is dropped by `from_dict` — it MUST be in the PASSTHROUGH list, or the adapter's `getattr(cfg, "", default)` silently falls back to its default forever. - See [config/AGENTS.md](../../config/AGENTS.md) for the decision tree. ## `boot_transformers` vs `boot_native` -- `boot_transformers` is the HF path. Goes through `boot()` in `transformers.py` → `build_bridge_from_module` in `_bridge_builder.py` → adapter init. +- `boot_transformers` is the HF path. Goes through `boot()` in [`transformers/source.py`](transformers/source.py) → `build_bridge_config_from_hf` in `_bridge_builder.py` → adapter init → `TransformersDriver`. - `boot_native` builds a TL-native transformer from a `TransformerBridgeConfig` directly (no HF dependency). Uses [`native.py`](../supported_architectures/native.py) as the single adapter; the model class is in [`native/model.py`](native/model.py). Almost every contributor change goes through the `boot_transformers` path. Touch `native/` only when adding a primitive (new norm type, new positional embedding variant) that the cfg-driven dispatch in `native/model.py` doesn't already cover. diff --git a/transformer_lens/model_bridge/sources/_bridge_builder.py b/transformer_lens/model_bridge/sources/_bridge_builder.py index 040c4b6485..13faea9eb3 100644 --- a/transformer_lens/model_bridge/sources/_bridge_builder.py +++ b/transformer_lens/model_bridge/sources/_bridge_builder.py @@ -108,6 +108,19 @@ def detect_tokenizer_bos_eos(tokenizer: Any) -> tuple[bool, bool]: return prepends_bos, appends_eos +def configure_tokenizer(tokenizer: Any, cfg: Any) -> Any: + """Shared boot step: normalize the tokenizer and record its BOS/EOS behavior on cfg. + + Every source must run this — skipping it leaves the dataclass default + ``tokenizer_prepends_bos=True``, which position-shifts every activation on + non-BOS-prepending tokenizers (Qwen family).""" + tokenizer = setup_tokenizer( + tokenizer, default_padding_side=getattr(cfg, "default_padding_side", None) + ) + cfg.tokenizer_prepends_bos, cfg.tokenizer_appends_eos = detect_tokenizer_bos_eos(tokenizer) + return tokenizer + + def build_bridge_from_module( model: nn.Module, architecture: str, @@ -196,12 +209,7 @@ def build_bridge_from_module( adapter.prepare_model(model) if tokenizer is not None: - default_padding_side = getattr(adapter.cfg, "default_padding_side", None) - tokenizer = setup_tokenizer(tokenizer, default_padding_side=default_padding_side) - ( - adapter.cfg.tokenizer_prepends_bos, - adapter.cfg.tokenizer_appends_eos, - ) = detect_tokenizer_bos_eos(tokenizer) + tokenizer = configure_tokenizer(tokenizer, adapter.cfg) from transformer_lens.model_bridge.sources.transformers_driver import ( TransformersDriver, diff --git a/transformer_lens/model_bridge/sources/inspect/_provider_base.py b/transformer_lens/model_bridge/sources/inspect/_provider_base.py index 6248bcbafe..55013994c6 100644 --- a/transformer_lens/model_bridge/sources/inspect/_provider_base.py +++ b/transformer_lens/model_bridge/sources/inspect/_provider_base.py @@ -18,6 +18,7 @@ import json import re import uuid +import warnings from typing import Any, Mapping import torch @@ -79,6 +80,34 @@ def _parse_tool_calls(text: str) -> list[ToolCall] | None: return calls or None +# Generation-semantics GenerateConfig fields neither provider maps; warn (once per field +# per process) instead of silently ignoring them. +_UNSUPPORTED_GENERATE_FIELDS = ( + "frequency_penalty", + "presence_penalty", + "logit_bias", + "best_of", + "num_choices", +) +_WARNED_UNSUPPORTED: set[str] = set() + + +def _warn_unsupported_config(config: GenerateConfig, provider: str) -> None: + """Warn once per process per set-but-unsupported GenerateConfig field.""" + new = [ + field + for field in _UNSUPPORTED_GENERATE_FIELDS + if getattr(config, field, None) is not None and field not in _WARNED_UNSUPPORTED + ] + if new: + _WARNED_UNSUPPORTED.update(new) + warnings.warn( + f"{provider}: ignoring unsupported GenerateConfig field(s) {new}.", + UserWarning, + stacklevel=3, + ) + + def _require_served(kind: str, served: frozenset[str], note: str, context: str) -> None: """Raise if ``kind`` was gated by the structural self-check — without this the eval path would silently return a derivation (e.g. ``resid_mid``) the driver path excludes.""" @@ -89,6 +118,19 @@ def _require_served(kind: str, served: frozenset[str], note: str, context: str) ) +def _require_interveneable(kind: str, served: frozenset[str], note: str, context: str) -> None: + """``_require_served`` plus the capture-only gate — shared by every provider's + intervention entry point so gated/capture-only kinds fail identically.""" + from .hooks import INTERVENEABLE_KINDS + + _require_served(kind, served, note, context) + if kind not in INTERVENEABLE_KINDS: + raise ValueError( + f"{context}: kind {kind!r} is capture-only " + f"(interveneable: {sorted(INTERVENEABLE_KINDS)})." + ) + + class _InspectModelAPIBase(ModelAPI): """Shared Inspect ModelAPI scaffolding for ``tl_bridge``-style providers. diff --git a/transformer_lens/model_bridge/sources/inspect/driver.py b/transformer_lens/model_bridge/sources/inspect/driver.py index 518036b276..2460b437df 100644 --- a/transformer_lens/model_bridge/sources/inspect/driver.py +++ b/transformer_lens/model_bridge/sources/inspect/driver.py @@ -57,6 +57,7 @@ def __init__(self, model: Any, adapter: Any, tokenizer: Any, profile: Any = None # Background event loop, created lazily on first forward. self._loop: asyncio.AbstractEventLoop | None = None self._loop_thread: threading.Thread | None = None + self._loop_lock = threading.Lock() # guards lazy creation / abandonment self._warned_missing: set[str] = set() # hooks we've already warned were absent def forward( @@ -143,37 +144,63 @@ def _warn_missing(self, missing: list[str]) -> None: def close(self) -> None: log = logging.getLogger("transformer_lens.inspect") - if self._loop is not None: + with self._loop_lock: + loop, self._loop = self._loop, None + thread, self._loop_thread = self._loop_thread, None + if loop is not None: try: - self._loop.call_soon_threadsafe(self._loop.stop) - if self._loop_thread is not None: - self._loop_thread.join(timeout=5) - self._loop.close() + loop.call_soon_threadsafe(loop.stop) + if thread is not None: + thread.join(timeout=5) + loop.close() except Exception as e: log.debug("event-loop teardown failed during close(): %s", e) - self._loop = None - self._loop_thread = None self._model = None # drop the provider reference; the server owns its own lifecycle # ---- helpers ---- def _ensure_loop(self) -> asyncio.AbstractEventLoop: """A private loop on a daemon thread — works even when the caller is already - inside a running loop (Jupyter), unlike asyncio.run().""" - if self._loop is None: - self._loop = asyncio.new_event_loop() - self._loop_thread = threading.Thread( - target=self._loop.run_forever, daemon=True, name="inspect-driver-loop" - ) - self._loop_thread.start() - return self._loop + inside a running loop (Jupyter), unlike asyncio.run(). Locked: unlocked + check-then-create would leak loops under concurrent first calls.""" + loop = self._loop + if loop is not None: # GIL-safe fast path; the lock only guards create/abandon + return loop + with self._loop_lock: + if self._loop is None: + self._loop = asyncio.new_event_loop() + self._loop_thread = threading.Thread( + target=self._loop.run_forever, daemon=True, name="inspect-driver-loop" + ) + self._loop_thread.start() + return self._loop + + def _abandon_loop(self) -> None: + """After a timeout the loop thread is still occupied by the hung forward (cancel + can't interrupt sync work), so every later call would queue behind it and time out. + Abandon it — the daemon thread stops once the hung call returns — and rebuild + lazily on the next forward.""" + with self._loop_lock: + loop, self._loop, self._loop_thread = self._loop, None, None + if loop is not None: + try: + loop.call_soon_threadsafe(loop.stop) # takes effect when the hung call ends + except Exception: + pass + warnings.warn( + "InspectDriver: abandoning the wedged event-loop thread after a provider " + "timeout; a fresh loop will be created on the next forward.", + UserWarning, + stacklevel=3, + ) def _run_coro(self, coro: Any) -> Any: future = asyncio.run_coroutine_threadsafe(coro, self._ensure_loop()) try: return future.result(timeout=_PROVIDER_TIMEOUT_S) # re-raises provider errors except FutureTimeout: - future.cancel() + future.cancel() # best-effort; can't interrupt a sync forward on the loop thread + self._abandon_loop() raise TimeoutError( f"Inspect provider call exceeded {_PROVIDER_TIMEOUT_S:.0f}s " "(set TL_INSPECT_TIMEOUT_S to change) — the remote/provider forward looks " diff --git a/transformer_lens/model_bridge/sources/inspect/eval.py b/transformer_lens/model_bridge/sources/inspect/eval.py index 176d6036a4..006679daef 100644 --- a/transformer_lens/model_bridge/sources/inspect/eval.py +++ b/transformer_lens/model_bridge/sources/inspect/eval.py @@ -73,8 +73,13 @@ async def solve(state: TaskState, generate: Generate) -> TaskState: ) activations = {name_by_wire[wk]: arr for wk, arr in decoded.items()} os.makedirs(output_dir, exist_ok=True) - path = os.path.join(output_dir, f"{state.sample_id}.npz") - np.savez_compressed(path, **activations) + # Epoch in the name — multi-epoch runs reuse sample_ids and would overwrite. + epoch = getattr(state, "epoch", None) + stem = f"{state.sample_id}_epoch{epoch}" if epoch is not None else str(state.sample_id) + path = os.path.join(output_dir, f"{stem}.npz") + # Explicit allow_pickle: plain float arrays never need pickle, and it keeps + # the **activations unpack off numpy 2.3's typed keyword slot. + np.savez_compressed(path, allow_pickle=False, **activations) store().set(store_key, reduce_fn(activations)) store().set(f"{store_key}_path", path) return state diff --git a/transformer_lens/model_bridge/sources/inspect/profiles.py b/transformer_lens/model_bridge/sources/inspect/profiles.py index 225317bb7a..7be2ae40cb 100644 --- a/transformer_lens/model_bridge/sources/inspect/profiles.py +++ b/transformer_lens/model_bridge/sources/inspect/profiles.py @@ -141,4 +141,10 @@ def for_provider(provider: str) -> Any: """Pick the codec for a provider name (the part before ``/`` in get_model).""" if provider.startswith("vllm-lens"): return VLLMLensProfile() - return TLBridgeProfile() + if provider in ("tl_bridge", "tl_bridge_vllm"): + return TLBridgeProfile() + # An unknown provider would otherwise get full-capability codec and NaN downstream. + raise ValueError( + f"No Inspect codec for provider {provider!r}. Known providers: 'tl_bridge', " + "'tl_bridge_vllm', 'vllm-lens*'." + ) diff --git a/transformer_lens/model_bridge/sources/inspect/source.py b/transformer_lens/model_bridge/sources/inspect/source.py index 656d31558b..0d25338dd0 100644 --- a/transformer_lens/model_bridge/sources/inspect/source.py +++ b/transformer_lens/model_bridge/sources/inspect/source.py @@ -13,9 +13,8 @@ from transformer_lens.model_bridge.remote_bridge import RemoteBridge from transformer_lens.model_bridge.sources._bridge_builder import ( build_bridge_config_from_hf, - detect_tokenizer_bos_eos, + configure_tokenizer, ) -from transformer_lens.model_bridge.sources._hf_format import setup_tokenizer from transformer_lens.utilities.hf_utils import get_hf_token from . import profiles @@ -78,13 +77,7 @@ def boot_inspect( if tokenizer is None: tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token) # Match boot_transformers' tokenizer setup so to_tokens(str) is token-identical. - tokenizer = setup_tokenizer( - tokenizer, default_padding_side=getattr(adapter.cfg, "default_padding_side", None) - ) - ( - adapter.cfg.tokenizer_prepends_bos, - adapter.cfg.tokenizer_appends_eos, - ) = detect_tokenizer_bos_eos(tokenizer) + tokenizer = configure_tokenizer(tokenizer, adapter.cfg) if provider == "tl_bridge": # The provider's raw HF forward must match boot_transformers' load: same dtype, @@ -93,6 +86,10 @@ def boot_inspect( inspect_kwargs["model_kwargs"] = _provider_model_kwargs( dict(inspect_kwargs.get("model_kwargs", {})), adapter, resolved_dtype, hf_token ) + elif provider == "tl_bridge_vllm": + # Otherwise the provider defaults to the HF-config dtype and bridge_config.dtype + # lies about what the engine actually loaded. + inspect_kwargs["dtype"] = resolved_dtype # memoize=False: inspect_ai caches get_model by name, which would (a) return a stale # model ignoring a changed dtype/kwargs on re-boot and (b) keep weights resident past diff --git a/transformer_lens/model_bridge/sources/inspect/transformers_provider.py b/transformer_lens/model_bridge/sources/inspect/transformers_provider.py index 74ca10b513..fef520df9e 100644 --- a/transformer_lens/model_bridge/sources/inspect/transformers_provider.py +++ b/transformer_lens/model_bridge/sources/inspect/transformers_provider.py @@ -30,7 +30,13 @@ ) from . import hooks, wire -from ._provider_base import _InspectModelAPIBase, _parse_tool_calls, _require_served +from ._provider_base import ( + _InspectModelAPIBase, + _parse_tool_calls, + _require_interveneable, + _require_served, + _warn_unsupported_config, +) # NOT "transformer_lens" — inspect_ai ships a built-in provider by that name (the # reverse direction: serving a HookedTransformer as an Inspect model for generation). @@ -126,17 +132,13 @@ def _generate_capture(self, input: Any, extra_args: Mapping[str, Any], config: G _, _, kind = key.partition(":") _require_served(kind, self._kinds, self._capability_note, f"capture {key!r}") interventions: Mapping[str, Any] = extra_args.get("interventions", {}) - # The driver validates before sending, but this direct interface (extra_args) is - # documented — validate here too so a gated/capture-only kind fails loud instead - # of silently no-op'ing when no hook installs for it. + # extra_args is a documented surface: gated/capture-only kinds must fail here, + # not silently no-op when no hook installs for them. for key in interventions: _, _, kind = key.partition(":") - _require_served(kind, self._kinds, self._capability_note, f"intervention {key!r}") - if kind not in hooks.INTERVENEABLE_KINDS: - raise ValueError( - f"intervention {key!r}: kind {kind!r} is capture-only " - f"(interveneable: {sorted(hooks.INTERVENEABLE_KINDS)})." - ) + _require_interveneable( + kind, self._kinds, self._capability_note, f"intervention {key!r}" + ) want_logits = bool(extra_args.get("return_logits", True)) capture, intervene = _plan(capture_keys, interventions) @@ -189,6 +191,7 @@ def _generate_eval(self, input: Any, config: GenerateConfig, tools: Any): """Plain Inspect generation: HF generate from the chat input (rendering ``tools`` into the template), honoring max_tokens/sampling, with optional per-token logprobs, token usage, parsed tool calls, and per-turn activation capture (agent rollouts).""" + _warn_unsupported_config(config, PROVIDER_NAME) ids = self._messages_to_ids(input, tools) prompt_len = int(ids.shape[1]) max_new = int(config.max_tokens) if config.max_tokens else 16 @@ -201,6 +204,10 @@ def _generate_eval(self, input: Any, config: GenerateConfig, tools: Any): "output_scores": True, "pad_token_id": self._tokenizer.pad_token_id or self._tokenizer.eos_token_id, } + if config.stop_seqs: + # transformers turns stop_strings into a StopStringCriteria; needs the tokenizer. + gen["stop_strings"] = list(config.stop_seqs) + gen["tokenizer"] = self._tokenizer if temperature is not None and temperature > 0: gen["temperature"] = float(temperature) if config.top_p is not None: @@ -300,6 +307,8 @@ def _install_hooks(self, capture, intervene, raw, call_id: str) -> list: continue attn = _first_attr(block, _ATTN_ATTRS) _, mlp_out_mod = _locate_mlp(block) + # Capabilities were probed on layer 0; hybrid archs can lack the module here. + _require_layer_modules(layer, attn, mlp_out_mod, cap_kinds, iv_kinds) if "resid_pre" in cap_kinds or "resid_pre" in iv_kinds: handles.append( block.register_forward_pre_hook( @@ -407,6 +416,26 @@ def _install_head_hooks(self, layer, attn, cap_kinds, iv_kinds, raw, call_id: st return handles +def _require_layer_modules( + layer: int, attn: Any, mlp_out_mod: Any, cap_kinds: Any, iv_kinds: Any +) -> None: + """Fail loud when this layer lacks a targeted submodule — detection ran on layer 0, + so hybrid attn/SSM stacks would otherwise install nothing and silently no-op.""" + wanted = set(cap_kinds) | set(iv_kinds) + if attn is None: + needs_attn = wanted & ({"attn_out"} | (hooks.HEAD_KINDS - {"pattern"})) + if needs_attn: + raise RuntimeError( + f"blocks.{layer} has no attention submodule: cannot serve " + f"{sorted(needs_attn)} at layer {layer} (heterogeneous layers)." + ) + if mlp_out_mod is None and "mlp_out" in wanted: + raise RuntimeError( + f"blocks.{layer} has no MLP submodule: cannot serve mlp_out at layer " + f"{layer} (heterogeneous layers)." + ) + + def _plan(capture_keys, interventions): """Resolve wire keys → per-layer kinds to capture (resid_mid needs pre+attn) and intervene.""" capture: dict[int, set[str]] = defaultdict(set) diff --git a/transformer_lens/model_bridge/sources/inspect/vllm_provider.py b/transformer_lens/model_bridge/sources/inspect/vllm_provider.py index f04abb15a8..43f4c59843 100644 --- a/transformer_lens/model_bridge/sources/inspect/vllm_provider.py +++ b/transformer_lens/model_bridge/sources/inspect/vllm_provider.py @@ -36,7 +36,13 @@ ) from . import hooks, wire -from ._provider_base import _InspectModelAPIBase, _parse_tool_calls, _require_served +from ._provider_base import ( + _InspectModelAPIBase, + _parse_tool_calls, + _require_interveneable, + _require_served, + _warn_unsupported_config, +) # Distinct from the HF ``tl_bridge`` provider and from inspect_ai's built-in ``vllm``. PROVIDER_NAME = "tl_bridge_vllm" @@ -96,7 +102,15 @@ def __init__( ) -> None: super().__init__(model_name, base_url, api_key, [], config) from transformers import AutoConfig, AutoTokenizer - from vllm import LLM + + try: + from vllm import LLM + except ImportError as exc: + raise ImportError( + "The tl_bridge_vllm provider requires vLLM (Linux + CUDA). Install with " + 'pip install "transformer-lens[vllm]" or uv sync --extra vllm; ' + "validated against vllm 0.20.x." + ) from exc from transformer_lens.utilities.hf_utils import get_hf_token @@ -146,25 +160,28 @@ def __init__( # isn't visible to worker subprocesses and hooks silently fail to install. os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0" - self._llm = LLM( - model=model_name, - gpu_memory_utilization=gpu_memory_utilization, - max_model_len=max_model_len, - max_num_batched_tokens=max_num_batched_tokens, - # Worker extension's tl_* methods are reachable via collective_rpc. - worker_extension_cls=_WORKER_EXTENSION_CLS, - # Full-vocab logprobs so _generate_capture can synthesize logits at the - # generated position (vLLM caps logprobs to this value; default 20 is too - # small for mech interp). - max_logprobs=int(hf_config_preview.vocab_size), - dtype=str(resolved_dtype).replace("torch.", "") if dtype is not None else "auto", - **_LOCKED_VLLM_KWARGS, - **vllm_kwargs, - ) + try: + self._llm = LLM( + model=model_name, + gpu_memory_utilization=gpu_memory_utilization, + max_model_len=max_model_len, + max_num_batched_tokens=max_num_batched_tokens, + # Worker extension's tl_* methods are reachable via collective_rpc. + worker_extension_cls=_WORKER_EXTENSION_CLS, + # Full-vocab logprobs so _generate_capture can synthesize logits at the + # generated position (vLLM caps logprobs to this value; default 20 is too + # small for mech interp). + max_logprobs=int(hf_config_preview.vocab_size), + dtype=str(resolved_dtype).replace("torch.", "") if dtype is not None else "auto", + **_LOCKED_VLLM_KWARGS, + **vllm_kwargs, + ) + finally: + # Always clear, even on a failed boot — stale specs would make the next + # in-process vllm.LLM(...) walk our dot-paths on a foreign model. + plugin._config.clear() # Capture-path validity was enforced inside patched_load_model during LLM(...). hf_config = extract_hf_config(self._llm) - # Don't leak our specs to a subsequent non-TL vllm.LLM(...) in the same process. - plugin._config.clear() # Capture-relevant constants used by _generate_capture. self._d_vocab = int(hf_config.vocab_size) @@ -218,6 +235,17 @@ def _generate_capture(self, input: Any, extra_args: Mapping[str, Any], config: G interventions: Mapping[str, Any] = extra_args.get("interventions", {}) intervention_specs: dict[str, Any] = {} for wk, spec in interventions.items(): + # extra_args is a documented surface: gated/capture-only kinds must fail + # here, not deep in the worker. + _, _, kind = wk.partition(":") + _require_interveneable(kind, self._kinds, self._capability_note, f"intervention {wk!r}") + if isinstance(spec, Mapping) and spec.get("pos") is not None: + raise ValueError( + f"intervention {wk!r}: per-position 'pos' is not supported on the " + "tl_bridge_vllm provider (its worker runs without position " + "interventions). Use boot_inspect(provider='tl_bridge') for " + "position-targeted patching." + ) name = hooks.name_from_wire_key(wk) if name is None: raise ValueError(f"intervention wire key {wk!r} is not a fireable hook.") @@ -279,6 +307,8 @@ def _generate_eval(self, input: Any, config: GenerateConfig, tools: Any): from vllm import SamplingParams from vllm.inputs import TokensPrompt + _warn_unsupported_config(config, PROVIDER_NAME) + # Worker-side intervention buffers are persistent: any prior capture-path call that # pushed specs (e.g. bridge.forward(intervene=...)) would still be applied here # without a reset. The HF provider installs hooks per-call so it's leak-immune. @@ -296,6 +326,8 @@ def _generate_eval(self, input: Any, config: GenerateConfig, tools: Any): temperature = float(config.temperature) if config.temperature is not None else 0.0 sp_kwargs: dict[str, Any] = {"max_tokens": max_new, "temperature": temperature} + if config.stop_seqs: + sp_kwargs["stop"] = list(config.stop_seqs) if temperature > 0: if config.top_p is not None: sp_kwargs["top_p"] = float(config.top_p) diff --git a/transformer_lens/model_bridge/sources/transformers/source.py b/transformer_lens/model_bridge/sources/transformers/source.py index ce0c7def5b..4b358e28a6 100644 --- a/transformer_lens/model_bridge/sources/transformers/source.py +++ b/transformer_lens/model_bridge/sources/transformers/source.py @@ -57,7 +57,17 @@ def boot( n_devices: int | None = None, max_memory: dict[str | int, str] | None = None, ) -> TransformerBridge: - """Boot a model from HuggingFace. + """Boot a model from HuggingFace (exposed as ``TransformerBridge.boot_transformers``). + + Returns raw HF weights by default — logits/activations match HF, *not* + legacy ``HookedTransformer`` (which folds LayerNorm + centers weights). + Call ``enable_compatibility_mode()`` on the result for HookedTransformer- + equivalent numerics. Generation, argmax, and CE loss are unaffected. + + Attention implementation is forced to ``"eager"`` so hooks can capture scores + and patterns. For an apples-to-apples HF comparison, load the HF model with + ``attn_implementation="eager"`` too; comparing against the default ``"sdpa"`` + shows ~1e-3 fp32 drift from kernel-level op reordering, not a bridge bug. Args: model_name: The name of the model to load. diff --git a/transformer_lens/model_bridge/sources/transformers_driver.py b/transformer_lens/model_bridge/sources/transformers_driver.py index c745788e2b..bff87ae6b9 100644 --- a/transformer_lens/model_bridge/sources/transformers_driver.py +++ b/transformer_lens/model_bridge/sources/transformers_driver.py @@ -37,6 +37,25 @@ def forward( return_logits: bool = True, **kwargs: Any, ) -> ForwardResult: + # Module-replacement dialect: these args are served by the bridge's + # HookPoints, not here — silently ignoring them would be a lie. + if capture: + raise NotImplementedError( + "TransformersDriver.forward does not serve capture=: on the HF " + "backend activations are captured through the bridge's HookPoint " + "system — use run_with_cache()/run_with_hooks() on the bridge." + ) + if intervene is not None: + raise NotImplementedError( + "TransformersDriver.forward does not serve intervene=: on the HF " + "backend interventions are torch hooks — use run_with_hooks() on " + "the bridge." + ) + if max_new_tokens != 1: + raise NotImplementedError( + "TransformersDriver.forward does not generate; use " + "bridge.generate() for multi-token decoding." + ) if input_ids is not None: raw = self._model(input_ids, **kwargs) else: diff --git a/transformer_lens/model_bridge/sources/vllm/driver.py b/transformer_lens/model_bridge/sources/vllm/driver.py index 91e0270dce..aec008f45d 100644 --- a/transformer_lens/model_bridge/sources/vllm/driver.py +++ b/transformer_lens/model_bridge/sources/vllm/driver.py @@ -3,6 +3,8 @@ import gc import logging +import threading +import warnings from typing import Any, Mapping import torch @@ -14,7 +16,13 @@ ) from transformer_lens.model_bridge.sources._driver_base import DriverBase -from .intervention_specs import SUPPORTED_OPS +from .intervention_specs import validate_spec + +# vLLM's distributed teardown operates on process-wide globals, so close() may only run +# it when no other VLLMDriver-owned engine is alive (notebook re-binding boots B before +# dropping A; A's __del__ must not destroy the process groups B is using). +_LIVE_DRIVERS = 0 +_LIVE_DRIVERS_LOCK = threading.Lock() class VLLMDriver(DriverBase): @@ -52,8 +60,22 @@ def __init__( # Logprobs per forward = real vocab (boot's max_logprobs). d_vocab can be # padded larger, which vLLM would reject; the logits tensor stays d_vocab. self._n_logprobs = int(getattr(hf_config, "vocab_size", self.bridge_config.d_vocab)) - - self.supported_hook_points = frozenset(overlay.capture_specs(hf_config).keys()) + # Unembedding cache: (weight_fp32, bias_fp32|None) once probe_logit_reconstruction + # runs — a per-forward re-fetch clones a d_vocab×d_model tensor to CPU every call. + self._unembed: tuple[torch.Tensor, Any] | None = None + self._unembed_probed = False + # fp32 reciprocal of the guarded final-norm weight; None after a failed probe. + self._lnf_inv_denom: torch.Tensor | None = None + self._lnf_probed = False + + capture_specs = overlay.capture_specs(hf_config) + self._hook_widths = {name: width for name, (_path, width) in capture_specs.items()} + self._capture_paths = {name: path for name, (path, _width) in capture_specs.items()} + self.supported_hook_points = frozenset(capture_specs.keys()) + + global _LIVE_DRIVERS + with _LIVE_DRIVERS_LOCK: + _LIVE_DRIVERS += 1 n_layers = getattr(hf_config, "num_hidden_layers", 0) if not isinstance(n_layers, int) or n_layers <= 0: @@ -89,6 +111,9 @@ def forward( "overwrite the prefill buffer; multi-step capture is multi-buffer work." ) intervene_specs = self._validate_interventions(intervene or {}) + # Pad tokens fed to vLLM are real content to it (no mask concept) — honor a + # caller-supplied mask by trimming rows to their true lengths, never swallow it. + attention_mask = kwargs.pop("attention_mask", None) # capture is authoritative — the bridge sends exactly the hooked names, so () # means "capture nothing" and a plain forward(tokens) skips the GPU→CPU copy @@ -97,9 +122,14 @@ def forward( names = list(capture) if self._enable_batching: - return self._forward_batched(input_ids, intervene_specs, return_logits, names) + return self._forward_batched( + input_ids, intervene_specs, return_logits, names, attention_mask + ) ids_list = self._normalize_input_ids(input_ids) + if attention_mask is not None: + n_real = int(torch.as_tensor(attention_mask).sum()) + ids_list = ids_list[:n_real] if len(ids_list) > self._max_num_batched_tokens: # Worker buffers silently clamp on overflow — fail loud here instead. raise ValueError( @@ -121,22 +151,17 @@ def forward( # subsequent forwards self-copy. Without this, repeated bridge.forward calls would # see the gate closed from the prior call and read stale buffers. self._llm.collective_rpc("tl_reset_capture_flags") - # Full-vocab logprobs → position -1 of the synthesized logits (see _n_logprobs). outputs = self._llm.generate( prompts=[TokensPrompt(prompt_token_ids=ids_list)], sampling_params=SamplingParams( max_tokens=int(max_new_tokens), temperature=0.0, - logprobs=self._n_logprobs if return_logits else None, + logprobs=self._sampler_logprobs(return_logits), ), ) n_tokens = len(ids_list) - # Reconstructing logits needs ln_final; force it into the read even if the caller - # didn't request it (dropped from `captured` below so the surface stays as asked). - read_names = names - if return_logits and self._LN_FINAL not in names: - read_names = names + [self._LN_FINAL] + read_names = self._read_names(names, return_logits) # collective_rpc returns one result per worker; single-rank, so [0]. Nothing to # read (no captures, logits off) → skip the crossing altogether. worker_captures = ( @@ -155,16 +180,41 @@ def forward( else self._synthesize_logits(outputs[0], n_tokens, self.bridge_config.d_vocab) ) - # Add batch dim; expose only the caller's requested hooks (drop the forced ln_final). - captured = {name: t.unsqueeze(0) for name, t in worker_captures.items() if name in names} + captured = self._expose_captured( + {name: t.unsqueeze(0) for name, t in worker_captures.items()}, names + ) return ForwardResult(logits=logits, captured=captured, raw_output=outputs[0]) + def _sampler_logprobs(self, return_logits: bool) -> int | None: + # Sampler logprobs are the reconstruction fallback only; skipping them avoids + # marshaling a d_vocab-entry Logprob dict host-side per request. + return self._n_logprobs if return_logits and self._unembed is None else None + + def _read_names(self, names: list[str], return_logits: bool) -> list[str]: + # Reconstruction needs ln_final even when uncaptured; skip once probed-unavailable. + recon_possible = self._unembed is not None or not self._unembed_probed + if return_logits and recon_possible and self._LN_FINAL not in names: + return names + [self._LN_FINAL] + return names + + def _expose_captured( + self, captured: Mapping[str, torch.Tensor], names: list[str] + ) -> dict[str, torch.Tensor]: + """Filter to the caller's requested hooks (dropping any forced ln_final) and + convert the exposed ln_final to the pre-weight convention its name promises — + reconstruction consumed the raw post-weight value already.""" + out = {name: t for name, t in captured.items() if name in names} + if self._LN_FINAL in out: + out[self._LN_FINAL] = self._unfold_ln_final(out[self._LN_FINAL]) + return out + def _forward_batched( self, input_ids: TensorLike, intervene_specs: dict, return_logits: bool, names: list[str], + attention_mask: Any = None, ) -> ForwardResult: """Eager batched path: per-request capture, right-padded to (B, S, W). @@ -176,6 +226,18 @@ def _forward_batched( from vllm.inputs import TokensPrompt prompts_ids = self._normalize_input_ids_batched(input_ids) + if attention_mask is not None: + # Right-padded tensor batches carry pad ids vLLM would treat as content; + # trim each row to its masked length so per-row final positions are real. + mask = torch.as_tensor(attention_mask) + if mask.dim() == 1: + mask = mask.unsqueeze(0) + if mask.shape[0] != len(prompts_ids): + raise ValueError( + f"attention_mask batch dim {mask.shape[0]} != number of prompts " + f"{len(prompts_ids)}." + ) + prompts_ids = [ids[: int(row.sum())] for ids, row in zip(prompts_ids, mask)] prompt_lens = [len(ids) for ids in prompts_ids] # Reset accumulators so prior-forward chunks don't leak into the cat. @@ -189,15 +251,11 @@ def _forward_batched( sampling_params=SamplingParams( max_tokens=1, temperature=0.0, - logprobs=self._n_logprobs if return_logits else None, + logprobs=self._sampler_logprobs(return_logits), ), ) - # Force ln_final into the read so logits can be reconstructed (dropped below if - # the caller didn't ask for it). - read_names = names - if return_logits and self._LN_FINAL not in names: - read_names = names + [self._LN_FINAL] + read_names = self._read_names(names, return_logits) # Keyed by req_id (no guaranteed order) — _assemble_padded joins to slot # k via outputs[k].request_id, not by position. Empty read → skip both the # crossing AND the join (_assemble_padded requires one worker key per request, @@ -215,13 +273,18 @@ def _forward_batched( recon = self._reconstruct_logits( captured.get(self._LN_FINAL) ) # (batch, max_seq, d_vocab) + if recon is not None: + # Pad rows reconstruct from zero-filled ln_final into finite garbage + # (0 @ W = plausible uniform logits) — mask them to the -inf convention + # the fallback and per-row consumers rely on. + for k, n in enumerate(prompt_lens): + recon[k, n:] = float("-inf") logits = ( recon if recon is not None else self._synthesize_logits_batched(outputs, prompt_lens, d_vocab) ) - if self._LN_FINAL not in names: - captured.pop(self._LN_FINAL, None) + captured = self._expose_captured(captured, names) return ForwardResult(logits=logits, captured=captured, raw_output=outputs) @@ -304,63 +367,126 @@ def get_param(self, dotted_name: str) -> torch.Tensor | None: return None return self._llm.collective_rpc("tl_get_param", args=(dotted_name,))[0] + def probe_logit_reconstruction(self) -> bool: + """One-time unembedding fetch + cache; downgrades ``provides_sequence_logits`` + honestly when no unembedding is reachable (the fallback path is final-position + log-probs, which cannot back a loss). Idempotent — later calls return the + cached availability.""" + if self._unembed_probed: + return self._unembed is not None + self._unembed_probed = True + weight = self.get_param("lm_head.weight") + if weight is None: # tied embeddings expose no separate lm_head + weight = self.get_param("model.embed_tokens.weight") + if weight is None: + self.provides_sequence_logits = False + return False + bias = self.get_param("lm_head.bias") + d_vocab = int(self.bridge_config.d_vocab) + # Slice vLLM's vocab-pad rows at cache time; fp32 residency (~2× checkpoint + # dtype on CPU) trades memory for skipping a full-matrix upcast per forward. + self._unembed = ( + weight.to(torch.float32)[:d_vocab], + bias.to(torch.float32)[:d_vocab] if bias is not None else None, + ) + self.provides_sequence_logits = True + return True + def _reconstruct_logits(self, ln_final: Any) -> torch.Tensor | None: """Rebuild real logits from the captured post-weight ln_final: ``ln_final @ lm_head.weight.T`` (+ bias, + Gemma-family tanh soft-cap). vLLM's ``ln_final.hook_normalized`` is the POST-weight RMSNorm value lm_head consumes (verified empirically: it equals HF's pre-weight value times the norm - weight), so no un-fold is needed. Accepts any ``(..., d_model)`` tensor and returns - ``(..., d_vocab)`` on CPU. ``None`` if ln_final wasn't captured or no unembedding - weight is fetchable — the caller then falls back to the sampler's log-probs. + weight), so no un-fold is needed here — the raw worker value feeds this directly. + Accepts any ``(..., d_model)`` tensor and returns ``(..., d_vocab)`` on CPU. + ``None`` if ln_final wasn't captured or no unembedding weight is fetchable — + the caller then falls back to the sampler's log-probs. """ - if ln_final is None: + if ln_final is None or not self.probe_logit_reconstruction(): return None - weight = self.get_param("lm_head.weight") - if weight is None: # tied embeddings expose no separate lm_head - weight = self.get_param("model.embed_tokens.weight") - if weight is None: - return None - lf = ln_final.to(device=weight.device, dtype=torch.float32) - logits = lf @ weight.to(torch.float32).T - bias = self.get_param("lm_head.bias") - if bias is not None: - logits = logits + bias.to(device=logits.device, dtype=torch.float32) + assert self._unembed is not None # probe returned True + weight32, bias32 = self._unembed + lf = ln_final.to(device=weight32.device, dtype=torch.float32) + logits = lf @ weight32.T + if bias32 is not None: + logits = logits + bias32.to(device=logits.device) + d_vocab = int(self.bridge_config.d_vocab) + if logits.shape[-1] > d_vocab: + # vLLM pads vocab embeddings to a multiple of 64; its own sampler slices to + # org_vocab_size before sampling — mirror that, or pad columns (zero-filled + # at load) become phantom argmax candidates and bias softmax denominators. + logits = logits[..., :d_vocab] cap = getattr(self.bridge_config, "output_logits_soft_cap", None) if cap is not None and cap > 0: # Gemma-family cap; -1.0 is the "disabled" sentinel logits = float(cap) * torch.tanh(logits / float(cap)) - d_vocab = int(self.bridge_config.d_vocab) if logits.shape[-1] < d_vocab: # pad the padded-vocab tail (never predicted) pad = logits.new_full((*logits.shape[:-1], d_vocab - logits.shape[-1]), float("-inf")) logits = torch.cat([logits, pad], dim=-1) return logits.cpu() + def _unfold_ln_final(self, t: torch.Tensor) -> torch.Tensor: + """Convert vLLM's post-weight RMSNorm capture to the pre-weight value the + canonical hook name promises (÷ weight; Gemma folds ``1 + weight``). Warns + once and serves the raw value when the norm weight is unreachable — loud + beats silent cross-backend mismatch.""" + if not self._lnf_probed: + self._lnf_probed = True + # The overlay's capture spec owns the module path; derive the weight from it. + path = self._capture_paths.get(self._LN_FINAL, "model.norm") + weight = self.get_param(f"{path}.weight") + if weight is None: + warnings.warn( + "ln_final.hook_normalized: norm weight unreachable — the captured " + "value stays POST-weight and will not match boot_transformers.", + UserWarning, + stacklevel=3, + ) + else: + w = weight.detach().to(torch.float32) + denom = (1.0 + w) if "gemma" in self.architecture.lower() else w + # Near-zero weight entries would blow up the division; identity beats inf. + denom = torch.where(denom.abs() < 1e-6, torch.ones_like(denom), denom) + self._lnf_inv_denom = denom.reciprocal() + if self._lnf_inv_denom is None: + return t + inv = self._lnf_inv_denom.to(device=t.device) + return (t.to(torch.float32) * inv).to(t.dtype) + def close(self) -> None: + if self._llm is None: # already closed — keep the refcount single-shot + return # Detach hooks before dropping the LLM so they don't stay registered on # worker modules for the life of the process (long-running notebooks). log = logging.getLogger("transformer_lens.vllm") - if self._llm is not None: - try: - self._llm.collective_rpc("tl_remove_hooks") - except Exception as e: - # Best-effort: engine may already be torn down or the RPC surface - # gone. Log so hook-leak debugging has a thread to pull. - log.debug("tl_remove_hooks failed during close(): %s", e) - self._llm = None - # vLLM 0.20.2 has no LLM.shutdown() — model weights and KV cache stay - # resident until process exit unless we explicitly tear down the - # distributed environment vLLM set up at construction. Both calls are - # best-effort: they're no-ops if there's no distributed state. try: - from vllm.distributed.parallel_state import ( - destroy_distributed_environment, - destroy_model_parallel, - ) - - destroy_model_parallel() - destroy_distributed_environment() + self._llm.collective_rpc("tl_remove_hooks") except Exception as e: - log.debug("vLLM distributed teardown failed during close(): %s", e) + # Best-effort: engine may already be torn down or the RPC surface + # gone. Log so hook-leak debugging has a thread to pull. + log.debug("tl_remove_hooks failed during close(): %s", e) + self._llm = None + self._unembed = None + self._lnf_inv_denom = None + + global _LIVE_DRIVERS + with _LIVE_DRIVERS_LOCK: + _LIVE_DRIVERS -= 1 + last_driver = _LIVE_DRIVERS <= 0 + # vLLM 0.20.2 has no LLM.shutdown(); the distributed teardown below hits + # process-global state, so it runs only for the last live driver (see + # _LIVE_DRIVERS). Both calls are best-effort no-ops otherwise. + if last_driver: + try: + from vllm.distributed.parallel_state import ( + destroy_distributed_environment, + destroy_model_parallel, + ) + + destroy_model_parallel() + destroy_distributed_environment() + except Exception as e: + log.debug("vLLM distributed teardown failed during close(): %s", e) # Free the caching allocator's blocks here so the caller doesn't have to. gc.collect() try: @@ -392,40 +518,15 @@ def _synthesize_logits(request_output: Any, n_tokens: int, d_vocab: int) -> torc return logits def _validate_interventions(self, intervene: Mapping[str, Any]) -> dict: - """Reject callables, validate spec format and hook names; return a plain dict.""" + """Shared spec validation plus driver-side gating (hook membership, pos support).""" out: dict = {} for hook_name, spec in intervene.items(): - if callable(spec): - raise NotImplementedError( - "VLLMDriver requires intervention specs (dict), not callables. " - "Supported ops: suppress, scale (factor: float), add (value: scalar or width-shaped), " - "set (value: scalar or width-shaped)." - ) - if not isinstance(spec, Mapping) or "op" not in spec: - raise ValueError( - f"Intervention spec for {hook_name!r} must be a dict with 'op' key; got {spec!r}" - ) - op = spec["op"] - if op not in SUPPORTED_OPS: - raise ValueError( - f"Unsupported intervention op {op!r} for {hook_name!r}. " - f"Supported: {sorted(SUPPORTED_OPS)}." - ) - if op == "scale" and "factor" not in spec: - raise ValueError( - f"Intervention {hook_name!r}: op='scale' requires 'factor' (float)." - ) - if op in ("add", "set") and "value" not in spec: - raise ValueError( - f"Intervention {hook_name!r}: op={op!r} requires 'value' " - "(scalar or width-shaped tensor/list)." - ) + validated = validate_spec(hook_name, spec, width=self._hook_widths.get(hook_name)) if hook_name not in self.supported_hook_points: raise ValueError( f"Cannot intervene on {hook_name!r}: not in supported_hook_points." ) - pos = spec.get("pos") - if pos is not None: + if validated.get("pos") is not None: if self._enable_batching: # The batched/eager path applies ops to the raw tensor, not the # (max_n, width) affine buffers, so it has no position surface. @@ -443,20 +544,7 @@ def _validate_interventions(self, intervene: Mapping[str, Any]) -> dict: "buffers broadcast across all positions). Use the Inspect/HF backend for " "position-scoped patching, or drop 'pos' for a whole-sequence edit." ) - if not ( - isinstance(pos, int) - or (isinstance(pos, (list, tuple)) and all(isinstance(p, int) for p in pos)) - ): - raise ValueError( - f"Intervention {hook_name!r}: 'pos' must be an int or list of ints " - f"(sequence positions to patch); got {pos!r}." - ) - bad = [p for p in ([pos] if isinstance(pos, int) else pos) if p < 0] - if bad: - raise ValueError( - f"Intervention {hook_name!r}: 'pos' must be non-negative; got {bad}." - ) - out[hook_name] = dict(spec) + out[hook_name] = validated return out @staticmethod diff --git a/transformer_lens/model_bridge/sources/vllm/intervention_specs.py b/transformer_lens/model_bridge/sources/vllm/intervention_specs.py index ab35dac72d..4181de8b75 100644 --- a/transformer_lens/model_bridge/sources/vllm/intervention_specs.py +++ b/transformer_lens/model_bridge/sources/vllm/intervention_specs.py @@ -1,9 +1,88 @@ -"""Intervention op vocabulary shared by the driver (spec validation) and the -worker extension (spec → buffer translation). +"""Intervention op vocabulary + spec validation shared by every producer path +(VLLMDriver, the worker RPCs — which also cover the Inspect vLLM provider). -Adding a new op requires (a) listing it here, (b) handling it in -``worker_extension._apply_intervention``. +Adding a new op requires (a) listing it here, (b) allowing its keys in +``validate_spec``, (c) handling it in ``worker_extension._apply_intervention``. """ from __future__ import annotations +from typing import Any, Mapping + +import torch + SUPPORTED_OPS = frozenset({"suppress", "scale", "add", "set"}) + + +def validate_spec(hook_name: str, spec: Any, *, width: int | None = None) -> dict: + """Validate one declarative intervention spec; returns a plain-dict copy. + + ``width`` enables the value-shape check where the caller knows the hook width. + """ + if callable(spec): + raise NotImplementedError( + "vLLM accepts intervention specs (dict), not callables. " + "Supported ops: suppress, scale (factor: float), add (value: scalar or " + "width-shaped), set (value: scalar or width-shaped)." + ) + if not isinstance(spec, Mapping) or "op" not in spec: + raise ValueError( + f"Intervention spec for {hook_name!r} must be a dict with 'op' key; got {spec!r}" + ) + op = spec["op"] + if op not in SUPPORTED_OPS: + raise ValueError( + f"Unsupported intervention op {op!r} for {hook_name!r}. " + f"Supported: {sorted(SUPPORTED_OPS)}." + ) + if op == "scale" and "factor" not in spec: + raise ValueError(f"Intervention {hook_name!r}: op='scale' requires 'factor' (float).") + if op in ("add", "set") and "value" not in spec: + raise ValueError( + f"Intervention {hook_name!r}: op={op!r} requires 'value' " + "(scalar or width-shaped tensor/list)." + ) + # Unknown keys must fail loud: a typo'd "position"/"positions" would otherwise + # silently become a whole-sequence edit. + allowed = {"op", "pos"} + if op == "scale": + allowed.add("factor") + if op in ("add", "set"): + allowed.add("value") + extra = set(spec) - allowed + if extra: + raise ValueError( + f"Intervention {hook_name!r}: unknown spec key(s) {sorted(extra)}; " + f"allowed for op={op!r}: {sorted(allowed)}." + ) + value = spec.get("value") + if value is not None and not isinstance(value, (int, float)): + try: + n_elements = int(torch.as_tensor(value).numel()) + except (TypeError, ValueError, RuntimeError) as exc: + raise ValueError( + f"Intervention {hook_name!r}: 'value' must be a scalar or a " + f"width-shaped tensor/list; got {type(value).__name__}." + ) from exc + if width is not None and n_elements != width: + # A mis-shaped value would otherwise surface as a broadcast error + # mid-forward — or broadcast along the wrong axis in square cases. + raise ValueError( + f"Intervention {hook_name!r}: 'value' has {n_elements} elements " + f"but the hook width is {width}." + ) + pos = spec.get("pos") + if pos is not None: + valid_pos = isinstance(pos, int) and not isinstance(pos, bool) + if not valid_pos and isinstance(pos, (list, tuple)): + valid_pos = all(isinstance(p, int) and not isinstance(p, bool) for p in pos) + if not valid_pos: + raise ValueError( + f"Intervention {hook_name!r}: 'pos' must be an int or list of ints " + f"(sequence positions to patch); got {pos!r}." + ) + negative = [p for p in ([pos] if isinstance(pos, int) else pos) if p < 0] + if negative: + raise ValueError( + f"Intervention {hook_name!r}: 'pos' must be non-negative; got {negative}." + ) + return dict(spec) diff --git a/transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py b/transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py index 0ce38efb37..50b2e80d3c 100644 --- a/transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py +++ b/transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py @@ -15,11 +15,11 @@ separately (fused-residual). The plugin's hook materializes the sum so the captured value matches HF's "post-MLP residual stream". - ``ln_final.hook_normalized``: vLLM exposes ``x * rsqrt(var+eps) * weight``; - HF/HT exposes the pre-weight value. They are NOT auto-converted — the cache - carries vLLM's post-weight value under this name, so a direct diff against - ``boot_transformers`` will mismatch here. To convert, fetch the weight via - ``bridge._driver.get_param("model.norm.weight")`` and divide the capture by it - (or by ``1 + weight`` for Gemma). + HF/HT exposes the pre-weight value. The driver un-folds the user-facing + capture (÷ weight, or ÷ (1 + weight) for Gemma) so the cache matches + ``boot_transformers``; logit reconstruction consumes the raw post-weight + value internally. If the norm weight is unreachable the driver warns and + serves the raw post-weight value. """ from __future__ import annotations @@ -58,6 +58,17 @@ def nonfiring_hooks(self) -> List[str]: "blocks.{i}.attn.hook_attn_scores", "blocks.{i}.attn.hook_rot_q", "blocks.{i}.attn.hook_rot_k", + # Head-split projections live inside the fused QKVParallelLinear / + # RowParallelLinear kernels — unobservable per-head. + "blocks.{i}.attn.hook_q", + "blocks.{i}.attn.hook_k", + "blocks.{i}.attn.hook_v", + "blocks.{i}.attn.hook_z", + # Block/MLP inputs: vLLM passes (hidden, residual) fused between layers, + # so the HF-convention pre-block/pre-MLP stream is never materialized. + "blocks.{i}.hook_in", + "blocks.{i}.mlp.hook_in", + "blocks.{i}.attn.hook_in", # vLLM's sampler bypasses lm_head.__call__ — capture-via-forward-hook # never fires; driver synthesizes argmax-matching logits from the # sampler's returned token. diff --git a/transformer_lens/model_bridge/sources/vllm/source.py b/transformer_lens/model_bridge/sources/vllm/source.py index 6bb6351103..e8661c4681 100644 --- a/transformer_lens/model_bridge/sources/vllm/source.py +++ b/transformer_lens/model_bridge/sources/vllm/source.py @@ -3,6 +3,7 @@ import logging import os +import threading import warnings from typing import Any, Dict, Optional @@ -14,6 +15,7 @@ from transformer_lens.model_bridge.remote_bridge import RemoteBridge from transformer_lens.model_bridge.sources._bridge_builder import ( build_bridge_config_from_hf, + configure_tokenizer, ) from transformer_lens.utilities.hf_utils import get_hf_token @@ -23,13 +25,22 @@ from .overlays import get_overlay # Forced LLM(...) kwargs that the capture-hook design depends on. Caller override → ValueError. +# enable_prefix_caching MUST stay off: a prefix-cache hit makes the prefill compute only the +# uncached suffix, so hooks fire for a subset of positions — captures land row-misaligned, +# interventions skip cached positions, and an intervened forward writes poisoned K/V that a +# later clean forward on the same prompt would silently reuse. _LOCKED_KWARGS = { "tensor_parallel_size": 1, "pipeline_parallel_size": 1, "skip_tokenizer_init": True, "disable_log_stats": True, + "enable_prefix_caching": False, } +# Serializes the configure() → LLM(...) → clear() handoff: the spec channel is a module +# global, so interleaved boots would cross-wire capture specs between engines. +_BOOT_LOCK = threading.Lock() + def boot_vllm( model_name: str, @@ -54,21 +65,23 @@ def boot_vllm( affine transform ``output = output * scale + bias`` (default identity), so interventions (``suppress`` / ``scale`` / ``add`` / ``set``) propagate to downstream layers. The hook's return value replaces the module output per - PyTorch ``register_forward_hook`` semantics. **The mutation path under - torch.compile + CUDA graphs is verified end-to-end by** - ``demos/vLLM_Bridge_Integration_Test.ipynb``; unit tests cover the dispatch - protocol only. + PyTorch ``register_forward_hook`` semantics. The mutation path under + torch.compile + CUDA graphs is exercised end-to-end by + ``demos/vLLM_Bridge_Integration_Test.ipynb`` (a manual GPU run, not CI); + unit tests cover the dispatch protocol only. Some captures use vLLM-native conventions that differ from HF/HT; see :mod:`transformer_lens.model_bridge.sources.vllm.overlays.decoder_only` for which hooks diverge and the conversion to apply for HT-equivalent values. - **Returned logits are log-probs, not raw logits.** vLLM bypasses ``lm_head``; - the driver fills the final position from the sampler's ``log_softmax`` output. - Correct for argmax / next-token, but absolute scale is off — temperature - scaling and logit-lens magnitude analysis will be wrong. Only the final - position is populated (earlier positions are ``-inf``); ``return_type`` in - ``{"loss", "both"}`` is rejected for that reason. + **Returned logits are reconstructed full-sequence logits.** vLLM's sampler + bypasses ``lm_head``, so the driver rebuilds real logits host-side as + ``ln_final @ lm_head.weight.T`` (+ bias, + Gemma soft-cap) from the captured + final-norm activation — valid at every position, so ``return_type`` in + ``{"loss", "both"}`` works. If the unembedding weight is unreachable the + driver falls back to the sampler's final-position log-probs (earlier + positions ``-inf``), declares ``provides_sequence_logits=False``, and the + bridge then rejects loss. GPU memory cost: each capture buffer is ``max_num_batched_tokens × width`` at the model's dtype. For Llama-3.2-1B at fp16 with ``max_num_batched_tokens=2048``, @@ -102,6 +115,16 @@ def boot_vllm( "with enable_batching=True (the batched/eager path has no affine buffers)." ) _reject_locked_overrides(vllm_kwargs) + # Import-check first: fail with an actionable message before any network I/O + # or plugin state mutation. + try: + from vllm import LLM + except ImportError as exc: + raise ImportError( + "boot_vllm requires vLLM (Linux + CUDA). Install with " + 'pip install "transformer-lens[vllm]" or uv sync --extra vllm; ' + "the driver is validated against vllm 0.20.x." + ) from exc from transformers import AutoConfig, AutoTokenizer @@ -113,14 +136,6 @@ def boot_vllm( overlay = get_overlay(architecture) resolved_dtype = dtype or _dtype_from_hf_config(hf_config_preview) - plugin.configure( - capture_specs=overlay.capture_specs(hf_config_preview), - max_num_batched_tokens=max_num_batched_tokens, - dtype=resolved_dtype, - enable_batching=enable_batching, - enable_position_interventions=enable_position_interventions, - ) - plugin.register() # The plugin's _config singleton must be visible to the worker, which only # holds with single-process execution. Multi-GPU is unsupported. Override @@ -136,43 +151,55 @@ def boot_vllm( ) os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0" - from vllm import LLM - # Batched capture reads query_start_loc from the forward context, untraceable # under torch.compile — so the batched path must run eager. eager_kwargs: Dict[str, Any] = {"enforce_eager": True} if enable_batching else {} - llm = LLM( - model=model_name, - gpu_memory_utilization=gpu_memory_utilization, - max_model_len=max_model_len, - # Critical: vLLM defaults max_num_batched_tokens to 8192 for chunked prefill. - # We size our capture buffer to this same value, so vLLM must compile for - # the matching dynamic-shape range — otherwise Dynamo's symbolic-shape - # hint exceeds the buffer dim and narrow() fails at compile time. - max_num_batched_tokens=max_num_batched_tokens, - # Register TLWorkerExtension so its tl_* methods are reachable via - # collective_rpc. Passed as a dotted path; vLLM imports the class at - # worker construction and mixes it into the Worker via multiple - # inheritance (asserts no attribute name collisions — hence the tl_ prefix). - worker_extension_cls="transformer_lens.model_bridge.sources.vllm.worker_extension.TLWorkerExtension", - # Allow full-vocab logprobs so the driver can synthesize real logits for - # the generated position (vLLM caps logprobs to this value; default 20 is - # too small for mech-interp). One ~512 KB buffer per call; negligible. - max_logprobs=hf_config_preview.vocab_size, - dtype=str(resolved_dtype).replace("torch.", "") if dtype is not None else "auto", - **eager_kwargs, - **_LOCKED_KWARGS, - **vllm_kwargs, - ) + with _BOOT_LOCK: + plugin.configure( + capture_specs=overlay.capture_specs(hf_config_preview), + max_num_batched_tokens=max_num_batched_tokens, + dtype=resolved_dtype, + enable_batching=enable_batching, + enable_position_interventions=enable_position_interventions, + ) + plugin.register() + try: + llm = LLM( + model=model_name, + gpu_memory_utilization=gpu_memory_utilization, + max_model_len=max_model_len, + # Critical: vLLM defaults max_num_batched_tokens to 8192 for chunked prefill. + # We size our capture buffer to this same value, so vLLM must compile for + # the matching dynamic-shape range — otherwise Dynamo's symbolic-shape + # hint exceeds the buffer dim and narrow() fails at compile time. + max_num_batched_tokens=max_num_batched_tokens, + # Register TLWorkerExtension so its tl_* methods are reachable via + # collective_rpc. Passed as a dotted path; vLLM imports the class at + # worker construction and mixes it into the Worker via multiple + # inheritance (asserts no attribute name collisions — hence the tl_ prefix). + worker_extension_cls="transformer_lens.model_bridge.sources.vllm.worker_extension.TLWorkerExtension", + # Allow full-vocab logprobs so the fallback path can synthesize logits when + # host-side reconstruction is unavailable (vLLM caps logprobs to this value; + # default 20 is too small for mech-interp). + max_logprobs=hf_config_preview.vocab_size, + # Always explicit — vLLM's "auto" downcasts fp32 checkpoints to fp16, which + # would leave the capture/affine buffers (allocated at resolved_dtype) at a + # different dtype than the engine's activations. + dtype=str(resolved_dtype).replace("torch.", ""), + **eager_kwargs, + **_LOCKED_KWARGS, + **vllm_kwargs, + ) + finally: + # Always clear, even on a failed boot: a stale populated _config would make the + # next in-process vllm.LLM(...) walk our dot-paths on a foreign model. + plugin._config.clear() # Capture-path validity is enforced inside patched_load_model during # LLM(...) above — any missing dot-path raises AttributeError there. By # the time we reach this line every spec has already been walked successfully. hf_config = extract_hf_config(llm) - # _config has been consumed into per-Worker state; clear so a non-TL - # vllm.LLM(...) in the same process doesn't inherit our specs. - plugin._config.clear() if tokenizer is None: tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token) @@ -184,6 +211,9 @@ def boot_vllm( bridge_config = build_bridge_config_from_hf(hf_config, architecture, model_name, resolved_dtype) adapter = ArchitectureAdapterFactory.select_architecture_adapter(bridge_config) + # Match boot_transformers' tokenizer setup so to_tokens(str) is token-identical. + tokenizer = configure_tokenizer(tokenizer, adapter.cfg) + driver = VLLMDriver( llm=llm, adapter=adapter, @@ -194,6 +224,9 @@ def boot_vllm( enable_batching=enable_batching, enable_position_interventions=enable_position_interventions, ) + # One-time unembedding fetch: caches the weight for per-forward reconstruction and + # downgrades provides_sequence_logits honestly if no unembedding is reachable. + driver.probe_logit_reconstruction() bridge = RemoteBridge(adapter=adapter, tokenizer=tokenizer, driver=driver) _log_hook_summary(model_name, architecture, driver) return bridge @@ -204,7 +237,8 @@ def _reject_locked_overrides(vllm_kwargs: Dict[str, Any]) -> None: if key in vllm_kwargs and vllm_kwargs[key] != locked: raise ValueError( f"boot_vllm forces {key}={locked}; caller passed {key}={vllm_kwargs[key]}. " - "Multi-device / continuous batching / vLLM-owned tokenizer are unsupported." + "Multi-device, prefix caching, continuous batching, and vLLM-owned " + "tokenizers are unsupported — each breaks the row=position capture invariant." ) diff --git a/transformer_lens/model_bridge/sources/vllm/worker_extension.py b/transformer_lens/model_bridge/sources/vllm/worker_extension.py index b8cbcca048..036ee36645 100644 --- a/transformer_lens/model_bridge/sources/vllm/worker_extension.py +++ b/transformer_lens/model_bridge/sources/vllm/worker_extension.py @@ -20,7 +20,7 @@ import torch -from .intervention_specs import SUPPORTED_OPS +from .intervention_specs import SUPPORTED_OPS, validate_spec class TLWorkerExtension: @@ -69,6 +69,9 @@ def tl_set_interventions(self, specs: Dict[str, Dict[str, Any]]) -> None: for hook_name, spec in specs.items(): if hook_name not in scale_bufs: raise KeyError(f"Unknown hook for intervention: {hook_name!r}") + # Authoritative validation: producers that bypass VLLMDriver (the Inspect + # vLLM provider pushes specs over this same RPC) get identical rejection. + validate_spec(hook_name, spec, width=scale_bufs[hook_name].shape[-1]) _apply_intervention(scale_bufs[hook_name], bias_bufs[hook_name], spec) def tl_get_param(self, dotted_name: str) -> Optional[torch.Tensor]: @@ -129,12 +132,8 @@ def tl_read_batched_captures( def tl_set_batched_interventions(self, specs: Dict[str, Dict[str, Any]]) -> None: """Store the global spec dict the eager hook reads; ``{}`` clears.""" - for spec in specs.values(): - op = spec.get("op") - if op not in SUPPORTED_OPS: - raise ValueError( - f"Unsupported intervention op: {op!r}. Supported: {sorted(SUPPORTED_OPS)}" - ) + for hook_name, spec in specs.items(): + validate_spec(hook_name, spec) self._tl_intervention_specs = dict(specs) def tl_remove_hooks(self) -> None: diff --git a/transformer_lens/model_bridge/supported_architectures/mamba.py b/transformer_lens/model_bridge/supported_architectures/mamba.py index 0023bbb5c5..e08aa501f2 100644 --- a/transformer_lens/model_bridge/supported_architectures/mamba.py +++ b/transformer_lens/model_bridge/supported_architectures/mamba.py @@ -20,7 +20,7 @@ class MambaArchitectureAdapter(ArchitectureAdapter): SSM config fields (state_size, conv_kernel, expand, time_step_rank, intermediate_size) are propagated from the HF config via - ``_HF_PASSTHROUGH_ATTRS`` in sources/transformers.py. + ``_HF_PASSTHROUGH_ATTRS`` in sources/_bridge_builder.py. """ # verify_models is transformer-shaped today and would need a dedicated diff --git a/transformer_lens/model_bridge/transformer_bridge.py b/transformer_lens/model_bridge/transformer_bridge.py index 9038a3e1e2..96c4a88199 100644 --- a/transformer_lens/model_bridge/transformer_bridge.py +++ b/transformer_lens/model_bridge/transformer_bridge.py @@ -11,6 +11,7 @@ TYPE_CHECKING, Any, Callable, + ClassVar, Dict, Iterator, List, @@ -67,13 +68,6 @@ def _resolve_attr_path(obj: nn.Module, attr_path: str) -> torch.Tensor: return cast(torch.Tensor, result) -# build_alias_to_canonical_map lives in bridge_core.py; re-import for the module's -# internal use (run_with_cache, hooks() context manager in this file). -from transformer_lens.model_bridge.bridge_core import ( # noqa: E402 - build_alias_to_canonical_map, -) - - class TransformerBridge(BridgeCore, HookIntrospectionMixin, nn.Module): """Torch-backed bridge: HF, vLLM-via-torch, anything that wraps an ``nn.Module``. @@ -165,97 +159,10 @@ def __init__( validate_driver(self._driver, after_bridge_construction=True) self.processor = None - @classmethod - def boot_transformers( - cls, - model_name: str, - hf_config_overrides: Optional[dict] = None, - device: Optional[Union[str, torch.device]] = None, - dtype: torch.dtype = torch.float32, - tokenizer: Optional[Any] = None, - load_weights: bool = True, - trust_remote_code: bool = False, - model_class: Optional[type] = None, - hf_model: Optional[Any] = None, - device_map: Optional[Union[str, Dict[str, Union[str, int]]]] = None, - n_devices: Optional[int] = None, - max_memory: Optional[Dict[Union[str, int], str]] = None, - n_ctx: Optional[int] = None, - revision: Optional[str] = None, - checkpoint_index: Optional[int] = None, - checkpoint_value: Optional[int] = None, - ) -> "TransformerBridge": - """Boot a model from HuggingFace (alias for sources.transformers.boot). - - Returns raw HF weights by default — logits/activations match HF, *not* - legacy ``HookedTransformer`` (which folds LayerNorm + centers weights). - Call ``enable_compatibility_mode()`` on the result for HookedTransformer- - equivalent numerics. Generation, argmax, and CE loss are unaffected. - - Attention implementation is forced to ``"eager"`` so hooks can capture scores - and patterns. For an apples-to-apples HF comparison, load the HF model with - ``attn_implementation="eager"`` too; comparing against the default ``"sdpa"`` - shows ~1e-3 fp32 drift from kernel-level op reordering, not a bridge bug. - - Args: - model_name: The name of the model to load. - hf_config_overrides: Optional overrides applied to the HuggingFace config before model load. - device: The device to use. If None, will be determined automatically. Mutually exclusive - with ``device_map``. - dtype: The dtype to use for the model. - tokenizer: Optional pre-initialized tokenizer to use; if not provided one will be created. - load_weights: If False, load model without weights (on meta device) for config inspection only. - trust_remote_code: Whether to trust remote code for custom model architectures. - model_class: Optional HuggingFace model class to use instead of the default - auto-detected class (e.g., BertForNextSentencePrediction). - hf_model: Optional pre-loaded HuggingFace model to use instead of loading one. Useful - for models loaded with custom configurations (e.g., quantization via - BitsAndBytesConfig). When provided, load_weights is ignored. If the pre-loaded - model was built with a ``device_map``, ``cfg.device`` and ``cfg.n_devices`` are - derived from its ``hf_device_map`` automatically. - device_map: HuggingFace-style device map for multi-GPU inference. Pass ``"auto"``, - ``"balanced"``, ``"sequential"``, or an explicit ``{submodule_path: device}`` dict. - Mutually exclusive with ``device``. - n_devices: Convenience shortcut: split the model across this many CUDA devices. - Translated to a ``max_memory`` dict over devices 0..n_devices-1 and passed as - ``device_map`` to HF. Requires CUDA with at least this many visible devices. - max_memory: Optional per-device memory budget, passed through to HF's dispatcher. - Only used when ``device_map`` or ``n_devices`` is in effect. - n_ctx: Optional context length override. Writes to the appropriate HF config field - for this model automatically (callers don't need to know the field name). - Warns if larger than the model's default context length. - revision: Optional HF revision (branch, tag, or commit). Forwarded to the underlying - ``AutoConfig.from_pretrained`` and ``AutoModelForCausalLM.from_pretrained`` calls. - Mutually exclusive with ``checkpoint_index`` / ``checkpoint_value``. - checkpoint_index: Index into the available training checkpoints for the model family - (currently ``EleutherAI/pythia*`` and ``stanford-crfm/*``). Resolved to a revision - string via known per-family naming conventions. - checkpoint_value: Training step or token count of the desired checkpoint. Alternative - to ``checkpoint_index``; must match an entry in the family's checkpoint label list. - - Returns: - The bridge to the loaded model. - """ - from transformer_lens.model_bridge.sources.transformers import boot - - return boot( - model_name=model_name, - hf_config_overrides=hf_config_overrides, - device=device, - dtype=dtype, - tokenizer=tokenizer, - load_weights=load_weights, - trust_remote_code=trust_remote_code, - model_class=model_class, - hf_model=hf_model, - device_map=device_map, - n_devices=n_devices, - max_memory=max_memory, - n_ctx=n_ctx, - revision=revision, - checkpoint_index=checkpoint_index, - checkpoint_value=checkpoint_value, - ) + # boot_transformers / list_supported_models / check_model_support are + # attached by sources.transformers.__init__ (setattr on this class) so the + # source package owns its own boot entry point. + boot_transformers: ClassVar[Callable[..., "TransformerBridge"]] @property def original_model(self) -> nn.Module: @@ -696,39 +603,6 @@ def process_weights( component_mapping=self.real_components, ) - def _calculate_loss(self, logits, tokens, loss_per_token=False): - """Calculate cross-entropy loss.""" - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = tokens[..., 1:].contiguous() - loss_fct = torch.nn.CrossEntropyLoss(reduction="none" if loss_per_token else "mean") - flat_logits = shift_logits.view(-1, shift_logits.size(-1)) - flat_labels = shift_labels.view(-1) - loss = loss_fct(flat_logits, flat_labels) - if loss_per_token: - return loss.view(shift_labels.shape) - else: - return loss - - def _extract_hf_weights(self): - """Extract weights from the original HuggingFace model.""" - hf_state_dict = self.state_dict() - for layer_idx in range(self.cfg.n_layers): - combined_qkv_key = f"transformer.h.{layer_idx}.attn.c_attn.weight" - combined_qkv_bias_key = f"transformer.h.{layer_idx}.attn.c_attn.bias" - if combined_qkv_key in hf_state_dict: - separate_keys_to_remove = [ - f"transformer.h.{layer_idx}.attn.q.weight", - f"transformer.h.{layer_idx}.attn.q.bias", - f"transformer.h.{layer_idx}.attn.k.weight", - f"transformer.h.{layer_idx}.attn.k.bias", - f"transformer.h.{layer_idx}.attn.v.weight", - f"transformer.h.{layer_idx}.attn.v.bias", - ] - for key_to_remove in separate_keys_to_remove: - if key_to_remove in hf_state_dict: - del hf_state_dict[key_to_remove] - return hf_state_dict - def to_tokens( self, input: Union[str, List[str]], diff --git a/transformer_lens/utilities/tokenize_utils.py b/transformer_lens/utilities/tokenize_utils.py index 22d1702b61..0ba674febc 100644 --- a/transformer_lens/utilities/tokenize_utils.py +++ b/transformer_lens/utilities/tokenize_utils.py @@ -7,7 +7,7 @@ import os from copy import deepcopy -from typing import Any +from typing import Any, Optional import einops import numpy as np @@ -27,7 +27,7 @@ def tokenize_and_concatenate( max_length: int = 1024, column_name: str = "text", add_bos_token: bool = True, - num_proc: int = 10, + num_proc: Optional[int] = 10, set_format: bool = True, ) -> Dataset | IterableDataset: """Tokenize each document, join with token-level EOS between docs, and reshape into ``(batch, sequence_length)`` rows. @@ -44,7 +44,8 @@ def tokenize_and_concatenate( max_length (int, optional): The length of the context window of the sequence. Defaults to 1024. column_name (str, optional): The name of the text column in the dataset. Defaults to 'text'. add_bos_token (bool, optional): Whether to prepend ``bos_token_id`` to each output row. Defaults to True. - num_proc (int, optional): Number of processes for parallel tokenization. Ignored when ``streaming=True``. Defaults to 10. + num_proc (int, optional): Number of processes for parallel tokenization; ``None`` runs in + the main process (``datasets`` pools even for ``num_proc=1``). Ignored when ``streaming=True``. Defaults to 10. set_format (bool, optional): If True, calls ``set_format(type="torch")`` on the result. Set False for ``IterableDataset`` (which doesn't support format setting); wrap the output in ``(torch.LongTensor(ex["tokens"]) for ex in tokenized_dataset)`` instead. Defaults to True. diff --git a/uv.lock b/uv.lock index 13a86aec5f..0ce3944b59 100644 --- a/uv.lock +++ b/uv.lock @@ -2,17 +2,37 @@ version = 1 revision = 3 requires-python = ">=3.10, <4.0" resolution-markers = [ - "python_full_version == '3.12.*' and sys_platform == 'linux'", - "python_full_version == '3.12.*' and sys_platform != 'linux'", - "python_full_version == '3.11.*' and sys_platform == 'linux'", - "python_full_version == '3.11.*' and sys_platform != 'linux'", - "python_full_version < '3.11' and sys_platform == 'linux'", - "python_full_version < '3.11' and sys_platform != 'linux'", - "python_full_version >= '3.14' and sys_platform == 'linux'", - "python_full_version == '3.13.*' and sys_platform == 'linux'", - "python_full_version >= '3.14' and sys_platform != 'linux'", - "python_full_version == '3.13.*' and sys_platform != 'linux'", -] + "python_full_version == '3.12.*' and sys_platform == 'linux' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.12.*' and sys_platform == 'darwin' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version < '3.11' and sys_platform == 'darwin' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version >= '3.14' and sys_platform == 'linux' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.13.*' and sys_platform == 'linux' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version >= '3.14' and sys_platform == 'darwin' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.13.*' and sys_platform == 'darwin' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version >= '3.14' and sys_platform == 'win32' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-16-transformer-lens-lit' and extra == 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.12.*' and extra == 'extra-16-transformer-lens-lit' and extra != 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.11.*' and extra == 'extra-16-transformer-lens-lit' and extra != 'extra-16-transformer-lens-vllm'", + "python_full_version < '3.11' and extra == 'extra-16-transformer-lens-lit' and extra != 'extra-16-transformer-lens-vllm'", + "python_full_version >= '3.14' and extra == 'extra-16-transformer-lens-lit' and extra != 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.13.*' and extra == 'extra-16-transformer-lens-lit' and extra != 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.12.*' and extra != 'extra-16-transformer-lens-lit' and extra != 'extra-16-transformer-lens-vllm'", + "python_full_version == '3.11.*' and extra != 'extra-16-transformer-lens-lit' and extra != 'extra-16-transformer-lens-vllm'", + "python_full_version < '3.11' and extra != 'extra-16-transformer-lens-lit' and extra != 'extra-16-transformer-lens-vllm'", + "python_full_version >= '3.13' and extra != 'extra-16-transformer-lens-lit' and extra != 'extra-16-transformer-lens-vllm'", +] +conflicts = [[ + { package = "transformer-lens", extra = "lit" }, + { package = "transformer-lens", extra = 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.../test_inspect_vllm_provider.py | 23 +- tests/unit/model_bridge/test_vllm_boot.py | 125 ++++++++- tests/unit/model_bridge/test_vllm_driver.py | 263 +++++++++++++++++- .../unit/model_bridge/test_vllm_internals.py | 37 ++- tests/unit/model_bridge/test_vllm_plugin.py | 119 ++++++++ .../test_vllm_worker_extension.py | 78 +++++- .../sources/inspect/vllm_provider.py | 64 ++--- .../model_bridge/sources/vllm/driver.py | 184 ++++++++++-- .../model_bridge/sources/vllm/internals.py | 28 ++ .../model_bridge/sources/vllm/plugin.py | 111 ++++++-- .../model_bridge/sources/vllm/source.py | 216 +++++++++----- .../sources/vllm/worker_extension.py | 118 +++++++- 20 files changed, 1575 insertions(+), 220 deletions(-) create mode 100644 tests/acceptance/model_bridge/_vllm_multigpu_common.py create mode 100644 tests/acceptance/model_bridge/test_vllm_multigpu.py create mode 100644 tests/acceptance/model_bridge/test_vllm_multigpu_pos.py create mode 100644 tests/acceptance/model_bridge/test_vllm_multigpu_pp.py diff --git a/docs/source/content/drivers.md b/docs/source/content/drivers.md index c3b66ac2f4..405188fa3c 100644 --- a/docs/source/content/drivers.md +++ b/docs/source/content/drivers.md @@ -76,6 +76,8 @@ Notes grounded in the source docstrings ([`sources/vllm/source.py`](https://gith - **Returned logits are reconstructed full-sequence logits**: vLLM's sampler bypasses `lm_head`, so the driver rebuilds them host-side as `ln_final @ lm_head.weight.T` (+ bias, + Gemma soft-cap) — valid at every position, so loss works. If the unembedding weight is unreachable it falls back to final-position log-probs and the bridge rejects `return_type="loss"`. - **Convention alignment**: vLLM materializes `ln_final` *post-weight*, but the driver un-folds the exposed capture (÷ weight, or ÷ (1 + weight) for Gemma) so `ln_final.hook_normalized` matches the pre-weight value `boot_transformers` serves. If the norm weight is unreachable it warns and serves the raw post-weight value. See [`sources/vllm/overlays/decoder_only.py`](https://github.com/TransformerLensOrg/TransformerLens/blob/dev-4.x/transformer_lens/model_bridge/sources/vllm/overlays/decoder_only.py) for which hooks diverge from HF conventions. - `enable_batching=True` switches to the eager batched path (`batch_size > 1`, chunked prefill) for data collection; `enable_position_interventions=True` lets a spec carry a `pos` field (int or list) to scope an edit to specific sequence positions. +- `tensor_parallel_size=2` enables single-node tensor parallelism (GPU-validated: capture/intervention/logit parity vs TP=1 within the standard band): every served hook point is post-all-reduce and replicated across a stage's TP ranks, with a first-forward cross-rank check that fails loud if that ever stops holding; vocab-sharded unembeddings are gathered for logit reconstruction. Incompatible with `enable_batching`; multi-node (Ray) remains unsupported. +- `pipeline_parallel_size=2` enables single-node pipeline parallelism (GPU-validated: capture/intervention/logit parity vs single-rank within the standard band): each stage owns a disjoint layer slice, so the driver merges per-rank capture reads (fail-loud if a requested hook is served by no rank) and each rank applies only the intervention specs whose hooks it owns. Ownership is determined by whether a hook actually fired, not by module presence — tied-embedding models alias `embed_tokens` onto the last stage and some builds instantiate the final norm everywhere. The first-forward layout check also verifies TP replicas of each stage fired identically under vLLM's PP microbatching. Composes with `tensor_parallel_size`; same `enable_batching` restriction. - Requires a CUDA GPU. Install with `pip install "transformer-lens[vllm]"` (or `uv sync --extra vllm`). The extra is Linux-only (vLLM ships no macOS/Windows wheels), pins the validated `vllm 0.20.x` band — which in turn pins its matching `torch` — and cannot co-install with the `[lit]` extra (numpy version conflict). See [`sources/vllm/internals.py`](https://github.com/TransformerLensOrg/TransformerLens/blob/dev-4.x/transformer_lens/model_bridge/sources/vllm/internals.py) before bumping the band. ### Inspect — interp inside `inspect_ai` evals diff --git a/pyproject.toml b/pyproject.toml index 771176d1ae..d78a8d1961 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -58,6 +58,14 @@ tl_bridge="transformer_lens.model_bridge.sources.inspect.transformers_provider" tl_bridge_vllm="transformer_lens.model_bridge.sources.inspect.vllm_provider" + # vLLM loads general_plugins in EVERY process it owns — including spawned TP + # worker processes, which is the only way the Worker.load_model capture patch + # reaches them (boot_vllm's direct register() call only covers in-process + # workers). register() is idempotent and no-ops without a configured spec + # channel, so non-TL vllm usage is unaffected. + [project.entry-points."vllm.general_plugins"] + tl_vllm_capture="transformer_lens.model_bridge.sources.vllm.plugin:register" + [dependency-groups] demo=["gradio>=4.0.0", "orjson>=3.11.7,<4.0"] dev=[ @@ -128,6 +136,7 @@ markers=[ "slow: marks tests as slow (deselect with '-m \"not slow\"')", "inspect: requires inspect_ai (deselect with '-m \"not inspect\"')", + "multigpu: requires vllm and >= 2 CUDA devices (select with '-m multigpu')", ] pythonpath=["."] testpaths=["tests", "transformer_lens"] # Only test these directories diff --git a/scripts/vllm_parity_report.py b/scripts/vllm_parity_report.py index 1abf934774..21950a9589 100644 --- a/scripts/vllm_parity_report.py +++ b/scripts/vllm_parity_report.py @@ -21,10 +21,15 @@ Run: uv run python scripts/vllm_parity_report.py Env: TL_PARITY_MODELS="id1,id2,..." overrides the model list. TL_VLLM_ATOL / TL_VLLM_RTOL override tolerance (defaults 2e-2). + TL_PARITY_TP=2 boot vLLM with tensor_parallel_size=2 — + same PASS bar as TP=1 (needs >= TP GPUs). + TL_PARITY_PP=2 boot vLLM with pipeline_parallel_size=2 + (needs >= TP*PP GPUs). """ from __future__ import annotations import gc +import json import os import sys import warnings @@ -50,6 +55,14 @@ # A wrong hook mapping (e.g. un-un-folded ln_final) diverges by O(1), well outside this. ATOL = float(os.environ.get("TL_VLLM_ATOL", "2e-2")) RTOL = float(os.environ.get("TL_VLLM_RTOL", "2e-2")) +# Scale-aware band: final-layer residual streams reach O(1e3) magnitudes ("massive +# activations") and kernel-order noise grows with scale. Measured across 4 archs +# (2×A6000, fp32, 2026-07-15): worst relative diff 5.9e-4, always at the final +# layer, with downstream ln_final back in band — while a mapping bug is O(1) +# relative. 2e-3 keeps >3 orders of magnitude of discrimination. +REL_BAND = float(os.environ.get("TL_VLLM_REL", "2e-3")) +TP = int(os.environ.get("TL_PARITY_TP", "1")) +PP = int(os.environ.get("TL_PARITY_PP", "1")) # vLLM capture kind -> TransformerBridge-native hook name (per-layer uses {i}). DIRECT_KINDS = { @@ -67,12 +80,18 @@ def _to2d(t: torch.Tensor) -> torch.Tensor: return t.reshape(-1, t.shape[-1]) -def _diff(a: torch.Tensor, b: torch.Tensor) -> tuple[float, bool]: +def _diff(a: torch.Tensor, b: torch.Tensor) -> tuple[float, bool, str]: + """(max abs diff, in-band?, scale-aware profile line). The profile shows the diff + relative to the reference's own magnitude — distinguishes depth-accumulated kernel + noise (rel stays small as scale grows) from a structural capture error.""" a2, b2 = _to2d(a), _to2d(b) if a2.shape != b2.shape: - return float("inf"), False + return float("inf"), False, "shape mismatch" d = (a2 - b2).abs().max().item() - return d, torch.allclose(a2, b2, atol=ATOL, rtol=RTOL) + scale = b2.abs().max().item() + # Effective atol scales with the reference tensor's magnitude (see REL_BAND). + ok = torch.allclose(a2, b2, atol=max(ATOL, REL_BAND * scale), rtol=RTOL) + return d, ok, f"abs={d:.2e} rel={d / max(scale, 1e-9):.2e} scale={scale:.1f}" def _refold_lnf(vllm_val: torch.Tensor, weight: torch.Tensor, is_gemma: bool) -> torch.Tensor: @@ -100,7 +119,13 @@ def verify(model_id: str) -> dict: n_layers = int(hf.cfg.n_layers) toks = hf.to_tokens(PROMPT) - vllm = boot_vllm(model_id, dtype=torch.float32, max_model_len=2048) + vllm = boot_vllm( + model_id, + dtype=torch.float32, + max_model_len=2048, + tensor_parallel_size=TP, + pipeline_parallel_size=PP, + ) offered = vllm._driver.supported_hook_points hf_logits, hf_cache = hf.run_with_cache(toks) @@ -110,9 +135,13 @@ def verify(model_id: str) -> dict: mism: list[str] = [] notes: list[str] = [] - # embed + the three per-layer direct boundaries (first & last layer). + # embed + the three per-layer direct boundaries. Default checks first & last + # layer; TL_PARITY_ALL_LAYERS=1 diffs every layer and prints the depth profile — + # gradual growth = kernel-noise accumulation, a cliff at one layer = capture bug. + all_layers = bool(os.environ.get("TL_PARITY_ALL_LAYERS")) + layer_ids = range(n_layers) if all_layers else sorted({0, n_layers - 1}) checks = [(EMBED_HOOK, EMBED_HOOK)] - for i in sorted({0, n_layers - 1}): + for i in layer_ids: for name in DIRECT_KINDS.values(): hk = name.format(i=i) checks.append((hk, hk)) @@ -122,16 +151,25 @@ def verify(model_id: str) -> dict: if hk not in v_cache or hk not in hf_cache: mism.append(f"{hk} missing") continue - d, ok = _diff(hf_cache[hk], v_cache[hk]) + d, ok, profile = _diff(hf_cache[hk], v_cache[hk]) worst = max(worst, d if d != float("inf") else worst) if not ok: - mism.append(f"{hk} maxdiff={d:.2e}") + mism.append(f"{hk} {profile}") + if all_layers: + print(f" depth profile for {model_id} (rel = abs/tensor-scale):", flush=True) + for i in layer_ids: + cells = [] + for kind, name in DIRECT_KINDS.items(): + hk = name.format(i=i) + if hk in v_cache and hk in hf_cache: + cells.append(f"{kind} {_diff(hf_cache[hk], v_cache[hk])[2]}") + print(f" L{i:>2}: " + " | ".join(cells), flush=True) # ln_final: the driver already un-folds to the pre-weight convention — compare # directly. The refolded diff is diagnostic: "direct FAIL + refolded ok" means # the driver's un-fold regressed (serving raw post-weight values again). if LNF_HOOK in offered and LNF_HOOK in v_cache and LNF_HOOK in hf_cache: - d, ok = _diff(hf_cache[LNF_HOOK], v_cache[LNF_HOOK]) + d, ok, _profile = _diff(hf_cache[LNF_HOOK], v_cache[LNF_HOOK]) worst = max(worst, d if d != float("inf") else worst) if ok: notes.append(f"ln_final direct={d:.2e}") @@ -140,7 +178,7 @@ def verify(model_id: str) -> dict: detail = f"{LNF_HOOK} maxdiff={d:.2e}" if weight is not None: refolded = _refold_lnf(v_cache[LNF_HOOK], weight, is_gemma) - rf_d, rf_ok = _diff(hf_cache[LNF_HOOK], refolded) + rf_d, rf_ok, _rf = _diff(hf_cache[LNF_HOOK], refolded) detail += f" (refolded={rf_d:.2e}{', un-fold regressed' if rf_ok else ''})" mism.append(detail) @@ -183,6 +221,11 @@ def _preflight() -> str | None: """Return a human reason to abort (no GPU / no vllm), or None if runnable.""" if not torch.cuda.is_available(): return "no CUDA device — vLLM capture only materializes in a real GPU forward" + if TP * PP > torch.cuda.device_count(): + return ( + f"TL_PARITY_TP={TP} x TL_PARITY_PP={PP} needs {TP * PP} GPUs but only " + f"{torch.cuda.device_count()} visible" + ) try: import vllm # noqa: F401 except Exception as e: @@ -190,7 +233,49 @@ def _preflight() -> str | None: return None +# Child-process result marker (see main): the parent parses the child's last +# marker line back into a row dict. +_ROW_MARKER = "##TL_PARITY_ROW## " + + +def _verify_in_subprocess(model_id: str) -> dict: + """One engine per process: an in-process vLLM engine does not reliably release + GPU memory before exit, so sequential same-process boots leak until later + models can't reserve their budget (observed: 47→18 GiB free across 4 boots). + Child stdout streams through; the marker line carries the structured row.""" + import subprocess + + proc = subprocess.run( + [sys.executable, os.path.abspath(__file__), "--one", model_id], + capture_output=True, + text=True, + ) + row = None + for line in proc.stdout.splitlines(): + if line.startswith(_ROW_MARKER): + row = json.loads(line[len(_ROW_MARKER) :]) + else: + print(line, flush=True) + if proc.stderr: + tail = proc.stderr.strip().splitlines()[-3:] + for line in tail: + print(f" [child stderr] {line}", flush=True) + if row is None: + return { + "model": model_id, + "arch": "?", + "status": "SKIP", + "detail": f"child exited {proc.returncode} without a result row", + } + return row + + def main() -> None: + if len(sys.argv) > 2 and sys.argv[1] == "--one": + r = verify(sys.argv[2]) + print(_ROW_MARKER + json.dumps(r), flush=True) + sys.exit(0) + reason = _preflight() if reason is not None: print(f"SKIP ALL: {reason}", flush=True) @@ -201,11 +286,11 @@ def main() -> None: models = [m.strip() for m in ids.split(",")] if ids else DEFAULT_MODELS rows = [] for m in models: - r = verify(m) + r = _verify_in_subprocess(m) rows.append(r) print(f"[{r['status']:4}] {r['arch']:28} {r['model']:40} {r['detail']}", flush=True) - print("\n================ vLLM PARITY REPORT CARD ================") + print(f"\n================ vLLM PARITY REPORT CARD (TP={TP}, PP={PP}) ================") for status in ("PASS", "FAIL", "SKIP"): sel = [r for r in rows if r["status"] == status] print(f"\n{status} ({len(sel)}):") diff --git a/tests/QUARANTINES.md b/tests/QUARANTINES.md index 47f03ca50b..3de13899eb 100644 --- a/tests/QUARANTINES.md +++ b/tests/QUARANTINES.md @@ -44,6 +44,14 @@ A `[vllm]` extra exists (Linux-only marker; declared conflicting with `[lit]` in **Un-skip:** a GPU CI lane that installs vllm, or locally on a CUDA machine with `vllm==0.20.2` installed alongside the project env. +## Multi-GPU tier — `-m multigpu` (no automated lane) + +[`acceptance/model_bridge/test_vllm_multigpu.py`](acceptance/model_bridge/test_vllm_multigpu.py) validates the vLLM driver's `tensor_parallel_size=2` path (capture replication, sharded-unembed gather, intervention parity vs TP=1). Gated on `importorskip("vllm")` + `torch.cuda.device_count() >= 2`, so it skips everywhere except a provisioned multi-GPU box. **Validated 2026-07-15 on 2×A6000** (vllm 0.20.2, Qwen2.5-0.5B): both multigpu files pass, plus `TL_PARITY_TP=2` parity and the TP=1 parity regression. + +[`acceptance/model_bridge/test_vllm_multigpu_pp.py`](acceptance/model_bridge/test_vllm_multigpu_pp.py) validates the `pipeline_parallel_size=2` path (cross-stage capture merge, layout tripwire, first/last-stage intervention parity, logit reconstruction from last-stage gathers). Same gating. **Validated 2026-07-15 on 2×A6000** (vllm 0.20.2, Qwen2.5-0.5B): 6/6 pass, plus `TL_PARITY_PP=2` parity and the single-rank parity regression. + +**Un-skip:** run on a >= 2-GPU Linux box with the `vllm` extra: `uv run pytest tests/acceptance/model_bridge/test_vllm_multigpu.py -m multigpu -v` (and the `_pos` / `_pp` siblings, each in its own process), plus `TL_PARITY_TP=2` / `TL_PARITY_PP=2` runs of `scripts/vllm_parity_report.py`. + --- ## Permanent — hardware requirement diff --git a/tests/acceptance/model_bridge/_vllm_multigpu_common.py b/tests/acceptance/model_bridge/_vllm_multigpu_common.py new file mode 100644 index 0000000000..fc0800d84f --- /dev/null +++ b/tests/acceptance/model_bridge/_vllm_multigpu_common.py @@ -0,0 +1,80 @@ +"""Shared constants/helpers for the vLLM multi-GPU acceptance suites. + +The suites stay in separate files — each boots its own engine(s) and in-process +vLLM engines under-release GPU memory, so every file gets a fresh pytest process +(see each file's docstring for its run command). Everything they must agree on +lives here so the tolerance band and boot profile can't drift between them. +""" +from __future__ import annotations + +import pytest +import torch + +MULTIGPU_MARKS = [ + pytest.mark.multigpu, + pytest.mark.skipif( + not torch.cuda.is_available() or torch.cuda.device_count() < 2, + reason="needs >= 2 CUDA devices", + ), +] + +# Qwen2.5-0.5B: 14 attention heads / 2 KV heads — both divisible by TP=2. +# (SmolLM2-135M has 9 heads and cannot tensor-parallelize; keep any +# replacement model even-headed.) 24 layers -> 12 per PP=2 stage. +MODEL = "Qwen/Qwen2.5-0.5B" +N_LAYERS = 24 +PROMPT_IDS = [504, 4674, 1442, 29892, 322] # fixed ids: no tokenizer variance in scope + +# vLLM kernel scheduling differs across parallel layouts (all-reduce / send-recv +# order), so exact equality is not expected; band matches scripts/vllm_parity_report.py, +# including its scale-aware atol (final-layer streams reach O(1e3) and noise grows +# with scale). +ATOL = RTOL = 2e-2 +REL_BAND = 2e-3 + + +def close(t1: torch.Tensor, t2: torch.Tensor) -> bool: + """Scale-aware allclose: atol widens with the reference tensor's magnitude.""" + atol_eff = max(ATOL, REL_BAND * t2.abs().max().item()) + return torch.allclose(t1, t2, atol=atol_eff, rtol=RTOL) + + +def boot_multigpu(**parallel_kwargs): + """Boot MODEL with the suites' shared fp32 profile; parallel sizes per test.""" + from transformer_lens.model_bridge.sources.vllm.source import boot_vllm + + return boot_vllm( + MODEL, + dtype=torch.float32, + max_model_len=2048, + gpu_memory_utilization=0.35, + **parallel_kwargs, + ) + + +def bridge_pair_fixture(**test_boot_kwargs): + """Module fixture: single-rank reference then the parallel bridge under test; + serial boots (never two engines booting at once), closed in reverse.""" + + @pytest.fixture(scope="module") + def bridges(): + b1 = boot_multigpu() + b2 = boot_multigpu(**test_boot_kwargs) + yield b1, b2 + b2.close() + b1.close() + + return bridges + + +def assert_caches_match(cache1, cache2) -> None: + """Full key-set equality plus scale-aware per-hook comparison — a missing key + means a rank/stage was dropped, a band miss means numerical drift.""" + assert set(cache1.keys()) == set(cache2.keys()) + for name in cache1: + t1, t2 = cache1[name].float(), cache2[name].float() + assert t1.shape == t2.shape, name + assert close(t1, t2), ( + f"{name}: max abs diff {(t1 - t2).abs().max().item():.3e} " + f"(scale {t2.abs().max().item():.1f})" + ) diff --git a/tests/acceptance/model_bridge/test_vllm_multigpu.py b/tests/acceptance/model_bridge/test_vllm_multigpu.py new file mode 100644 index 0000000000..8e9f79912d --- /dev/null +++ b/tests/acceptance/model_bridge/test_vllm_multigpu.py @@ -0,0 +1,85 @@ +"""TP=2 validation for the vLLM driver — real engine, real model, >= 2 GPUs. + +Run on a multi-GPU box with the ``vllm`` extra installed: + + uv run pytest tests/acceptance/model_bridge/test_vllm_multigpu.py -m multigpu -v + +Every test compares tensor_parallel_size=2 against the GPU-validated TP=1 path on +the same tiny model, so a pass means TP introduces no capture/intervention/logit +drift beyond kernel-order noise. Engines boot once per size (module fixtures); +position-scoped interventions run separately (test_vllm_multigpu_pos.py) so the +third engine gets a fresh process. +""" +from __future__ import annotations + +import pytest +import torch + +pytest.importorskip("vllm") + +from ._vllm_multigpu_common import ( + MULTIGPU_MARKS, + PROMPT_IDS, + assert_caches_match, + bridge_pair_fixture, + close, +) + +pytestmark = MULTIGPU_MARKS + +bridges = bridge_pair_fixture(tensor_parallel_size=2) + + +class TestTPCaptureParity: + def test_captures_match_tp1(self, bridges): + b1, b2 = bridges + toks = torch.tensor([PROMPT_IDS]) + _, cache1 = b1.run_with_cache(toks) + _, cache2 = b2.run_with_cache(toks) + assert_caches_match(cache1, cache2) + + def test_logits_argmax_matches_tp1(self, bridges): + b1, b2 = bridges + toks = torch.tensor([PROMPT_IDS]) + l1 = b1.forward(toks, return_type="logits") + l2 = b2.forward(toks, return_type="logits") + assert l1.shape == l2.shape + # Full-sequence argmax agreement — exercises the sharded-lm_head gather. + assert torch.equal(l1.argmax(dim=-1), l2.argmax(dim=-1)) + + def test_sequence_logits_and_loss_under_tp(self, bridges): + _, b2 = bridges + assert b2._driver.provides_sequence_logits is True # gather succeeded + loss = b2.forward(torch.tensor([PROMPT_IDS]), return_type="loss") + assert torch.isfinite(loss) + + def test_layout_check_ran_clean(self, bridges): + """The first capture-bearing forward cross-checked ranks without raising.""" + _, b2 = bridges + assert b2._driver._tp_size == 2 + assert b2._driver._layout_verified is True + + +class TestTPInterventionParity: + def test_suppress_matches_tp1(self, bridges): + b1, b2 = bridges + toks = torch.tensor([PROMPT_IDS]) + spec = {"blocks.0.mlp.hook_out": {"op": "suppress"}} + l1 = b1.forward(toks, return_type="logits", intervene=spec) + l2 = b2.forward(toks, return_type="logits", intervene=spec) + assert torch.equal(l1.argmax(dim=-1), l2.argmax(dim=-1)) + assert close(l1.float(), l2.float()) + + def test_intervention_actually_bites_under_tp(self, bridges): + """Guard against a TP no-op passing the parity test trivially.""" + _, b2 = bridges + toks = torch.tensor([PROMPT_IDS]) + clean = b2.forward(toks, return_type="logits") + edited = b2.forward( + toks, return_type="logits", intervene={"blocks.0.mlp.hook_out": {"op": "suppress"}} + ) + assert not torch.allclose(clean.float(), edited.float(), atol=1e-4, rtol=1e-4) + + +# Position-scoped interventions live in test_vllm_multigpu_pos.py — a third +# engine boot gets its own process (in-process engines under-release GPU memory). diff --git a/tests/acceptance/model_bridge/test_vllm_multigpu_pos.py b/tests/acceptance/model_bridge/test_vllm_multigpu_pos.py new file mode 100644 index 0000000000..44fd4db2a4 --- /dev/null +++ b/tests/acceptance/model_bridge/test_vllm_multigpu_pos.py @@ -0,0 +1,51 @@ +"""TP=2 position-scoped interventions — separate file so it runs in a fresh process. + +In-process vLLM engines don't reliably release GPU memory before process exit, so +this third engine boot (TP=2 + position-interventions buffers) gets its own pytest +invocation instead of stacking on test_vllm_multigpu.py's two resident engines: + + uv run pytest tests/acceptance/model_bridge/test_vllm_multigpu_pos.py -m multigpu -v +""" +from __future__ import annotations + +import pytest +import torch + +pytest.importorskip("vllm") + +from ._vllm_multigpu_common import MULTIGPU_MARKS, PROMPT_IDS, boot_multigpu + +pytestmark = MULTIGPU_MARKS + + +@pytest.fixture(scope="module") +def pos_bridge(): + bridge = boot_multigpu(tensor_parallel_size=2, enable_position_interventions=True) + yield bridge + bridge.close() + + +class TestTPPositionInterventions: + def test_pos_scoped_edit_is_row_scoped(self, pos_bridge): + toks = torch.tensor([PROMPT_IDS]) + _, clean = pos_bridge.run_with_cache(toks) + _, edited = pos_bridge.run_with_cache( + toks, intervene={"embed.hook_out": {"op": "suppress", "pos": 2}} + ) + name = "embed.hook_out" + assert torch.allclose( + edited[name][0, 2].float(), torch.zeros_like(edited[name][0, 2].float()) + ) + # Off-target rows untouched (row-scoped affine survived TP). + for row in (0, 1, 3, 4): + assert torch.allclose( + clean[name][0, row].float(), edited[name][0, row].float(), atol=1e-5, rtol=1e-5 + ) + + def test_pos_beyond_prompt_rejected_under_tp(self, pos_bridge): + """The prompt-length guard must hold with spawned TP workers too.""" + with pytest.raises(ValueError, match="beyond the prompt length"): + pos_bridge.forward( + torch.tensor([PROMPT_IDS]), + intervene={"embed.hook_out": {"op": "suppress", "pos": 50}}, + ) diff --git a/tests/acceptance/model_bridge/test_vllm_multigpu_pp.py b/tests/acceptance/model_bridge/test_vllm_multigpu_pp.py new file mode 100644 index 0000000000..7dfeacf5f7 --- /dev/null +++ b/tests/acceptance/model_bridge/test_vllm_multigpu_pp.py @@ -0,0 +1,85 @@ +"""PP=2 validation for the vLLM driver — real engine, real model, >= 2 GPUs. + +Run on a multi-GPU box with the ``vllm`` extra installed (own process — the +sibling TP suite boots its own engines): + + uv run pytest tests/acceptance/model_bridge/test_vllm_multigpu_pp.py -m multigpu -v + +Compares pipeline_parallel_size=2 against the GPU-validated single-rank path: +captures must merge across stages with no gaps, interventions must bite on +hooks owned by BOTH stages, and the fire-counter layout check must hold under +vLLM's PP microbatching. +""" +from __future__ import annotations + +import pytest +import torch + +pytest.importorskip("vllm") + +from ._vllm_multigpu_common import ( + MULTIGPU_MARKS, + N_LAYERS, + PROMPT_IDS, + assert_caches_match, + bridge_pair_fixture, + close, +) + +pytestmark = MULTIGPU_MARKS + +bridges = bridge_pair_fixture(pipeline_parallel_size=2) + + +class TestPPCaptureParity: + def test_captures_merge_across_stages_and_match(self, bridges): + b1, b2 = bridges + toks = torch.tensor([PROMPT_IDS]) + _, cache1 = b1.run_with_cache(toks) + _, cache2 = b2.run_with_cache(toks) + # Key-set equality doubles as the merge check: a missing later-stage hook + # means the per-rank read or the merge dropped a stage. + assert_caches_match(cache1, cache2) + + def test_layout_check_ran_clean(self, bridges): + """First forward verified per-stage ownership + replica fire-counter + agreement under PP microbatching without raising.""" + _, b2 = bridges + assert b2._driver._layout_verified is True + + def test_logits_argmax_matches(self, bridges): + b1, b2 = bridges + toks = torch.tensor([PROMPT_IDS]) + l1 = b1.forward(toks, return_type="logits") + l2 = b2.forward(toks, return_type="logits") + # Exercises the stage-local gathers: lm_head + final norm live on stage 1. + assert torch.equal(l1.argmax(dim=-1), l2.argmax(dim=-1)) + + def test_sequence_logits_and_loss_under_pp(self, bridges): + _, b2 = bridges + assert b2._driver.provides_sequence_logits is True + loss = b2.forward(torch.tensor([PROMPT_IDS]), return_type="loss") + assert torch.isfinite(loss) + + +class TestPPInterventionParity: + @pytest.mark.parametrize( + "hook", + [ + "blocks.0.mlp.hook_out", # first stage + f"blocks.{N_LAYERS - 1}.mlp.hook_out", # last stage + ], + ) + def test_suppress_bites_and_matches_single_rank(self, bridges, hook): + """Interventions must apply on whichever stage owns the hook.""" + b1, b2 = bridges + toks = torch.tensor([PROMPT_IDS]) + spec = {hook: {"op": "suppress"}} + clean2 = b2.forward(toks, return_type="logits") + l1 = b1.forward(toks, return_type="logits", intervene=spec) + l2 = b2.forward(toks, return_type="logits", intervene=spec) + assert not torch.allclose( + clean2.float(), l2.float(), atol=1e-4, rtol=1e-4 + ), f"{hook}: intervention was a no-op under PP" + assert torch.equal(l1.argmax(dim=-1), l2.argmax(dim=-1)) + assert close(l1.float(), l2.float()) diff --git a/tests/unit/model_bridge/test_inspect_vllm_provider.py b/tests/unit/model_bridge/test_inspect_vllm_provider.py index 995d23d199..2d9b1e81fd 100644 --- a/tests/unit/model_bridge/test_inspect_vllm_provider.py +++ b/tests/unit/model_bridge/test_inspect_vllm_provider.py @@ -24,6 +24,13 @@ def _install_vllm_mocks(monkeypatch, llm_class: Any) -> None: in ``sys.modules`` so the provider's lazy ``from vllm import …`` resolves to mocks.""" vllm = types.ModuleType("vllm") vllm.LLM = llm_class + # Provider __init__ runs the tl_absent_hooks coverage check; give bare mocks a + # passing default (side_effect-configured mocks override this). + if ( + isinstance(llm_class, MagicMock) + and llm_class.return_value.collective_rpc.side_effect is None + ): + llm_class.return_value.collective_rpc.return_value = [[]] vllm.SamplingParams = MagicMock(name="SamplingParams") vllm_inputs = types.ModuleType("vllm.inputs") vllm_inputs.TokensPrompt = MagicMock(name="TokensPrompt") @@ -108,7 +115,11 @@ def _make_provider(monkeypatch, llm_instance: Any, hf_config: Any | None = None) TransformerLensVLLMModelAPI, ) - return TransformerLensVLLMModelAPI("any/model", device="cpu"), llm_class + provider = TransformerLensVLLMModelAPI("any/model", device="cpu") + # Drop __init__-time RPC history (coverage check) so call_count assertions + # measure only the generate path. + llm_instance.collective_rpc.reset_mock() + return provider, llm_class class TestModuleSafety: @@ -344,7 +355,8 @@ def _make_capture_provider(self, monkeypatch, *, captures=None, next_token: int ) def rpc(method, args=()): - return [captures] if method == "tl_read_captures" else [None] + # [[]] default keeps the boot-time tl_absent_hooks coverage check green. + return [captures] if method == "tl_read_captures" else [[]] llm.collective_rpc.side_effect = rpc provider, llm_class = _make_provider(monkeypatch, llm) @@ -586,7 +598,8 @@ def _make_provider_with_capture(self, monkeypatch, capture: list[str]): captures = {"blocks.0.hook_out": torch.zeros(4, 4, dtype=torch.float32)} def rpc(method, args=()): - return [captures] if method == "tl_read_captures" else [None] + # [[]] default keeps the boot-time tl_absent_hooks coverage check green. + return [captures] if method == "tl_read_captures" else [[]] llm.collective_rpc.side_effect = rpc @@ -598,7 +611,9 @@ def rpc(method, args=()): TransformerLensVLLMModelAPI, ) - return TransformerLensVLLMModelAPI("any/model", device="cpu", capture=capture), llm + provider = TransformerLensVLLMModelAPI("any/model", device="cpu", capture=capture) + llm.collective_rpc.reset_mock() # drop the __init__-time coverage check call + return provider, llm def test_eval_capture_during_generate_no_extra_forward(self, monkeypatch): from inspect_ai.model import GenerateConfig diff --git a/tests/unit/model_bridge/test_vllm_boot.py b/tests/unit/model_bridge/test_vllm_boot.py index a54a2438b6..3592d56b0d 100644 --- a/tests/unit/model_bridge/test_vllm_boot.py +++ b/tests/unit/model_bridge/test_vllm_boot.py @@ -48,10 +48,23 @@ def _cfg() -> TransformerBridgeConfig: ) +def _fake_rpc(absent_per_rank): + """Method-dispatch collective_rpc stub: tl_absent_hooks feeds the boot-time + coverage check (list per rank); every other method returns a real tensor + (the reconstruction probe's get_param is beartype-enforced).""" + + def rpc(method, *args, **kwargs): + if method == "tl_absent_hooks": + return absent_per_rank + return [torch.ones(16, 4)] + + return MagicMock(side_effect=rpc) + + @pytest.fixture def mocked_boot(monkeypatch): """Mock every external boundary boot_vllm crosses; yield handles for assertions.""" - plugin._config.clear() + plugin.clear_config() hf_config = _hf_config() cfg = _cfg() adapter = ArchitectureAdapter(cfg) @@ -63,9 +76,7 @@ def mocked_boot(monkeypatch): monkeypatch.setattr("transformers.AutoTokenizer.from_pretrained", auto_tokenizer) fake_llm = MagicMock(name="llm") - # The boot-time reconstruction probe fetches the unembedding via get_param, whose - # return type is beartype-enforced — the RPC must yield a real tensor. - fake_llm.collective_rpc = MagicMock(return_value=[torch.ones(16, 4)]) + fake_llm.collective_rpc = _fake_rpc([[]]) fake_vllm = MagicMock() fake_vllm.LLM = MagicMock(return_value=fake_llm) monkeypatch.setitem(sys.modules, "vllm", fake_vllm) @@ -115,13 +126,13 @@ def _fake_configure_tokenizer(tokenizer, cfg_): "configure_tok": configure_tok, } - plugin._config.clear() + plugin.clear_config() def test_rejects_locked_kwarg_override(): - """tensor_parallel_size != 1 fails fast — before any I/O.""" - with pytest.raises(ValueError, match="tensor_parallel_size"): - boot_vllm("any-model", tensor_parallel_size=2) + """Locked kwargs fail fast — before any I/O.""" + with pytest.raises(ValueError, match="skip_tokenizer_init"): + boot_vllm("any-model", skip_tokenizer_init=False) def test_rejects_position_interventions_with_batching(): @@ -190,7 +201,7 @@ def test_env_var_zero_does_not_warn(mocked_boot, monkeypatch): def test_plugin_config_cleared_after_boot(mocked_boot): """No leak to non-TL vllm.LLM users in the same process.""" boot_vllm("any-model") - assert plugin._config == {} + assert plugin._ENV_CONFIG_KEY not in os.environ def test_plugin_config_cleared_when_llm_construction_fails(mocked_boot): @@ -199,7 +210,38 @@ def test_plugin_config_cleared_when_llm_construction_fails(mocked_boot): mocked_boot["vllm_llm"].side_effect = RuntimeError("CUDA out of memory") with pytest.raises(RuntimeError, match="CUDA out of memory"): boot_vllm("any-model") - assert plugin._config == {} + assert plugin._ENV_CONFIG_KEY not in os.environ + + +def test_env_channel_populated_during_llm_construction(mocked_boot): + """Spawned workers only see the env var — it must be live while LLM(...) runs.""" + seen: dict = {} + + def _capture_env(*args, **kwargs): + seen["env"] = os.environ.get(plugin._ENV_CONFIG_KEY) + return mocked_boot["vllm_llm"].return_value + + mocked_boot["vllm_llm"].side_effect = _capture_env + boot_vllm("any-model") + assert seen["env"], "env spec channel was empty during worker construction" + restored = plugin._deserialize_config(seen["env"]) + assert "embed.hook_out" in restored["capture_specs"] + + +def test_boot_fails_loud_when_hook_absent_on_all_ranks(mocked_boot): + """A spec that installed on no rank is a broken dot-path — silent zeros otherwise.""" + fake_llm = mocked_boot["vllm_llm"].return_value + fake_llm.collective_rpc = _fake_rpc([["blocks.0.hook_out"], ["blocks.0.hook_out"]]) + with pytest.raises(RuntimeError, match="blocks.0.hook_out"): + boot_vllm("any-model") + + +def test_boot_accepts_per_rank_absence(mocked_boot): + """PP shards legally lack some layers — only absent-everywhere is an error.""" + fake_llm = mocked_boot["vllm_llm"].return_value + # Disjoint per rank — union covers every spec. + fake_llm.collective_rpc = _fake_rpc([["blocks.1.hook_out"], ["blocks.0.hook_out"]]) + assert boot_vllm("any-model") is not None def test_rejects_prefix_caching_override(): @@ -256,4 +298,65 @@ def test_missing_vllm_raises_actionable_import_error(monkeypatch): monkeypatch.setitem(sys.modules, "vllm", None) # forces ImportError on import with pytest.raises(ImportError, match=r"transformer-lens\[vllm\]"): boot_vllm("any-model") - assert plugin._config == {} + assert plugin._ENV_CONFIG_KEY not in os.environ + + +class TestParallelBoot: + """TP/PP plumbing: kwarg validation, LLM wiring, env handling. GPU behavior is + validated by tests/acceptance/model_bridge/test_vllm_multigpu*.py.""" + + PARALLEL_KWARGS = pytest.mark.parametrize( + "kwarg", ["tensor_parallel_size", "pipeline_parallel_size"] + ) + + @PARALLEL_KWARGS + def test_size_passed_to_llm_and_layout_unverified(self, mocked_boot, kwarg): + bridge = boot_vllm("any-model", **{kwarg: 2}) + assert mocked_boot["vllm_llm"].call_args.kwargs[kwarg] == 2 + assert bridge._driver._layout_verified is False # first forward cross-checks + + @PARALLEL_KWARGS + def test_default_is_single_rank(self, mocked_boot, kwarg): + bridge = boot_vllm("any-model") + assert mocked_boot["vllm_llm"].call_args.kwargs[kwarg] == 1 + assert bridge._driver._layout_verified is True # nothing to cross-check + + @PARALLEL_KWARGS + def test_rejects_batching(self, kwarg): + with pytest.raises(ValueError, match="parallelism is unsupported"): + boot_vllm("any-model", enable_batching=True, **{kwarg: 2}) + + @PARALLEL_KWARGS + def test_invalid_value_rejected(self, kwarg): + with pytest.raises(ValueError, match="positive int"): + boot_vllm("any-model", **{kwarg: 0}) + + @PARALLEL_KWARGS + def test_parallel_boot_clears_stale_mp_zero(self, mocked_boot, monkeypatch, kwarg): + """A prior single-rank boot leaves '0' in the env; parallel boots must not + inherit it — it would force the uni-process executor and workers never spawn.""" + monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0") + boot_vllm("any-model", **{kwarg: 2}) + assert "VLLM_ENABLE_V1_MULTIPROCESSING" not in os.environ + + def test_single_rank_still_forces_in_process(self, mocked_boot, monkeypatch): + monkeypatch.delenv("VLLM_ENABLE_V1_MULTIPROCESSING", raising=False) + boot_vllm("any-model") + assert os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] == "0" + + def test_tp_and_pp_combine(self, mocked_boot): + bridge = boot_vllm("any-model", tensor_parallel_size=2, pipeline_parallel_size=2) + kwargs = mocked_boot["vllm_llm"].call_args.kwargs + assert kwargs["tensor_parallel_size"] == 2 + assert kwargs["pipeline_parallel_size"] == 2 + assert bridge._driver._tp_size == 2 + assert bridge._driver._layout_verified is False + + +def test_compile_cache_disabled_for_tl_boots(mocked_boot, monkeypatch): + """Hooks are traced into the compiled graph; a cross-process cached artifact + either crashes at AOT load (closure bytecode mismatch) or silently serves a + hookless graph — TL boots must never share vLLM's compile cache.""" + monkeypatch.delenv("VLLM_DISABLE_COMPILE_CACHE", raising=False) + boot_vllm("any-model") + assert os.environ["VLLM_DISABLE_COMPILE_CACHE"] == "1" diff --git a/tests/unit/model_bridge/test_vllm_driver.py b/tests/unit/model_bridge/test_vllm_driver.py index a5b174c128..223b47199d 100644 --- a/tests/unit/model_bridge/test_vllm_driver.py +++ b/tests/unit/model_bridge/test_vllm_driver.py @@ -313,11 +313,10 @@ def test_forward_pushes_interventions_before_generate(self): torch.tensor([[1, 2, 3]]), intervene={"embed.hook_out": {"op": "suppress"}}, ) - rpc_calls = [c.args for c in driver._llm.collective_rpc.call_args_list] assert any( - args[0] == "tl_set_interventions" - and args[1] == ({"embed.hook_out": {"op": "suppress"}},) - for args in rpc_calls + c.args[0] == "tl_set_interventions" + and c.kwargs.get("args") == ({"embed.hook_out": {"op": "suppress"}},) + for c in driver._llm.collective_rpc.call_args_list ) def test_forward_always_pushes_interventions_for_reset(self): @@ -325,8 +324,10 @@ def test_forward_always_pushes_interventions_for_reset(self): pytest.importorskip("vllm") driver = _driver(captures={"embed.hook_out": torch.zeros(3, 4)}) driver.forward(torch.tensor([[1, 2, 3]])) - rpc_calls = [c.args for c in driver._llm.collective_rpc.call_args_list] - assert any(args[0] == "tl_set_interventions" and args[1] == ({},) for args in rpc_calls) + assert any( + c.args[0] == "tl_set_interventions" and c.kwargs.get("args") == ({},) + for c in driver._llm.collective_rpc.call_args_list + ) def test_forward_rejects_max_new_tokens_gt_one(self): """Decode-step writes overwrite the prefill buffer — silent capture corruption.""" @@ -352,14 +353,25 @@ def test_zero_capture_skips_worker_read(self): ] assert reads == [], f"hookless forward still read the buffers: {reads}" - driver2 = _driver(captures={"ln_final.hook_normalized": torch.zeros(3, 4)}) + # Method-aware mock: a blanket return value would hand the reconstruction + # probe a captures dict as a "weight". + driver2 = _driver(captures={}) + lnf = {"ln_final.hook_normalized": torch.zeros(3, 4)} + + def rpc2(method, args=(), **kwargs): + if method == "tl_read_captures": + return [lnf] + if method == "tl_get_param": + return [None] # no unembedding -> sampler-logprob fallback + return [[]] + + driver2._llm.collective_rpc = MagicMock(side_effect=rpc2) driver2.forward(torch.tensor([[1, 2, 3]])) # return_logits=True default reads2 = [ - c.args - for c in driver2._llm.collective_rpc.call_args_list - if c.args[0] == "tl_read_captures" + c for c in driver2._llm.collective_rpc.call_args_list if c.args[0] == "tl_read_captures" ] - assert len(reads2) == 1 and reads2[0][1][1] == ["ln_final.hook_normalized"] + assert len(reads2) == 1 + assert reads2[0].kwargs.get("args")[1] == ["ln_final.hook_normalized"] def _batched_request_output(request_id, generated_token=None, top_logprobs=None): @@ -572,11 +584,10 @@ def test_batched_intervention_spec_pushed(self): captures_by_req={"r0": {"embed.hook_out": torch.ones(2, 4)}}, ) driver.forward([[1, 2]], intervene={"embed.hook_out": {"op": "suppress"}}) - calls = [c.args for c in driver._llm.collective_rpc.call_args_list] assert any( - a[0] == "tl_set_batched_interventions" - and a[1] == ({"embed.hook_out": {"op": "suppress"}},) - for a in calls + c.args[0] == "tl_set_batched_interventions" + and c.kwargs.get("args") == ({"embed.hook_out": {"op": "suppress"}},) + for c in driver._llm.collective_rpc.call_args_list ) @@ -873,3 +884,225 @@ def test_attention_mask_batch_mismatch_raises(self): attention_mask=torch.tensor([[1, 1]]), return_logits=False, ) + + +class TestGatherParam: + """Cross-rank param reads: replicated → rank 0; vocab-sharded → rank-order concat.""" + + def test_single_rank_returns_shard(self): + driver = _driver(captures={}) + driver._llm.collective_rpc = MagicMock(return_value=[torch.ones(16, 4)]) + assert torch.equal(driver._gather_param("lm_head.weight"), torch.ones(16, 4)) + + def test_replicated_shards_return_rank_zero(self): + """Norm weights are identical on every rank — must NOT concatenate.""" + driver = _driver(captures={}) + w = torch.full((4,), 2.0) + driver._llm.collective_rpc = MagicMock(return_value=[w, w.clone()]) + assert driver._gather_param("model.norm.weight").shape == (4,) + + def test_sharded_shards_concat_in_rank_order(self): + driver = _driver(captures={}) + shard0, shard1 = torch.zeros(8, 4), torch.ones(8, 4) + driver._llm.collective_rpc = MagicMock(return_value=[shard0, shard1]) + full = driver._gather_param("lm_head.weight") + assert full.shape == (16, 4) + assert torch.equal(full[:8], shard0) and torch.equal(full[8:], shard1) + + def test_missing_on_all_ranks_returns_none(self): + driver = _driver(captures={}) + driver._llm.collective_rpc = MagicMock(return_value=[None, None]) + assert driver._gather_param("lm_head.bias") is None + + def test_probe_reconstructs_across_sharded_vocab(self): + """End-to-end: a 2-rank sharded unembedding must reassemble so tokens in the + UPPER half of the vocab can win argmax (concat-order regression).""" + driver = _driver(captures={}) + # Rank 0 rows favor token 0; rank 1 rows favor the ln_final direction strongly. + shard0 = torch.zeros(8, 4) + shard1 = torch.zeros(8, 4) + shard1[3] = 10.0 # global token id 8 + 3 = 11 + + def rpc(method, args=(), **kwargs): + if method == "tl_get_param" and args[0] == "lm_head.weight": + return [shard0, shard1] + if method == "tl_get_param": + return [None, None] # no bias; no tied-embedding fallback needed + return [[]] + + driver._llm.collective_rpc = MagicMock(side_effect=rpc) + assert driver.probe_logit_reconstruction() is True + logits = driver._reconstruct_logits(torch.ones(3, 4)) + assert logits.shape == (3, 16) + assert int(logits[0].argmax()) == 11 + + +class TestRpcTensorCoercion: + """Multiproc collective_rpc (TP>1) serializes tensors to nested lists — + every RPC read must coerce back.""" + + def test_get_param_coerces_list_payload(self): + driver = _driver(captures={}) + driver._llm.collective_rpc = MagicMock(return_value=[[[1.0, 2.0], [3.0, 4.0]]]) + out = driver.get_param("model.norm.weight") + assert isinstance(out, torch.Tensor) and out.shape == (2, 2) + + def test_gather_param_concats_list_shards(self): + """List shards have no .shape — coercion must precede the replicated-vs-sharded check.""" + driver = _driver(captures={}) + shard0 = [[0.0] * 4] * 8 + shard1 = [[1.0] * 4] * 8 + driver._llm.collective_rpc = MagicMock(return_value=[shard0, shard1]) + full = driver._gather_param("lm_head.weight") + assert isinstance(full, torch.Tensor) and full.shape == (16, 4) + assert torch.equal(full[8:], torch.ones(8, 4)) + + def test_gather_param_detects_replicated_list_shards(self): + driver = _driver(captures={}) + weight = [2.0, 2.0, 2.0, 2.0] + driver._llm.collective_rpc = MagicMock(return_value=[weight, list(weight)]) + out = driver._gather_param("model.norm.weight") + assert out.shape == (4,) + + def test_rpc_captures_coerces_dict_values(self): + coerced = VLLMDriver._rpc_captures({"embed.hook_out": [[1.0, 2.0]]}) + assert isinstance(coerced["embed.hook_out"], torch.Tensor) + + def test_tensor_payloads_pass_through_unchanged(self): + t = torch.randn(3, 4) + assert VLLMDriver._rpc_tensor(t) is t + + def test_get_param_none_payload_stays_none(self): + """None (missing path) is filtered before coercion at the call sites.""" + driver = _driver(captures={}) + driver._llm.collective_rpc = MagicMock(return_value=[None]) + assert driver.get_param("no.such.param") is None + + +class TestRankLayoutVerification: + """First capture-bearing forward under TP/PP cross-checks the rank layout.""" + + def _multi_rank_driver(self, tp=2, pp=1): + driver = _driver(captures={}) + driver._tp_size = tp + driver._pp_size = pp + driver._layout_verified = False + return driver + + def _rpc(self, counters): + def rpc(method, *args, **kwargs): + if method == "tl_read_counter": + return counters + return [[]] + + return MagicMock(side_effect=rpc) + + def test_tp_replicated_captures_pass(self): + driver = self._multi_rank_driver(tp=2) + t = torch.randn(3, 4) + driver._llm.collective_rpc = self._rpc([4, 4]) + driver._verify_rank_layout([{"embed.hook_out": t}, {"embed.hook_out": t.clone()}]) + + def test_divergent_replicas_fail_loud(self): + driver = self._multi_rank_driver(tp=2) + with pytest.raises(RuntimeError, match="no longer replicated"): + driver._verify_rank_layout( + [ + {"embed.hook_out": torch.zeros(3, 4)}, + {"embed.hook_out": torch.ones(3, 4)}, + ] + ) + + def test_wrong_copy_count_fails_loud(self): + """tp=2 but a hook appears on one rank only — installation drifted.""" + driver = self._multi_rank_driver(tp=2) + with pytest.raises(RuntimeError, match="expected exactly tp_size"): + driver._verify_rank_layout([{"embed.hook_out": torch.zeros(3, 4)}, {}]) + + def test_pp_disjoint_ownership_passes(self): + """pp=2, tp=1: each hook on exactly one rank; stage counters differ freely + (different hook counts per stage) — only same-stage replicas must agree.""" + driver = self._multi_rank_driver(tp=1, pp=2) + driver._llm.collective_rpc = self._rpc([3, 1]) + driver._verify_rank_layout( + [ + {"embed.hook_out": torch.randn(3, 4)}, + {"blocks.1.hook_out": torch.randn(3, 4)}, + ] + ) + + def test_tp_replica_counter_mismatch_fails_loud(self): + """Ranks serving the same hook set are TP replicas of one stage; unequal + fire counters mean their forwards diverged.""" + driver = self._multi_rank_driver(tp=2, pp=1) + driver._llm.collective_rpc = self._rpc([4, 3]) + t = torch.randn(3, 4) + with pytest.raises(RuntimeError, match="Fire-counter mismatch"): + driver._verify_rank_layout([{"embed.hook_out": t}, {"embed.hook_out": t.clone()}]) + + def test_single_rank_driver_skips_verification(self): + driver = _driver(captures={}) + assert driver._tp_size == 1 and driver._layout_verified is True + + +class TestMergeRankCaptures: + """PP stages own disjoint hook subsets; the merge reassembles the full dict.""" + + def test_disjoint_ranks_merge(self): + driver = _driver(captures={}) + merged = driver._merge_rank_captures( + [ + {"embed.hook_out": torch.zeros(3, 4)}, + {"blocks.0.hook_out": torch.ones(3, 4)}, + ], + ["embed.hook_out", "blocks.0.hook_out"], + ) + assert set(merged) == {"embed.hook_out", "blocks.0.hook_out"} + assert torch.equal(merged["blocks.0.hook_out"], torch.ones(3, 4)) + + def test_tp_replicas_first_copy_wins(self): + driver = _driver(captures={}) + t = torch.randn(3, 4) + merged = driver._merge_rank_captures( + [{"embed.hook_out": t}, {"embed.hook_out": t.clone()}], ["embed.hook_out"] + ) + assert merged["embed.hook_out"] is t + + def test_supported_hook_served_by_no_rank_fails_loud(self): + driver = _driver(captures={}) + with pytest.raises(RuntimeError, match="returned by no rank"): + driver._merge_rank_captures([{}, {}], ["embed.hook_out"]) + + def test_unsupported_requested_name_does_not_false_alarm(self): + """The forced ln_final read may be legitimately absent on fallback paths.""" + driver = _driver(captures={}) + merged = driver._merge_rank_captures([{}], ["not.a.supported.hook"]) + assert merged == {} + + +class TestPPWorkerInterventionDispatch: + """Specs for hooks another PP stage owns are skipped, not KeyError'd.""" + + def test_absent_hook_spec_skipped(self): + from transformer_lens.model_bridge.sources.vllm.worker_extension import ( + TLWorkerExtension, + ) + + worker = TLWorkerExtension() + worker._tl_scale_buffers = {"embed.hook_out": torch.ones(4)} + worker._tl_bias_buffers = {"embed.hook_out": torch.zeros(4)} + worker._tl_absent_hooks = {"blocks.9.hook_out"} # owned by another stage + worker.tl_set_interventions({"blocks.9.hook_out": {"op": "suppress"}}) + assert torch.equal(worker._tl_scale_buffers["embed.hook_out"], torch.ones(4)) + + def test_truly_unknown_hook_still_raises(self): + from transformer_lens.model_bridge.sources.vllm.worker_extension import ( + TLWorkerExtension, + ) + + worker = TLWorkerExtension() + worker._tl_scale_buffers = {"embed.hook_out": torch.ones(4)} + worker._tl_bias_buffers = {"embed.hook_out": torch.zeros(4)} + worker._tl_absent_hooks = set() + with pytest.raises(KeyError, match="Unknown hook"): + worker.tl_set_interventions({"tpyo.hook_out": {"op": "suppress"}}) diff --git a/tests/unit/model_bridge/test_vllm_internals.py b/tests/unit/model_bridge/test_vllm_internals.py index cbca9315e8..7373398c92 100644 --- a/tests/unit/model_bridge/test_vllm_internals.py +++ b/tests/unit/model_bridge/test_vllm_internals.py @@ -9,9 +9,13 @@ from types import SimpleNamespace import numpy as np +import pytest import torch -from transformer_lens.model_bridge.sources.vllm.internals import segment_by_request +from transformer_lens.model_bridge.sources.vllm.internals import ( + segment_by_request, + verify_hook_coverage, +) def _model_runner(query_start_loc, req_ids): @@ -49,3 +53,34 @@ def test_single_request(self): offsets, req_ids = segment_by_request(mr) assert offsets.tolist() == [0, 5] assert req_ids == ["only"] + + +class TestVerifyHookCoverage: + """Boot-time fail-loud when a spec installed on no rank (broken dot-path).""" + + def _llm(self, absent_per_rank): + from unittest.mock import MagicMock + + llm = MagicMock() + llm.collective_rpc.return_value = absent_per_rank + return llm + + def test_all_installed_passes(self): + verify_hook_coverage(self._llm([[], []])) + + def test_disjoint_per_rank_absence_passes(self): + """PP shards each lack the other's layers — union coverage is what matters.""" + verify_hook_coverage(self._llm([["blocks.1.hook_out"], ["blocks.0.hook_out"]])) + + def test_installation_never_ran_raises(self): + """A rank returning None never ran installation (plugin patch absent) — + must not pass coverage vacuously.""" + + with pytest.raises(RuntimeError, match="never ran on rank"): + verify_hook_coverage(self._llm([[], None])) + + def test_absent_everywhere_raises(self): + with pytest.raises(RuntimeError, match="blocks.9.hook_out"): + verify_hook_coverage( + self._llm([["blocks.9.hook_out"], ["blocks.9.hook_out", "embed.hook_out"]]) + ) diff --git a/tests/unit/model_bridge/test_vllm_plugin.py b/tests/unit/model_bridge/test_vllm_plugin.py index 4de4383589..f8510c9296 100644 --- a/tests/unit/model_bridge/test_vllm_plugin.py +++ b/tests/unit/model_bridge/test_vllm_plugin.py @@ -6,11 +6,17 @@ """ from __future__ import annotations +import json +import os from types import SimpleNamespace +import pytest import torch +import torch.nn as nn +from transformer_lens.model_bridge.sources.vllm import plugin from transformer_lens.model_bridge.sources.vllm.plugin import _make_batched_hook +from transformer_lens.model_bridge.sources.vllm.worker_extension import resolve_dot_path def _worker(specs): @@ -54,3 +60,116 @@ def test_no_spec_leaves_output_unchanged(self): out = hook(None, None, torch.ones(2, 4) * 3) assert torch.equal(out, torch.ones(2, 4) * 3) + + +class TestSpecChannel: + """The driver→worker spec channel must survive process boundaries (env var).""" + + def _sample_config(self): + return { + "capture_specs": { + "embed.hook_out": ("model.embed_tokens", 4), + "blocks.0.hook_out": ("model.layers.0", 4), + }, + "max_num_batched_tokens": 2048, + "dtype": torch.bfloat16, + "enable_batching": False, + "enable_position_interventions": True, + } + + def test_serialize_round_trip(self): + config = self._sample_config() + restored = plugin._deserialize_config(plugin._serialize_config(config)) + assert restored == config + assert restored["dtype"] is torch.bfloat16 + + def test_deserialize_rejects_non_dtype(self): + raw = plugin._serialize_config(self._sample_config()) + data = json.loads(raw) + data["dtype"] = "nn" # torch.nn exists but is not a dtype + with pytest.raises(ValueError, match="not a torch dtype"): + plugin._deserialize_config(json.dumps(data)) + + def test_configure_sets_env_and_clear_config_removes_it(self): + config = self._sample_config() + try: + plugin.configure(**config) + assert plugin._ENV_CONFIG_KEY in os.environ + assert plugin._active_config()["capture_specs"] == config["capture_specs"] + finally: + plugin.clear_config() + assert plugin._ENV_CONFIG_KEY not in os.environ + + def test_active_config_reaches_spawned_workers(self, monkeypatch): + """A spawned worker re-imports the module fresh but inherits the env var.""" + config = self._sample_config() + monkeypatch.setenv(plugin._ENV_CONFIG_KEY, plugin._serialize_config(config)) + assert plugin._active_config() == config + + def test_active_config_none_when_no_channel(self, monkeypatch): + monkeypatch.delenv(plugin._ENV_CONFIG_KEY, raising=False) + assert plugin._active_config() is None + + +class TestResolveDotPath: + """Per-rank module resolution: missing segments mean 'not on this rank', not a crash.""" + + def _model(self): + model = nn.Module() + model.layers = nn.ModuleList([nn.Linear(2, 2)]) + model.norm = nn.LayerNorm(2) + return SimpleNamespace(model=model) + + def test_resolves_nested_and_indexed(self): + root = self._model() + assert resolve_dot_path(root, "model.norm") is root.model.norm + assert resolve_dot_path(root, "model.layers.0") is root.model.layers[0] + + def test_missing_attribute_returns_none(self): + assert resolve_dot_path(self._model(), "model.mlp") is None + + def test_out_of_range_index_returns_none(self): + """A PP rank owning layers [0..k) legally lacks the later indices.""" + assert resolve_dot_path(self._model(), "model.layers.7") is None + + def test_pp_missing_layer_stub_treated_as_absent(self): + """vLLM PP fills non-owned slots (layers, embed_tokens, norm) with + PPMissingLayer identity stubs the forward never calls — resolving one would + install a hook that serves its dead zero buffer as a real capture.""" + + class PPMissingLayer(nn.Module): # matched by name, as vLLM's real class is + pass + + root = self._model() + root.model.layers.append(PPMissingLayer()) + root.model.norm = PPMissingLayer() + assert resolve_dot_path(root, "model.layers.0") is root.model.layers[0] + assert resolve_dot_path(root, "model.layers.1") is None + assert resolve_dot_path(root, "model.norm") is None + + +class TestCompiledHookInstrumentationSafety: + """Compile-traced hook internals must stay annotation-free: pytest's jaxtyping + hook wraps annotated functions, and dynamo tracing the wrapper corrupts + jaxtyping's thread-local memo stack process-wide (GPU-verified).""" + + def test_gated_capture_has_no_annotations(self): + from transformer_lens.model_bridge.sources.vllm.plugin import _gated_capture + + assert ( + _gated_capture.__wrapped__.__annotations__ == {} + if hasattr(_gated_capture, "__wrapped__") + else _gated_capture.__annotations__ == {} + ) + + +class TestVllmPluginEntryPoint: + def test_general_plugin_entry_point_declared(self): + """Spawned TP workers only get the load_model patch via vllm.general_plugins — + without this entry point, workers boot hookless and captures are empty.""" + from importlib.metadata import entry_points + + eps = entry_points(group="vllm.general_plugins") + assert any( + ep.value == "transformer_lens.model_bridge.sources.vllm.plugin:register" for ep in eps + ) diff --git a/tests/unit/model_bridge/test_vllm_worker_extension.py b/tests/unit/model_bridge/test_vllm_worker_extension.py index 3d254347c2..ba96343a6a 100644 --- a/tests/unit/model_bridge/test_vllm_worker_extension.py +++ b/tests/unit/model_bridge/test_vllm_worker_extension.py @@ -8,12 +8,15 @@ from types import SimpleNamespace +import pytest import torch from transformer_lens.model_bridge.sources.vllm.worker_extension import ( TLWorkerExtension, _apply_intervention, _apply_op, + decode_tensor, + encode_tensor, ) @@ -26,9 +29,9 @@ def test_reads_named_tensor(self): ext.model_runner = SimpleNamespace( # type: ignore[attr-defined] model=SimpleNamespace(norm=SimpleNamespace(weight=weight)) ) - out = ext.tl_get_param("norm.weight") + out = decode_tensor(ext.tl_get_param("norm.weight")) assert torch.equal(out, weight) - assert out is not weight # cloned, not a live reference + assert out is not weight # cloned bytes, not a live reference def test_missing_path_returns_none(self): ext = TLWorkerExtension() @@ -54,12 +57,23 @@ def _ext(self): def test_names_filters(self): out = self._ext().tl_read_captures([2], names=["a"]) assert set(out) == {"a"} - assert tuple(out["a"].shape) == (2, 4) + assert tuple(decode_tensor(out["a"]).shape) == (2, 4) def test_none_reads_all(self): out = self._ext().tl_read_captures([2]) assert set(out) == {"a", "b"} + def test_unfired_hook_not_served(self): + """A still-open capture flag means the hook never ran this forward — under PP + that's an installed-but-not-owned module (tied-embedding alias, norm on a + non-final stage) whose buffer holds no data; serving it would hand dead zeros + to the rank merge. No flag entry keeps legacy single-rank behavior.""" + ext = self._ext() + ext._tl_capture_flags = {"a": torch.ones(1), "b": torch.zeros(1)} + assert set(ext.tl_read_captures([2])) == {"a"} + ext._tl_capture_flags = {"a": torch.ones(1)} # "b" flagless -> still served + assert set(ext.tl_read_captures([2])) == {"a", "b"} + def test_batched_names_filters(self): ext = TLWorkerExtension() ext._tl_accum = { @@ -68,6 +82,7 @@ def test_batched_names_filters(self): } out = ext.tl_read_batched_captures(names=["a"]) assert set(out["r"]) == {"a"} + assert torch.equal(decode_tensor(out["r"]["a"]), torch.ones(2, 4)) class TestFireCounter: @@ -252,9 +267,10 @@ def test_read_concatenates_chunks_in_order(self): } out = ext.tl_read_batched_captures() # req-A's two chunks cat to (3, 4); chunk order is token order. - assert tuple(out["req-A"]["embed.hook_out"].shape) == (3, 4) - assert torch.equal(out["req-A"]["embed.hook_out"][2], torch.full((4,), 2.0)) - assert tuple(out["req-B"]["embed.hook_out"].shape) == (3, 4) + req_a = decode_tensor(out["req-A"]["embed.hook_out"]) + assert tuple(req_a.shape) == (3, 4) + assert torch.equal(req_a[2], torch.full((4,), 2.0)) + assert tuple(decode_tensor(out["req-B"]["embed.hook_out"]).shape) == (3, 4) def test_reset_clears_accumulator(self): ext = TLWorkerExtension() @@ -273,3 +289,53 @@ def test_set_batched_interventions_stores_specs(self): ext = TLWorkerExtension() ext.tl_set_batched_interventions({"h": {"op": "suppress"}}) assert ext._tl_intervention_specs == {"h": {"op": "suppress"}} + + +class TestAbsentHooks: + """tl_absent_hooks reports per-rank installation gaps for the boot coverage check.""" + + def test_never_installed_returns_none(self): + """No _tl_absent_hooks attr = installation never ran — must NOT read as + 'nothing absent'.""" + worker = TLWorkerExtension() + assert worker.tl_absent_hooks() is None + + def test_reports_recorded_names_sorted(self): + worker = TLWorkerExtension() + worker._tl_absent_hooks = {"blocks.1.hook_out", "blocks.0.hook_out"} + assert worker.tl_absent_hooks() == ["blocks.0.hook_out", "blocks.1.hook_out"] + + def test_installed_with_no_gaps_returns_empty_list(self): + worker = TLWorkerExtension() + worker._tl_absent_hooks = set() + assert worker.tl_absent_hooks() == [] + + def test_remove_hooks_clears(self): + worker = TLWorkerExtension() + worker._tl_absent_hooks = {"blocks.0.hook_out"} + worker.tl_remove_hooks() + assert worker.tl_absent_hooks() == [] + + +class TestTensorWireFormat: + """encode/decode must round-trip exactly — vLLM's multiproc RPC can't carry + raw tensors from extension methods (GPU-verified header leak on 0.20.2).""" + + @pytest.mark.parametrize( + "dtype", + [torch.float32, torch.float16, torch.bfloat16, torch.int64], + ) + def test_round_trip_exact(self, dtype): + t = (torch.randn(3, 5) * 4).to(dtype) + payload = encode_tensor(t) + assert isinstance(payload["data"], bytes) + out = decode_tensor(payload) + assert out.dtype == dtype and out.shape == t.shape + assert torch.equal(out, t) + + def test_payload_is_msgpack_native(self): + """Only str/bool/int-list/bytes — nothing vLLM's serializer would mangle.""" + payload = encode_tensor(torch.ones(2, 2)) + assert set(payload) == {"__tl_tensor__", "dtype", "shape", "data"} + assert isinstance(payload["dtype"], str) + assert all(isinstance(d, int) for d in payload["shape"]) diff --git a/transformer_lens/model_bridge/sources/inspect/vllm_provider.py b/transformer_lens/model_bridge/sources/inspect/vllm_provider.py index 43f4c59843..b4d7f003e8 100644 --- a/transformer_lens/model_bridge/sources/inspect/vllm_provider.py +++ b/transformer_lens/model_bridge/sources/inspect/vllm_provider.py @@ -17,7 +17,6 @@ from __future__ import annotations import gc -import os from typing import Any, Mapping import numpy as np @@ -47,22 +46,17 @@ # Distinct from the HF ``tl_bridge`` provider and from inspect_ai's built-in ``vllm``. PROVIDER_NAME = "tl_bridge_vllm" -# Forced ``LLM(...)`` kwargs that the capture-hook design depends on (matches the same -# set in ``sources/vllm/source.py``). Multi-device / vLLM-owned tokenizer break the wire -# path; we own the tokenizer and the plugin assumes single-process workers. +# Forced ``LLM(...)`` kwargs the capture-hook design depends on. Multi-device and a +# vLLM-owned tokenizer break the wire path; prefix caching would misalign capture rows +# (see ``construct_instrumented_llm``, which supplies the caching/parallelism values). _LOCKED_VLLM_KWARGS = { "tensor_parallel_size": 1, "pipeline_parallel_size": 1, + "enable_prefix_caching": False, "skip_tokenizer_init": True, "disable_log_stats": True, } -# Dotted path to the worker extension whose ``tl_*`` methods this provider drives via -# ``collective_rpc`` (capture reads, intervention specs, hook teardown). -_WORKER_EXTENSION_CLS = ( - "transformer_lens.model_bridge.sources.vllm.worker_extension.TLWorkerExtension" -) - def _kinds_from_specs(specs: dict[str, Any]) -> frozenset[str]: """Boundary kinds (resid_post/attn_out/...) served by the overlay's ``capture_specs``. @@ -104,7 +98,7 @@ def __init__( from transformers import AutoConfig, AutoTokenizer try: - from vllm import LLM + import vllm # noqa: F401 — import check; construction is in construct_instrumented_llm except ImportError as exc: raise ImportError( "The tl_bridge_vllm provider requires vLLM (Linux + CUDA). Install with " @@ -114,9 +108,10 @@ def __init__( from transformer_lens.utilities.hf_utils import get_hf_token - from ..vllm import plugin from ..vllm.internals import extract_hf_config from ..vllm.overlays import get_overlay + from ..vllm.source import _dtype_from_hf_config, construct_instrumented_llm + from ..vllm.worker_extension import dtype_name # Caller-overridable LLM kwargs go through ``vllm_kwargs``; the locked set above # may not be overridden (multi-device / vLLM-owned tokenizer break our wire path). @@ -149,38 +144,25 @@ def __init__( overlay = get_overlay(architecture) resolved_dtype = dtype if dtype is not None else _dtype_from_hf_config(hf_config_preview) capture_specs = overlay.capture_specs(hf_config_preview) - plugin.configure( + self._llm = construct_instrumented_llm( + model_name, capture_specs=capture_specs, max_num_batched_tokens=max_num_batched_tokens, dtype=resolved_dtype, enable_batching=False, # eager batched path is a later increment - ) - plugin.register() - # Single-process workers are required — otherwise the plugin's _config singleton - # isn't visible to worker subprocesses and hooks silently fail to install. - os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0" - - try: - self._llm = LLM( - model=model_name, - gpu_memory_utilization=gpu_memory_utilization, - max_model_len=max_model_len, - max_num_batched_tokens=max_num_batched_tokens, - # Worker extension's tl_* methods are reachable via collective_rpc. - worker_extension_cls=_WORKER_EXTENSION_CLS, + llm_kwargs={ + "gpu_memory_utilization": gpu_memory_utilization, + "max_model_len": max_model_len, # Full-vocab logprobs so _generate_capture can synthesize logits at the # generated position (vLLM caps logprobs to this value; default 20 is too # small for mech interp). - max_logprobs=int(hf_config_preview.vocab_size), - dtype=str(resolved_dtype).replace("torch.", "") if dtype is not None else "auto", - **_LOCKED_VLLM_KWARGS, + "max_logprobs": int(hf_config_preview.vocab_size), + "dtype": dtype_name(resolved_dtype) if dtype is not None else "auto", + "skip_tokenizer_init": True, + "disable_log_stats": True, **vllm_kwargs, - ) - finally: - # Always clear, even on a failed boot — stale specs would make the next - # in-process vllm.LLM(...) walk our dot-paths on a foreign model. - plugin._config.clear() - # Capture-path validity was enforced inside patched_load_model during LLM(...). + }, + ) hf_config = extract_hf_config(self._llm) # Capture-relevant constants used by _generate_capture. @@ -441,16 +423,6 @@ def close(self) -> None: pass -def _dtype_from_hf_config(hf_config: Any) -> torch.dtype: - """Best-effort dtype from an HF config — vLLM prefers fp16 on GPU.""" - raw = getattr(hf_config, "torch_dtype", None) - if isinstance(raw, torch.dtype): - return raw - if isinstance(raw, str): - return getattr(torch, raw, torch.float16) - return torch.float16 - - def _synthesize_logits(request_output: Any, n_tokens: int, d_vocab: int) -> torch.Tensor: """Build a ``(1, n_tokens, d_vocab)`` logits-like tensor from vLLM's sampler output — log-probs (not raw logits), with earlier positions ``-inf`` (lm_head is bypassed so diff --git a/transformer_lens/model_bridge/sources/vllm/driver.py b/transformer_lens/model_bridge/sources/vllm/driver.py index aec008f45d..3fcb33ab81 100644 --- a/transformer_lens/model_bridge/sources/vllm/driver.py +++ b/transformer_lens/model_bridge/sources/vllm/driver.py @@ -5,7 +5,7 @@ import logging import threading import warnings -from typing import Any, Mapping +from typing import Any, Mapping, Optional, Sequence, Union import torch @@ -17,6 +17,7 @@ from transformer_lens.model_bridge.sources._driver_base import DriverBase from .intervention_specs import validate_spec +from .worker_extension import _TL_TENSOR_KEY, decode_tensor # vLLM's distributed teardown operates on process-wide globals, so close() may only run # it when no other VLLMDriver-owned engine is alive (notebook re-binding boots B before @@ -49,11 +50,20 @@ def __init__( max_num_batched_tokens: int, enable_batching: bool = False, enable_position_interventions: bool = False, + tensor_parallel_size: int = 1, + pipeline_parallel_size: int = 1, ) -> None: super().__init__(adapter.cfg, tokenizer) self._llm = llm self._max_num_batched_tokens = max_num_batched_tokens self._enable_batching = enable_batching + # Rank layout: PP stages own disjoint layer (hence hook) subsets; the TP + # ranks within a stage hold replicas of that stage's post-all-reduce hook + # points. Reads merge across stages and take the first replica within one; + # the first capture-bearing forward cross-checks the whole layout + # (see _verify_rank_layout) and then trusts it. + self._tp_size = tensor_parallel_size + self._layout_verified = tensor_parallel_size == 1 and pipeline_parallel_size == 1 # Position-scoped 'pos' interventions need (max_n, width) affine buffers, # allocated at boot only when this is set (see plugin.patched_load_model). self._enable_position_interventions = enable_position_interventions @@ -95,7 +105,9 @@ def __init__( def forward( self, - input_ids: TensorLike | None = None, + # Wider than the protocol's TensorLike: the batched path documents plain + # (ragged) list[int] / list[list[int]] prompts, which have no shape/dtype. + input_ids: Optional[Union[TensorLike, Sequence[Any]]] = None, *, capture: tuple[str, ...] = (), intervene: Mapping[str, Intervention] | None = None, @@ -162,13 +174,23 @@ def forward( n_tokens = len(ids_list) read_names = self._read_names(names, return_logits) - # collective_rpc returns one result per worker; single-rank, so [0]. Nothing to - # read (no captures, logits off) → skip the crossing altogether. - worker_captures = ( - self._llm.collective_rpc("tl_read_captures", args=([n_tokens], read_names))[0] - if read_names - else {} - ) + # collective_rpc returns one result per worker; each returns only the hooks + # it owns (PP shards) with TP replicas within a stage — merge across ranks. + # Nothing to read (no captures, logits off) → skip the crossing altogether. + if read_names: + per_rank_raw = self._llm.collective_rpc( + "tl_read_captures", args=([n_tokens], read_names) + ) + if not self._layout_verified: + self._verify_rank_layout([self._rpc_captures(caps) for caps in per_rank_raw]) + self._layout_verified = True + # Merge on the wire dicts, decode only the survivors — decoding every TP + # replica just to discard all but the first is wasted CPU per forward. + worker_captures = self._rpc_captures( + self._merge_rank_captures(per_rank_raw, read_names) + ) + else: + worker_captures = {} logits: torch.Tensor | None = None if return_logits: @@ -210,7 +232,7 @@ def _expose_captured( def _forward_batched( self, - input_ids: TensorLike, + input_ids: Union[TensorLike, Sequence[Any]], intervene_specs: dict, return_logits: bool, names: list[str], @@ -242,7 +264,6 @@ def _forward_batched( # Reset accumulators so prior-forward chunks don't leak into the cat. self._llm.collective_rpc("tl_reset_accumulators") - self._llm.collective_rpc("tl_reset_counter") self._llm.collective_rpc("tl_set_batched_interventions", args=(intervene_specs,)) d_vocab = self.bridge_config.d_vocab @@ -264,6 +285,11 @@ def _forward_batched( worker_captures = self._llm.collective_rpc( "tl_read_batched_captures", args=(read_names,) )[0] + # Batched runs single-rank today, but coerce anyway — multiproc RPC + # serializes tensors to lists (see _rpc_tensor). + worker_captures = { + req_id: self._rpc_captures(caps) for req_id, caps in worker_captures.items() + } captured = self._assemble_padded(outputs, worker_captures, prompt_lens) else: captured = {} @@ -360,12 +386,129 @@ def _synthesize_logits_batched( return logits def get_param(self, dotted_name: str) -> torch.Tensor | None: - """Fetch a named worker tensor (e.g. ``model.norm.weight``) for conversions + """Fetch a named model tensor (e.g. ``model.norm.weight``) for conversions the bridge can't otherwise do (ln_final post→pre-weight; see the overlay). - None if closed or the path is missing.""" + Gathered across ranks: replicated params return one copy, vocab-sharded + weights concatenate, stage-local params (PP) come from whichever rank owns + them. None if closed or the path resolves nowhere.""" + return self._gather_param(dotted_name) + + @staticmethod + def _rpc_tensor(value: Any) -> torch.Tensor: + """Decode a non-None collective_rpc payload back to a tensor. + + Worker methods return the explicit wire format (see + ``worker_extension.encode_tensor``) because vLLM's multiproc RPC can't + round-trip raw tensors. Raw tensors still pass through for mocks and any + legacy in-process payloads; anything else is best-effort coerced.""" + if isinstance(value, torch.Tensor): + return value + if isinstance(value, Mapping) and value.get(_TL_TENSOR_KEY): + return decode_tensor(dict(value)) + return torch.as_tensor(value) + + @classmethod + def _rpc_captures(cls, captures: Mapping[str, Any]) -> dict[str, torch.Tensor]: + return {name: cls._rpc_tensor(value) for name, value in captures.items()} + + def _gather_param(self, dotted_name: str, dim: int = 0) -> torch.Tensor | None: + """Fetch a param across all TP ranks: replicated → rank 0; sharded → concat. + + Vocab-parallel weights (``lm_head.weight``, ``embed_tokens.weight``) hold + contiguous per-rank slices in rank order, which is also collective_rpc's + result order — concatenation along ``dim`` reassembles the full tensor + (vocab padding lands in the tail rows, sliced off by reconstruction). + """ if self._llm is None: return None - return self._llm.collective_rpc("tl_get_param", args=(dotted_name,))[0] + shards = [ + self._rpc_tensor(s) + for s in self._llm.collective_rpc("tl_get_param", args=(dotted_name,)) + if s is not None + ] + if not shards: + return None + if len(shards) == 1: + return shards[0] + if all(s.shape == shards[0].shape and torch.equal(s, shards[0]) for s in shards[1:]): + return shards[0] # replicated (norm weights, biases on some archs) + return torch.cat(shards, dim=dim) + + def _merge_rank_captures(self, per_rank_captures: list, requested: list[str]) -> dict[str, Any]: + """Merge per-rank capture dicts into one (values stay opaque — callers decode): + PP stages own disjoint hook subsets; TP replicas within a stage agree (verified + once by _verify_rank_layout), so the first copy wins. A requested, supported + hook served by NO rank fails loud — zero-filling would be silent data loss + (same policy as the batched join).""" + merged: dict[str, Any] = {} + for captures in per_rank_captures: + for name, tensor in captures.items(): + if name not in merged: + merged[name] = tensor + missing = [ + name for name in requested if name not in merged and name in self.supported_hook_points + ] + if missing: + raise RuntimeError( + f"Capture(s) {missing} returned by no rank — a hook fired nowhere " + "despite passing the boot-time coverage check. Report this." + ) + return merged + + def _verify_rank_layout(self, per_rank_captures: list) -> None: + """One-time cross-rank check of the capture layout under TP/PP. + + Invariant: each hook lives on exactly one PP stage and is replicated across + that stage's ``tp_size`` ranks (every overlay hook point is post-all-reduce). + A wrong copy count means installation drift; divergent replicas mean a hook + point moved pre-all-reduce — either way rank-merged reads would be silently + wrong, so fail loud on the first capture-bearing forward. Ranks serving the + same hook set are TP replicas of one stage and must report identical fire + counters; counters are NOT comparable across stages — per-rank installed + counts overcount modules that exist but never run (tied-embedding aliases, + archs that instantiate norm on every rank). + """ + all_names: set[str] = set() + for captures in per_rank_captures: + all_names |= set(captures.keys()) + for name in sorted(all_names): + copies = [ + (rank, caps[name]) for rank, caps in enumerate(per_rank_captures) if name in caps + ] + if len(copies) != self._tp_size: + raise RuntimeError( + f"Rank-layout check failed for {name!r}: found on {len(copies)} rank(s) " + f"but expected exactly tp_size={self._tp_size} (one PP stage × its TP " + "replicas). Per-rank installation drifted — report this." + ) + rank0, t0 = copies[0] + for rank, t_r in copies[1:]: + if t0.shape != t_r.shape or not torch.allclose( + t0.float(), t_r.float(), atol=1e-5, rtol=1e-5 + ): + diff = ( + (t0.float() - t_r.float()).abs().max().item() + if t0.shape == t_r.shape + else float("inf") + ) + raise RuntimeError( + f"TP replication check failed for {name!r}: rank {rank0} vs rank " + f"{rank} max abs diff {diff:.3e}. This hook point is no longer " + "replicated across ranks (vLLM may have moved it pre-all-reduce) — " + "merged capture reads would be silently wrong. Report this." + ) + counters = [int(c) for c in self._llm.collective_rpc("tl_read_counter")] + stages: dict = {} + for rank, captures in enumerate(per_rank_captures): + stages.setdefault(frozenset(captures.keys()), []).append(rank) + for ranks in stages.values(): + group = [counters[r] for r in ranks] + if len(set(group)) > 1: + raise RuntimeError( + f"Fire-counter mismatch within a TP replica group: ranks {ranks} " + f"serve the same hooks but fired {group} times. Replicated forwards " + "diverged — merged capture reads would be silently wrong. Report this." + ) def probe_logit_reconstruction(self) -> bool: """One-time unembedding fetch + cache; downgrades ``provides_sequence_logits`` @@ -375,13 +518,14 @@ def probe_logit_reconstruction(self) -> bool: if self._unembed_probed: return self._unembed is not None self._unembed_probed = True - weight = self.get_param("lm_head.weight") + # Gathered reads: under TP the unembedding is vocab-sharded per rank. + weight = self._gather_param("lm_head.weight") if weight is None: # tied embeddings expose no separate lm_head - weight = self.get_param("model.embed_tokens.weight") + weight = self._gather_param("model.embed_tokens.weight") if weight is None: self.provides_sequence_logits = False return False - bias = self.get_param("lm_head.bias") + bias = self._gather_param("lm_head.bias") d_vocab = int(self.bridge_config.d_vocab) # Slice vLLM's vocab-pad rows at cache time; fp32 residency (~2× checkpoint # dtype on CPU) trades memory for skipping a full-matrix upcast per forward. @@ -432,9 +576,11 @@ def _unfold_ln_final(self, t: torch.Tensor) -> torch.Tensor: beats silent cross-backend mismatch.""" if not self._lnf_probed: self._lnf_probed = True - # The overlay's capture spec owns the module path; derive the weight from it. + # The overlay's capture spec owns the module path; derive the weight from + # it. Gathered read: under PP the final norm lives only on the last stage, + # so a rank-0 get_param would miss it. path = self._capture_paths.get(self._LN_FINAL, "model.norm") - weight = self.get_param(f"{path}.weight") + weight = self._gather_param(f"{path}.weight") if weight is None: warnings.warn( "ln_final.hook_normalized: norm weight unreachable — the captured " diff --git a/transformer_lens/model_bridge/sources/vllm/internals.py b/transformer_lens/model_bridge/sources/vllm/internals.py index 185a5a9a4b..4ec1efc621 100644 --- a/transformer_lens/model_bridge/sources/vllm/internals.py +++ b/transformer_lens/model_bridge/sources/vllm/internals.py @@ -24,6 +24,34 @@ def extract_hf_config(llm: Any) -> Any: ) from e +def verify_hook_coverage(llm: Any) -> None: + """Raise if any configured capture hook installed on NO rank. + + Per-rank absence is legal (pipeline-parallel ranks own layer subsets), so + hook installation skips missing modules instead of raising — this boot-time + check restores the fail-loud contract: a hook absent everywhere is a broken + overlay dot-path and would otherwise read back as silent zeros. + """ + absent_per_rank = llm.collective_rpc("tl_absent_hooks") + if not absent_per_rank: + return + never_ran = [rank for rank, absent in enumerate(absent_per_rank) if absent is None] + if never_ran: + raise RuntimeError( + f"Capture-hook installation never ran on rank(s) {never_ran}: the TL vLLM " + "plugin did not execute in those worker processes. The vllm.general_plugins " + "entry point registers it — if this install predates that, rerun `uv sync`." + ) + absent_everywhere = set(absent_per_rank[0]) + for rank_absent in absent_per_rank[1:]: + absent_everywhere &= set(rank_absent) + if absent_everywhere: + raise RuntimeError( + f"Capture hooks failed to install on every worker: {sorted(absent_everywhere)}. " + "The overlay's dot-paths don't match this model's module tree." + ) + + # Cumulative per-request query offsets: FlashAttention/Triton name them # query_start_loc, FlashInfer names them qo_indptr. Request i = rows i:i+1. _QUERY_OFFSET_ATTRS = ("query_start_loc", "qo_indptr") diff --git a/transformer_lens/model_bridge/sources/vllm/plugin.py b/transformer_lens/model_bridge/sources/vllm/plugin.py index 51e5d70fb5..dcd7b238e7 100644 --- a/transformer_lens/model_bridge/sources/vllm/plugin.py +++ b/transformer_lens/model_bridge/sources/vllm/plugin.py @@ -25,21 +25,27 @@ """ from __future__ import annotations +import json +import os import re -from typing import Any, Dict, Tuple +from typing import Any, Dict, Optional, Tuple import torch from .internals import segment_by_request -from .worker_extension import _apply_op +from .worker_extension import _apply_op, dtype_name, resolve_dot_path # Decoder layers return the fused-residual (mlp_delta, residual) 2-tuple, so # their hooks materialize the sum (see _make_capture_hook); other modules don't. _DECODER_LAYER_PATH = re.compile(r"^model\.layers\.\d+$") -# Transient signal driver → worker during LLM construction. Per-Worker buffers -# live on Worker instances so concurrent boot_vllm calls don't collide. -_config: Dict[str, Any] = {} +# Transient signal driver → worker during LLM construction: an env var, because +# spawned worker processes (TP/PP > 1) re-import this module and would see any +# module global empty — vLLM runs ``register()`` in every worker, so the patch +# itself propagates; only this payload needs a cross-process channel. Per-Worker +# buffers live on Worker instances so concurrent boot_vllm calls don't collide. +# Single-node only: env vars don't reach Ray remote workers. +_ENV_CONFIG_KEY = "TL_VLLM_PLUGIN_CONFIG" _install_patched = False _orig_load_model = None @@ -52,11 +58,56 @@ def configure( enable_position_interventions: bool = False, ) -> None: """Set capture specs, buffer length, dtype, and hook flavor before ``LLM(...)``.""" - _config["capture_specs"] = capture_specs - _config["max_num_batched_tokens"] = max_num_batched_tokens - _config["dtype"] = dtype - _config["enable_batching"] = enable_batching - _config["enable_position_interventions"] = enable_position_interventions + os.environ[_ENV_CONFIG_KEY] = _serialize_config( + { + "capture_specs": capture_specs, + "max_num_batched_tokens": max_num_batched_tokens, + "dtype": dtype, + "enable_batching": enable_batching, + "enable_position_interventions": enable_position_interventions, + } + ) + + +def clear_config() -> None: + """Unset the spec channel. Boot sites call this in a ``finally`` after + ``LLM(...)`` so a later non-TL engine can't inherit the specs.""" + os.environ.pop(_ENV_CONFIG_KEY, None) + + +def _serialize_config(config: Dict[str, Any]) -> str: + return json.dumps( + { + "capture_specs": { + name: [path, width] for name, (path, width) in config["capture_specs"].items() + }, + "max_num_batched_tokens": config["max_num_batched_tokens"], + "dtype": dtype_name(config["dtype"]), + "enable_batching": config["enable_batching"], + "enable_position_interventions": config["enable_position_interventions"], + } + ) + + +def _deserialize_config(raw: str) -> Dict[str, Any]: + data = json.loads(raw) + dtype = getattr(torch, data["dtype"], None) + if not isinstance(dtype, torch.dtype): + raise ValueError(f"{_ENV_CONFIG_KEY}: {data['dtype']!r} is not a torch dtype.") + return { + "capture_specs": { + name: (path, int(width)) for name, (path, width) in data["capture_specs"].items() + }, + "max_num_batched_tokens": int(data["max_num_batched_tokens"]), + "dtype": dtype, + "enable_batching": bool(data["enable_batching"]), + "enable_position_interventions": bool(data["enable_position_interventions"]), + } + + +def _active_config() -> Optional[Dict[str, Any]]: + raw = os.environ.get(_ENV_CONFIG_KEY) + return _deserialize_config(raw) if raw else None def register() -> None: @@ -67,9 +118,9 @@ def register() -> None: don't double-wrap. No ``unregister()`` symmetry: the patch stays for process lifetime. Benign - because ``patched_load_model`` no-ops when ``_config["capture_specs"]`` is - absent — and ``boot_vllm`` clears ``_config`` after each ``LLM(...)``, so - any subsequent non-TL ``LLM(...)`` in the same process hits the no-op path. + because ``patched_load_model`` no-ops when no spec channel is populated — and + boot sites call ``clear_config()`` after each ``LLM(...)``, so any subsequent + non-TL ``LLM(...)`` in the same process hits the no-op path. """ global _install_patched, _orig_load_model if _install_patched: @@ -80,15 +131,16 @@ def register() -> None: def patched_load_model(self): _orig_load_model(self) - specs = _config.get("capture_specs") - if not specs: + config = _active_config() + if config is None: return # not a TL-driven LLM; no hooks to install - max_n = _config["max_num_batched_tokens"] - dtype = _config["dtype"] - enable_batching = _config.get("enable_batching", False) + specs = config["capture_specs"] + max_n = config["max_num_batched_tokens"] + dtype = config["dtype"] + enable_batching = config.get("enable_batching", False) # Per-position affine buffers are (max_n, width) instead of (width,), so each # sequence row can carry a distinct scale/bias (position-scoped patching). - per_position = _config.get("enable_position_interventions", False) + per_position = config.get("enable_position_interventions", False) device = next(self.model_runner.model.parameters()).device # Detach prior handles before reassigning — vLLM doesn't double-load @@ -107,12 +159,16 @@ def patched_load_model(self): # Batched-mode per-(req_id, hook) accumulators + global spec dict. self._tl_accum = {} self._tl_intervention_specs = {} + # Specs whose module isn't on this rank (PP layer shards); the boot site + # verifies via tl_absent_hooks that every spec landed somewhere. + self._tl_absent_hooks = set() # Shared counter — surfaces hook double-fire under compile via tl_read_counter. self._tl_fire_counter = torch.zeros(1, device=device, dtype=torch.int64) for canonical_name, (dot_path, width) in specs.items(): - target = self.model_runner.model - for seg in dot_path.split("."): - target = target[int(seg)] if seg.isdigit() else getattr(target, seg) + target = resolve_dot_path(self.model_runner.model, dot_path) + if target is None: + self._tl_absent_hooks.add(canonical_name) + continue # Decoder layers return vLLM's (mlp_delta, residual) tuple; the # hook materializes their sum so the capture semantically matches # HF's full residual stream. Other modules use the default path. @@ -192,7 +248,11 @@ def _make_capture_hook( ``fire_counter`` is incremented per call for the fire-once check. """ - def _affine(t: torch.Tensor, n: Any) -> torch.Tensor: + # NO type annotations on _affine: the pytest jaxtyping hook wraps annotated + # transformer_lens functions, and dynamo tracing that wrapper into the compiled + # graph corrupts jaxtyping's thread-local memo stack for the whole process + # (GPU-verified: unrelated outer calls die with 'pop from empty list'). + def _affine(t, n): # per_position is a closure constant → torch.compile specializes this branch. # narrow(0, 0, n) keeps the SymInt dynamic shape (same trick as _gated_capture); # the (width,) path broadcasts across all rows. @@ -234,9 +294,8 @@ def hook(_module, _inputs, output): return hook -def _gated_capture( - capture_buf: torch.Tensor, n: Any, modified: torch.Tensor, capture_flag: torch.Tensor -) -> None: +# NO type annotations: this is traced into the compiled graph — see _affine's note. +def _gated_capture(capture_buf, n, modified, capture_flag): """First-write-wins via torch.where, compile-safe (no Python branching). When ``capture_flag == 0`` (open), writes ``modified`` to ``capture_buf[:n]``. diff --git a/transformer_lens/model_bridge/sources/vllm/source.py b/transformer_lens/model_bridge/sources/vllm/source.py index e8661c4681..7901028eaf 100644 --- a/transformer_lens/model_bridge/sources/vllm/source.py +++ b/transformer_lens/model_bridge/sources/vllm/source.py @@ -21,8 +21,9 @@ from . import plugin from .driver import VLLMDriver -from .internals import extract_hf_config +from .internals import extract_hf_config, verify_hook_coverage from .overlays import get_overlay +from .worker_extension import dtype_name # Forced LLM(...) kwargs that the capture-hook design depends on. Caller override → ValueError. # enable_prefix_caching MUST stay off: a prefix-cache hit makes the prefill compute only the @@ -30,17 +31,105 @@ # interventions skip cached positions, and an intervened forward writes poisoned K/V that a # later clean forward on the same prompt would silently reuse. _LOCKED_KWARGS = { - "tensor_parallel_size": 1, - "pipeline_parallel_size": 1, "skip_tokenizer_init": True, "disable_log_stats": True, "enable_prefix_caching": False, } -# Serializes the configure() → LLM(...) → clear() handoff: the spec channel is a module -# global, so interleaved boots would cross-wire capture specs between engines. +# Serializes the configure() → LLM(...) → clear() handoff: the spec channel is a +# process-wide env var, so interleaved boots would cross-wire capture specs between engines. _BOOT_LOCK = threading.Lock() +_WORKER_EXTENSION_CLS = ( + "transformer_lens.model_bridge.sources.vllm.worker_extension.TLWorkerExtension" +) + + +def construct_instrumented_llm( + model_name: str, + *, + capture_specs: Dict[str, Any], + max_num_batched_tokens: int, + dtype: torch.dtype, + enable_batching: bool = False, + enable_position_interventions: bool = False, + tensor_parallel_size: int = 1, + pipeline_parallel_size: int = 1, + llm_kwargs: Dict[str, Any], +) -> Any: + """The one place a TL-instrumented ``vllm.LLM`` is constructed — shared by + ``boot_vllm`` and the Inspect vLLM provider so the capture contract can't drift + between them. Owns everything hook correctness depends on: + + - ``VLLM_DISABLE_COMPILE_CACHE=1``: our hooks are traced INTO vLLM's compiled + graph, but its compile cache is keyed only on its own config — a cached + artifact from a differently-instrumented process either crashes at AOT load + (bytecode binds the hook closures) or silently serves a hookless graph. + - ``VLLM_ENABLE_V1_MULTIPROCESSING``: forced ``"0"`` single-rank (in-process + worker, the historical GPU-validated path); parallel boots spawn workers, + which read the specs via the plugin's env channel — and must not inherit a + stale ``"0"`` that would force the uni-process executor. + - ``enable_prefix_caching=False``: a prefix-cache hit computes only the uncached + suffix, so captures land row-misaligned, interventions skip cached positions, + and an intervened forward writes poisoned K/V a later clean forward reuses. + - configure → register → ``LLM(...)`` → clear under ``_BOOT_LOCK``, then a + hook-coverage check so a spec that landed on no rank fails here, not as zeros. + + ``llm_kwargs`` carries the caller's remaining ``LLM(...)`` arguments; restating a + contract kwarg is allowed only at the same value (callers validate overrides). + """ + from vllm import LLM + + os.environ["VLLM_DISABLE_COMPILE_CACHE"] = "1" + if tensor_parallel_size == 1 and pipeline_parallel_size == 1: + existing_mp = os.environ.get("VLLM_ENABLE_V1_MULTIPROCESSING") + if existing_mp not in (None, "0"): + warnings.warn( + f"VLLM_ENABLE_V1_MULTIPROCESSING={existing_mp!r} overridden to '0' — " + "single-rank TL boots keep the worker in-process.", + UserWarning, + stacklevel=3, + ) + os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0" + elif os.environ.get("VLLM_ENABLE_V1_MULTIPROCESSING") == "0": + os.environ.pop("VLLM_ENABLE_V1_MULTIPROCESSING") + + with _BOOT_LOCK: + plugin.configure( + capture_specs=capture_specs, + max_num_batched_tokens=max_num_batched_tokens, + dtype=dtype, + enable_batching=enable_batching, + enable_position_interventions=enable_position_interventions, + ) + plugin.register() + try: + llm = LLM( + **{ + "model": model_name, + # vLLM defaults this to 8192 for chunked prefill; the capture buffers + # are sized to it, so the compiled dynamic-shape range must match — + # otherwise Dynamo's symbolic hint exceeds the buffer dim at compile. + "max_num_batched_tokens": max_num_batched_tokens, + # Mixes tl_* methods into the Worker for collective_rpc (multiple + # inheritance; the tl_ prefix avoids attribute collisions). + "worker_extension_cls": _WORKER_EXTENSION_CLS, + "enable_prefix_caching": False, + "tensor_parallel_size": tensor_parallel_size, + "pipeline_parallel_size": pipeline_parallel_size, + **llm_kwargs, + } + ) + finally: + # Always clear, even on a failed boot: stale specs would make the next + # in-process vllm.LLM(...) walk our dot-paths on a foreign model. + plugin.clear_config() + + # Hook installation skips modules absent on a rank (PP shards); a spec that + # landed on NO rank is a broken dot-path and must fail here, not read zeros. + verify_hook_coverage(llm) + return llm + def boot_vllm( model_name: str, @@ -51,6 +140,8 @@ def boot_vllm( max_num_batched_tokens: int = 2048, enable_batching: bool = False, enable_position_interventions: bool = False, + tensor_parallel_size: int = 1, + pipeline_parallel_size: int = 1, **vllm_kwargs: Any, ) -> RemoteBridge: """Boot a model via vLLM and wrap it in a :class:`RemoteBridge` via :class:`VLLMDriver`. @@ -108,12 +199,35 @@ def boot_vllm( Costs ~2× extra resident GPU memory across all hooks (the scale and bias buffers join the already-``(max_n, width)`` capture buffer), so it is opt-in and defaults ``False``. Compiled-path only — incompatible with ``enable_batching``. + + ``tensor_parallel_size`` > 1 enables single-node tensor parallelism. Every + capture point the overlay hooks is post-all-reduce and therefore replicated + across a stage's TP ranks; ``pipeline_parallel_size`` > 1 shards layers across + stages, each owning the hooks that actually fire on it. + Capture reads merge across ranks with a first-forward layout check that fails + loud on installation drift or non-replicated hook points; sharded/stage-local + weights (``lm_head``, final norm) are gathered for logit reconstruction and the + ln_final un-fold. Single-node only (the spec channel is an env var, which never + reaches Ray remote workers); both are incompatible with ``enable_batching`` + (per-rank chunk boundaries are unvalidated). """ if enable_position_interventions and enable_batching: raise ValueError( "enable_position_interventions requires the compiled path and is incompatible " "with enable_batching=True (the batched/eager path has no affine buffers)." ) + for kwarg_name, size in ( + ("tensor_parallel_size", tensor_parallel_size), + ("pipeline_parallel_size", pipeline_parallel_size), + ): + if not isinstance(size, int) or size < 1: + raise ValueError(f"{kwarg_name} must be a positive int; got {size!r}.") + if (tensor_parallel_size > 1 or pipeline_parallel_size > 1) and enable_batching: + raise ValueError( + "enable_batching=True with tensor/pipeline parallelism is unsupported: the " + "eager batched path's per-rank chunk boundaries are unvalidated. " + "Use the compiled single-prompt path." + ) _reject_locked_overrides(vllm_kwargs) # Import-check first: fail with an actionable message before any network I/O # or plugin state mutation. @@ -137,68 +251,35 @@ def boot_vllm( resolved_dtype = dtype or _dtype_from_hf_config(hf_config_preview) - # The plugin's _config singleton must be visible to the worker, which only - # holds with single-process execution. Multi-GPU is unsupported. Override - # any user setting — silent capture failure otherwise. - existing_mp = os.environ.get("VLLM_ENABLE_V1_MULTIPROCESSING") - if existing_mp not in (None, "0"): - warnings.warn( - f"VLLM_ENABLE_V1_MULTIPROCESSING={existing_mp!r} overridden to '0' — " - "boot_vllm needs single-process execution for capture hooks to install. " - "Multi-GPU vLLM is unsupported.", - UserWarning, - stacklevel=2, - ) - os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0" - # Batched capture reads query_start_loc from the forward context, untraceable # under torch.compile — so the batched path must run eager. eager_kwargs: Dict[str, Any] = {"enforce_eager": True} if enable_batching else {} - with _BOOT_LOCK: - plugin.configure( - capture_specs=overlay.capture_specs(hf_config_preview), - max_num_batched_tokens=max_num_batched_tokens, - dtype=resolved_dtype, - enable_batching=enable_batching, - enable_position_interventions=enable_position_interventions, - ) - plugin.register() - try: - llm = LLM( - model=model_name, - gpu_memory_utilization=gpu_memory_utilization, - max_model_len=max_model_len, - # Critical: vLLM defaults max_num_batched_tokens to 8192 for chunked prefill. - # We size our capture buffer to this same value, so vLLM must compile for - # the matching dynamic-shape range — otherwise Dynamo's symbolic-shape - # hint exceeds the buffer dim and narrow() fails at compile time. - max_num_batched_tokens=max_num_batched_tokens, - # Register TLWorkerExtension so its tl_* methods are reachable via - # collective_rpc. Passed as a dotted path; vLLM imports the class at - # worker construction and mixes it into the Worker via multiple - # inheritance (asserts no attribute name collisions — hence the tl_ prefix). - worker_extension_cls="transformer_lens.model_bridge.sources.vllm.worker_extension.TLWorkerExtension", - # Allow full-vocab logprobs so the fallback path can synthesize logits when - # host-side reconstruction is unavailable (vLLM caps logprobs to this value; - # default 20 is too small for mech-interp). - max_logprobs=hf_config_preview.vocab_size, - # Always explicit — vLLM's "auto" downcasts fp32 checkpoints to fp16, which - # would leave the capture/affine buffers (allocated at resolved_dtype) at a - # different dtype than the engine's activations. - dtype=str(resolved_dtype).replace("torch.", ""), - **eager_kwargs, - **_LOCKED_KWARGS, - **vllm_kwargs, - ) - finally: - # Always clear, even on a failed boot: a stale populated _config would make the - # next in-process vllm.LLM(...) walk our dot-paths on a foreign model. - plugin._config.clear() - - # Capture-path validity is enforced inside patched_load_model during - # LLM(...) above — any missing dot-path raises AttributeError there. By - # the time we reach this line every spec has already been walked successfully. + llm = construct_instrumented_llm( + model_name, + capture_specs=overlay.capture_specs(hf_config_preview), + max_num_batched_tokens=max_num_batched_tokens, + dtype=resolved_dtype, + enable_batching=enable_batching, + enable_position_interventions=enable_position_interventions, + tensor_parallel_size=tensor_parallel_size, + pipeline_parallel_size=pipeline_parallel_size, + llm_kwargs={ + "gpu_memory_utilization": gpu_memory_utilization, + "max_model_len": max_model_len, + # Allow full-vocab logprobs so the fallback path can synthesize logits when + # host-side reconstruction is unavailable (vLLM caps logprobs to this value; + # default 20 is too small for mech-interp). + "max_logprobs": hf_config_preview.vocab_size, + # Always explicit — vLLM's "auto" downcasts fp32 checkpoints to fp16, which + # would leave the capture/affine buffers (allocated at resolved_dtype) at a + # different dtype than the engine's activations. + "dtype": dtype_name(resolved_dtype), + **eager_kwargs, + **_LOCKED_KWARGS, + **vllm_kwargs, + }, + ) hf_config = extract_hf_config(llm) if tokenizer is None: tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token) @@ -223,6 +304,8 @@ def boot_vllm( max_num_batched_tokens=max_num_batched_tokens, enable_batching=enable_batching, enable_position_interventions=enable_position_interventions, + tensor_parallel_size=tensor_parallel_size, + pipeline_parallel_size=pipeline_parallel_size, ) # One-time unembedding fetch: caches the weight for per-forward reconstruction and # downgrades provides_sequence_logits honestly if no unembedding is reachable. @@ -237,8 +320,9 @@ def _reject_locked_overrides(vllm_kwargs: Dict[str, Any]) -> None: if key in vllm_kwargs and vllm_kwargs[key] != locked: raise ValueError( f"boot_vllm forces {key}={locked}; caller passed {key}={vllm_kwargs[key]}. " - "Multi-device, prefix caching, continuous batching, and vLLM-owned " - "tokenizers are unsupported — each breaks the row=position capture invariant." + "Prefix caching, continuous batching, and vLLM-owned tokenizers are " + "unsupported — each breaks the capture-read invariants. (TP/PP are " + "supported via the tensor_parallel_size/pipeline_parallel_size kwargs.)" ) @@ -274,4 +358,4 @@ def _log_hook_summary(model_name: str, architecture: str, driver: VLLMDriver) -> ) -__all__ = ["boot_vllm"] +__all__ = ["boot_vllm", "construct_instrumented_llm"] diff --git a/transformer_lens/model_bridge/sources/vllm/worker_extension.py b/transformer_lens/model_bridge/sources/vllm/worker_extension.py index 036ee36645..9622e1eceb 100644 --- a/transformer_lens/model_bridge/sources/vllm/worker_extension.py +++ b/transformer_lens/model_bridge/sources/vllm/worker_extension.py @@ -16,12 +16,79 @@ """ from __future__ import annotations -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, Set import torch from .intervention_specs import SUPPORTED_OPS, validate_spec +# Explicit tensor wire format for collective_rpc returns. vLLM's multiproc RPC +# cannot round-trip raw tensors from extension methods — its tensor-aware msgpack +# encoder's header (['float32', [rows, cols], buf_idx]) leaks through undecoded on +# the response path (GPU-verified on vllm 0.20.2). dtype/shape/bytes are all +# msgpack-native, so this survives any executor topology; the in-process executor +# pays one extra CPU copy. +_TL_TENSOR_KEY = "__tl_tensor__" + + +def dtype_name(dtype: torch.dtype) -> str: + """``torch.float32`` → ``"float32"`` — the one spelling for every dtype-string site.""" + return str(dtype).removeprefix("torch.") + + +def is_pp_missing_layer(module: Any) -> bool: + """vLLM PP keeps full-length module lists on every rank, filling non-owned slots + (layers, embed_tokens, norm, lm_head) with PPMissingLayer identity stubs that the + forward never calls — a hook installed there would serve its dead zero buffer as + a real capture. Matched by name so non-vllm test doubles work; the pinned band's + class is named exactly this, and a rename fails loud via the rank-layout check.""" + return type(module).__name__ == "PPMissingLayer" + + +def resolve_dot_path(root: Any, dot_path: str) -> Any: + """Walk a dot-path; ``None`` when any segment is missing or the target is a + PPMissingLayer stub. Per-rank absence is legal under pipeline parallelism (each + rank owns a layer subset) — the boot site verifies every hook landed on at least + one rank.""" + target = root + for seg in dot_path.split("."): + if seg.isdigit(): + try: + target = target[int(seg)] + except (IndexError, KeyError, TypeError): + return None + else: + target = getattr(target, seg, None) + if target is None: + return None + return None if is_pp_missing_layer(target) else target + + +def encode_tensor(t: torch.Tensor) -> Dict[str, Any]: + """Encode a CPU tensor for the RPC wire. bf16 rides as fp32 bytes (exact).""" + t = t.detach().cpu().contiguous() + name = dtype_name(t.dtype) + if t.dtype == torch.bfloat16: + t = t.to(torch.float32) + return { + _TL_TENSOR_KEY: True, + "dtype": name, + "shape": list(t.shape), + "data": t.numpy().tobytes(), + } + + +def decode_tensor(payload: Dict[str, Any]) -> torch.Tensor: + """Inverse of :func:`encode_tensor`; used driver-side.""" + import numpy as np + + dtype = getattr(torch, payload["dtype"]) + wire_dtype = torch.float32 if dtype == torch.bfloat16 else dtype + np_dtype = torch.empty(0, dtype=wire_dtype).numpy().dtype + array = np.frombuffer(payload["data"], dtype=np_dtype).copy() + tensor = torch.from_numpy(array).reshape(payload["shape"]) + return tensor.to(torch.bfloat16) if dtype == torch.bfloat16 else tensor + class TLWorkerExtension: """Mixed into vLLM's ``Worker`` via ``worker_extension_cls``.""" @@ -36,11 +103,22 @@ class TLWorkerExtension: # Batched-mode state (eager). _tl_accum: Dict[tuple, List[torch.Tensor]] _tl_intervention_specs: Dict[str, Dict[str, Any]] + # Specs whose module doesn't exist on this rank (PP layer shards). + _tl_absent_hooks: set + + def tl_absent_hooks(self) -> Optional[List[str]]: + """Spec names that installed no hook on this rank — the boot site verifies + their union across ranks covers every spec. ``None`` means installation + never ran at all (plugin patch absent or spec channel empty), which the + coverage check must treat as fatal rather than vacuously complete.""" + if not hasattr(self, "_tl_absent_hooks"): + return None + return sorted(self._tl_absent_hooks) def tl_read_captures( self, prompt_lens: List[int], names: Optional[List[str]] = None - ) -> Dict[str, torch.Tensor]: - """Slice each capture buffer to ``sum(prompt_lens)`` rows; CPU copies. + ) -> Dict[str, Dict[str, Any]]: + """Slice each capture buffer to ``sum(prompt_lens)`` rows; wire-encoded CPU copies. ``names`` restricts the read (``None`` = all) — this is the only GPU→CPU crossing, so it's where a names_filtered run saves bandwidth. Caller gates @@ -48,8 +126,17 @@ def tl_read_captures( """ total = sum(prompt_lens) buffers: Dict[str, torch.Tensor] = getattr(self, "_tl_buffers", {}) + # Ownership is dynamic, not structural: a hook can be installed on a module + # this rank holds but never runs (PP stages share tied-embedding aliases and + # some archs instantiate norm on every rank), so a still-open first-write + # flag means the buffer holds no data from this forward — don't serve it. + flags: Dict[str, torch.Tensor] = getattr(self, "_tl_capture_flags", {}) wanted = buffers.keys() if names is None else [n for n in names if n in buffers] - return {name: buffers[name][:total].detach().cpu().clone() for name in wanted} + return { + name: encode_tensor(buffers[name][:total]) + for name in wanted + if name not in flags or bool(flags[name].item()) + } def tl_set_interventions(self, specs: Dict[str, Dict[str, Any]]) -> None: """Reset all affine buffers to identity, then apply each spec. @@ -66,7 +153,12 @@ def tl_set_interventions(self, specs: Dict[str, Dict[str, Any]]) -> None: for name, sb in scale_bufs.items(): sb.fill_(1.0) bias_bufs[name].zero_() + absent: Set[str] = getattr(self, "_tl_absent_hooks", set()) for hook_name, spec in specs.items(): + if hook_name in absent: + # Another PP stage owns this hook; its rank applies the spec. The + # driver's supported_hook_points check already caught real typos. + continue if hook_name not in scale_bufs: raise KeyError(f"Unknown hook for intervention: {hook_name!r}") # Authoritative validation: producers that bypass VLLMDriver (the Inspect @@ -74,18 +166,15 @@ def tl_set_interventions(self, specs: Dict[str, Dict[str, Any]]) -> None: validate_spec(hook_name, spec, width=scale_bufs[hook_name].shape[-1]) _apply_intervention(scale_bufs[hook_name], bias_bufs[hook_name], spec) - def tl_get_param(self, dotted_name: str) -> Optional[torch.Tensor]: - """Read a named model tensor (e.g. ``model.norm.weight``) as a CPU clone. + def tl_get_param(self, dotted_name: str) -> Optional[Dict[str, Any]]: + """Read a named model tensor (e.g. ``model.norm.weight``) as a wire-encoded + CPU clone. ``None`` if the path doesn't resolve to a tensor. The bridge has no general weight surface, so this is how callers reach e.g. the ln_final weight. """ - target = getattr(self, "model_runner").model - for seg in dotted_name.split("."): - target = target[int(seg)] if seg.isdigit() else getattr(target, seg, None) - if target is None: - return None - return target.detach().cpu().clone() if isinstance(target, torch.Tensor) else None + target = resolve_dot_path(getattr(self, "model_runner").model, dotted_name) + return encode_tensor(target) if isinstance(target, torch.Tensor) else None def tl_reset_counter(self) -> None: """Zero the shared hook-fire counter before a forward.""" @@ -123,11 +212,11 @@ def tl_read_batched_captures( """ accum: Dict[tuple, List[torch.Tensor]] = getattr(self, "_tl_accum", {}) nameset = None if names is None else set(names) - out: Dict[str, Dict[str, torch.Tensor]] = {} + out: Dict[str, Dict[str, Any]] = {} for (req_id, name), chunks in accum.items(): if nameset is not None and name not in nameset: continue - out.setdefault(req_id, {})[name] = torch.cat(chunks, dim=0) + out.setdefault(req_id, {})[name] = encode_tensor(torch.cat(chunks, dim=0)) return out def tl_set_batched_interventions(self, specs: Dict[str, Dict[str, Any]]) -> None: @@ -147,6 +236,7 @@ def tl_remove_hooks(self) -> None: self._tl_capture_flags = {} self._tl_accum = {} self._tl_intervention_specs = {} + self._tl_absent_hooks = set() def _apply_op(t: torch.Tensor, spec: Dict[str, Any]) -> torch.Tensor: From 003b20f0ce6c5da3091ad693ceeb9a8fdd5bc707 Mon Sep 17 00:00:00 2001 From: jlarson4 Date: Mon, 20 Jul 2026 10:05:31 -0500 Subject: [PATCH 12/43] lock fixed --- uv.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/uv.lock b/uv.lock index 07df9b9792..0da72f87bf 100644 --- a/uv.lock +++ b/uv.lock @@ -9645,7 +9645,7 @@ quantization = [ [[package]] name = "transformers" -version = "5.14.1" +version = "5.13.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "huggingface-hub" }, @@ -9660,9 +9660,9 @@ dependencies = [ { name = "tqdm" }, { name = "typer" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/5a/fb/2a2ba88f325e68a921d8b69ff63b477830b2e73ade9a3c8c8cab2f06d741/transformers-5.14.1.tar.gz", hash = "sha256:60d196c27781eacf8637e2b533f517582907ad6f9ae142046d6b69431a5b2173", size = 9295927, upload-time = "2026-07-16T09:41:57.773Z" } +sdist = { url = "https://files.pythonhosted.org/packages/ec/e1/720ff7ff666b04279fea5bb7ac3ef8675e98f0ddbc1b8cb8bc9f3889d62e/transformers-5.13.0.tar.gz", hash = "sha256:940c1428e42a4238f9ccf0cd41e63c590701aa63c19fd2ce3d7d602222d68495", size = 9195801, upload-time = "2026-07-03T16:05:39.362Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/6f/67/8d85ca2323233ae3c0365a659c4e52ee1f587b440e4bc577e7d8e4416d0f/transformers-5.14.1-py3-none-any.whl", hash = "sha256:9db974c4079ede2d1a3ea7ca5a240df33f2cc26fc2b36ba64c5f2a4f43b6e725", size = 11625234, upload-time = "2026-07-16T09:41:54.143Z" }, + { url = "https://files.pythonhosted.org/packages/a5/3a/d99704c5effe10c6339c98cb236259161103e159bb99a78468b6729572ec/transformers-5.13.0-py3-none-any.whl", hash = "sha256:8adbc1d20bd5463cd6876b2eb7cb31971e1065788e7dc6bc12bab597a7c504b7", size = 11503730, upload-time = "2026-07-03T16:05:35.569Z" }, ] [[package]] From 44a26ab6d362da53bf7d16cf98e00bb0ad6162c7 Mon Sep 17 00:00:00 2001 From: jlarson4 Date: Mon, 20 Jul 2026 13:44:10 -0500 Subject: [PATCH 13/43] fix re-export --- .../model_bridge/sources/transformers/__init__.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/transformer_lens/model_bridge/sources/transformers/__init__.py b/transformer_lens/model_bridge/sources/transformers/__init__.py index 1da07bc4b6..942a65e1c7 100644 --- a/transformer_lens/model_bridge/sources/transformers/__init__.py +++ b/transformer_lens/model_bridge/sources/transformers/__init__.py @@ -2,10 +2,11 @@ from __future__ import annotations # Re-exported so external code that patches ``AutoConfig.from_pretrained`` / -# ``AutoTokenizer.from_pretrained`` via this module path keeps working after the -# package split. Class-method monkey-patches reach the same class objects that -# ``source.py`` imports directly, so this re-export keeps tests stable. -from transformers import AutoConfig, AutoTokenizer +# ``AutoModelForCausalLM.from_pretrained`` / ``AutoTokenizer.from_pretrained`` via this +# module path keeps working after the package split. Class-method monkey-patches reach +# the same class objects that ``source.py`` / ``helpers.py`` use directly, so this +# re-export keeps tests stable. +from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer from transformer_lens.model_bridge.bridge import TransformerBridge @@ -34,6 +35,7 @@ __all__ = [ "AutoConfig", + "AutoModelForCausalLM", "AutoTokenizer", "boot", "check_model_support", From d92683d00b4a1cc650fc98c428fef95186017ba5 Mon Sep 17 00:00:00 2001 From: Jonah Larson Date: Fri, 24 Jul 2026 15:55:26 -0500 Subject: [PATCH 14/43] Initial Setup for HookedTransformers Deprecation (#1538) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add dedicated GraniteMoe adapter tests (#1302) (#1524) granite_moe had substantive test coverage, but embedded inside test_granite_adapter.py rather than in its own file — inconsistent with every other architecture listed in #1302 (including granite_moe_hybrid), which each get a dedicated test__adapter.py. Moves that coverage into test_granite_moe_adapter.py and adds the one piece that was missing: an explicit config-flag test class (GraniteMoe doesn't override config setup, so it inherits dense Granite's flags, but nothing asserted that directly before). test_granite_adapter.py goes back to covering only GraniteArchitectureAdapter, matching its own docstring. * Updating documentation for future deprecation (#1523) * feat: add RWKV-7 (Goose) TransformerBridge adapter (RWKV7ForCausalLM) (#1521) * feat: add RWKV-7 (Goose) TransformerBridge adapter (RWKV7ForCausalLM) Signed-off-by: Mukund Pandey * refactor: use OpaqueBlockBridge for RWKV-7; introduce OpaqueBlockBridge base class Signed-off-by: Mukund Pandey * added hook_out_is_single_residual_stream default to OpaqueBlockBridge * style: apply black formatting to opaque_block, ssm_block, __init__ Signed-off-by: Mukund Pandey * style: fix import order in generalized_components/__init__.py (isort) Signed-off-by: Mukund Pandey * style: fix import order in generalized_components/__init__.py (isort) Signed-off-by: Mukund Pandey * style: fix import order in generalized_components/__init__.py (isort) Signed-off-by: Mukund Pandey * refactor: replace SSM2MixerBridge with GeneralizedComponent for RWKV-7 attn/ffn sublayers Signed-off-by: Mukund Pandey * style: fix black formatting in generate_report.py Signed-off-by: Mukund Pandey --------- Signed-off-by: Mukund Pandey Co-authored-by: jlarson4 * feat: add RavenForCausalLM (Huginn) depth-recurrent adapter (#1520) * Add RavenForCausalLM (Huginn) TransformerBridge adapter Adds a TransformerBridge architecture adapter for RavenForCausalLM (tomg-group-umd/huginn-0125), a depth-recurrent decoder with a prelude / weight-tied recurrent core / coda structure, resolving #1469. - raven.py delegates the recurrence to the remote-code HF forward and maps the three physical block lists (prelude / core_block / coda) via SSMBlockBridge, with combined-QKV native attention and a gated MLP. Sets applicable_phases=[] (a random initial latent state and post-residual sandwich norms diverge from the verify_models phases) and supports_fold_ln=False (ln_f is reused mid-network). prepare_loading patches Huginn's remote code for transformers v5 (tied-weights-keys dict form + a weight re-init guard). - Registers in the adapter factory, the model registry (canonical author and description) and supported_models.json; surfaces the recurrence-shape config via both _HF_PASSTHROUGH_ATTRS lists. - Adds synthetic-config unit tests and CI-gated integration tests. * fix: pass num_steps as int to iterate_forward (0-d tensor has no len) * fix: remove unused torch import and use setattr for dynamic cfg attrs * style: apply black formatting to raven.py Signed-off-by: Mukund Pandey * refactor: introduce OpaqueBlockBridge; use it for Raven instead of SSMBlockBridge Signed-off-by: Mukund Pandey * style: apply black formatting to opaque_block, ssm_block, __init__ Signed-off-by: Mukund Pandey * style: fix import order in generalized_components/__init__.py (isort) Signed-off-by: Mukund Pandey * fix: strip dead hook_q/k/v aliases from Raven AttentionBridge Raven uses a combined Wqkv projection (no separate q/k/v submodules), so AttentionBridge's default hook_q/hook_k/hook_v aliases (which target q.hook_out / k.hook_out / v.hook_out) are unresolvable. The upstream test_every_hook_alias_resolves_to_hookpoint audit catches these as 9 dead aliases across prelude / core_block / coda. Strip them by setting an instance-level hook_aliases that omits those three keys, leaving only the aliases that have real HookPoint targets. Signed-off-by: Mukund Pandey * chore: merge upstream/dev — add RWKV-7 passthrough attrs and registry entry Brings in the RWKV-7 additions from #1521 that landed in dev: - _bridge_builder.py: added RWKV-7 passthrough attrs (num_heads, value_dim, decay/gate/a/v_low_rank_dim, norm_first, norm_bias, fuse_norm, attn_mode, hidden_act) - generate_report.py: added RWKV7ForCausalLM description entry Signed-off-by: Mukund Pandey --------- Signed-off-by: Mukund Pandey * Fix Tracr demo output_label: compiled labels are auto-numbered (#1525) The Tracr demo passes output_label="reverse", but Tracr builds labels as f"{name}_{unique_id}", so the compiled expression's residual labels are reverse_1:* and the demo raises ValueError at the state-dict cell. Pass reverse.label instead of hardcoding the name, and correct the same wrong label shape in the unit-test fixture (real Tracr never emits "reverse:1") and in the infer_tracr_output_label docstring example. Co-authored-by: Claude Fable 5 * Add TransformerBridge adapter for lightweight decoder-only pretraining models (#1519) * Add TransformerLens pretrain bridge adapter * Add TransformerLens pretrain bridge adapter * Setup registry for hookedtransformer deprecation * bug fixes and rebase to 4.x * Hook management improvements * implemented `stop_at_layer` on TransformerBridge * input_to_embed and pos_slice added to bridge * prevent corruption, check_hooks_to_add placed in all locations that need it * Adjusted head detector to be HookedTransformer agnostic, added key value injection to bridge * Add specific migration differences section * Attempting to let pytest run in parallel * Attempting to fix the OOM on the multi-device Full Coverage run --------- Signed-off-by: Mukund Pandey Co-authored-by: Delaida Muminovic Co-authored-by: Mukund Pandey Co-authored-by: Joseph Quevedo <41499530+dewstend@users.noreply.github.com> Co-authored-by: Claude Fable 5 Co-authored-by: Cacapice --- .github/workflows/checks.yml | 22 +- demos/Tracr_to_Transformer_Lens_Demo.ipynb | 6 +- docs/source/content/migrating_to_v3.md | 24 +- makefile | 7 +- .../compatibility/test_hook_duplication.py | 5 +- .../test_bridge_hook_behavior.py | 4 +- .../model_bridge/test_bridge_caching_hooks.py | 102 ++ .../test_bridge_input_to_embed.py | 52 ++ .../test_bridge_manual_kv_cache.py | 71 ++ .../test_bridge_run_with_cache_pos_slice.py | 70 ++ .../test_bridge_start_at_layer.py | 134 +++ .../model_bridge/test_pretrain_adapter.py | 312 +++++++ .../model_bridge/test_raven_adapter.py | 251 +++++ .../model_bridge/test_rwkv7_adapter.py | 161 ++++ tests/mocks/tiny_pretrain_model.py | 193 ++++ .../test_moe_bridge_tuple_output.py | 88 ++ .../_pretrain_mocks.py | 214 +++++ .../test_granite_adapter.py | 60 +- .../test_granite_moe_adapter.py | 212 +++++ .../test_hook_orchestration_parity.py | 180 ++++ .../test_pretrain_adapter.py | 453 +++++++++ .../test_pretrain_model_container.py | 537 +++++++++++ .../test_raven_adapter.py | 335 +++++++ .../test_rwkv7_adapter.py | 301 ++++++ .../test_bridge_train_mode_propagation.py | 100 ++ .../model_bridge/test_checkpoint_revision.py | 12 +- tests/unit/test_tracr_conversion.py | 12 +- tests/unit/tools/test_model_registry.py | 112 +++ transformer_lens/ActivationCache.py | 7 +- transformer_lens/HookedAudioEncoder.py | 11 + transformer_lens/HookedEncoderDecoder.py | 11 + transformer_lens/HookedTransformer.py | 11 + .../conversion_utils/hook_conversion_utils.py | 4 +- transformer_lens/evals.py | 4 +- .../factories/architecture_adapter_factory.py | 6 + transformer_lens/head_detector.py | 4 +- transformer_lens/hook_points.py | 2 +- transformer_lens/loading_from_pretrained.py | 23 +- transformer_lens/model_bridge/bridge_core.py | 387 ++++++-- .../generalized_components/__init__.py | 55 +- .../generalized_components/block.py | 67 +- .../generalized_components/moe.py | 17 +- .../generalized_components/opaque_block.py | 112 +++ .../generalized_components/ssm_block.py | 98 +- .../model_bridge/remote_bridge.py | 15 +- .../model_bridge/sources/_bridge_builder.py | 22 + .../sources/transformers/helpers.py | 4 +- .../sources/transformers/source.py | 15 +- .../supported_architectures/__init__.py | 12 + .../supported_architectures/pretrain.py | 442 +++++++++ .../supported_architectures/raven.py | 303 ++++++ .../supported_architectures/rwkv7.py | 296 ++++++ .../model_bridge/transformer_bridge.py | 137 ++- transformer_lens/model_protocol.py | 65 ++ transformer_lens/patching.py | 8 +- transformer_lens/supported_models.py | 400 +------- transformer_lens/tools/__init__.py | 19 +- .../tools/model_registry/__init__.py | 23 + .../tools/model_registry/checkpoints.py | 45 + .../model_registry/data/model_aliases.json | 883 ++++++++++++++++++ .../model_registry/data/supported_models.json | 14 + .../tools/model_registry/generate_report.py | 2 + .../tools/model_registry/registry_io.py | 17 + .../tools/model_registry/verify_models.py | 23 +- transformer_lens/utilities/tracr.py | 2 +- 65 files changed, 6868 insertions(+), 728 deletions(-) create mode 100644 tests/integration/model_bridge/test_bridge_caching_hooks.py create mode 100644 tests/integration/model_bridge/test_bridge_input_to_embed.py create mode 100644 tests/integration/model_bridge/test_bridge_manual_kv_cache.py create mode 100644 tests/integration/model_bridge/test_bridge_run_with_cache_pos_slice.py create mode 100644 tests/integration/model_bridge/test_bridge_start_at_layer.py create mode 100644 tests/integration/model_bridge/test_pretrain_adapter.py create mode 100644 tests/integration/model_bridge/test_raven_adapter.py create mode 100644 tests/integration/model_bridge/test_rwkv7_adapter.py create mode 100644 tests/mocks/tiny_pretrain_model.py create mode 100644 tests/unit/model_bridge/generalized_components/test_moe_bridge_tuple_output.py create mode 100644 tests/unit/model_bridge/supported_architectures/_pretrain_mocks.py create mode 100644 tests/unit/model_bridge/supported_architectures/test_granite_moe_adapter.py create mode 100644 tests/unit/model_bridge/supported_architectures/test_hook_orchestration_parity.py create mode 100644 tests/unit/model_bridge/supported_architectures/test_pretrain_adapter.py create mode 100644 tests/unit/model_bridge/supported_architectures/test_pretrain_model_container.py create mode 100644 tests/unit/model_bridge/supported_architectures/test_raven_adapter.py create mode 100644 tests/unit/model_bridge/supported_architectures/test_rwkv7_adapter.py create mode 100644 tests/unit/model_bridge/test_bridge_train_mode_propagation.py create mode 100644 transformer_lens/model_bridge/generalized_components/opaque_block.py create mode 100644 transformer_lens/model_bridge/supported_architectures/pretrain.py create mode 100644 transformer_lens/model_bridge/supported_architectures/raven.py create mode 100644 transformer_lens/model_bridge/supported_architectures/rwkv7.py create mode 100644 transformer_lens/model_protocol.py create mode 100644 transformer_lens/tools/model_registry/checkpoints.py create mode 100644 transformer_lens/tools/model_registry/data/model_aliases.json diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml index f792c1af15..62329c3765 100644 --- a/.github/workflows/checks.yml +++ b/.github/workflows/checks.yml @@ -247,9 +247,22 @@ jobs: coverage-test: name: Full Code Coverage Test runs-on: ubuntu-latest - timeout-minutes: 90 + # Suite runs under pytest-xdist on a 2-vCPU runner (~40 min with swap); + # 60 catches a real hang well below the old 90. + timeout-minutes: 60 steps: - uses: actions/checkout@v4 + - name: Add swap space + # The 2-vCPU runner has ~7GB RAM; two xdist workers each holding + # torch + resident models overflow it (OOM-killed the runner at ~83%). + # Swap absorbs the model-load spikes. Mirrors the notebook-checks job. + run: | + sudo swapoff /swapfile 2>/dev/null || true + sudo rm -f /swapfile + sudo fallocate -l 8G /swapfile + sudo chmod 600 /swapfile + sudo mkswap /swapfile + sudo swapon /swapfile - name: Install uv uses: astral-sh/setup-uv@v7 with: @@ -293,6 +306,13 @@ jobs: run: make coverage-report-test env: HF_TOKEN: ${{ secrets.HF_TOKEN }} + # One BLAS thread per worker: N xdist workers each spawning ncores + # OMP threads would oversubscribe the 4-vCPU runner and erase the gain. + OMP_NUM_THREADS: "1" + MKL_NUM_THREADS: "1" + # Worker count knob (this runner is 2-vCPU, so -n auto = 2). If swap + # still can't hold peak memory, drop to "-n 1" or grow the swapfile. + XDIST_ARGS: "-n auto --dist loadscope" - name: Build check run: uv build - name: Upload Coverage Report Artifact diff --git a/demos/Tracr_to_Transformer_Lens_Demo.ipynb b/demos/Tracr_to_Transformer_Lens_Demo.ipynb index 30fd67eced..25d9cc450d 100644 --- a/demos/Tracr_to_Transformer_Lens_Demo.ipynb +++ b/demos/Tracr_to_Transformer_Lens_Demo.ipynb @@ -147,7 +147,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Extract the state dict. The helper reconstructs Tracr's categorical unembed from the output basis labels, so it still works when the final RASP expression is explicitly named and its residual coordinates are not first." + "Extract the state dict. The helper reconstructs Tracr's categorical unembed from the output basis labels, so it still works when the final RASP expression is explicitly named and its residual coordinates are not first.\n", + "\n", + "Note that Tracr suffixes every label with a unique id — the expression named `reverse` compiles to residual labels like `reverse_1:1` — so we pass `reverse.label` rather than hardcoding the name. (Omitting `output_label` entirely also works: the helper infers it from the output encoder's value set.)" ] }, { @@ -157,7 +159,7 @@ "outputs": [], "source": [ "\n", - "sd = make_tracr_transformer_bridge_state_dict(model, output_label=\"reverse\")\n", + "sd = make_tracr_transformer_bridge_state_dict(model, output_label=reverse.label)\n", "print(sd.keys())" ] }, diff --git a/docs/source/content/migrating_to_v3.md b/docs/source/content/migrating_to_v3.md index f6d2ed0058..a275c2ddc7 100644 --- a/docs/source/content/migrating_to_v3.md +++ b/docs/source/content/migrating_to_v3.md @@ -4,6 +4,8 @@ TransformerLens 3 introduces **TransformerBridge**, a new way of loading and ins This page explains the differences and gives side-by-side migration recipes for the most common patterns. +> **Deprecation status.** `HookedTransformer.from_pretrained` — along with the `HookedEncoderDecoder` and `HookedAudioEncoder` load paths — now emits a `DeprecationWarning`. `HookedTransformer` and the other `Hooked*` classes are slated for removal in a future major release; every feature is being migrated to `TransformerBridge` and the driver system (features that aren't a fit for a driver, such as train-from-scratch, are moving to bridge-based homes rather than staying on `HookedTransformer`). New code should use `TransformerBridge.boot_transformers(...)`. Follow the migration progress in the deprecation plan. + ## Why the change? `HookedTransformer` was a single unified implementation that every supported architecture had to be mapped into. That was beautiful in theory — interpretability code written once worked everywhere — but in practice it meant that adding a new architecture required reimplementing its forward pass inside TransformerLens, and any divergence from the HuggingFace version was a latent source of bugs. @@ -36,7 +38,9 @@ Weight-processing flags (`fold_ln`, `center_writing_weights`, `center_unembed`, ### Parameters that were removed -`n_devices`, `move_to_device`, and `first_n_layers` are not part of `boot_transformers`. If you relied on any of these, file an issue describing your use case — the right pattern for multi-GPU loads under the bridge is still being worked out. +`move_to_device` and `first_n_layers` are not part of `boot_transformers`. If you relied on either, file an issue describing your use case. + +> **Multi-GPU update.** The 3.0 release notes listed `n_devices` among the removed parameters. This feature has since been restored alongside `device_map` and `max_memory`. See [Parameters that are new](#parameters-that-are-new) below. ### Parameters that are new @@ -44,6 +48,8 @@ Weight-processing flags (`fold_ln`, `center_writing_weights`, `center_unembed`, - `trust_remote_code: bool = False` — pass through to HuggingFace for models that ship custom modeling code. - `hf_config_overrides: dict | None = None` — override specific fields of the HF config before the model is constructed. - `n_ctx: int | None = None` — override the model's context length. The bridge writes to whichever HF config field this architecture uses (`n_positions` / `max_position_embeddings` / etc.) so callers don't need to know the field name. Warns if larger than the model's default. +- `n_devices` / `device_map` / `max_memory` — multi-GPU loading. `device_map` takes a HuggingFace-style map (`"auto"`, `"balanced"`, or an explicit dict) and is passed straight to `from_pretrained`; `n_devices=N` is a convenience that splits the model across `N` visible CUDA devices (translated to a `max_memory` dict internally); `max_memory` sets a per-device budget. `device` and `device_map` are mutually exclusive. +- `revision` / `checkpoint_index` / `checkpoint_value` — load a specific HF revision, or a training checkpoint for checkpointed families (`EleutherAI/pythia*`, `stanford-crfm/*`). `checkpoint_index` / `checkpoint_value` resolve to a revision string, mirroring the old `HookedTransformer.from_pretrained` checkpoint arguments. - `hf_model` / `model_class` — advanced: pass in a pre-loaded HF model or a specific model class. ## Weight processing is now opt-in @@ -202,6 +208,18 @@ pattern = cache["blocks.0.attn.hook_pattern"] The cache, hook, and config APIs are the same. The only lines that had to change are the import, the load call, and — if you want the old weight-processing behavior — one extra call to `enable_compatibility_mode`. -## When to stay on HookedTransformer +## Migrating specific `HookedTransformer` APIs + +`HookedTransformer` is deprecated and will be removed in a future major release. The compatibility layer keeps existing code running in the meantime, but new work should target `TransformerBridge`, and migrating existing projects is the long-term supported path. + +Most `HookedTransformer` methods and properties exist on `TransformerBridge` under the same name — see [APIs that are unchanged](#apis-that-are-unchanged). The table below covers the cases where the name or access path differs. + +> If you hit a `HookedTransformer` API whose bridge equivalent isn't obvious and isn't listed here, [open an issue](https://github.com/TransformerLensOrg/TransformerLens/issues); when you (or we) work out the equivalent, add a row below. + +Weight-matrix rows return **raw** HuggingFace weights by default. `HookedTransformer.from_pretrained` applies weight processing (LayerNorm folding, `center_writing_weights`, `center_unembed`) at load, so those properties differ numerically from the bridge's unless the bridge is in compatibility mode — see [Will my numbers match HookedTransformer?](#will-my-numbers-match-hookedtransformer). -If your code runs unchanged on TransformerLens 3 via the compatibility layer and you don't need architectures beyond what `HookedTransformer` already supported, there is no hard deadline to migrate. But new architectures, weight-processing controls, and hook refinements are landing on the bridge side — new work should target the bridge, and migrating existing projects is the long-term supported path. +| `HookedTransformer` | `TransformerBridge` equivalent | Notes | +|---|---|---| +| `model.W_pos` | `bridge.pos_embed.W_pos` | Raw weight (also `bridge.pos_embed.weight`). `center_writing_weights` centers `W_pos` in default HT loads, so it matches HT's only under matching processing (`enable_compatibility_mode()`, or HT loaded with no processing). | +| `model.W_E_pos` | `torch.cat([bridge.W_E, bridge.pos_embed.W_pos], dim=0)` | No single accessor — concatenate the token + positional matrices. Same weight-processing caveat as `W_pos` (both `W_E` and `W_pos` are centered writing-weights). | +| `HookedTransformer.from_pretrained_no_processing(name)` | `TransformerBridge.boot_transformers(name, no_processing=True)` | Both load raw weights, so these match. | diff --git a/makefile b/makefile index 9f9ea9044d..d9655e7d08 100644 --- a/makefile +++ b/makefile @@ -7,6 +7,11 @@ UV_SYNC := uv sync$(ACTIVE_FLAG) # Remove this line when no longer needed RERUN_ARGS := --reruns 2 --reruns-delay 5 +# Parallelism for the coverage run. loadscope keeps a module's tests + its +# module/session fixtures on one worker (models load once per worker, not +# re-scattered). Override to -n 2 (or -n 0) if a runner OOMs. +XDIST_ARGS ?= -n auto --dist loadscope + dep: $(UV_SYNC) @@ -33,7 +38,7 @@ benchmark-test: $(RUN) pytest tests/benchmarks $(RERUN_ARGS) coverage-report-test: - $(RUN) pytest --cov=transformer_lens/ --cov-report=html --cov-branch -m "not slow" tests/integration tests/unit tests/acceptance $(RERUN_ARGS) + $(RUN) pytest -o "addopts=--jaxtyping-packages=transformer_lens,beartype.beartype -W ignore::beartype.roar.BeartypeDecorHintPep585DeprecationWarning" --cov=transformer_lens/ --cov-report=html --cov-branch $(XDIST_ARGS) -m "not slow" tests/integration tests/unit tests/acceptance $(RERUN_ARGS) docstring-test: $(RUN) pytest transformer_lens/ $(RERUN_ARGS) diff --git a/tests/acceptance/model_bridge/compatibility/test_hook_duplication.py b/tests/acceptance/model_bridge/compatibility/test_hook_duplication.py index 254b5a7414..bc85f20a8d 100644 --- a/tests/acceptance/model_bridge/compatibility/test_hook_duplication.py +++ b/tests/acceptance/model_bridge/compatibility/test_hook_duplication.py @@ -21,7 +21,8 @@ def count_hooked_calls(acts, hook): hooked_model.blocks[0].hook_mlp_out.add_hook(count_hooked_calls, is_permanent=True) _ = hooked_model(test_input) - hooked_model.reset_hooks() + # Session-scoped fixture: clear the permanent hook too, or it leaks into later tests. + hooked_model.reset_hooks(including_permanent=True) bridge_call_count = 0 @@ -88,7 +89,7 @@ def use_cached_activations(acts, hook): except KeyError: success = False finally: - bridge_model.reset_hooks() + bridge_model.reset_hooks(including_permanent=True) assert success, ( "Stateful hook pattern failed - hook was likely called multiple times, " diff --git a/tests/integration/model_bridge/compatibility/test_bridge_hook_behavior.py b/tests/integration/model_bridge/compatibility/test_bridge_hook_behavior.py index 5e4e580f4c..8d6daa7c6f 100644 --- a/tests/integration/model_bridge/compatibility/test_bridge_hook_behavior.py +++ b/tests/integration/model_bridge/compatibility/test_bridge_hook_behavior.py @@ -101,7 +101,9 @@ def hook_fn(tensor, hook): bridge("World") assert count == 2 finally: - bridge.reset_hooks() + # Session-scoped fixture: plain reset_hooks() keeps permanent hooks, + # leaking this one onto blocks.0.hook_in for every later test. + bridge.reset_hooks(including_permanent=True) class TestHookModification: diff --git a/tests/integration/model_bridge/test_bridge_caching_hooks.py b/tests/integration/model_bridge/test_bridge_caching_hooks.py new file mode 100644 index 0000000000..aeb2773382 --- /dev/null +++ b/tests/integration/model_bridge/test_bridge_caching_hooks.py @@ -0,0 +1,102 @@ +"""HookedRootModule caching-hook API on TransformerBridge. + +get_caching_hooks / add_caching_hooks / cache_all / cache_some, plus +run_with_cache(incl_bwd=...). The correctness anchor is that caches built via +these paths match run_with_cache, and that incl_bwd captures nonzero gradients +under "_grad". +""" + +import warnings + +import pytest +import torch + + +@pytest.fixture() +def bridge(distilgpt2_bridge): + return distilgpt2_bridge + + +def _tokens(): + return torch.randint(0, 100, (1, 6)) + + +def test_get_caching_hooks_matches_run_with_cache(bridge): + tokens = _tokens() + with torch.no_grad(): + _, ref = bridge.run_with_cache(tokens) + + cache, fwd_hooks, bwd_hooks = bridge.get_caching_hooks( + names_filter=lambda n: n.endswith("hook_out") + ) + assert bwd_hooks == [] + with torch.no_grad(), bridge.hooks(fwd_hooks=fwd_hooks): + bridge.forward(tokens) + + assert cache, "nothing cached" + for name in cache: + assert torch.allclose(cache[name], ref[name], atol=1e-5), name + + +def test_add_caching_hooks_persists_until_reset(bridge): + tokens = _tokens() + with torch.no_grad(): + _, ref = bridge.run_with_cache(tokens) + + cache = bridge.add_caching_hooks(names_filter="blocks.2.hook_out") + try: + with torch.no_grad(): + bridge.forward(tokens) + assert torch.allclose(cache["blocks.2.hook_out"], ref["blocks.2.hook_out"], atol=1e-5) + finally: + bridge.reset_hooks() + + cache.clear() + with torch.no_grad(): + bridge.forward(tokens) + assert cache == {}, "hooks should be gone after reset_hooks" + + +def test_cache_all_and_cache_some_are_deprecated(bridge): + tokens = _tokens() + for call in ( + lambda c: bridge.cache_all(c), + lambda c: bridge.cache_some(c, names=lambda n: n.endswith("hook_out")), + ): + cache: dict = {} + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + call(cache) + assert any(issubclass(w.category, DeprecationWarning) for w in caught) + try: + with torch.no_grad(): + bridge.forward(tokens) + assert cache, "deprecated helper cached nothing" + finally: + bridge.reset_hooks() + + +def test_run_with_cache_incl_bwd_caches_gradients(bridge): + tokens = _tokens() + output, cache = bridge.run_with_cache(tokens, incl_bwd=True, return_type="loss") + + assert output.dim() == 0, "incl_bwd needs a scalar output (return_type='loss')" + grad_key = "blocks.4.hook_out_grad" + assert grad_key in cache + grad = cache[grad_key] + assert grad.shape == (1, 6, bridge.cfg.d_model) + assert grad.abs().sum() > 0, "gradient should be nonzero" + # forward activation is also present and detached + assert "blocks.4.hook_out" in cache + assert not cache["blocks.4.hook_out"].requires_grad + + +def test_incl_bwd_leaves_no_hooks_attached(bridge): + tokens = _tokens() + bridge.run_with_cache(tokens, incl_bwd=True, return_type="loss") + leftover = [hp.name for hp in bridge.hook_points() if hp.has_hooks(dir="both")] + assert leftover == [], leftover + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/integration/model_bridge/test_bridge_input_to_embed.py b/tests/integration/model_bridge/test_bridge_input_to_embed.py new file mode 100644 index 0000000000..4a3fd0176c --- /dev/null +++ b/tests/integration/model_bridge/test_bridge_input_to_embed.py @@ -0,0 +1,52 @@ +"""input_to_embed on TransformerBridge — the residual entering block 0. + +Bridge analog of HookedTransformer.input_to_embed. The correctness anchor is +that feeding its residual to forward(..., start_at_layer=0) reproduces the full +forward exactly. +""" + +import pytest +import torch + + +@pytest.fixture() +def bridge(distilgpt2_bridge): + return distilgpt2_bridge + + +def test_returns_residual_tokens_and_none_shortformer(bridge): + tokens = torch.randint(0, 100, (2, 7)) + residual, out_tokens, shortformer_pos_embed, attention_mask = bridge.input_to_embed(tokens) + + assert residual.shape == (2, 7, bridge.cfg.d_model) + assert torch.equal(out_tokens, tokens) + assert shortformer_pos_embed is None + + +def test_round_trips_through_start_at_layer_0(bridge): + tokens = torch.randint(0, 100, (2, 7)) + full = bridge.forward(tokens) + + residual, _, _, _ = bridge.input_to_embed(tokens) + resumed = bridge.forward(residual, start_at_layer=0) + + assert torch.allclose(resumed, full, atol=1e-4) + + +def test_residual_matches_resid_pre_of_block_0(bridge): + tokens = torch.randint(0, 100, (2, 7)) + _, cache = bridge.run_with_cache(tokens) + residual, _, _, _ = bridge.input_to_embed(tokens) + assert torch.allclose(residual, cache["blocks.0.hook_in"], atol=1e-4) + + +def test_string_input(bridge): + residual, tokens, _, _ = bridge.input_to_embed("Hello, world!") + assert residual.ndim == 3 + assert residual.shape[0] == 1 + assert residual.shape[-1] == bridge.cfg.d_model + assert tokens.shape[1] == residual.shape[1] + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/integration/model_bridge/test_bridge_manual_kv_cache.py b/tests/integration/model_bridge/test_bridge_manual_kv_cache.py new file mode 100644 index 0000000000..08ef745c39 --- /dev/null +++ b/tests/integration/model_bridge/test_bridge_manual_kv_cache.py @@ -0,0 +1,71 @@ +"""Manual KV-cache injection into TransformerBridge.forward (HF-native). + +forward(past_key_values=...) hands HF back its own cache object and computes +only the new tokens' keys/values. The correctness anchor is that incremental +decoding (token-by-token or chunked) matches a single full forward, and that +use_cache is enabled automatically when a cache is passed. +""" + +import pytest +import torch + + +@pytest.fixture() +def bridge(distilgpt2_bridge): + return distilgpt2_bridge + + +def _seq(): + return torch.randint(0, 100, (1, 7)) + + +def test_token_by_token_matches_full_forward(bridge): + seq = _seq() + with torch.no_grad(): + ref = bridge.forward(seq) + + cache = None + step_logits = [] + with torch.no_grad(): + for i in range(seq.shape[1]): + logits, cache = bridge.forward( + seq[:, i : i + 1], past_key_values=cache, return_type="logits_and_cache" + ) + step_logits.append(logits[:, -1, :]) + incremental = torch.stack(step_logits, dim=1) + + assert torch.allclose(ref, incremental, atol=1e-3) + + +def test_chunked_prefill_matches_full_forward(bridge): + seq = _seq() + with torch.no_grad(): + ref = bridge.forward(seq) + _, cache = bridge.forward(seq[:, :4], return_type="logits_and_cache") + tail_logits, _ = bridge.forward( + seq[:, 4:], past_key_values=cache, return_type="logits_and_cache" + ) + assert torch.allclose(ref[:, -1], tail_logits[:, -1], atol=1e-3) + + +def test_use_cache_enabled_automatically(bridge): + """Passing a cache alone (no explicit use_cache) must return a grown cache.""" + seq = _seq() + with torch.no_grad(): + _, cache = bridge.forward(seq[:, :3], return_type="logits_and_cache") + assert cache is not None + _, cache2 = bridge.forward( + seq[:, 3:5], past_key_values=cache, return_type="logits_and_cache" + ) + assert cache2 is not None + + +def test_normal_forward_unaffected(bridge): + seq = _seq() + with torch.no_grad(): + out = bridge.forward(seq) + assert out.shape == (1, seq.shape[1], bridge.cfg.d_vocab) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/integration/model_bridge/test_bridge_run_with_cache_pos_slice.py b/tests/integration/model_bridge/test_bridge_run_with_cache_pos_slice.py new file mode 100644 index 0000000000..6d5e92b492 --- /dev/null +++ b/tests/integration/model_bridge/test_bridge_run_with_cache_pos_slice.py @@ -0,0 +1,70 @@ +"""run_with_cache(pos_slice=...) on TransformerBridge. + +Mirrors HookedTransformer's pos_slice: each cached activation is sliced along +its position dimension (dim 1 for resid/per-head/token-id activations; the +query position -2 for attention patterns/scores). An int slice keeps the +position dim at size 1, matching HookedTransformer. +""" + +import pytest +import torch + + +@pytest.fixture() +def bridge(distilgpt2_bridge): + return distilgpt2_bridge + + +def _pos_dim(name: str) -> int: + return -2 if name.endswith(("hook_pattern", "hook_attn_scores")) else 1 + + +def test_int_slice_keeps_pos_dim_and_matches_full_cache(bridge): + tokens = torch.randint(0, 100, (1, 8)) + _, full = bridge.run_with_cache(tokens) + + for i in (0, 4, 7, -1): + _, sliced = bridge.run_with_cache(tokens, pos_slice=i) + for name, tensor in full.items(): + if not isinstance(tensor, torch.Tensor) or tensor.dim() < 2: + continue + dim = _pos_dim(name) + abs_dim = dim if dim >= 0 else tensor.dim() + dim + expected = tensor.index_select(abs_dim, torch.tensor([i % tensor.shape[abs_dim]])) + assert sliced[name].shape == expected.shape, name + assert torch.allclose(sliced[name], expected, atol=1e-5), name + + +def test_per_head_and_pattern_shapes(bridge): + tokens = torch.randint(0, 100, (1, 8)) + _, sliced = bridge.run_with_cache(tokens, pos_slice=2) + n_heads, d_head = bridge.cfg.n_heads, bridge.cfg.d_head + + # per-head projection: [batch, pos, head, d_head] -> pos sliced to 1 + per_head = sliced["blocks.0.attn.q.hook_out"] + assert per_head.shape == (1, 1, n_heads, d_head) + + # attention pattern: [batch, head, q_pos, k_pos] -> query pos sliced to 1 + pattern = sliced["blocks.0.attn.hook_pattern"] + assert pattern.shape == (1, n_heads, 1, 8) + + +def test_tuple_range_slice(bridge): + tokens = torch.randint(0, 100, (1, 8)) + _, full = bridge.run_with_cache(tokens) + _, sliced = bridge.run_with_cache(tokens, pos_slice=(1, 4)) + + resid = sliced["blocks.1.hook_out"] + assert resid.shape == (1, 3, bridge.cfg.d_model) + assert torch.allclose(resid, full["blocks.1.hook_out"][:, 1:4], atol=1e-5) + + +def test_none_pos_slice_is_unchanged(bridge): + tokens = torch.randint(0, 100, (1, 8)) + _, full = bridge.run_with_cache(tokens) + _, no_slice = bridge.run_with_cache(tokens, pos_slice=None) + assert full["blocks.0.hook_out"].shape == no_slice["blocks.0.hook_out"].shape + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/integration/model_bridge/test_bridge_start_at_layer.py b/tests/integration/model_bridge/test_bridge_start_at_layer.py new file mode 100644 index 0000000000..62c80a14d4 --- /dev/null +++ b/tests/integration/model_bridge/test_bridge_start_at_layer.py @@ -0,0 +1,134 @@ +"""start_at_layer (residual-stream input) on TransformerBridge. + +The correctness anchor is a round trip: cache the residual entering block k +from a full run, resume from it with start_at_layer=k, and require the logits +and the downstream cache to match the full run bit-for-bit. Blocks below k +execute internally (the bridge delegates to HF) but must not appear in the +cache or fire hooks, matching HookedTransformer. +""" + +import pytest +import torch + + +@pytest.fixture() +def bridge(distilgpt2_bridge): + return distilgpt2_bridge + + +def _tokens(): + return torch.randint(0, 100, (2, 8)) + + +def test_resume_matches_full_run_logits_and_downstream_cache(bridge): + tokens = _tokens() + full_logits, full_cache = bridge.run_with_cache(tokens) + k = 3 + resid = full_cache[f"blocks.{k}.hook_in"] + + logits, cache = bridge.run_with_cache(resid, start_at_layer=k) + + assert torch.allclose(logits, full_logits, atol=1e-4) + # Every downstream key present in the full run must match exactly. + for name, value in full_cache.items(): + if name.startswith("blocks.") and int(name.split(".")[1]) >= k: + assert name in cache, f"{name} missing from resumed cache" + assert torch.allclose(cache[name], value, atol=1e-4), name + + +def test_upstream_hooks_absent_downstream_present(bridge): + tokens = _tokens() + _, full_cache = bridge.run_with_cache(tokens) + k = 3 + _, cache = bridge.run_with_cache(full_cache[f"blocks.{k}.hook_in"], start_at_layer=k) + + keys = set(cache.keys()) + embedding_stage = [ + n + for n in keys + if n in ("hook_embed", "hook_pos_embed", "hook_tokens") + or n.startswith(("embed.", "pos_embed.")) + ] + assert embedding_stage == [], f"embedding-stage hooks should be bypassed: {embedding_stage}" + for below in range(k): + assert not any(n.startswith(f"blocks.{below}.") for n in keys), f"block {below} leaked" + assert any(n.startswith(f"blocks.{k}.") for n in keys), "start block missing" + assert any("ln_final" in n for n in keys), "final norm missing" + assert any("unembed" in n for n in keys), "unembed (output stage) should be present" + + +def test_negative_start_at_layer(bridge): + tokens = _tokens() + full_logits, full_cache = bridge.run_with_cache(tokens) + last = bridge.cfg.n_layers - 1 + resid = full_cache[f"blocks.{last}.hook_in"] + + logits, cache = bridge.run_with_cache(resid, start_at_layer=-1) + + assert torch.allclose(logits, full_logits, atol=1e-4) + assert not any(n.startswith(f"blocks.{last - 1}.") for n in cache.keys()) + + +def test_start_and_stop_bound_a_subrange(bridge): + tokens = _tokens() + _, full_cache = bridge.run_with_cache(tokens) + _, cache = bridge.run_with_cache( + full_cache["blocks.1.hook_in"], start_at_layer=1, stop_at_layer=3 + ) + keys = set(cache.keys()) + assert any(n.startswith("blocks.1.") for n in keys) + assert any(n.startswith("blocks.2.") for n in keys) + assert not any(n.startswith("blocks.0.") for n in keys) + assert not any(n.startswith("blocks.3.") for n in keys) + assert not any("ln_final" in n for n in keys) + + +def test_run_with_hooks_skips_sub_start_layers(bridge): + tokens = _tokens() + _, full_cache = bridge.run_with_cache(tokens) + k = 3 + fired = [] + + def rec(activation, hook): + fired.append(hook.name) + return None + + logits = bridge.run_with_hooks( + full_cache[f"blocks.{k}.hook_in"], + start_at_layer=k, + fwd_hooks=[ + ("blocks.0.attn.hook_out", rec), + ("blocks.2.attn.hook_out", rec), + ("blocks.3.attn.hook_out", rec), + ("blocks.5.attn.hook_out", rec), + ], + ) + assert logits is not None + assert all(int(n.split(".")[1]) >= k for n in fired), fired + assert any(n.startswith("blocks.3.") for n in fired) + assert any(n.startswith("blocks.5.") for n in fired) + + +def test_token_input_with_start_at_layer_raises(bridge): + with pytest.raises(ValueError, match="residual-stream tensor"): + bridge.forward(_tokens(), start_at_layer=1) + + +def test_out_of_range_start_at_layer_raises(bridge): + tokens = _tokens() + _, full_cache = bridge.run_with_cache(tokens) + resid = full_cache["blocks.0.hook_in"] + with pytest.raises(ValueError, match="out of range"): + bridge.forward(resid, start_at_layer=bridge.cfg.n_layers) + + +def test_state_cleaned_up_after_start_at_layer(bridge): + tokens = _tokens() + full_logits, full_cache = bridge.run_with_cache(tokens) + bridge.forward(full_cache["blocks.2.hook_in"], start_at_layer=2) + # A subsequent normal forward must be unaffected by the injection state. + assert torch.allclose(bridge.forward(tokens), full_logits, atol=1e-4) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/integration/model_bridge/test_pretrain_adapter.py b/tests/integration/model_bridge/test_pretrain_adapter.py new file mode 100644 index 0000000000..5878532e38 --- /dev/null +++ b/tests/integration/model_bridge/test_pretrain_adapter.py @@ -0,0 +1,312 @@ +"""Integration tests for PretrainArchitectureAdapter -- numerical parity +against a real (not structural-mock) reference model. + +Uses `tests/mocks/tiny_pretrain_model.py`'s `TinyPretrainModel`, which +implements genuine adjacent-pair RoPE and RMSNorm math (unlike +`_pretrain_mocks.py`'s unit-test mocks, which stub RoPE with constant +cos/sin and only need to get attribute names and call-signatures right). + +Covers: source/bridge logit parity (dense, full-MoE, mixed dense/MoE, +untied embeddings); that this file's fixtures supply a `cfg` truthfully +describing the wrapped model's head/vocab dimensions (a fixture property, +not something `build_pretrain_bridge` itself enforces -- see +`TestIntegrationFixturesSupplyATruthfulConfig`); residual-stream +decomposition via `run_with_cache`; a forward-hook intervention +cross-checked against an independent way of expressing the same edit; and +float64 construction-time preservation. + +Note on scope: since the adapter delegates the whole forward to the +source model rather than reimplementing attention/RoPE, exact logit +parity mainly demonstrates that wrapping and output normalization don't +alter an unhooked forward, not that the RoPE convention itself is +correct (the same implementation runs on both sides of the comparison). +`TestResidualStreamDecomposition` and `TestHookIntervention` are the +stronger evidence for correct intervention points. +""" + +from __future__ import annotations + +import torch + +from tests.mocks.tiny_pretrain_model import TinyPretrainModel +from transformer_lens.config import TransformerBridgeConfig +from transformer_lens.model_bridge.supported_architectures.pretrain import ( + ARCHITECTURE_NAME, + build_pretrain_bridge, +) + + +def _make_cfg( + *, + d_model: int, + n_layers: int, + n_heads: int = 2, + d_vocab: int = 64, +) -> TransformerBridgeConfig: + """Build a bridge config matching this file's TinyPretrainModel + fixtures (n_heads=2, vocab_size=64 by default). See + `TestIntegrationFixturesSupplyATruthfulConfig` for why this matching + matters and what happens when it doesn't.""" + return TransformerBridgeConfig( + d_model=d_model, + d_head=d_model // n_heads, + n_layers=n_layers, + n_ctx=128, + n_heads=n_heads, + d_vocab=d_vocab, + d_mlp=d_model * 2, + architecture=ARCHITECTURE_NAME, + ) + + +class TestSourceBridgeLogitParity: + """Bridge output must equal the source model's own `"logits"` output + exactly -- not within tolerance. The adapter never translates weights + into a second parameter layout, so there is no floating-point drift + to tolerate; any mismatch here is a real bug, not numerical noise.""" + + def test_dense_model_logit_parity_exact(self) -> None: + torch.manual_seed(0) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=2, d_ff=32, vocab_size=64) + model.eval() + tokens = torch.randint(0, 64, (1, 5)) + with torch.no_grad(): + source_logits = model(tokens)["logits"] + + bridge = build_pretrain_bridge(model, _make_cfg(d_model=16, n_layers=2)) + with torch.no_grad(): + bridge_logits = bridge(tokens) + + torch.testing.assert_close(bridge_logits, source_logits, atol=0, rtol=0) + + def test_full_moe_model_logit_parity_exact(self) -> None: + """Every block's MLP is MoE (moe_layer_indices covers all layers).""" + torch.manual_seed(0) + model = TinyPretrainModel( + d_model=16, + n_heads=2, + n_layers=2, + d_ff=32, + vocab_size=64, + n_experts=4, + top_k=2, + moe_layer_indices=frozenset({0, 1}), + ) + model.eval() + tokens = torch.randint(0, 64, (1, 5)) + with torch.no_grad(): + source_logits = model(tokens)["logits"] + + bridge = build_pretrain_bridge(model, _make_cfg(d_model=16, n_layers=2)) + with torch.no_grad(): + bridge_logits = bridge(tokens) + + torch.testing.assert_close(bridge_logits, source_logits, atol=0, rtol=0) + + def test_mixed_dense_moe_model_logit_parity_exact(self) -> None: + """One block dense, one block MoE -- the dispatcher must route each + block's MLP correctly within a single model, not just across + separately-constructed all-dense/all-MoE models.""" + torch.manual_seed(0) + model = TinyPretrainModel( + d_model=16, + n_heads=2, + n_layers=2, + d_ff=32, + vocab_size=64, + n_experts=4, + top_k=2, + moe_layer_indices=frozenset({1}), + ) + model.eval() + tokens = torch.randint(0, 64, (1, 5)) + with torch.no_grad(): + source_logits = model(tokens)["logits"] + + bridge = build_pretrain_bridge(model, _make_cfg(d_model=16, n_layers=2)) + with torch.no_grad(): + bridge_logits = bridge(tokens) + + torch.testing.assert_close(bridge_logits, source_logits, atol=0, rtol=0) + + def test_untied_embeddings_logit_parity_exact(self) -> None: + torch.manual_seed(0) + model = TinyPretrainModel( + d_model=16, + n_heads=2, + n_layers=1, + d_ff=32, + vocab_size=64, + tie_embeddings=False, + ) + model.eval() + tokens = torch.randint(0, 64, (1, 5)) + with torch.no_grad(): + source_logits = model(tokens)["logits"] + + bridge = build_pretrain_bridge(model, _make_cfg(d_model=16, n_layers=1)) + with torch.no_grad(): + bridge_logits = bridge(tokens) + + torch.testing.assert_close(bridge_logits, source_logits, atol=0, rtol=0) + + +class TestIntegrationFixturesSupplyATruthfulConfig: + """Integration fixtures must supply a config matching the source + model. `build_pretrain_bridge` does not itself validate this -- `cfg` + is caller-owned, per ordinary TransformerBridge convention -- so a + mismatch shows up only in `bridge.cfg`, silently, never in the + logits (see the second test).""" + + def test_cfg_matches_model_head_and_vocab_dimensions(self) -> None: + torch.manual_seed(0) + n_heads = 2 + vocab_size = 64 + model = TinyPretrainModel( + d_model=16, n_heads=n_heads, n_layers=2, d_ff=32, vocab_size=vocab_size + ) + model.eval() + cfg = _make_cfg(d_model=16, n_layers=2, n_heads=n_heads, d_vocab=vocab_size) + bridge = build_pretrain_bridge(model, cfg) + + assert bridge.cfg.n_heads == n_heads + assert bridge.cfg.d_head == 16 // n_heads + assert bridge.cfg.d_vocab == model.embed.num_embeddings + + def test_build_pretrain_bridge_does_not_validate_cfg_against_model(self) -> None: + """A cfg describing a different architecture than the wrapped + model still builds and still produces exact logit parity + (delegation never reads cfg) -- only bridge.cfg is wrong. + + Current framework convention; not an adapter guarantee. This test + documents present behavior, not a requirement -- if + build_pretrain_bridge later gains cfg-vs-model validation, this + test should be updated (or removed) rather than treated as a + regression to preserve.""" + torch.manual_seed(0) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64) + model.eval() + tokens = torch.randint(0, 64, (1, 5)) + with torch.no_grad(): + source_logits = model(tokens)["logits"] + + wrong_cfg = _make_cfg(d_model=16, n_layers=1, n_heads=4, d_vocab=256) + bridge = build_pretrain_bridge(model, wrong_cfg) + with torch.no_grad(): + bridge_logits = bridge(tokens) + + torch.testing.assert_close(bridge_logits, source_logits, atol=0, rtol=0) + assert bridge.cfg.n_heads != model.blocks[0].attn.n_heads + assert bridge.cfg.d_vocab != model.embed.num_embeddings + + +class TestResidualStreamDecomposition: + """`run_with_cache`'s resid hooks must decompose the way the residual + stream actually works, not just be present under the right names + (attribute/key presence is already covered by the structural unit + tests) -- resid_pre + attn_out == resid_mid, resid_mid + mlp_out == + resid_post, and one block's resid_post is the next block's resid_pre.""" + + def test_resid_stream_decomposes_exactly_via_run_with_cache(self) -> None: + torch.manual_seed(0) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=2, d_ff=32, vocab_size=64) + model.eval() + bridge = build_pretrain_bridge(model, _make_cfg(d_model=16, n_layers=2)) + tokens = torch.randint(0, 64, (1, 5)) + + with torch.no_grad(): + _, cache = bridge.run_with_cache(tokens) + + torch.testing.assert_close( + cache["blocks.0.hook_resid_pre"], cache["hook_embed"], atol=0, rtol=0 + ) + torch.testing.assert_close( + cache["blocks.0.hook_resid_mid"], + cache["blocks.0.hook_resid_pre"] + cache["blocks.0.hook_attn_out"], + ) + torch.testing.assert_close( + cache["blocks.0.hook_resid_post"], + cache["blocks.0.hook_resid_mid"] + cache["blocks.0.hook_mlp_out"], + ) + torch.testing.assert_close( + cache["blocks.1.hook_resid_pre"], + cache["blocks.0.hook_resid_post"], + atol=0, + rtol=0, + ) + torch.testing.assert_close( + cache["blocks.1.hook_resid_post"], + cache["blocks.1.hook_resid_mid"] + cache["blocks.1.hook_mlp_out"], + ) + + +class TestHookIntervention: + """A forward-hook edit at `blocks.{i}.mlp.hook_out` must land at the + point the residual-stream decomposition says it should -- cross- + checked against an independently-expressed version of the same edit + (forcing resid_post to equal resid_mid), rather than only checking + that *some* change occurred.""" + + def test_mlp_hook_ablation_matches_independent_resid_post_override(self) -> None: + torch.manual_seed(0) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=2, d_ff=32, vocab_size=64) + model.eval() + bridge = build_pretrain_bridge(model, _make_cfg(d_model=16, n_layers=2)) + tokens = torch.randint(0, 64, (1, 5)) + + with torch.no_grad(): + baseline_logits, baseline_cache = bridge.run_with_cache(tokens) + resid_mid0 = baseline_cache["blocks.0.hook_resid_mid"].clone() + + def zero_mlp_out(value: torch.Tensor, hook) -> torch.Tensor: + return torch.zeros_like(value) + + def force_resid_post_to_resid_mid(value: torch.Tensor, hook) -> torch.Tensor: + return resid_mid0 + + with torch.no_grad(): + via_mlp_ablation = bridge.run_with_hooks( + tokens, fwd_hooks=[("blocks.0.mlp.hook_out", zero_mlp_out)] + ) + via_resid_override = bridge.run_with_hooks( + tokens, + fwd_hooks=[("blocks.0.hook_resid_post", force_resid_post_to_resid_mid)], + ) + + # Sanity: the intervention actually changed something. + assert not torch.equal(via_mlp_ablation, baseline_logits) + # The real check: two different-looking edits that are + # mathematically the same edit must produce identical output. + torch.testing.assert_close(via_mlp_ablation, via_resid_override, atol=0, rtol=0) + + +class TestDtypePreservation: + """float64 is checked for preservation through construction only, not + full numerical parity at that dtype -- RoPE's cos/sin computation + involves trig functions whose float32-vs-float64 rounding can + legitimately differ from the bridge's own dtype-casting path in ways + unrelated to correctness, so exact-equality parity isn't a meaningful + check at non-default dtypes the way it is at float32.""" + + def test_float64_construction_preserves_dtype_and_tied_weights(self) -> None: + torch.manual_seed(0) + model = TinyPretrainModel( + d_model=16, + n_heads=2, + n_layers=1, + d_ff=32, + vocab_size=64, + tie_embeddings=True, + ).to(torch.float64) + assert model.lm_head.weight is model.embed.weight + + bridge = build_pretrain_bridge(model, _make_cfg(d_model=16, n_layers=1)) + + assert next(model.parameters()).dtype == torch.float64 + assert model.lm_head.weight is model.embed.weight + + tokens = torch.randint(0, 64, (1, 4)) + with torch.no_grad(): + output = bridge(tokens) + assert output.dtype == torch.float64 + assert torch.isfinite(output).all() diff --git a/tests/integration/model_bridge/test_raven_adapter.py b/tests/integration/model_bridge/test_raven_adapter.py new file mode 100644 index 0000000000..9a3d5801a0 --- /dev/null +++ b/tests/integration/model_bridge/test_raven_adapter.py @@ -0,0 +1,251 @@ +"""Integration tests for the Raven / Huginn architecture adapter (RavenForCausalLM). + +Model: tomg-group-umd/huginn-0125 + - Remote-code (auto_map → raven_modeling_minimal.RavenForCausalLM), so no + tiny random checkpoint exists; this loads the real ~3.5B weights (~14GB + download, ~28GB RAM for the two fp32 copies). Gated out of CI on + network/memory budget; run locally with: + uv run pytest tests/integration/model_bridge/test_raven_adapter.py -v -m slow + +Huginn is a depth-recurrent decoder: prelude (2 blocks) → weight-tied +recurrent core (4 blocks applied N times, default N = mean_recurrence = 32) → +coda (2 blocks). The recurrence, the per-step prelude re-injection, the +sandwich norms and RoPE all live inside the remote-code forward, which the +bridge delegates to, so logit parity holds unchanged. + +Two behaviours make Huginn different from a flat decoder and shape these tests: + +1. Random initial latent state. ``iterate_forward`` seeds the recurrence with + ``torch.randn_like`` (``initialize_state``), so the forward is + non-deterministic across calls. Parity is only meaningful with the RNG + pinned identically before the bridge and HF calls; ``_seeded`` does that. + +2. Weight-tied recurrent core. The four ``core_block`` blocks run once per + recurrence step, so their hooks fire N times per forward and + ``run_with_cache`` keeps the final step (pinned by TestRavenRecurrentCore). + +Comparing against an independent HF load (never ``bridge.original_model``, +whose modules are hook-wrapped) keeps the parity check honest. +""" + +import os +import platform + +import pytest +import torch +from transformers import AutoConfig, AutoModelForCausalLM + +from transformer_lens.model_bridge import TransformerBridge + +MODEL = "tomg-group-umd/huginn-0125" + +pytestmark = [ + pytest.mark.slow, + pytest.mark.skipif( + bool(os.getenv("CI")), + reason="tomg-group-umd/huginn-0125: ~14GB download + ~28GB RAM, too large for CI", + ), +] + +# Depth-recurrent sandwich-norm model with 100+ effective layer applications at +# fp32; allow a wider op-order noise floor on GH Actions macOS-arm64 (same idiom +# as the Ouro and SmolLM3 bridge-vs-HF parity tests). +_MACOS_ARM64 = platform.system() == "Darwin" and platform.machine() == "arm64" +FP32_NOISE_TOL = 1e-2 if _MACOS_ARM64 else 1e-3 + +# Reduced recurrence for a tractable local run; passed identically to both the +# bridge and HF forward so parity is exact regardless of the value. +NUM_STEPS = 8 + + +def _seeded(fn, seed: int = 0): + """Run ``fn`` with the global torch RNG pinned. + + Huginn's ``initialize_state`` draws the initial latent from + ``torch.randn_like``; pinning the seed makes the bridge and HF forwards + start from the same latent so their logits are comparable. + """ + torch.manual_seed(seed) + return fn() + + +@pytest.fixture(scope="module") +def bridge() -> TransformerBridge: + return TransformerBridge.boot_transformers( + MODEL, device="cpu", dtype=torch.float32, trust_remote_code=True + ) + + +@pytest.fixture(scope="module") +def hf_eager(bridge: TransformerBridge) -> torch.nn.Module: + """HF model loaded independently of the bridge's wrapped instance. + + Depends on the bridge fixture only for ordering: booting the bridge runs + the adapter's prepare_loading patch (guarding transformers v5 weight + re-init) on the cached raven modeling module, and this plain + from_pretrained load benefits from that patch too. The weights are still a + separate copy. + """ + config = AutoConfig.from_pretrained(MODEL, trust_remote_code=True) + return AutoModelForCausalLM.from_pretrained( + MODEL, + config=config, + torch_dtype=torch.float32, + trust_remote_code=True, + ).eval() + + +@pytest.fixture(scope="module") +def tokens(hf_eager: torch.nn.Module) -> torch.Tensor: + """A short, deterministic random token sequence.""" + generator = torch.Generator().manual_seed(0) + return torch.randint(0, hf_eager.config.vocab_size, (1, 16), generator=generator) + + +class TestRavenBridgeCreation: + """The bridge loads the remote-code model and wires the three phases.""" + + def test_boot_transformers_succeeds(self, bridge: TransformerBridge) -> None: + assert bridge is not None + + def test_phase_block_lists_present(self, bridge: TransformerBridge) -> None: + """Prelude / core_block / coda are exposed as separate block lists.""" + assert len(bridge.prelude) == bridge.cfg.n_layers_in_prelude + assert len(bridge.core_block) == bridge.cfg.n_layers_in_recurrent_block + assert len(bridge.coda) == bridge.cfg.n_layers_in_coda + + def test_physical_layer_split( + self, bridge: TransformerBridge, hf_eager: torch.nn.Module + ) -> None: + """The three lists sum to num_hidden_layers (the physical block count).""" + physical = len(bridge.prelude) + len(bridge.core_block) + len(bridge.coda) + assert physical == hf_eager.config.num_hidden_layers + + def test_config_flags(self, bridge: TransformerBridge) -> None: + assert bridge.cfg.normalization_type == "RMS" + assert bridge.cfg.positional_embedding_type == "rotary" + assert bridge.cfg.gated_mlp is True + assert bridge.cfg.final_rms is True + + def test_sandwich_norms_wired(self, bridge: TransformerBridge) -> None: + """All four per-block RMSNorms are bridged on each phase.""" + for phase in (bridge.prelude, bridge.core_block, bridge.coda): + block = phase[0] + for ln in ("norm_1", "norm_2", "norm_3", "norm_4"): + assert hasattr(block, ln), f"missing {ln}" + + def test_recurrence_config_propagates( + self, bridge: TransformerBridge, hf_eager: torch.nn.Module + ) -> None: + assert bridge.cfg.mean_recurrence == hf_eager.config.mean_recurrence + + +class TestRavenHFDelegation: + """The bridge wraps the live remote-code modules in place (no copies).""" + + def test_core_block_is_the_hf_tree_module(self, bridge: TransformerBridge) -> None: + assert bridge.core_block[0] is bridge.original_model.transformer.core_block[0] + assert type(bridge.core_block[0].original_component).__name__ == "SandwichBlock" + + def test_attention_combined_qkv_wraps_live_hf_modules(self, bridge: TransformerBridge) -> None: + attn = bridge.core_block[0].attn + assert type(attn.original_component).__name__ == "CausalSelfAttention" + # Huginn uses a combined Wqkv projection (not split q/k/v). + assert attn.qkv is attn.original_component.Wqkv + assert attn.o is attn.original_component.proj + + def test_mlp_combined_fc_wraps_live_hf_modules(self, bridge: TransformerBridge) -> None: + mlp = bridge.core_block[0].mlp + assert type(mlp.original_component).__name__ == "GatedMLP" + # Combined gate+up "fc" and output "proj". + assert getattr(mlp, "in") is mlp.original_component.fc + assert mlp.out is mlp.original_component.proj + + def test_sandwich_norms_wrap_live_hf_modules(self, bridge: TransformerBridge) -> None: + block = bridge.core_block[0] + layer = block.original_component + assert block.norm_1 is layer.norm_1 + assert block.norm_2 is layer.norm_2 + assert block.norm_3 is layer.norm_3 + assert block.norm_4 is layer.norm_4 + + +class TestRavenForwardEquivalence: + """Bridge output reproduces the HF eager reference through the recurrence. + + Both forwards are given the same reduced ``num_steps`` and the same pinned + RNG seed, so the random initial latent state and recurrence depth match and + the logits are directly comparable. + """ + + def test_forward_logits_match_hf_eager( + self, bridge: TransformerBridge, hf_eager: torch.nn.Module, tokens: torch.Tensor + ) -> None: + with torch.inference_mode(): + bridge_logits = _seeded(lambda: bridge(tokens, num_steps=NUM_STEPS)) + hf_logits = _seeded(lambda: hf_eager(tokens, num_steps=NUM_STEPS).logits) + max_diff = (bridge_logits - hf_logits).abs().max().item() + assert max_diff < FP32_NOISE_TOL, ( + f"Raven bridge vs HF eager logit drift={max_diff:.2e} exceeds the " + f"fp32-noise tolerance {FP32_NOISE_TOL:.0e}." + ) + + +class TestRavenRecurrentCore: + """Pin the weight-tied recurrent-core semantics the adapter documents.""" + + def test_core_block_hook_fires_once_per_recurrence_step( + self, bridge: TransformerBridge, tokens: torch.Tensor + ) -> None: + """The same physical core block executes num_steps times per forward. + + This is the load-bearing difference from a flat decoder: hooks on + core_block.{i} fire once per recurrence step and run_with_cache keeps + the final step. If HF's remote code changes the loop (or the bridge + stops delegating it), this fails. + """ + fired: list[bool] = [] + _seeded( + lambda: bridge.run_with_hooks( + tokens, + num_steps=NUM_STEPS, + fwd_hooks=[("core_block.0.hook_out", lambda value, hook: fired.append(True))], + ) + ) + assert len(fired) == NUM_STEPS + + def test_prelude_hook_fires_once(self, bridge: TransformerBridge, tokens: torch.Tensor) -> None: + """Prelude blocks are non-recurrent: their hooks fire exactly once.""" + fired: list[bool] = [] + _seeded( + lambda: bridge.run_with_hooks( + tokens, + num_steps=NUM_STEPS, + fwd_hooks=[("prelude.0.hook_out", lambda value, hook: fired.append(True))], + ) + ) + assert len(fired) == 1 + + +class TestRavenHookShapes: + """Residual-stream hooks fire with the expected shape on each phase.""" + + @pytest.mark.parametrize("phase", ["prelude", "core_block", "coda"]) + def test_hook_out_shape_matches_residual_stream( + self, bridge: TransformerBridge, tokens: torch.Tensor, phase: str + ) -> None: + captured: list[torch.Tensor] = [] + + def _capture(value, hook): + captured.append(value[0] if isinstance(value, tuple) else value) + return value + + _seeded( + lambda: bridge.run_with_hooks( + tokens, + num_steps=NUM_STEPS, + fwd_hooks=[(f"{phase}.0.hook_out", _capture)], + ) + ) + assert captured + assert captured[-1].shape == (1, tokens.shape[1], bridge.cfg.d_model) diff --git a/tests/integration/model_bridge/test_rwkv7_adapter.py b/tests/integration/model_bridge/test_rwkv7_adapter.py new file mode 100644 index 0000000000..4c47889670 --- /dev/null +++ b/tests/integration/model_bridge/test_rwkv7_adapter.py @@ -0,0 +1,161 @@ +"""Integration tests for the RWKV-7 architecture adapter (RWKV7ForCausalLM). + +Model: fla-hub/rwkv7-0.1B-g1 + - Remote-code (``trust_remote_code=True``); the modeling classes live in the + flash-linear-attention (``fla``) library, which must be installed: + pip install flash-linear-attention + - ~191M params (~0.8GB download). Gated out of CI on the network/dependency + budget; run locally with: + uv run pytest tests/integration/model_bridge/test_rwkv7_adapter.py -v -m slow + +RWKV-7 is an attention-free recurrent decoder: a flat stack of pre-norm blocks, +each a generalized-delta-rule time-mixing sublayer (``attn``) plus a +token-shifted squared-ReLU channel-mixing sublayer (``ffn``), wrapped by standard +biased LayerNorm. The recurrence, the token shift, and the cross-block +``v_first`` threading all live inside the ``fla`` remote-code forward, which the +bridge delegates to, so logit parity holds unchanged. + +Comparing against an independent HF load (never ``bridge.original_model``, whose +modules are hook-wrapped) keeps the parity check honest. +""" + +import os +import platform + +import pytest +import torch + +from transformer_lens.model_bridge import TransformerBridge + +pytest.importorskip("fla", reason="RWKV-7 needs flash-linear-attention (pip install it)") + +from transformers import AutoConfig, AutoModelForCausalLM # noqa: E402 + +MODEL = "fla-hub/rwkv7-0.1B-g1" + +pytestmark = [ + pytest.mark.slow, + pytest.mark.skipif( + bool(os.getenv("CI")), + reason="fla-hub/rwkv7-0.1B-g1: remote code + flash-linear-attention dep, skipped in CI", + ), +] + +# Recurrent fp32 model; allow a wider op-order noise floor on GH Actions +# macOS-arm64 (same idiom as the Ouro / Raven bridge-vs-HF parity tests). +_MACOS_ARM64 = platform.system() == "Darwin" and platform.machine() == "arm64" +FP32_NOISE_TOL = 1e-2 if _MACOS_ARM64 else 1e-3 + + +@pytest.fixture(scope="module") +def bridge() -> TransformerBridge: + return TransformerBridge.boot_transformers( + MODEL, device="cpu", dtype=torch.float32, trust_remote_code=True + ) + + +@pytest.fixture(scope="module") +def hf_eager(bridge: TransformerBridge) -> torch.nn.Module: + """HF model loaded independently of the bridge's wrapped instance. + + Depends on the bridge fixture only for ordering: booting the bridge runs the + adapter's prepare_loading patch (guarding transformers v5 weight re-init) on + the cached rwkv7 modeling module, and this plain from_pretrained load benefits + from that patch too. The weights are still a separate copy. + """ + config = AutoConfig.from_pretrained(MODEL, trust_remote_code=True) + return AutoModelForCausalLM.from_pretrained( + MODEL, + config=config, + torch_dtype=torch.float32, + trust_remote_code=True, + ).eval() + + +@pytest.fixture(scope="module") +def tokens(hf_eager: torch.nn.Module) -> torch.Tensor: + """A short, deterministic random token sequence.""" + generator = torch.Generator().manual_seed(0) + return torch.randint(0, hf_eager.config.vocab_size, (1, 16), generator=generator) + + +class TestRWKV7BridgeCreation: + """The bridge loads the remote-code model and wires the flat block stack.""" + + def test_boot_transformers_succeeds(self, bridge: TransformerBridge) -> None: + assert bridge is not None + + def test_block_count_matches_config( + self, bridge: TransformerBridge, hf_eager: torch.nn.Module + ) -> None: + assert len(bridge.blocks) == hf_eager.config.num_hidden_layers + + def test_config_flags(self, bridge: TransformerBridge) -> None: + assert bridge.cfg.normalization_type == "LN" + assert bridge.cfg.positional_embedding_type == "none" + assert bridge.cfg.gated_mlp is False + assert bridge.cfg.final_rms is False + + def test_block_submodules_wired(self, bridge: TransformerBridge) -> None: + """attn_norm / attn / ffn_norm / ffn are bridged on each block.""" + block = bridge.blocks[0] + for sub in ("attn_norm", "attn", "ffn_norm", "ffn"): + assert hasattr(block, sub), f"missing {sub}" + + def test_attn_projections_wrap_live_hf_modules(self, bridge: TransformerBridge) -> None: + attn = bridge.blocks[0].attn + hf_attn = bridge.original_model.model.layers[0].attn + assert attn.r_proj is hf_attn.r_proj + assert attn.k_proj is hf_attn.k_proj + assert attn.v_proj is hf_attn.v_proj + assert attn.o_proj is hf_attn.o_proj + + def test_ffn_projections_wrap_live_hf_modules(self, bridge: TransformerBridge) -> None: + ffn = bridge.blocks[0].ffn + hf_ffn = bridge.original_model.model.layers[0].ffn + assert ffn.key is hf_ffn.key + assert ffn.value is hf_ffn.value + + +class TestRWKV7ForwardEquivalence: + """Bridge output reproduces the HF eager reference through the recurrence.""" + + def test_forward_logits_match_hf_eager( + self, bridge: TransformerBridge, hf_eager: torch.nn.Module, tokens: torch.Tensor + ) -> None: + with torch.inference_mode(): + bridge_logits = bridge(tokens) + hf_logits = hf_eager(tokens).logits + max_diff = (bridge_logits - hf_logits).abs().max().item() + assert max_diff < FP32_NOISE_TOL, ( + f"RWKV-7 bridge vs HF eager logit drift={max_diff:.2e} exceeds the " + f"fp32-noise tolerance {FP32_NOISE_TOL:.0e}." + ) + + +class TestRWKV7Hooks: + """Residual-stream and sublayer hooks fire with the expected shape.""" + + @pytest.mark.parametrize( + "hook_name", + ["blocks.0.hook_out", "blocks.0.attn.hook_out", "blocks.0.ffn.hook_out"], + ) + def test_hook_fires_with_residual_shape( + self, bridge: TransformerBridge, tokens: torch.Tensor, hook_name: str + ) -> None: + captured: list[torch.Tensor] = [] + + def _capture(value, hook): + captured.append(value[0] if isinstance(value, tuple) else value) + return value + + bridge.run_with_hooks(tokens, fwd_hooks=[(hook_name, _capture)]) + assert captured, f"{hook_name} did not fire" + assert captured[-1].shape == (1, tokens.shape[1], bridge.cfg.d_model) + + def test_run_with_cache_has_block_hooks( + self, bridge: TransformerBridge, tokens: torch.Tensor + ) -> None: + _, cache = bridge.run_with_cache(tokens) + for key in ("blocks.0.hook_out", "blocks.0.attn.hook_out", "blocks.0.ffn.hook_out"): + assert key in cache, f"{key} missing from cache" diff --git a/tests/mocks/tiny_pretrain_model.py b/tests/mocks/tiny_pretrain_model.py new file mode 100644 index 0000000000..2c3d9434ca --- /dev/null +++ b/tests/mocks/tiny_pretrain_model.py @@ -0,0 +1,193 @@ +"""A tiny, self-contained decoder-only reference model (RoPE, RMSNorm, +gated SwiGLU MLP, optional MoE) used only by the pretrain-adapter +integration tests, to exercise real numerical parity rather than a purely +structural mock. + +Deliberately uses the *adjacent-pair* RoPE convention +(`[x0, x1] -> [-x1, x0]`), not HuggingFace's rotate-half (contiguous-half) +convention -- this is the concrete case `PretrainArchitectureAdapter`'s +docstring is about: an existing HF-oriented attention bridge would silently +apply the wrong rotation to a model using this convention. + +Also deliberately computes RoPE `cos`/`sin` once per forward pass and +passes them into every block call as extra positional args +(`block(x, cos, sin)`), matching the target architecture's convention. +This matters beyond efficiency: `BlockBridge` wraps a bare-tensor output +in a 1-tuple for single-positional-argument "standalone hidden_states" +calls (an HF-compatibility convention). A loop calling `block(x)` alone +would need to unwrap `[0]` from each result -- modifying the source +model's forward specifically because it's being bridged. Passing +`cos`/`sin` positionally avoids that, so the forward loop needs no +changes at all. +""" + +from __future__ import annotations + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def _rotate_adjacent_pairs(x: torch.Tensor) -> torch.Tensor: + x1 = x[..., 0::2] + x2 = x[..., 1::2] + return torch.stack((-x2, x1), dim=-1).flatten(-2) + + +def _apply_rotary_adjacent_pairs( + x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor +) -> torch.Tensor: + cos = cos.repeat_interleave(2, dim=-1) + sin = sin.repeat_interleave(2, dim=-1) + return x * cos + _rotate_adjacent_pairs(x) * sin + + +class RMSNorm(nn.Module): + def __init__(self, d_model: int, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(d_model)) + self.eps = eps + + def forward(self, x: torch.Tensor) -> torch.Tensor: + variance = x.pow(2).mean(-1, keepdim=True) + x = x * torch.rsqrt(variance + self.eps) + return x * self.weight + + +class Attention(nn.Module): + """Receives precomputed `cos`/`sin` rather than computing them + internally -- matches the real target architecture, where rotary + tables are computed once per forward pass and shared across blocks.""" + + def __init__(self, d_model: int, n_heads: int): + super().__init__() + self.n_heads = n_heads + self.d_head = d_model // n_heads + self.qkv = nn.Linear(d_model, 3 * d_model, bias=False) + self.o = nn.Linear(d_model, d_model, bias=False) + + def forward(self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor: + b, s, d = x.shape + qkv = self.qkv(x).view(b, s, 3, self.n_heads, self.d_head).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # each: [b, n_heads, s, d_head] + + q = _apply_rotary_adjacent_pairs(q, cos, sin) + k = _apply_rotary_adjacent_pairs(k, cos, sin) + + attn = torch.softmax( + (q @ k.transpose(-2, -1)) / (self.d_head**0.5) + + torch.triu(torch.full((s, s), float("-inf"), device=x.device), diagonal=1), + dim=-1, + ) + out = (attn @ v).transpose(1, 2).reshape(b, s, d) + return self.o(out) + + +class DenseMLP(nn.Module): + def __init__(self, d_model: int, d_ff: int): + super().__init__() + self.gate = nn.Linear(d_model, d_ff, bias=False) + self.up = nn.Linear(d_model, d_ff, bias=False) + self.down = nn.Linear(d_ff, d_model, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.down(F.silu(self.gate(x)) * self.up(x)) + + +class MoEMLP(nn.Module): + def __init__(self, d_model: int, d_ff: int, n_experts: int, top_k: int): + super().__init__() + self.top_k = top_k + self.router = nn.Linear(d_model, n_experts, bias=False) + self.experts = nn.ModuleList([DenseMLP(d_model, d_ff) for _ in range(n_experts)]) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + weights = torch.softmax(self.router(x), dim=-1) + topk_weights, topk_idx = weights.topk(self.top_k, dim=-1) + out = torch.zeros_like(x) + for e in range(len(self.experts)): + mask = topk_idx == e + if not mask.any(): + continue + gate = (mask.float() * topk_weights).sum(dim=-1, keepdim=True) + out = out + gate * self.experts[e](x) + return out + + +class Block(nn.Module): + """Takes `cos`/`sin` as extra positional args alongside the hidden + state -- see the module docstring for why this matters beyond RoPE + plumbing.""" + + def __init__(self, d_model: int, n_heads: int, d_ff: int, mlp: nn.Module): + super().__init__() + self.norm1 = RMSNorm(d_model) + self.attn = Attention(d_model, n_heads) + self.norm2 = RMSNorm(d_model) + self.mlp = mlp + + def forward(self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor: + x = x + self.attn(self.norm1(x), cos, sin) + x = x + self.mlp(self.norm2(x)) + return x + + +class TinyPretrainModel(nn.Module): + """Full tiny reference model for numerical-parity integration tests. + Matches `PretrainArchitectureAdapter`'s expected attribute paths: + `embed`, `blocks`, `norm_f`, `lm_head`. + """ + + def __init__( + self, + d_model: int = 32, + n_heads: int = 4, + n_layers: int = 2, + d_ff: int = 64, + vocab_size: int = 256, + n_experts: int = 4, + top_k: int = 2, + moe_layer_indices: frozenset[int] = frozenset(), + tie_embeddings: bool = True, + rope_base: float = 10000.0, + ): + super().__init__() + self.embed = nn.Embedding(vocab_size, d_model) + self.blocks = nn.ModuleList( + [ + Block( + d_model, + n_heads, + d_ff, + mlp=( + MoEMLP(d_model, d_ff, n_experts, top_k) + if i in moe_layer_indices + else DenseMLP(d_model, d_ff) + ), + ) + for i in range(n_layers) + ] + ) + self.norm_f = RMSNorm(d_model) + self.lm_head = nn.Linear(d_model, vocab_size, bias=False) + if tie_embeddings: + self.lm_head.weight = self.embed.weight + + d_head = d_model // n_heads + inv_freq = 1.0 / (rope_base ** (torch.arange(0, d_head, 2).float() / d_head)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + def forward(self, tokens: torch.Tensor) -> dict: + b, s = tokens.shape + x = self.embed(tokens) + + # Computed once per forward pass, passed into every block -- the + # real target architecture's convention (see module docstring). + pos = torch.arange(s, device=tokens.device, dtype=self.inv_freq.dtype) + freqs = torch.outer(pos, self.inv_freq) + cos, sin = freqs.cos(), freqs.sin() + + for block in self.blocks: + x = block(x, cos, sin) + x = self.norm_f(x) + return {"logits": self.lm_head(x)} diff --git a/tests/unit/model_bridge/generalized_components/test_moe_bridge_tuple_output.py b/tests/unit/model_bridge/generalized_components/test_moe_bridge_tuple_output.py new file mode 100644 index 0000000000..3b644497bc --- /dev/null +++ b/tests/unit/model_bridge/generalized_components/test_moe_bridge_tuple_output.py @@ -0,0 +1,88 @@ +"""Tests for MoEBridge tuple-output validation and router-score hooks. + +Unlike the pretrain-adapter's TestDenseOrMoEFeedForwardBridgeTupleOutput +(which patches _delegate.forward and therefore exercises +DenseOrMoEFeedForwardBridge.forward in isolation), these tests call +MoEBridge.forward() directly via a stub original_component, so they cover +MoEBridge's own tuple-validation contract rather than the dispatcher's. +""" + +from __future__ import annotations + +import pytest +import torch +import torch.nn as nn + +from transformer_lens.model_bridge.generalized_components import MoEBridge + + +class _StubMoEModule(nn.Module): + """Minimal nn.Module whose forward is swapped in per-test, standing in + for a real MoE layer wrapped by MoEBridge.""" + + def __init__(self, fake_forward): + super().__init__() + # Give the module at least one parameter so + # `next(self.parameters())` in MoEBridge.forward doesn't raise + # StopIteration and skip the dtype-cast branch entirely. + self.dummy = nn.Linear(4, 4) + self._fake_forward = fake_forward + + def forward(self, *args, **kwargs): + return self._fake_forward(*args, **kwargs) + + +def _bridge_with_stub(fake_forward) -> MoEBridge: + bridge = MoEBridge(name="mlp") + bridge.set_original_component(_StubMoEModule(fake_forward)) + return bridge + + +class TestMoEBridgeTupleOutput: + def test_empty_tuple_raises_clear_type_error(self) -> None: + bridge = _bridge_with_stub(lambda *a, **kw: ()) + with pytest.raises(TypeError, match="torch.Tensor"): + bridge(torch.ones(1, 3, 4)) + + def test_non_tensor_metadata_preserved_without_router_hook(self) -> None: + bridge = _bridge_with_stub(lambda *a, **kw: (torch.zeros(1, 3, 4), "not_router_scores")) + fired = {"called": False} + + def mark_fired(value, hook): + fired["called"] = True + + bridge.hook_router_scores.add_hook(mark_fired) + + output = bridge(torch.ones(1, 3, 4)) + + assert isinstance(output, tuple) + assert output[1] == "not_router_scores" + torch.testing.assert_close(output[0], torch.zeros(1, 3, 4)) + assert fired["called"] is False + + def test_tensor_router_scores_still_fire_hook(self) -> None: + router_scores = torch.rand(1, 3, 8) + bridge = _bridge_with_stub(lambda *a, **kw: (torch.zeros(1, 3, 4), router_scores)) + captured = {} + + def capture_router_scores(value, hook): + captured["router_scores"] = value + return value + + bridge.hook_router_scores.add_hook(capture_router_scores) + + output = bridge(torch.ones(1, 3, 4)) + + assert isinstance(output, tuple) + torch.testing.assert_close(output[1], router_scores) + assert "router_scores" in captured + torch.testing.assert_close(captured["router_scores"], router_scores) + + def test_non_tensor_first_element_raises_clear_type_error(self) -> None: + """Exercises the output[0]-validation added to MoEBridge itself + (not just the dispatcher) -- a malformed first element should + raise a clear TypeError here rather than failing inside + HookPoint's own type checking.""" + bridge = _bridge_with_stub(lambda *a, **kw: ("not_a_tensor", torch.zeros(1, 3, 4))) + with pytest.raises(TypeError, match="torch.Tensor"): + bridge(torch.ones(1, 3, 4)) diff --git a/tests/unit/model_bridge/supported_architectures/_pretrain_mocks.py b/tests/unit/model_bridge/supported_architectures/_pretrain_mocks.py new file mode 100644 index 0000000000..28c4f94f44 --- /dev/null +++ b/tests/unit/model_bridge/supported_architectures/_pretrain_mocks.py @@ -0,0 +1,214 @@ +"""Shared mock modules and fixtures for PretrainArchitectureAdapter and +PretrainModelContainer unit tests. Not a test file itself (no `test_` +prefix, so pytest won't collect it) -- imported by both +test_pretrain_adapter.py and test_pretrain_model_container.py. + +Fully self-contained: builds tiny mock `nn.Module`s that reproduce the +relevant module tree (embed / blocks / norm1 / attn / norm2 / mlp / norm_f / +lm_head) rather than depending on any external training-framework package. +No weight loading, no network access. +""" + +from __future__ import annotations + +import torch +import torch.nn as nn + +from transformer_lens.config.transformer_bridge_config import TransformerBridgeConfig +from transformer_lens.model_bridge.supported_architectures.pretrain import ( + ARCHITECTURE_NAME, +) + +# -------------------------------------------------------------------------- +# Tiny mock module tree, matching the real target architecture's +# block-calling convention (cos/sin as extra positional args -- see +# tiny_pretrain_model.py's module docstring). Real RoPE/RMSNorm math isn't +# needed here (covered by integration parity tests); only correct +# attribute names/shapes/call-signatures for component_mapping to wire up. +# -------------------------------------------------------------------------- + + +class TinyRMSNorm(nn.Module): + def __init__(self, d_model: int): + super().__init__() + self.weight = nn.Parameter(torch.ones(d_model)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return x * self.weight + + +class TinyAttention(nn.Module): + """Opaque attention stand-in: linear-in, linear-out, no real RoPE math. + Takes (and ignores) cos/sin positionally, matching the real target + architecture's block-calling convention.""" + + def __init__(self, d_model: int, n_heads: int): + super().__init__() + self.n_heads = n_heads + self.qkv = nn.Linear(d_model, 3 * d_model) + self.out = nn.Linear(d_model, d_model) + + def forward(self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor: + b, s, d = x.shape + qkv = self.qkv(x) + q, k, v = qkv.chunk(3, dim=-1) + attn = torch.softmax(q @ k.transpose(-2, -1) / (d**0.5), dim=-1) + return self.out(attn @ v) + + +class TinyDenseMLP(nn.Module): + def __init__(self, d_model: int, d_ff: int): + super().__init__() + self.gate = nn.Linear(d_model, d_ff, bias=False) + self.up = nn.Linear(d_model, d_ff, bias=False) + self.down = nn.Linear(d_ff, d_model, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.down(torch.nn.functional.silu(self.gate(x)) * self.up(x)) + + +class TinyMoE(nn.Module): + def __init__(self, d_model: int, d_ff: int, n_experts: int, top_k: int): + super().__init__() + self.top_k = top_k + self.router = nn.Linear(d_model, n_experts, bias=False) + self.experts = nn.ModuleList([TinyDenseMLP(d_model, d_ff) for _ in range(n_experts)]) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + weights = torch.softmax(self.router(x), dim=-1) + topk_weights, topk_idx = weights.topk(self.top_k, dim=-1) + out = torch.zeros_like(x) + for expert_idx in range(len(self.experts)): + mask = (topk_idx == expert_idx).any(dim=-1, keepdim=True) + if mask.any(): + out = out + mask * self.experts[expert_idx](x) + return out + + +class TinyBlock(nn.Module): + def __init__(self, d_model: int, n_heads: int, d_ff: int, mlp: nn.Module): + super().__init__() + self.norm1 = TinyRMSNorm(d_model) + self.attn = TinyAttention(d_model, n_heads) + self.norm2 = TinyRMSNorm(d_model) + self.mlp = mlp + + def forward(self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor: + x = x + self.attn(self.norm1(x), cos, sin) + x = x + self.mlp(self.norm2(x)) + return x + + +class TinyPretrainModel(nn.Module): + """Minimal decoder-only model matching the adapter's expected paths: + embed / blocks / norm_f / lm_head. `moe_layer_indices` (possibly empty) + controls which blocks get a `TinyMoE` mlp instead of a dense one. + """ + + def __init__( + self, + d_model: int = 32, + n_heads: int = 4, + n_layers: int = 2, + d_ff: int = 64, + vocab_size: int = 256, + n_experts: int = 4, + top_k: int = 2, + moe_layer_indices: frozenset[int] = frozenset(), + tie_embeddings: bool = True, + ): + super().__init__() + self.embed = nn.Embedding(vocab_size, d_model) + self.blocks = nn.ModuleList( + [ + TinyBlock( + d_model, + n_heads, + d_ff, + mlp=( + TinyMoE(d_model, d_ff, n_experts, top_k) + if i in moe_layer_indices + else TinyDenseMLP(d_model, d_ff) + ), + ) + for i in range(n_layers) + ] + ) + self.norm_f = TinyRMSNorm(d_model) + self.lm_head = nn.Linear(d_model, vocab_size, bias=False) + if tie_embeddings: + self.lm_head.weight = self.embed.weight + self.d_head = d_model // n_heads + + def _forward_impl(self, tokens: torch.Tensor) -> dict: + b, s = tokens.shape + x = self.embed(tokens) + # Dummy cos/sin (no real RoPE math needed for these structural + # tests) but real tensors, passed positionally into every block -- + # matches the real target architecture's convention. + cos = torch.ones(s, self.d_head // 2) + sin = torch.zeros(s, self.d_head // 2) + for block in self.blocks: + x = block(x, cos, sin) + x = self.norm_f(x) + return {"logits": self.lm_head(x)} + + def forward(self, tokens: torch.Tensor, **kwargs) -> dict: + # **kwargs declared so pytest fixtures can pass through + # run_with_cache's force-injected kwargs -- the actual kwarg- + # forwarding contracts (strict vs. **kwargs-accepting source + # forwards) are exercised directly against ForwardStrict/ + # ForwardVarKwargs below instead. + return self._forward_impl(tokens) + + +class ForwardStrict(nn.Module): + """Declares no **kwargs catch-all -- exercises the "must filter" + branch of PretrainModelContainer's kwarg handling.""" + + def __init__(self): + super().__init__() + self.seen_kwargs: dict = {} + + def forward(self, tokens: torch.Tensor, targets: torch.Tensor | None = None) -> dict: + self.seen_kwargs = {"targets": targets} + return {"logits": tokens.float()} + + +class ForwardVarKwargs(nn.Module): + """Declares **kwargs -- exercises the "pass everything through" branch + of PretrainModelContainer's kwarg handling.""" + + def __init__(self): + super().__init__() + self.seen_kwargs: dict = {} + + def forward(self, tokens: torch.Tensor, **kwargs) -> dict: + self.seen_kwargs = kwargs + return {"logits": tokens.float()} + + +class MalformedMLP(nn.Module): + """Has neither a dense MLP's (gate, up, down) nor an MoE layer's + (router, experts) attributes -- used to test the adapter's failure + path.""" + + def __init__(self, d_model: int): + super().__init__() + self.some_other_layer = nn.Linear(d_model, d_model) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.some_other_layer(x) + + +def make_cfg(d_model: int = 32, n_layers: int = 2) -> TransformerBridgeConfig: + return TransformerBridgeConfig( + d_model=d_model, + d_head=d_model // 4, + n_layers=n_layers, + n_ctx=128, + n_heads=4, + d_vocab=256, + d_mlp=d_model * 2, + architecture=ARCHITECTURE_NAME, + ) diff --git a/tests/unit/model_bridge/supported_architectures/test_granite_adapter.py b/tests/unit/model_bridge/supported_architectures/test_granite_adapter.py index b5b3f848b6..2603d0f56a 100644 --- a/tests/unit/model_bridge/supported_architectures/test_granite_adapter.py +++ b/tests/unit/model_bridge/supported_architectures/test_granite_adapter.py @@ -1,9 +1,12 @@ -"""Unit tests for GraniteArchitectureAdapter and GraniteMoeArchitectureAdapter. +"""Unit tests for GraniteArchitectureAdapter. Tests cover: - Component mapping structure (bridge types and HF module names) - Weight conversion key set - Config flags set by the adapter + +GraniteMoeArchitectureAdapter has its own dedicated test file, +test_granite_moe_adapter.py. """ import pytest @@ -14,7 +17,6 @@ EmbeddingBridge, GatedMLPBridge, LinearBridge, - MoEBridge, PositionEmbeddingsAttentionBridge, RMSNormalizationBridge, RotaryEmbeddingBridge, @@ -23,9 +25,6 @@ from transformer_lens.model_bridge.supported_architectures.granite import ( GraniteArchitectureAdapter, ) -from transformer_lens.model_bridge.supported_architectures.granite_moe import ( - GraniteMoeArchitectureAdapter, -) # --------------------------------------------------------------------------- # Helpers / fixtures @@ -74,16 +73,6 @@ def adapter(cfg: TransformerBridgeConfig) -> GraniteArchitectureAdapter: return GraniteArchitectureAdapter(cfg) -@pytest.fixture -def moe_cfg() -> TransformerBridgeConfig: - return _make_cfg() - - -@pytest.fixture -def moe_adapter(moe_cfg: TransformerBridgeConfig) -> GraniteMoeArchitectureAdapter: - return GraniteMoeArchitectureAdapter(moe_cfg) - - # --------------------------------------------------------------------------- # Config flag tests # --------------------------------------------------------------------------- @@ -212,44 +201,3 @@ def test_exact_conversion_key_set(self, adapter: GraniteArchitectureAdapter) -> "blocks.{i}.attn.v.weight", "blocks.{i}.attn.o.weight", } - - -# --------------------------------------------------------------------------- -# GraniteMoe component mapping tests -# --------------------------------------------------------------------------- - - -class TestGraniteMoeAdapterComponentMapping: - """GraniteMoe replaces dense MLP with MoE; everything else is identical to Granite.""" - - def test_top_level_keys(self, moe_adapter: GraniteMoeArchitectureAdapter) -> None: - assert set(moe_adapter.component_mapping.keys()) == { - "embed", - "rotary_emb", - "blocks", - "ln_final", - "unembed", - } - - def test_mlp_is_moe_bridge(self, moe_adapter: GraniteMoeArchitectureAdapter) -> None: - mlp = moe_adapter.component_mapping["blocks"].submodules["mlp"] - assert isinstance(mlp, MoEBridge) - - def test_moe_hf_path(self, moe_adapter: GraniteMoeArchitectureAdapter) -> None: - mlp = moe_adapter.component_mapping["blocks"].submodules["mlp"] - assert mlp.name == "block_sparse_moe" - - def test_non_mlp_components_match_dense( - self, - adapter: GraniteArchitectureAdapter, - moe_adapter: GraniteMoeArchitectureAdapter, - ) -> None: - """Embed, rotary_emb, ln_final, unembed, and attention are shared with dense Granite.""" - for key in ("embed", "rotary_emb", "ln_final", "unembed"): - assert type(moe_adapter.component_mapping[key]) is type(adapter.component_mapping[key]) - assert moe_adapter.component_mapping[key].name == adapter.component_mapping[key].name - - def test_attention_unchanged_in_moe(self, moe_adapter: GraniteMoeArchitectureAdapter) -> None: - attn = moe_adapter.component_mapping["blocks"].submodules["attn"] - assert isinstance(attn, PositionEmbeddingsAttentionBridge) - assert set(attn.submodules.keys()) == {"q", "k", "v", "o"} diff --git a/tests/unit/model_bridge/supported_architectures/test_granite_moe_adapter.py b/tests/unit/model_bridge/supported_architectures/test_granite_moe_adapter.py new file mode 100644 index 0000000000..0f96d26f83 --- /dev/null +++ b/tests/unit/model_bridge/supported_architectures/test_granite_moe_adapter.py @@ -0,0 +1,212 @@ +"""Unit tests for GraniteMoeArchitectureAdapter. + +GraniteMoe is dense Granite with the gated MLP replaced by a Sparse Mixture +of Experts block (``block_sparse_moe``); everything else (embed, rotary +embeddings, attention, final norm, unembed, and the common Granite config +flags) is inherited unchanged from ``GraniteArchitectureAdapter``. +""" + +import pytest + +from transformer_lens.config import TransformerBridgeConfig +from transformer_lens.model_bridge.generalized_components import ( + BlockBridge, + EmbeddingBridge, + LinearBridge, + MoEBridge, + PositionEmbeddingsAttentionBridge, + RMSNormalizationBridge, + RotaryEmbeddingBridge, + UnembeddingBridge, +) +from transformer_lens.model_bridge.supported_architectures.granite import ( + GraniteArchitectureAdapter, +) +from transformer_lens.model_bridge.supported_architectures.granite_moe import ( + GraniteMoeArchitectureAdapter, +) + +# --------------------------------------------------------------------------- +# Helpers / fixtures +# --------------------------------------------------------------------------- + +N_HEADS = 8 +N_KV_HEADS = 2 +D_MODEL = 64 +D_MLP = 256 +N_LAYERS = 2 +N_CTX = 256 +D_VOCAB = 1000 + + +def _make_dense_cfg() -> TransformerBridgeConfig: + """Same shape as the MoE config, but for dense Granite (comparison baseline).""" + return TransformerBridgeConfig( + d_model=D_MODEL, + d_head=D_MODEL // N_HEADS, + n_layers=N_LAYERS, + n_ctx=N_CTX, + n_heads=N_HEADS, + d_vocab=D_VOCAB, + d_mlp=D_MLP, + n_key_value_heads=N_KV_HEADS, + default_prepend_bos=False, + architecture="GraniteForCausalLM", + ) + + +def _make_moe_cfg() -> TransformerBridgeConfig: + """Return a minimal synthetic config for GraniteMoe adapter tests.""" + return TransformerBridgeConfig( + d_model=D_MODEL, + d_head=D_MODEL // N_HEADS, + n_layers=N_LAYERS, + n_ctx=N_CTX, + n_heads=N_HEADS, + d_vocab=D_VOCAB, + d_mlp=D_MLP, + n_key_value_heads=N_KV_HEADS, + num_experts=4, + experts_per_token=2, + default_prepend_bos=False, + architecture="GraniteMoeForCausalLM", + ) + + +@pytest.fixture +def dense_adapter() -> GraniteArchitectureAdapter: + return GraniteArchitectureAdapter(_make_dense_cfg()) + + +@pytest.fixture +def cfg() -> TransformerBridgeConfig: + return _make_moe_cfg() + + +@pytest.fixture +def adapter(cfg: TransformerBridgeConfig) -> GraniteMoeArchitectureAdapter: + return GraniteMoeArchitectureAdapter(cfg) + + +# --------------------------------------------------------------------------- +# Config flag tests +# --------------------------------------------------------------------------- + + +class TestGraniteMoeAdapterConfig: + """GraniteMoe doesn't override config setup, so it should inherit the same + flags as dense Granite -- checked explicitly here rather than only + implied by inheritance.""" + + def test_normalization_type(self, adapter: GraniteMoeArchitectureAdapter) -> None: + assert adapter.cfg.normalization_type == "RMS" + + def test_positional_embedding_type(self, adapter: GraniteMoeArchitectureAdapter) -> None: + assert adapter.cfg.positional_embedding_type == "rotary" + + def test_final_rms(self, adapter: GraniteMoeArchitectureAdapter) -> None: + assert adapter.cfg.final_rms is True + + def test_gated_mlp(self, adapter: GraniteMoeArchitectureAdapter) -> None: + assert adapter.cfg.gated_mlp is True + + def test_attn_only_false(self, adapter: GraniteMoeArchitectureAdapter) -> None: + assert adapter.cfg.attn_only is False + + def test_uses_rms_norm(self, adapter: GraniteMoeArchitectureAdapter) -> None: + assert adapter.cfg.uses_rms_norm is True + + def test_default_prepend_bos_false(self, adapter: GraniteMoeArchitectureAdapter) -> None: + assert adapter.cfg.default_prepend_bos is False + + def test_n_key_value_heads_propagated(self, adapter: GraniteMoeArchitectureAdapter) -> None: + assert adapter.cfg.n_key_value_heads == N_KV_HEADS + + +# --------------------------------------------------------------------------- +# Component mapping tests +# --------------------------------------------------------------------------- + + +class TestGraniteMoeAdapterComponentMapping: + """GraniteMoe replaces the dense MLP with MoE; everything else is + identical to dense Granite.""" + + def test_top_level_keys(self, adapter: GraniteMoeArchitectureAdapter) -> None: + assert set(adapter.component_mapping.keys()) == { + "embed", + "rotary_emb", + "blocks", + "ln_final", + "unembed", + } + + def test_top_level_bridge_types(self, adapter: GraniteMoeArchitectureAdapter) -> None: + mapping = adapter.component_mapping + assert isinstance(mapping["embed"], EmbeddingBridge) + assert isinstance(mapping["rotary_emb"], RotaryEmbeddingBridge) + assert isinstance(mapping["blocks"], BlockBridge) + assert isinstance(mapping["ln_final"], RMSNormalizationBridge) + assert isinstance(mapping["unembed"], UnembeddingBridge) + + def test_top_level_hf_paths(self, adapter: GraniteMoeArchitectureAdapter) -> None: + mapping = adapter.component_mapping + assert mapping["embed"].name == "model.embed_tokens" + assert mapping["rotary_emb"].name == "model.rotary_emb" + assert mapping["blocks"].name == "model.layers" + assert mapping["ln_final"].name == "model.norm" + assert mapping["unembed"].name == "lm_head" + + def test_block_submodule_keys(self, adapter: GraniteMoeArchitectureAdapter) -> None: + blocks = adapter.component_mapping["blocks"] + assert set(blocks.submodules.keys()) == {"ln1", "ln2", "attn", "mlp"} + + def test_mlp_is_moe_bridge(self, adapter: GraniteMoeArchitectureAdapter) -> None: + mlp = adapter.component_mapping["blocks"].submodules["mlp"] + assert isinstance(mlp, MoEBridge) + + def test_moe_hf_path(self, adapter: GraniteMoeArchitectureAdapter) -> None: + mlp = adapter.component_mapping["blocks"].submodules["mlp"] + assert mlp.name == "block_sparse_moe" + + def test_attention_unchanged_from_dense(self, adapter: GraniteMoeArchitectureAdapter) -> None: + attn = adapter.component_mapping["blocks"].submodules["attn"] + assert isinstance(attn, PositionEmbeddingsAttentionBridge) + assert set(attn.submodules.keys()) == {"q", "k", "v", "o"} + assert attn.submodules["q"].name == "q_proj" + assert attn.submodules["k"].name == "k_proj" + assert attn.submodules["v"].name == "v_proj" + assert attn.submodules["o"].name == "o_proj" + for submodule in attn.submodules.values(): + assert isinstance(submodule, LinearBridge) + + def test_non_mlp_components_match_dense( + self, + dense_adapter: GraniteArchitectureAdapter, + adapter: GraniteMoeArchitectureAdapter, + ) -> None: + """Embed, rotary_emb, ln_final, and unembed are shared with dense Granite.""" + for key in ("embed", "rotary_emb", "ln_final", "unembed"): + assert type(adapter.component_mapping[key]) is type( + dense_adapter.component_mapping[key] + ) + assert adapter.component_mapping[key].name == dense_adapter.component_mapping[key].name + + +# --------------------------------------------------------------------------- +# Weight conversion key tests +# --------------------------------------------------------------------------- + + +class TestGraniteMoeAdapterWeightConversions: + """GraniteMoe doesn't override weight_processing_conversions, so it should + carry the same qkvo conversion set as dense Granite (there is no + MoE-specific weight remapping).""" + + def test_exact_conversion_key_set(self, adapter: GraniteMoeArchitectureAdapter) -> None: + assert set(adapter.weight_processing_conversions.keys()) == { + "blocks.{i}.attn.q.weight", + "blocks.{i}.attn.k.weight", + "blocks.{i}.attn.v.weight", + "blocks.{i}.attn.o.weight", + } diff --git a/tests/unit/model_bridge/supported_architectures/test_hook_orchestration_parity.py b/tests/unit/model_bridge/supported_architectures/test_hook_orchestration_parity.py new file mode 100644 index 0000000000..de3aa2c809 --- /dev/null +++ b/tests/unit/model_bridge/supported_architectures/test_hook_orchestration_parity.py @@ -0,0 +1,180 @@ +"""HookedRootModule-parity for BridgeCore hook orchestration. + +`reset_hooks` direction/permanence/level/clear-contexts selectivity, the +`mod_dict` accessor, and the `check_hooks_to_add` extension point -- the +surface migrated hook code relies on when moving off HookedTransformer. + +Uses the network-free pretrain mock bridge (real block tree, HT-style +hook aliases like `blocks.0.hook_mlp_out`) rather than a thin stub, so the +"backward hook on a non-io hook point" case -- which the old registry walk +missed -- is actually exercised. +""" +from __future__ import annotations + +import pytest +import torch + +from transformer_lens.model_bridge.supported_architectures.pretrain import ( + build_pretrain_bridge, +) + +from ._pretrain_mocks import TinyPretrainModel, make_cfg + + +def _build(): + cfg = make_cfg(n_layers=2) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=2, d_ff=32, vocab_size=64) + return build_pretrain_bridge(model, cfg) + + +def _noop(value, hook=None): + return value + + +class TestResetHooks: + def test_removes_forward_and_backward_from_every_point(self): + """Default direction='both' clears bwd hooks even on non-io points + (block-level hook_mlp_in etc.), which the old fwd-only walk left behind.""" + bridge = _build() + for hp in bridge.hook_points(): + hp.add_hook(_noop, dir="fwd") + hp.add_hook(_noop, dir="bwd") + + bridge.reset_hooks() + + leftover = [ + hp.name + for hp in bridge.hook_points() + if hp.has_hooks(dir="both", including_permanent=False) + ] + assert leftover == [] + + def test_direction_is_selective(self): + bridge = _build() + hp = bridge._hook_registry["blocks.0.hook_out"] + hp.add_hook(_noop, dir="fwd") + hp.add_hook(_noop, dir="bwd") + + bridge.reset_hooks(direction="fwd") + assert not hp.has_hooks(dir="fwd") + assert hp.has_hooks(dir="bwd") + + bridge.reset_hooks(direction="bwd") + assert not hp.has_hooks(dir="bwd") + + def test_permanent_hooks_survive_default_reset(self): + bridge = _build() + hp = bridge._hook_registry["blocks.0.hook_out"] + bridge.add_perma_hook("blocks.0.hook_out", _noop) + + bridge.reset_hooks() + + assert hp.has_hooks(dir="fwd", including_permanent=True) + + def test_including_permanent_removes_permanent_hooks(self): + bridge = _build() + hp = bridge._hook_registry["blocks.0.hook_out"] + bridge.add_perma_hook("blocks.0.hook_out", _noop) + + bridge.reset_hooks(including_permanent=True) + + assert not hp.has_hooks(dir="fwd", including_permanent=True) + + def test_level_is_selective(self): + bridge = _build() + hp = bridge._hook_registry["blocks.0.hook_out"] + hp.add_hook(_noop, dir="fwd", level=0) + hp.add_hook(_noop, dir="fwd", level=1) + + bridge.reset_hooks(level=0) + + assert not hp.has_hooks(dir="fwd", level=0) + assert hp.has_hooks(dir="fwd", level=1) + + def test_clear_contexts_flag(self): + bridge = _build() + hp = bridge._hook_registry["blocks.0.hook_out"] + + hp.ctx["scratch"] = 1 + bridge.reset_hooks(clear_contexts=False) + assert hp.ctx == {"scratch": 1} + + bridge.reset_hooks(clear_contexts=True) + assert hp.ctx == {} + + +class TestModDict: + def test_canonical_and_alias_names_resolve_to_same_hook_point(self): + bridge = _build() + mod_dict = bridge.mod_dict + assert mod_dict["blocks.0.hook_mlp_out"] is mod_dict["blocks.0.mlp.hook_out"] + + def test_includes_non_hook_modules(self): + """mod_dict is a superset of hook_dict -- it also exposes container + modules (blocks, block 0) that hook_dict omits.""" + bridge = _build() + mod_dict = bridge.mod_dict + assert "blocks" in mod_dict + assert "blocks.0" in mod_dict + for name, hook_point in bridge.hook_dict.items(): + assert mod_dict[name] is hook_point + + +class TestCheckHooksToAdd: + def test_invoked_before_add(self, monkeypatch): + bridge = _build() + seen: list[str] = [] + + def recording( + self, hook_point, hook_point_name, hook, dir="fwd", is_permanent=False, prepend=False + ): + seen.append(hook_point_name) + + monkeypatch.setattr(type(bridge), "check_hooks_to_add", recording) + bridge.add_hook("blocks.0.hook_out", _noop) + assert seen == ["blocks.0.hook_out"] + + def test_can_veto_hook_addition(self, monkeypatch): + """A raising override blocks the add -- the hook must not land.""" + bridge = _build() + hp = bridge._hook_registry["blocks.0.hook_out"] + + def rejecting( + self, hook_point, hook_point_name, hook, dir="fwd", is_permanent=False, prepend=False + ): + raise AssertionError(f"hook {hook_point_name} rejected") + + monkeypatch.setattr(type(bridge), "check_hooks_to_add", rejecting) + with pytest.raises(AssertionError, match="rejected"): + bridge.add_hook("blocks.0.hook_out", _noop) + assert not hp.has_hooks(dir="fwd") + + def test_invoked_by_run_with_hooks(self, monkeypatch): + """The extension point must fire for run_with_hooks too, not just + add_hook -- run_with_hooks is the common hooking path (matches HT).""" + bridge = _build() + seen: list[str] = [] + + def recording( + self, hook_point, hook_point_name, hook, dir="fwd", is_permanent=False, prepend=False + ): + seen.append(hook_point_name) + + monkeypatch.setattr(type(bridge), "check_hooks_to_add", recording) + tokens = torch.randint(0, 64, (1, 4)) + bridge.run_with_hooks(tokens, fwd_hooks=[("blocks.0.hook_out", _noop)]) + assert "blocks.0.hook_out" in seen + + def test_invoked_by_hooks_context_manager(self, monkeypatch): + bridge = _build() + seen: list[str] = [] + + def recording( + self, hook_point, hook_point_name, hook, dir="fwd", is_permanent=False, prepend=False + ): + seen.append(hook_point_name) + + monkeypatch.setattr(type(bridge), "check_hooks_to_add", recording) + with bridge.hooks(fwd_hooks=[("blocks.0.hook_out", _noop)]): + pass + assert "blocks.0.hook_out" in seen diff --git a/tests/unit/model_bridge/supported_architectures/test_pretrain_adapter.py b/tests/unit/model_bridge/supported_architectures/test_pretrain_adapter.py new file mode 100644 index 0000000000..d2fa03f24e --- /dev/null +++ b/tests/unit/model_bridge/supported_architectures/test_pretrain_adapter.py @@ -0,0 +1,453 @@ +"""Unit tests for PretrainArchitectureAdapter and DenseOrMoEFeedForwardBridge +-- component mapping, structural dispatch, and bridge-construction-level +behavior. Container, builder, and wrapped-model lifecycle behavior (kwarg +filtering, output-contract normalization, hook registration for the hidden +MLP delegate, train/eval, dtype/device) lives in +test_pretrain_model_container.py. + +Fully self-contained (see _pretrain_mocks.py): tiny mock `nn.Module`s, no +external package dependency, no network access. +""" + +from __future__ import annotations + +import pytest +import torch + +from transformer_lens.model_bridge.generalized_components import ( + DelegatedAttentionBlockBridge, +) +from transformer_lens.model_bridge.sources._bridge_builder import ( + build_bridge_from_module, +) +from transformer_lens.model_bridge.supported_architectures.pretrain import ( + ARCHITECTURE_NAME, + DenseOrMoEFeedForwardBridge, + NativeForwardAttentionBridge, + PretrainArchitectureAdapter, + PretrainModelContainer, + build_pretrain_bridge, +) + +from ._pretrain_mocks import ( + MalformedMLP, + TinyDenseMLP, + TinyMoE, + TinyPretrainModel, + make_cfg, +) + +_make_cfg = make_cfg # local alias, matches call sites below + + +class TestPretrainAdapterConstruction: + def test_adapter_sets_required_config_flags(self) -> None: + adapter = PretrainArchitectureAdapter(_make_cfg()) + assert adapter.cfg.normalization_type == "RMS" + assert adapter.cfg.positional_embedding_type == "rotary" + assert adapter.cfg.final_rms is True + assert adapter.cfg.gated_mlp is True + assert adapter.cfg.attn_only is False + + def test_adapter_mutates_the_passed_in_cfg_object_in_place(self) -> None: + """Documented, intentional behavior (matches nanogpt.py's adapter + convention) -- not an accidental side effect. This test guards the + specific claim: the object passed in is the object mutated, not a + copy.""" + cfg = _make_cfg() + adapter = PretrainArchitectureAdapter(cfg) + assert adapter.cfg is cfg + assert cfg.normalization_type == "RMS" + + def test_component_mapping_has_expected_top_level_keys(self) -> None: + adapter = PretrainArchitectureAdapter(_make_cfg()) + mapping = adapter.get_component_mapping() + assert set(mapping.keys()) == {"embed", "blocks", "ln_final", "unembed"} + + def test_mlp_mapping_always_uses_the_dispatcher(self) -> None: + """Unconditional now -- no cfg.num_experts branch to depend on.""" + adapter = PretrainArchitectureAdapter(_make_cfg()) + mlp_component = adapter.component_mapping["blocks"].submodules["mlp"] + assert isinstance(mlp_component, DenseOrMoEFeedForwardBridge) + + +class TestNativeForwardAttentionBridge: + def test_opaque_attention_bridge_does_not_advertise_per_head_aliases(self) -> None: + """The opaque attention wrap has no Q/K/V/O submodules, so it must + not advertise AttentionBridge's class-level hook_aliases/ + property_aliases (which assume those submodules exist) or the + split-QKV-fork machinery -- see NativeForwardAttentionBridge's + docstring.""" + adapter = PretrainArchitectureAdapter(_make_cfg()) + attn = adapter.component_mapping["blocks"].submodules["attn"] + + assert isinstance(attn, NativeForwardAttentionBridge) + assert attn.hook_aliases == {} + assert attn.property_aliases == {} + assert attn.supports_split_qkv_fork is False + + # Also check the actual constructed runtime bridge, not just the + # adapter's own component_mapping -- proves bridge construction + # didn't reintroduce the aliases along the way. + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel(d_model=32, n_heads=4, n_layers=1, d_ff=64, vocab_size=256) + bridge = build_pretrain_bridge(model, cfg) + assert bridge.blocks[0].attn.hook_aliases == {} + + def test_block_level_attention_output_still_reaches_hook_out(self) -> None: + """Clearing the per-head aliases must not also silence the block's + own attn.hook_out -- that's the actual hook point this adapter's + block-level hook coverage relies on.""" + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel(d_model=32, n_heads=4, n_layers=1, d_ff=64, vocab_size=256) + bridge = build_pretrain_bridge(model, cfg) + tokens = torch.randint(0, 256, (1, 5)) + _, cache = bridge.run_with_cache(tokens) + assert "blocks.0.attn.hook_out" in cache + + def test_blocks_use_delegated_attention_block_bridge(self) -> None: + """The adapter reuses DelegatedAttentionBlockBridge rather than + plain BlockBridge, since NativeForwardAttentionBridge's + supports_split_qkv_fork = False means the block-level + hook_attn_in/hook_q_input/hook_k_input/hook_v_input aliases would + otherwise dangle (point at HookPoints that are never created).""" + adapter = PretrainArchitectureAdapter(_make_cfg()) + assert isinstance(adapter.component_mapping["blocks"], DelegatedAttentionBlockBridge) + + def test_split_qkv_hook_names_are_absent_but_attention_output_remains_available( + self, + ) -> None: + """hook_attn_in/hook_q_input/hook_k_input/hook_v_input must not + appear as resolvable hook names anywhere in the built bridge -- + DelegatedAttentionBlockBridge strips the block-level aliases, and + NativeForwardAttentionBridge never creates the underlying + attention-level HookPoints in the first place. hook_attn_out is + untouched by either change and must still resolve, both as the + block-level alias and as the attention component's own hook_out.""" + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel(d_model=32, n_heads=4, n_layers=1, d_ff=64, vocab_size=256) + bridge = build_pretrain_bridge(model, cfg) + hook_dict = bridge.hook_dict + for dangling in ( + "blocks.0.hook_attn_in", + "blocks.0.hook_q_input", + "blocks.0.hook_k_input", + "blocks.0.hook_v_input", + "blocks.0.attn.hook_attn_in", + "blocks.0.attn.hook_q_input", + "blocks.0.attn.hook_k_input", + "blocks.0.attn.hook_v_input", + ): + assert dangling not in hook_dict, f"{dangling} should not resolve" + assert "blocks.0.hook_attn_out" in hook_dict + assert "blocks.0.attn.hook_out" in hook_dict + + +class TestDenseOrMoEFeedForwardBridgeDispatch: + def test_dispatches_to_moe_delegate_for_moe_module(self) -> None: + cfg = _make_cfg() + bridge = DenseOrMoEFeedForwardBridge(name="mlp", config=cfg) + moe_module = TinyMoE(d_model=32, d_ff=64, n_experts=4, top_k=2) + bridge.set_original_component(moe_module) + + from transformer_lens.model_bridge.generalized_components import MoEBridge + + assert isinstance(bridge._delegate, MoEBridge) + + def test_dispatches_to_dense_delegate_for_dense_module(self) -> None: + cfg = _make_cfg() + bridge = DenseOrMoEFeedForwardBridge(name="mlp", config=cfg) + dense_module = TinyDenseMLP(d_model=32, d_ff=64) + bridge.set_original_component(dense_module) + + from transformer_lens.model_bridge.generalized_components import GatedMLPBridge + + assert isinstance(bridge._delegate, GatedMLPBridge) + + def test_raises_clear_error_for_malformed_module(self) -> None: + cfg = _make_cfg() + bridge = DenseOrMoEFeedForwardBridge(name="mlp", config=cfg) + malformed = MalformedMLP(d_model=32) + + with pytest.raises(ValueError, match="doesn't know how to wrap it"): + bridge.set_original_component(malformed) + + +class TestDenseOrMoEFeedForwardBridgeDispatchValidatesTypes: + """hasattr alone would let a module win a branch by attribute-name + coincidence even if the attributes are the wrong type. These tests + exercise the basic type checks added to set_original_component so + such a mismatch is caught here, with a clear message naming the + actual field and type, rather than failing later inside MoEBridge/ + GatedMLPBridge or during forward.""" + + def test_router_of_wrong_type_raises_type_error(self) -> None: + class BadRouterMoE(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.router = "not_a_module" # right name, wrong type + self.experts = torch.nn.ModuleList( + [TinyDenseMLP(d_model=32, d_ff=64) for _ in range(4)] + ) + + cfg = _make_cfg() + bridge = DenseOrMoEFeedForwardBridge(name="mlp", config=cfg) + with pytest.raises(TypeError, match="router"): + bridge.set_original_component(BadRouterMoE()) + + def test_experts_of_wrong_type_raises_type_error(self) -> None: + class BadExpertsMoE(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.router = torch.nn.Linear(32, 4) + self.experts = "not_a_module_collection" # right name, wrong type + + cfg = _make_cfg() + bridge = DenseOrMoEFeedForwardBridge(name="mlp", config=cfg) + with pytest.raises(TypeError, match="experts"): + bridge.set_original_component(BadExpertsMoE()) + + def test_plain_list_of_experts_is_rejected(self) -> None: + """Modules held in an ordinary Python list are not registered as + children -- their parameters would silently drop out of + parameters()/state_dict()/.to(...)/train()/eval(), contradicting + this adapter's lifecycle guarantees. Rejected even though every + element is itself a valid nn.Module.""" + + class ListExpertsMoE(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.router = torch.nn.Linear(32, 4) + self.experts = [TinyDenseMLP(d_model=32, d_ff=64) for _ in range(4)] + + cfg = _make_cfg() + bridge = DenseOrMoEFeedForwardBridge(name="mlp", config=cfg) + with pytest.raises(TypeError, match="registered module collection"): + bridge.set_original_component(ListExpertsMoE()) + + def test_gate_of_wrong_type_raises_type_error(self) -> None: + class BadGateDense(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.gate = "not_a_module" # right name, wrong type + self.up = torch.nn.Linear(32, 64, bias=False) + self.down = torch.nn.Linear(64, 32, bias=False) + + cfg = _make_cfg() + bridge = DenseOrMoEFeedForwardBridge(name="mlp", config=cfg) + with pytest.raises(TypeError, match="gate"): + bridge.set_original_component(BadGateDense()) + + +class TestDenseOrMoEFeedForwardBridgeTupleOutput: + """Exercises the dispatcher's own tuple-output handling directly, by + patching an already-wired delegate's forward -- independent of + whether GatedMLPBridge/MoEBridge happen to return a tuple today. The + dispatcher explicitly supports and validates this shape, so it's + tested as its own contract rather than assumed from what the real + delegates currently do.""" + + def _dispatcher_with_patched_delegate(self, fake_forward): + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel( + d_model=16, + n_heads=2, + n_layers=1, + d_ff=32, + vocab_size=64, + n_experts=4, + top_k=2, + moe_layer_indices=frozenset({0}), + ) + bridge = build_pretrain_bridge(model, cfg) + dispatcher = bridge.blocks[0].mlp + dispatcher._delegate.forward = fake_forward + return dispatcher + + def test_tensor_first_tuple_passes_through_with_hook_applied(self) -> None: + dispatcher = self._dispatcher_with_patched_delegate( + lambda *a, **kw: (torch.zeros(1, 3, 16), "aux") + ) + output = dispatcher(torch.ones(1, 3, 16)) + assert isinstance(output, tuple) + assert output[1] == "aux" + torch.testing.assert_close(output[0], torch.zeros(1, 3, 16)) + + def test_empty_tuple_raises_clear_type_error(self) -> None: + dispatcher = self._dispatcher_with_patched_delegate(lambda *a, **kw: ()) + with pytest.raises(TypeError, match="torch.Tensor"): + dispatcher(torch.ones(1, 3, 16)) + + def test_non_tensor_first_element_raises_clear_type_error(self) -> None: + dispatcher = self._dispatcher_with_patched_delegate( + lambda *a, **kw: ("not_a_tensor", torch.zeros(1)) + ) + with pytest.raises(TypeError, match="torch.Tensor"): + dispatcher(torch.ones(1, 3, 16)) + + +class TestPretrainAdapterBridgeConstruction: + @pytest.fixture + def dense_bridge(self): + cfg = _make_cfg(n_layers=2) + model = TinyPretrainModel(d_model=32, n_heads=4, n_layers=2, d_ff=64, vocab_size=256) + return build_pretrain_bridge(model, cfg) + + @pytest.fixture + def moe_bridge(self): + cfg = _make_cfg(n_layers=2) + model = TinyPretrainModel( + d_model=32, + n_heads=4, + n_layers=2, + d_ff=64, + vocab_size=256, + n_experts=4, + top_k=2, + moe_layer_indices=frozenset({1}), + ) + return build_pretrain_bridge(model, cfg) + + def test_bridge_has_correct_block_count(self, dense_bridge) -> None: + assert len(dense_bridge.blocks) == 2 + + def test_bridge_has_embed_unembed_and_final_norm(self, dense_bridge) -> None: + assert hasattr(dense_bridge, "embed") + assert hasattr(dense_bridge, "unembed") + assert hasattr(dense_bridge, "ln_final") + + def test_forward_returns_logits_of_expected_shape(self, dense_bridge) -> None: + tokens = torch.randint(0, 256, (1, 5)) + with torch.no_grad(): + output = dense_bridge(tokens) + assert output.shape == (1, 5, 256) + + def test_run_with_cache_exposes_resid_hooks(self, dense_bridge) -> None: + tokens = torch.randint(0, 256, (1, 5)) + _, cache = dense_bridge.run_with_cache(tokens) + assert any("resid_pre" in k for k in cache.keys()) + assert any("resid_post" in k for k in cache.keys()) + + def test_no_per_head_attention_hooks(self, dense_bridge) -> None: + """This adapter deliberately does not expose hook_q/hook_k/hook_v -- + see PretrainArchitectureAdapter's class docstring.""" + tokens = torch.randint(0, 256, (1, 5)) + _, cache = dense_bridge.run_with_cache(tokens) + assert not any("hook_q" in k for k in cache.keys()) + assert not any("hook_pattern" in k for k in cache.keys()) + + def test_moe_bridge_forward_runs(self, moe_bridge) -> None: + tokens = torch.randint(0, 256, (1, 5)) + with torch.no_grad(): + output = moe_bridge(tokens) + assert output.shape == (1, 5, 256) + assert not torch.isnan(output).any() + + def test_moe_block_uses_dispatcher(self, moe_bridge) -> None: + assert isinstance(moe_bridge.blocks[1].mlp, DenseOrMoEFeedForwardBridge) + + def test_moe_layer_hooks_are_registered_at_the_dispatcher_path(self, moe_bridge) -> None: + """Verifies actual cache keys, not just isinstance -- the claim in + DenseOrMoEFeedForwardBridge's docstring that hooks fire at + blocks.{i}.mlp.hook_in/hook_out regardless of dense-vs-MoE.""" + tokens = torch.randint(0, 256, (1, 5)) + _, cache = moe_bridge.run_with_cache(tokens) + assert "blocks.1.mlp.hook_in" in cache + assert "blocks.1.mlp.hook_out" in cache + # Layer 0 is dense in this fixture -- same path convention applies. + assert "blocks.0.mlp.hook_in" in cache + assert "blocks.0.mlp.hook_out" in cache + + +class TestMoEConfigFieldIndependence: + def test_bridge_behaves_identically_with_moe_config_fields_populated(self) -> None: + """MoEBridge itself (transformer_lens/model_bridge/generalized_ + components/moe.py) never reads cfg.num_experts or + cfg.experts_per_token. Populating them with realistic values must + not change behavior. + Uses an actual MoE layer (moe_layer_indices={0}) -- both models + being dense would let this pass without ever constructing + MoEBridge, so the dispatch type is asserted directly too.""" + from transformer_lens.model_bridge.generalized_components import MoEBridge + + torch.manual_seed(0) + model_a = TinyPretrainModel( + d_model=16, + n_heads=2, + n_layers=1, + d_ff=32, + vocab_size=64, + n_experts=4, + top_k=2, + moe_layer_indices=frozenset({0}), + ) + cfg_without = _make_cfg(n_layers=1) + bridge_without = build_pretrain_bridge(model_a, cfg_without) + + torch.manual_seed(0) + model_b = TinyPretrainModel( + d_model=16, + n_heads=2, + n_layers=1, + d_ff=32, + vocab_size=64, + n_experts=4, + top_k=2, + moe_layer_indices=frozenset({0}), + ) + cfg_with = _make_cfg(n_layers=1) + cfg_with.num_experts = 4 + cfg_with.experts_per_token = 2 + bridge_with = build_pretrain_bridge(model_b, cfg_with) + + assert isinstance(bridge_without.blocks[0].mlp._delegate, MoEBridge) + assert isinstance(bridge_with.blocks[0].mlp._delegate, MoEBridge) + + tokens = torch.randint(0, 64, (1, 3)) + with torch.no_grad(): + out_without = bridge_without(tokens) + out_with = bridge_with(tokens) + torch.testing.assert_close(out_without, out_with, atol=0, rtol=0) + + +class TestPretrainAdapterTiedEmbeddings: + def test_tied_embedding_bridge_construction_succeeds(self) -> None: + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel( + d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64, tie_embeddings=True + ) + bridge = build_pretrain_bridge(model, cfg) + tokens = torch.randint(0, 64, (1, 3)) + with torch.no_grad(): + output = bridge(tokens) + assert output.shape == (1, 3, 64) + + def test_untied_embedding_bridge_construction_succeeds(self) -> None: + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel( + d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64, tie_embeddings=False + ) + bridge = build_pretrain_bridge(model, cfg) + tokens = torch.randint(0, 64, (1, 3)) + with torch.no_grad(): + output = bridge(tokens) + assert output.shape == (1, 3, 64) + + +class TestBuildBridgeFromModuleDirectlyStillWorks: + """Advanced/internal path -- build_pretrain_bridge is the intended + public entry point, but direct build_bridge_from_module use (with the + container applied manually) must keep working too.""" + + def test_direct_build_bridge_from_module_with_manual_container(self) -> None: + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64) + bridge = build_bridge_from_module( + PretrainModelContainer(model), + architecture=ARCHITECTURE_NAME, + tl_config=cfg, + ) + tokens = torch.randint(0, 64, (1, 3)) + with torch.no_grad(): + output = bridge(tokens) + assert output.shape == (1, 3, 64) diff --git a/tests/unit/model_bridge/supported_architectures/test_pretrain_model_container.py b/tests/unit/model_bridge/supported_architectures/test_pretrain_model_container.py new file mode 100644 index 0000000000..43c40901ea --- /dev/null +++ b/tests/unit/model_bridge/supported_architectures/test_pretrain_model_container.py @@ -0,0 +1,537 @@ +"""Container, builder, and wrapped-model lifecycle behavior for +PretrainModelContainer and build_pretrain_bridge -- kwarg filtering, +output-contract normalization, train/eval mode propagation, and +dtype/tied-weight preservation. Hook registration tests for the hidden +MLP delegate +(implemented by DenseOrMoEFeedForwardBridge) live here too, since what +they verify -- real parameters staying reachable through +`original_model.inner` -- is a lifecycle concern, not a mapping one. +Adapter-mapping-level behavior lives in test_pretrain_adapter.py. + +Fully self-contained (see _pretrain_mocks.py): tiny mock `nn.Module`s, no +external package dependency, no network access. +""" + +from __future__ import annotations + +import pytest +import torch +import torch.nn as nn + +from transformer_lens.model_bridge.supported_architectures.pretrain import ( + PretrainModelContainer, + _LogitsAttrDict, + build_pretrain_bridge, +) + +from ._pretrain_mocks import ( + ForwardStrict, + ForwardVarKwargs, + TinyPretrainModel, + make_cfg, +) + +_make_cfg = make_cfg # local alias, matches call sites below + + +class TestPretrainModelContainerKwargFiltering: + def test_output_attentions_is_stripped_for_strict_signature(self) -> None: + """The one TransformerBridge-injected kwarg (run_with_cache + forces output_attentions=True) must not reach a source forward with + no **kwargs catch-all.""" + model = ForwardStrict() + container = PretrainModelContainer(model) + container(torch.tensor([[1, 2, 3]]), targets=None, output_attentions=True) + assert model.seen_kwargs == {"targets": None} + + def test_output_attentions_is_stripped_even_for_var_kwargs_signature(self) -> None: + """Stripped unconditionally, regardless of whether the source would + have accepted it anyway -- keeps behavior uniform across source + forward signatures.""" + model = ForwardVarKwargs() + container = PretrainModelContainer(model) + container(torch.tensor([[1, 2, 3]]), output_attentions=True, some_other_kwarg=42) + assert model.seen_kwargs == {"some_other_kwarg": 42} + + def test_unrelated_kwarg_typo_is_not_silently_swallowed(self) -> None: + """A genuine caller mistake (e.g. `target=` instead of `targets=`) + must raise, not be silently discarded alongside the one kwarg this + container is actually responsible for stripping.""" + model = ForwardStrict() + container = PretrainModelContainer(model) + with pytest.raises(TypeError): + container(torch.tensor([[1, 2, 3]]), target=torch.tensor([1])) + + +class TestPretrainModelContainerHookRegistration: + """The delegate is hidden from nn.Module registration (see + DenseOrMoEFeedForwardBridge), so blocks.{i}.mlp.hook_in/out are the + only publicly registered MLP hook points. Forward, gradients, dtype + conversion, and state_dict() still reach the hidden delegate's real + weights through the raw wrapped model.""" + + def _build(self): + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64) + return build_pretrain_bridge(model, cfg) + + def test_only_the_dispatcher_hook_out_is_registered(self) -> None: + bridge = self._build() + tokens = torch.randint(0, 64, (1, 4)) + with torch.no_grad(): + _, cache = bridge.run_with_cache(tokens) + assert "blocks.0.mlp.hook_out" in cache + assert "blocks.0.mlp._delegate.hook_out" not in cache + + def test_broad_hook_selector_applies_intervention_exactly_once(self) -> None: + bridge = self._build() + tokens = torch.randint(0, 64, (1, 4)) + calls: list[str] = [] + + def recording_add_one(value: torch.Tensor, hook) -> torch.Tensor: + calls.append(hook.name) + return value + 1.0 + + def broad_filter(name: str) -> bool: + return "mlp" in name and "hook_out" in name + + with torch.no_grad(): + bridge.run_with_hooks(tokens, fwd_hooks=[(broad_filter, recording_add_one)]) + + assert calls == ["blocks.0.mlp.hook_out"] + + def test_gradients_still_reach_the_hidden_delegates_weights(self) -> None: + bridge = self._build() + tokens = torch.randint(0, 64, (1, 4)) + bridge(tokens).sum().backward() + mlp = bridge.original_model.inner.blocks[0].mlp + assert mlp.gate.weight.grad is not None + assert mlp.gate.weight.grad.abs().sum().item() > 0 + + def test_bridge_to_dtype_reaches_the_hidden_delegates_weights(self) -> None: + bridge = self._build() + bridge.to(dtype=torch.float64) + + assert bridge.original_model.inner.embed.weight.dtype == torch.float64 + assert bridge.original_model.inner.blocks[0].mlp.gate.weight.dtype == torch.float64 + + tokens = torch.randint(0, 64, (1, 4)) + with torch.no_grad(): + output = bridge(tokens) + assert output.dtype == torch.float64 + + def test_state_dict_includes_the_hidden_delegates_weights(self) -> None: + bridge = self._build() + source_weight = bridge.original_model.inner.blocks[0].mlp.gate.weight + + state = bridge.state_dict() + matching = [v for k, v in state.items() if k.endswith("blocks.0.mlp.gate.weight")] + + assert len(matching) == 1 + torch.testing.assert_close(matching[0], source_weight) + + def test_repeated_hook_lifecycle_still_works(self) -> None: + """Guards against a future assumption that every internally-used + GeneralizedComponent must be registered -- the dispatcher's own + hooks are, and that's what reset_hooks()/run_with_cache rely on.""" + bridge = self._build() + tokens = torch.randint(0, 64, (1, 4)) + with torch.no_grad(): + bridge.run_with_cache(tokens) + bridge.reset_hooks() + _, cache = bridge.run_with_cache(tokens) + assert "blocks.0.mlp.hook_out" in cache + + +class TestTrainEvalModePropagation: + """bridge.train()/.eval() propagate to the wrapped model: + build_pretrain_bridge reassigns the returned bridge's class to a small + generated TransformerBridge subclass (see + _get_mode_propagating_bridge_class) whose overridden train() also sets + mode on original_model; inherited eval() calls train(False), so it + reaches the override too without needing its own override. This + closes a gap in TransformerBridge's own train()/eval(), which only + walk component_mapping and never reach original_model on their own.""" + + def test_bridge_eval_propagates_to_wrapped_model(self) -> None: + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64) + bridge = build_pretrain_bridge(model, cfg) + + assert model.training is True + bridge.eval() + assert model.training is False + + def test_bridge_train_propagates_to_wrapped_model(self) -> None: + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64) + bridge = build_pretrain_bridge(model, cfg) + + model.eval() + assert model.training is False + bridge.train() + assert model.training is True + + def test_caller_can_still_set_mode_directly_on_their_own_model_reference(self) -> None: + """Setting mode via the raw model reference still works and stays + in sync -- both paths write to the same underlying module.""" + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64) + build_pretrain_bridge(model, cfg) + + model.eval() + assert model.training is False + model.train() + assert model.training is True + + def test_train_and_eval_return_the_bridge_itself(self) -> None: + """nn.Module convention: train()/eval() return self, so callers can + chain (`model.train().to(device)`, etc). The generated subclass + must preserve this, not just the mode-propagation side effect.""" + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64) + bridge = build_pretrain_bridge(model, cfg) + + assert bridge.train(False) is bridge + assert bridge.train(True) is bridge + assert bridge.eval() is bridge + + def test_bridge_can_be_reconstructed_from_supported_state(self) -> None: + """The adapter's actual persistence contract is: rebuild the + bridge from the source model class + cfg + the source model's + *raw* state_dict, then confirm behavior (outputs, train/eval + propagation) survives the round trip. + + The state_dict must be snapshotted BEFORE `build_pretrain_bridge` + is called. Building the bridge wraps the model's submodules in + place (each one gains an `_original_component` wrapper for + hooking), so `model.state_dict()` taken *after* wrapping has + keys like `embed._original_component.weight` instead of + `embed.weight` -- a fresh, unwrapped `TinyPretrainModel` can't + load that. Capturing the state_dict first, then wrapping a + second fresh model and loading the pre-wrap snapshot into it, + avoids that trap. + + Only the source model's state_dict is used -- not + `bridge.state_dict()` -- since the bridge doesn't own an + independent set of learned weights; it exposes the wrapped + source model's parameters under bridge-shaped names. + + This is deliberately weaker than whole-object `pickle.dumps`, + which would also require every nested `TransformerBridge` + implementation detail (e.g. `AttentionBridge`'s hook + conversions) to be picklable -- a guarantee unrelated to this + adapter and not part of what it needs to promise. If a stronger + whole-object-pickle guarantee is later required, it belongs in a + `TransformerBridge`-level test, not here. + """ + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64) + + # Snapshot BEFORE wrapping -- see docstring. + raw_model_state = {key: value.detach().clone() for key, value in model.state_dict().items()} + + bridge = build_pretrain_bridge(model, cfg) + bridge.eval() + + restored_model = TinyPretrainModel( + d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64 + ) + restored_model.load_state_dict(raw_model_state) + + restored_bridge = build_pretrain_bridge(restored_model, cfg) + restored_bridge.eval() + + tokens = torch.randint(0, 64, (1, 4)) + with torch.no_grad(): + expected = bridge(tokens) + actual = restored_bridge(tokens) + + expected_logits = expected.logits if hasattr(expected, "logits") else expected + actual_logits = actual.logits if hasattr(actual, "logits") else actual + torch.testing.assert_close(actual_logits, expected_logits, atol=0, rtol=0) + + assert restored_bridge.training is False + assert restored_model.training is False + + restored_bridge.train() + assert restored_model.training is True + + def test_repeated_construction_reuses_the_same_generated_class(self) -> None: + """Guards the cache's stated purpose: build_pretrain_bridge should + not generate a new mode-propagating subclass on every call -- two + independently-built bridges from the same base bridge class must + share one generated class.""" + cfg_a = _make_cfg(n_layers=1) + model_a = TinyPretrainModel(d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64) + bridge_a = build_pretrain_bridge(model_a, cfg_a) + + cfg_b = _make_cfg(n_layers=1) + model_b = TinyPretrainModel(d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64) + bridge_b = build_pretrain_bridge(model_b, cfg_b) + + assert type(bridge_a) is type(bridge_b) + + +class TestDtypeAndTiedWeightPreservation: + def test_non_default_dtype_and_tied_weights_survive_construction(self) -> None: + torch.manual_seed(0) + model = TinyPretrainModel( + d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64, tie_embeddings=True + ).to(torch.float64) + assert model.lm_head.weight is model.embed.weight + + cfg = _make_cfg(n_layers=1) + bridge = build_pretrain_bridge(model, cfg) + + assert next(model.parameters()).dtype == torch.float64 + assert model.lm_head.weight is model.embed.weight + + tokens = torch.randint(0, 64, (1, 3)) + with torch.no_grad(): + output = bridge(tokens) + assert output.dtype == torch.float64 + + +class TestBuildPretrainBridgeErgonomics: + def test_explicit_model_name_is_forwarded(self) -> None: + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64) + bridge = build_pretrain_bridge(model, cfg, model_name="my-custom-name") + assert bridge.cfg.model_name == "my-custom-name" + + def test_omitting_optional_kwargs_still_works(self) -> None: + """The default (no device/dtype/model_name passed) path, exercised + everywhere else in this file, must keep working unchanged.""" + cfg = _make_cfg(n_layers=1) + model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64) + bridge = build_pretrain_bridge(model, cfg) + tokens = torch.randint(0, 64, (1, 3)) + with torch.no_grad(): + output = bridge(tokens) + assert output.shape == (1, 3, 64) + + +class TestPretrainModelContainerOutputContract: + def test_container_normalizes_dict_output_to_logits_attr_dict(self) -> None: + model = TinyPretrainModel( + d_model=16, + n_heads=2, + n_layers=1, + d_ff=32, + vocab_size=64, + ) + container = PretrainModelContainer(model) + tokens = torch.randint(0, 64, (1, 3)) + + with torch.no_grad(): + output = container(tokens) + + assert isinstance(output, _LogitsAttrDict) + assert output.logits is output["logits"] + assert output.logits.shape == (1, 3, 64) + + def test_container_returns_a_fresh_wrapper_on_each_call(self) -> None: + model = TinyPretrainModel( + d_model=16, + n_heads=2, + n_layers=1, + d_ff=32, + vocab_size=64, + ) + container = PretrainModelContainer(model) + tokens = torch.randint(0, 64, (1, 3)) + + with torch.no_grad(): + out1 = container(tokens) + out2 = container(tokens) + + assert isinstance(out1, _LogitsAttrDict) + assert isinstance(out2, _LogitsAttrDict) + assert out1 is not out2 + assert out1["logits"] is not out2["logits"] + torch.testing.assert_close(out1["logits"], out2["logits"]) + + def test_container_does_not_reuse_or_mutate_output_mapping(self) -> None: + model = TinyPretrainModel( + d_model=16, + n_heads=2, + n_layers=1, + d_ff=32, + vocab_size=64, + ).eval() + container = PretrainModelContainer(model) + tokens = torch.randint(0, 64, (1, 3)) + + with torch.no_grad(): + out1 = container(tokens) + + out1["sentinel"] = True + + with torch.no_grad(): + out2 = container(tokens) + + assert out1 is not out2 + assert "sentinel" not in out2 + torch.testing.assert_close(out1["logits"], out2["logits"]) + + def test_dict_missing_logits_key_raises_value_error(self) -> None: + class ForwardWrongKey(nn.Module): + def forward(self, tokens: torch.Tensor) -> dict: + return {"scores": tokens.float()} + + container = PretrainModelContainer(ForwardWrongKey()) + with pytest.raises(ValueError, match="'logits'"): + container(torch.tensor([[1, 2, 3]])) + + def test_dict_with_heterogeneous_keys_still_raises_value_error(self) -> None: + """sorted(output.keys()) would raise TypeError on non-comparable + mixed key types (e.g. str and int); the error message must use + list() instead, so an invalid model output produces the intended + ValueError, not an unrelated sorting error.""" + + class ForwardHeterogeneousKeys(nn.Module): + def forward(self, tokens: torch.Tensor) -> dict: + return {"scores": tokens.float(), 1: "metadata"} + + container = PretrainModelContainer(ForwardHeterogeneousKeys()) + with pytest.raises(ValueError, match="'logits'"): + container(torch.tensor([[1, 2, 3]])) + + def test_dict_with_non_tensor_logits_raises_type_error(self) -> None: + class ForwardBadLogitsType(nn.Module): + def forward(self, tokens: torch.Tensor) -> dict: + return {"logits": "not a tensor"} + + container = PretrainModelContainer(ForwardBadLogitsType()) + with pytest.raises(TypeError, match="torch.Tensor"): + container(torch.tensor([[1, 2, 3]])) + + def test_already_logits_attr_dict_passes_through_by_identity(self) -> None: + """An already-wrapped output must not be re-wrapped.""" + already_wrapped = _LogitsAttrDict({"logits": torch.zeros(1)}) + + class ForwardAlreadyWrapped(nn.Module): + def forward(self, tokens: torch.Tensor) -> _LogitsAttrDict: + return already_wrapped + + container = PretrainModelContainer(ForwardAlreadyWrapped()) + output = container(torch.tensor([[1, 2, 3]])) + assert output is already_wrapped + + def test_malformed_logits_attr_dict_raises_value_error_not_keyerror(self) -> None: + """Not a realistic target-model failure (_LogitsAttrDict is + private), but closes the contract: a malformed instance gets the + same clear ValueError an ordinary dict missing 'logits' gets, + rather than a raw KeyError leaking out.""" + malformed = _LogitsAttrDict({"scores": torch.zeros(1)}) + + class ForwardMalformedWrapped(nn.Module): + def forward(self, tokens: torch.Tensor) -> _LogitsAttrDict: + return malformed + + container = PretrainModelContainer(ForwardMalformedWrapped()) + with pytest.raises(ValueError, match="'logits'"): + container(torch.tensor([[1, 2, 3]])) + + def test_hf_style_object_with_logits_attribute_passes_through_unchanged(self) -> None: + """A source model that already satisfies hasattr(output, 'logits') + on its own (an HF-ModelOutput-like object, not a dict) needs no + normalization at all.""" + + class FakeModelOutput: + def __init__(self, logits: torch.Tensor): + self.logits = logits + + already_hf_style = FakeModelOutput(torch.zeros(1)) + + class ForwardHFStyle(nn.Module): + def forward(self, tokens: torch.Tensor) -> FakeModelOutput: + return already_hf_style + + container = PretrainModelContainer(ForwardHFStyle()) + output = container(torch.tensor([[1, 2, 3]])) + assert output is already_hf_style + + def test_property_backed_logits_is_only_evaluated_once(self) -> None: + """.logits must be read once and cached locally, not re-evaluated + on every access -- matters for a property-backed .logits, which + could otherwise do redundant (or side-effecting) work per access.""" + access_count = 0 + + class PropertyBackedOutput: + @property + def logits(self) -> torch.Tensor: + nonlocal access_count + access_count += 1 + return torch.zeros(1) + + class ForwardPropertyBacked(nn.Module): + def forward(self, tokens: torch.Tensor) -> PropertyBackedOutput: + return PropertyBackedOutput() + + container = PretrainModelContainer(ForwardPropertyBacked()) + container(torch.tensor([[1, 2, 3]])) + assert access_count == 1 + + def test_object_with_non_tensor_logits_attribute_raises_type_error(self) -> None: + class BadModelOutput: + logits = "not a tensor" + + class ForwardBadModelOutput(nn.Module): + def forward(self, tokens: torch.Tensor) -> BadModelOutput: + return BadModelOutput() + + container = PretrainModelContainer(ForwardBadModelOutput()) + with pytest.raises(TypeError, match=r"\.logits"): + container(torch.tensor([[1, 2, 3]])) + + def test_bare_tensor_output_passes_through_unchanged(self) -> None: + class ForwardBareTensor(nn.Module): + def forward(self, tokens: torch.Tensor) -> torch.Tensor: + return tokens.float() + + container = PretrainModelContainer(ForwardBareTensor()) + tokens = torch.tensor([[1, 2, 3]]) + output = container(tokens) + assert torch.equal(output, tokens.float()) + + def test_tuple_with_tensor_first_element_passes_through_unchanged(self) -> None: + """TransformerBridge extracts output[0] as logits for tuple + returns -- no normalization needed.""" + + class ForwardTuple(nn.Module): + def forward(self, tokens: torch.Tensor) -> tuple: + return (tokens.float(), "some_aux_value") + + container = PretrainModelContainer(ForwardTuple()) + output = container(torch.tensor([[1, 2, 3]])) + assert isinstance(output, tuple) + assert output[1] == "some_aux_value" + + def test_tuple_with_non_tensor_first_element_raises_type_error(self) -> None: + class ForwardBadTuple(nn.Module): + def forward(self, tokens: torch.Tensor) -> tuple: + return ("not a tensor", tokens) + + container = PretrainModelContainer(ForwardBadTuple()) + with pytest.raises(TypeError, match="torch.Tensor"): + container(torch.tensor([[1, 2, 3]])) + + def test_list_output_raises_type_error(self) -> None: + class ForwardList(nn.Module): + def forward(self, tokens: torch.Tensor) -> list: + return [tokens.float()] + + container = PretrainModelContainer(ForwardList()) + with pytest.raises(TypeError, match="must return"): + container(torch.tensor([[1, 2, 3]])) + + def test_string_output_raises_type_error(self) -> None: + class ForwardString(nn.Module): + def forward(self, tokens: torch.Tensor) -> str: + return "wrong" + + container = PretrainModelContainer(ForwardString()) + with pytest.raises(TypeError, match="must return"): + container(torch.tensor([[1, 2, 3]])) diff --git a/tests/unit/model_bridge/supported_architectures/test_raven_adapter.py b/tests/unit/model_bridge/supported_architectures/test_raven_adapter.py new file mode 100644 index 0000000000..3005b4dbbd --- /dev/null +++ b/tests/unit/model_bridge/supported_architectures/test_raven_adapter.py @@ -0,0 +1,335 @@ +"""Unit tests for RavenArchitectureAdapter (Huginn depth-recurrent decoder). + +Synthetic-config only — no HF Hub access, no weight loading. Covers the +architecture quirks: RMS/rotary/gated flags, ``supports_fold_ln = False``, +the empty ``applicable_phases`` (off the transformer-shaped verify path), +recurrence-config propagation, the three separate prelude / core_block / coda +block lists mapped via ``OpaqueBlockBridge``, the combined-QKV native attention +and combined gate+up MLP submodules, and four-place registration. +""" + +import pytest + +from transformer_lens.config import TransformerBridgeConfig +from transformer_lens.factories.architecture_adapter_factory import ( + ArchitectureAdapterFactory, +) +from transformer_lens.model_bridge.generalized_components import ( + AttentionBridge, + EmbeddingBridge, + LinearBridge, + MLPBridge, + OpaqueBlockBridge, + RMSNormalizationBridge, + UnembeddingBridge, +) +from transformer_lens.model_bridge.supported_architectures.raven import ( + RavenArchitectureAdapter, +) + +# The three physical block lists Huginn stores under model.transformer. +BLOCK_LISTS = ("prelude", "core_block", "coda") + + +def _make_cfg( + n_embd: int = 55, + n_heads: int = 55, + n_layers: int = 8, + n_ctx: int = 128, + d_vocab: int = 256, + mean_recurrence: int = 32, + n_layers_in_prelude: int = 2, + n_layers_in_recurrent_block: int = 4, + n_layers_in_coda: int = 2, + injection_type: str = "linear", + qk_bias: bool = True, +) -> TransformerBridgeConfig: + """Minimal TransformerBridgeConfig for Raven adapter tests. + + n_heads == n_embd keeps d_head == 1 so the tiny synthetic dims are legal + (Huginn is MHA: num_key_value_heads == num_attention_heads). + """ + cfg = TransformerBridgeConfig( + d_model=n_embd, + d_head=n_embd // n_heads, + n_layers=n_layers, + n_ctx=n_ctx, + n_heads=n_heads, + d_vocab=d_vocab, + d_mlp=4 * n_embd, + default_prepend_bos=False, + architecture="RavenForCausalLM", + ) + # Inject Huginn recurrence-shape fields the adapter reads via getattr. + cfg.mean_recurrence = mean_recurrence # type: ignore[attr-defined] + cfg.n_layers_in_prelude = n_layers_in_prelude # type: ignore[attr-defined] + cfg.n_layers_in_recurrent_block = n_layers_in_recurrent_block # type: ignore[attr-defined] + cfg.n_layers_in_coda = n_layers_in_coda # type: ignore[attr-defined] + cfg.injection_type = injection_type # type: ignore[attr-defined] + cfg.qk_bias = qk_bias # type: ignore[attr-defined] + return cfg + + +@pytest.fixture(scope="class") +def cfg() -> TransformerBridgeConfig: + return _make_cfg() + + +@pytest.fixture(scope="class") +def adapter(cfg: TransformerBridgeConfig) -> RavenArchitectureAdapter: + return RavenArchitectureAdapter(cfg) + + +# --------------------------------------------------------------------------- +# Config attributes +# --------------------------------------------------------------------------- + + +class TestRavenAdapterConfig: + """Adapter sets the required norm / positional / MLP flags.""" + + def test_normalization_type_rms(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.cfg.normalization_type == "RMS" + + def test_uses_rms_norm(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.cfg.uses_rms_norm is True + + def test_positional_embedding_type_rotary(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.cfg.positional_embedding_type == "rotary" + + def test_final_rms_true(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.cfg.final_rms is True + + def test_gated_mlp_true(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.cfg.gated_mlp is True + + def test_attn_only_false(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.cfg.attn_only is False + + def test_supports_fold_ln_false(self, adapter: RavenArchitectureAdapter) -> None: + # ln_f is reused mid-network (after recurrence, feeding the coda), so + # it must not be folded into W_U. + assert adapter.supports_fold_ln is False + + def test_applicable_phases_empty(self) -> None: + # Off the transformer-shaped verify_models path; correctness lives in + # the integration tests. + assert RavenArchitectureAdapter.applicable_phases == [] + + def test_weight_processing_conversions_empty(self, adapter: RavenArchitectureAdapter) -> None: + # Full delegation to the HF forward — no HT-format reshaping. + assert adapter.weight_processing_conversions == {} + + +class TestRavenRecurrenceConfigPropagation: + """Recurrence-shape attributes are surfaced on cfg.""" + + def test_mean_recurrence(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.cfg.mean_recurrence == 32 + + def test_n_layers_in_prelude(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.cfg.n_layers_in_prelude == 2 + + def test_n_layers_in_recurrent_block(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.cfg.n_layers_in_recurrent_block == 4 + + def test_n_layers_in_coda(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.cfg.n_layers_in_coda == 2 + + def test_injection_type(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.cfg.injection_type == "linear" + + def test_qk_bias(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.cfg.qk_bias is True + + def test_physical_layer_split_sums_to_n_layers(self, adapter: RavenArchitectureAdapter) -> None: + """prelude + recurrent-block + coda counts the 8 physical layers.""" + total = ( + adapter.cfg.n_layers_in_prelude + + adapter.cfg.n_layers_in_recurrent_block + + adapter.cfg.n_layers_in_coda + ) + assert total == adapter.cfg.n_layers + + def test_recurrence_defaults_when_absent(self) -> None: + """Adapter falls back to Huginn defaults if the attrs are missing.""" + bare = TransformerBridgeConfig( + d_model=55, + d_head=1, + n_layers=8, + n_ctx=128, + n_heads=55, + d_vocab=256, + architecture="RavenForCausalLM", + ) + a = RavenArchitectureAdapter(bare) + assert a.cfg.mean_recurrence == 32 + assert a.cfg.n_layers_in_prelude == 2 + assert a.cfg.n_layers_in_recurrent_block == 4 + assert a.cfg.n_layers_in_coda == 2 + + +# --------------------------------------------------------------------------- +# Top-level component mapping +# --------------------------------------------------------------------------- + + +class TestRavenTopLevelComponents: + """component_mapping exposes prelude / core_block / coda as three lists.""" + + def test_required_keys(self, adapter: RavenArchitectureAdapter) -> None: + assert set(adapter.component_mapping.keys()) == { + "embed", + "prelude", + "core_block", + "coda", + "ln_final", + "unembed", + } + + def test_embed_is_embedding_bridge(self, adapter: RavenArchitectureAdapter) -> None: + assert isinstance(adapter.component_mapping["embed"], EmbeddingBridge) + + def test_embed_name(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.component_mapping["embed"].name == "transformer.wte" + + @pytest.mark.parametrize("key", BLOCK_LISTS) + def test_block_list_is_opaque_block_bridge( + self, adapter: RavenArchitectureAdapter, key: str + ) -> None: + # OpaqueBlockBridge delegates the whole SandwichBlock so the post-residual + # norm placement is preserved (a standard BlockBridge would assume a + # pre-norm flow). + assert isinstance(adapter.component_mapping[key], OpaqueBlockBridge) + + @pytest.mark.parametrize("key", BLOCK_LISTS) + def test_block_list_name(self, adapter: RavenArchitectureAdapter, key: str) -> None: + assert adapter.component_mapping[key].name == f"transformer.{key}" + + def test_ln_final_is_rms_normalization_bridge(self, adapter: RavenArchitectureAdapter) -> None: + assert isinstance(adapter.component_mapping["ln_final"], RMSNormalizationBridge) + + def test_ln_final_name(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.component_mapping["ln_final"].name == "transformer.ln_f" + + def test_unembed_is_unembedding_bridge(self, adapter: RavenArchitectureAdapter) -> None: + assert isinstance(adapter.component_mapping["unembed"], UnembeddingBridge) + + def test_unembed_name(self, adapter: RavenArchitectureAdapter) -> None: + assert adapter.component_mapping["unembed"].name == "lm_head" + + +# --------------------------------------------------------------------------- +# SandwichBlock submodules (shared shape across all three block lists) +# --------------------------------------------------------------------------- + + +class TestRavenBlockSubmodules: + """Each block list wraps a SandwichBlock: four norms, native attn, gated MLP.""" + + @pytest.mark.parametrize("key", BLOCK_LISTS) + def test_submodule_keys(self, adapter: RavenArchitectureAdapter, key: str) -> None: + block = adapter.component_mapping[key] + assert set(block.submodules.keys()) == { + "norm_1", + "attn", + "norm_2", + "norm_3", + "mlp", + "norm_4", + } + + @pytest.mark.parametrize("key", BLOCK_LISTS) + @pytest.mark.parametrize("norm", ["norm_1", "norm_2", "norm_3", "norm_4"]) + def test_norms_are_rms(self, adapter: RavenArchitectureAdapter, key: str, norm: str) -> None: + block = adapter.component_mapping[key] + assert isinstance(block.submodules[norm], RMSNormalizationBridge) + assert block.submodules[norm].name == norm + + @pytest.mark.parametrize("key", BLOCK_LISTS) + def test_attn_is_native_attention_with_combined_qkv( + self, adapter: RavenArchitectureAdapter, key: str + ) -> None: + attn = adapter.component_mapping[key].submodules["attn"] + assert isinstance(attn, AttentionBridge) + assert attn.name == "attn" + # Combined Wqkv projection + output proj (no split q/k/v). + assert isinstance(attn.submodules["qkv"], LinearBridge) + assert attn.submodules["qkv"].name == "Wqkv" + assert isinstance(attn.submodules["o"], LinearBridge) + assert attn.submodules["o"].name == "proj" + assert set(attn.submodules.keys()) == {"qkv", "o"} + + @pytest.mark.parametrize("key", BLOCK_LISTS) + def test_mlp_is_gated_with_combined_fc( + self, adapter: RavenArchitectureAdapter, key: str + ) -> None: + mlp = adapter.component_mapping[key].submodules["mlp"] + assert isinstance(mlp, MLPBridge) + assert mlp.name == "mlp" + # Combined gate+up "fc" and output "proj". + assert isinstance(mlp.submodules["in"], LinearBridge) + assert mlp.submodules["in"].name == "fc" + assert isinstance(mlp.submodules["out"], LinearBridge) + assert mlp.submodules["out"].name == "proj" + + +class TestRavenBlockListsAreIndependent: + """The three block lists must not share bridge instances. + + Each OpaqueBlockBridge binds to a distinct HF ModuleList, so reusing a single + submodule bridge object across lists would cross-wire them. + """ + + def test_top_level_bridges_distinct(self, adapter: RavenArchitectureAdapter) -> None: + bridges = [adapter.component_mapping[k] for k in BLOCK_LISTS] + assert len({id(b) for b in bridges}) == len(BLOCK_LISTS) + + def test_submodule_bridges_distinct(self, adapter: RavenArchitectureAdapter) -> None: + attns = [adapter.component_mapping[k].submodules["attn"] for k in BLOCK_LISTS] + assert len({id(a) for a in attns}) == len(BLOCK_LISTS) + norms = [adapter.component_mapping[k].submodules["norm_1"] for k in BLOCK_LISTS] + assert len({id(n) for n in norms}) == len(BLOCK_LISTS) + + +# --------------------------------------------------------------------------- +# Factory registration + model registry +# --------------------------------------------------------------------------- + + +class TestRavenFactoryRegistration: + """RavenForCausalLM is wired into the adapter factory.""" + + def test_factory_returns_raven_adapter(self) -> None: + cfg = _make_cfg() + adapter = ArchitectureAdapterFactory.select_architecture_adapter(cfg) + assert isinstance(adapter, RavenArchitectureAdapter) + + def test_architecture_key_present(self) -> None: + from transformer_lens.factories.architecture_adapter_factory import ( + SUPPORTED_ARCHITECTURES, + ) + + assert "RavenForCausalLM" in SUPPORTED_ARCHITECTURES + assert SUPPORTED_ARCHITECTURES["RavenForCausalLM"] is RavenArchitectureAdapter + + +class TestRavenModelRegistry: + """RavenForCausalLM is listed in the model registry constants.""" + + def test_in_hf_supported(self) -> None: + from transformer_lens.tools.model_registry import HF_SUPPORTED_ARCHITECTURES + + assert "RavenForCausalLM" in HF_SUPPORTED_ARCHITECTURES + + def test_canonical_author_is_tomg_group(self) -> None: + from transformer_lens.tools.model_registry import CANONICAL_AUTHORS_BY_ARCH + + assert CANONICAL_AUTHORS_BY_ARCH.get("RavenForCausalLM") == ["tomg-group-umd"] + + def test_has_architecture_description(self) -> None: + from transformer_lens.tools.model_registry.generate_report import ( + ARCHITECTURE_DESCRIPTIONS, + ) + + assert "RavenForCausalLM" in ARCHITECTURE_DESCRIPTIONS diff --git a/tests/unit/model_bridge/supported_architectures/test_rwkv7_adapter.py b/tests/unit/model_bridge/supported_architectures/test_rwkv7_adapter.py new file mode 100644 index 0000000000..13844765e5 --- /dev/null +++ b/tests/unit/model_bridge/supported_architectures/test_rwkv7_adapter.py @@ -0,0 +1,301 @@ +"""Unit tests for RWKV7ArchitectureAdapter (RWKV-7 "Goose" recurrent decoder). + +Synthetic-config only — no HF Hub access, no weight loading. Covers the +architecture quirks: standard biased LayerNorm (``normalization_type == "LN"``), +no positional embeddings, ungated FFN, the empty ``applicable_phases`` (off the +transformer-shaped verify path), shape-attribute propagation, the single +``model.layers`` block list mapped via ``OpaqueBlockBridge``, the delegated +time-mixing (``attn``) and channel-mixing (``ffn``) sublayers wrapped by +``GeneralizedComponent`` with their projection submodules, the fused-signature +``ffn_norm`` likewise delegated via a plain ``GeneralizedComponent``, and four-place +registration. +""" + +import pytest + +from transformer_lens.config import TransformerBridgeConfig +from transformer_lens.factories.architecture_adapter_factory import ( + ArchitectureAdapterFactory, +) +from transformer_lens.model_bridge.generalized_components import ( + EmbeddingBridge, + LinearBridge, + NormalizationBridge, + OpaqueBlockBridge, + UnembeddingBridge, +) +from transformer_lens.model_bridge.generalized_components.base import ( + GeneralizedComponent, +) +from transformer_lens.model_bridge.supported_architectures.rwkv7 import ( + RWKV7ArchitectureAdapter, +) + + +def _make_cfg( + d_model: int = 64, + head_dim: int = 64, + num_heads: int = 1, + n_layers: int = 4, + n_ctx: int = 128, + d_vocab: int = 256, + inject_shape_attrs: bool = True, +) -> TransformerBridgeConfig: + """Minimal TransformerBridgeConfig for RWKV-7 adapter tests. + + ``head_dim == d_model`` keeps ``num_heads == 1`` so the tiny synthetic dims + are legal. + """ + cfg = TransformerBridgeConfig( + d_model=d_model, + d_head=head_dim, + n_layers=n_layers, + n_ctx=n_ctx, + n_heads=num_heads, + d_vocab=d_vocab, + d_mlp=4 * d_model, + default_prepend_bos=False, + architecture="RWKV7ForCausalLM", + ) + if inject_shape_attrs: + # Inject RWKV-7 shape fields the adapter reads via getattr. head_dim is a + # read-only alias of d_head on the config, so it is not set here. + cfg.num_heads = num_heads # type: ignore[attr-defined] + cfg.value_dim = [d_model] * n_layers # type: ignore[attr-defined] + cfg.decay_low_rank_dim = 64 # type: ignore[attr-defined] + cfg.gate_low_rank_dim = 128 # type: ignore[attr-defined] + cfg.a_low_rank_dim = 64 # type: ignore[attr-defined] + cfg.v_low_rank_dim = 16 # type: ignore[attr-defined] + cfg.norm_first = True # type: ignore[attr-defined] + cfg.norm_bias = True # type: ignore[attr-defined] + cfg.fuse_norm = True # type: ignore[attr-defined] + cfg.attn_mode = "chunk" # type: ignore[attr-defined] + cfg.hidden_act = "sqrelu" # type: ignore[attr-defined] + cfg.norm_eps = 1e-5 # type: ignore[attr-defined] + return cfg + + +@pytest.fixture(scope="class") +def cfg() -> TransformerBridgeConfig: + return _make_cfg() + + +@pytest.fixture(scope="class") +def adapter(cfg: TransformerBridgeConfig) -> RWKV7ArchitectureAdapter: + return RWKV7ArchitectureAdapter(cfg) + + +# --------------------------------------------------------------------------- +# Config attributes +# --------------------------------------------------------------------------- + + +class TestRWKV7AdapterConfig: + """Adapter sets the required norm / positional / MLP flags.""" + + def test_normalization_type_ln(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert adapter.cfg.normalization_type == "LN" + + def test_uses_rms_norm_false(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert adapter.cfg.uses_rms_norm is False + + def test_positional_embedding_type_none(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert adapter.cfg.positional_embedding_type == "none" + + def test_final_rms_false(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert adapter.cfg.final_rms is False + + def test_gated_mlp_false(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert adapter.cfg.gated_mlp is False + + def test_attn_only_false(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert adapter.cfg.attn_only is False + + def test_is_stateful_false(self, adapter: RWKV7ArchitectureAdapter) -> None: + # fla drives decode state through its own Cache, not cache_params. + assert adapter.cfg.is_stateful is False + + def test_applicable_phases_empty(self) -> None: + # Off the transformer-shaped verify_models path; correctness lives in + # the integration tests. + assert RWKV7ArchitectureAdapter.applicable_phases == [] + + def test_weight_processing_conversions_empty(self, adapter: RWKV7ArchitectureAdapter) -> None: + # Full delegation to the fla forward — no HT-format reshaping. + assert adapter.weight_processing_conversions == {} + + +class TestRWKV7ShapeAttrPropagation: + """RWKV-7 shape attributes are surfaced on cfg.""" + + def test_head_dim(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert adapter.cfg.head_dim == 64 + + def test_num_heads(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert adapter.cfg.num_heads == 1 + + def test_value_dim(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert adapter.cfg.value_dim == [64, 64, 64, 64] + + def test_low_rank_dims(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert adapter.cfg.decay_low_rank_dim == 64 + assert adapter.cfg.gate_low_rank_dim == 128 + assert adapter.cfg.a_low_rank_dim == 64 + assert adapter.cfg.v_low_rank_dim == 16 + + def test_norm_flags(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert adapter.cfg.norm_first is True + assert adapter.cfg.norm_bias is True + assert adapter.cfg.fuse_norm is True + + def test_attn_mode_and_hidden_act(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert adapter.cfg.attn_mode == "chunk" + assert adapter.cfg.hidden_act == "sqrelu" + + def test_defaults_when_absent(self) -> None: + """Adapter falls back to RWKV-7 defaults if the attrs are missing.""" + bare = _make_cfg(inject_shape_attrs=False) + a = RWKV7ArchitectureAdapter(bare) + assert a.cfg.head_dim == 64 + # num_heads defaults to d_model // head_dim. + assert a.cfg.num_heads == 1 + # value_dim defaults to [d_model] * n_layers. + assert a.cfg.value_dim == [64, 64, 64, 64] + assert a.cfg.decay_low_rank_dim == 64 + assert a.cfg.gate_low_rank_dim == 128 + assert a.cfg.a_low_rank_dim == 64 + assert a.cfg.v_low_rank_dim == 16 + assert a.cfg.norm_first is True + assert a.cfg.fuse_norm is True + assert a.cfg.hidden_act == "sqrelu" + + +# --------------------------------------------------------------------------- +# Top-level component mapping +# --------------------------------------------------------------------------- + + +class TestRWKV7TopLevelComponents: + """component_mapping exposes embed / blocks / ln_final / unembed.""" + + def test_required_keys(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert set(adapter.component_mapping.keys()) == { + "embed", + "blocks", + "ln_final", + "unembed", + } + + def test_embed_is_embedding_bridge(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert isinstance(adapter.component_mapping["embed"], EmbeddingBridge) + assert adapter.component_mapping["embed"].name == "model.embeddings" + + def test_blocks_is_ssm_block_bridge(self, adapter: RWKV7ArchitectureAdapter) -> None: + # OpaqueBlockBridge delegates the whole recurrent block so the internal + # mixing is preserved (a standard BlockBridge assumes a pre-norm attn flow). + blocks = adapter.component_mapping["blocks"] + assert isinstance(blocks, OpaqueBlockBridge) + assert blocks.name == "model.layers" + + def test_ln_final_is_normalization_bridge(self, adapter: RWKV7ArchitectureAdapter) -> None: + ln_final = adapter.component_mapping["ln_final"] + assert isinstance(ln_final, NormalizationBridge) + assert ln_final.name == "model.norm" + + def test_unembed_is_unembedding_bridge(self, adapter: RWKV7ArchitectureAdapter) -> None: + assert isinstance(adapter.component_mapping["unembed"], UnembeddingBridge) + assert adapter.component_mapping["unembed"].name == "lm_head" + + +# --------------------------------------------------------------------------- +# Block submodules +# --------------------------------------------------------------------------- + + +class TestRWKV7BlockSubmodules: + """Each block wraps attn_norm / attn / ffn_norm / ffn.""" + + def test_submodule_keys(self, adapter: RWKV7ArchitectureAdapter) -> None: + block = adapter.component_mapping["blocks"] + assert set(block.submodules.keys()) == {"attn_norm", "attn", "ffn_norm", "ffn"} + + def test_attn_norm_is_normalization_bridge(self, adapter: RWKV7ArchitectureAdapter) -> None: + attn_norm = adapter.component_mapping["blocks"].submodules["attn_norm"] + assert isinstance(attn_norm, NormalizationBridge) + assert attn_norm.name == "attn_norm" + + def test_ffn_norm_is_delegating_passthrough(self, adapter: RWKV7ArchitectureAdapter) -> None: + # Under config.fuse_norm the block calls ffn_norm(x, residual, True) -> + # (normed, residual); the reimplementing NormalizationBridge can't express + # that, so it is a plain delegating GeneralizedComponent, not a norm bridge. + ffn_norm = adapter.component_mapping["blocks"].submodules["ffn_norm"] + assert type(ffn_norm) is GeneralizedComponent + assert not isinstance(ffn_norm, NormalizationBridge) + assert ffn_norm.name == "ffn_norm" + + def test_attn_is_generalized_component_with_projections( + self, adapter: RWKV7ArchitectureAdapter + ) -> None: + attn = adapter.component_mapping["blocks"].submodules["attn"] + assert isinstance(attn, GeneralizedComponent) + assert attn.name == "attn" + assert set(attn.submodules.keys()) == {"r_proj", "k_proj", "v_proj", "o_proj"} + for proj in ("r_proj", "k_proj", "v_proj", "o_proj"): + assert isinstance(attn.submodules[proj], LinearBridge) + assert attn.submodules[proj].name == proj + + def test_ffn_is_generalized_component_with_projections( + self, adapter: RWKV7ArchitectureAdapter + ) -> None: + ffn = adapter.component_mapping["blocks"].submodules["ffn"] + assert isinstance(ffn, GeneralizedComponent) + assert ffn.name == "ffn" + # HF names the up-projection "key" and the (down) output projection "value". + assert set(ffn.submodules.keys()) == {"key", "value"} + assert isinstance(ffn.submodules["key"], LinearBridge) + assert ffn.submodules["key"].name == "key" + assert isinstance(ffn.submodules["value"], LinearBridge) + assert ffn.submodules["value"].name == "value" + + +# --------------------------------------------------------------------------- +# Factory registration + model registry +# --------------------------------------------------------------------------- + + +class TestRWKV7FactoryRegistration: + """RWKV7ForCausalLM is wired into the adapter factory.""" + + def test_factory_returns_rwkv7_adapter(self) -> None: + cfg = _make_cfg() + adapter = ArchitectureAdapterFactory.select_architecture_adapter(cfg) + assert isinstance(adapter, RWKV7ArchitectureAdapter) + + def test_architecture_key_present(self) -> None: + from transformer_lens.factories.architecture_adapter_factory import ( + SUPPORTED_ARCHITECTURES, + ) + + assert "RWKV7ForCausalLM" in SUPPORTED_ARCHITECTURES + assert SUPPORTED_ARCHITECTURES["RWKV7ForCausalLM"] is RWKV7ArchitectureAdapter + + +class TestRWKV7ModelRegistry: + """RWKV7ForCausalLM is listed in the model registry constants.""" + + def test_in_hf_supported(self) -> None: + from transformer_lens.tools.model_registry import HF_SUPPORTED_ARCHITECTURES + + assert "RWKV7ForCausalLM" in HF_SUPPORTED_ARCHITECTURES + + def test_canonical_author_is_fla_hub(self) -> None: + from transformer_lens.tools.model_registry import CANONICAL_AUTHORS_BY_ARCH + + assert CANONICAL_AUTHORS_BY_ARCH.get("RWKV7ForCausalLM") == ["fla-hub"] + + def test_has_architecture_description(self) -> None: + from transformer_lens.tools.model_registry.generate_report import ( + ARCHITECTURE_DESCRIPTIONS, + ) + + assert "RWKV7ForCausalLM" in ARCHITECTURE_DESCRIPTIONS diff --git a/tests/unit/model_bridge/test_bridge_train_mode_propagation.py b/tests/unit/model_bridge/test_bridge_train_mode_propagation.py new file mode 100644 index 0000000000..4d08d0c71e --- /dev/null +++ b/tests/unit/model_bridge/test_bridge_train_mode_propagation.py @@ -0,0 +1,100 @@ +"""Regression tests for TransformerBridge train/eval mode propagation. + +`original_model` is stored outside the registered module tree, so +inherited `nn.Module.train()` does not reach it. These tests verify that +bridge mode changes propagate to the wrapped model, whose mode-dependent +layers (Dropout) rely on that flag. Architecture-independent stub bridge; +no HF Hub access, no weights. +""" +from __future__ import annotations + +import torch +import torch.nn as nn + +from transformer_lens.config import TransformerBridgeConfig +from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter +from transformer_lens.model_bridge.bridge import TransformerBridge +from transformer_lens.model_bridge.generalized_components import LinearBridge + + +class _StubModel(nn.Module): + """Minimal source model with a mode-dependent layer (Dropout), so + train/eval propagation has an observable behavioral consequence, not + just a flag.""" + + def __init__(self) -> None: + super().__init__() + self.proj = nn.Linear(4, 4) + self.drop = nn.Dropout(0.5) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.drop(self.proj(x)) + + +class _StubAdapter(ArchitectureAdapter): + """Smallest adapter TransformerBridge will accept: a one-entry + component_mapping. The key is `stub_proj` (not `proj` or `embed`) + because TransformerBridge reserves several canonical attribute names + on itself; a colliding key fails at add_module time.""" + + def __init__(self, cfg: TransformerBridgeConfig) -> None: + super().__init__(cfg) + self.component_mapping = {"stub_proj": LinearBridge(name="proj")} + + +def _make_stub_bridge() -> tuple[TransformerBridge, _StubModel]: + cfg = TransformerBridgeConfig( + d_model=4, + d_head=2, + n_layers=1, + n_ctx=8, + n_heads=2, + d_vocab=8, + architecture="StubForTest", + ) + model = _StubModel() + bridge = TransformerBridge(model, _StubAdapter(cfg), tokenizer=None) + return bridge, model + + +class TestTrainEvalModePropagation: + """bridge.train()/.eval() must reach original_model. + + Without the TransformerBridge.train() override these fail with + original_model.training stuck at its wrap-time value (bridge.training + flips, the wrapped model's does not) -- i.e. bridge.eval() would leave + dropout active.""" + + def test_eval_propagates_to_original_model(self) -> None: + bridge, model = _make_stub_bridge() + assert model.training is True # nn.Module default at wrap time + bridge.eval() + assert bridge.training is False + assert model.training is False + + def test_train_propagates_to_original_model(self) -> None: + bridge, model = _make_stub_bridge() + bridge.eval() + assert model.training is False + bridge.train() + assert bridge.training is True + assert model.training is True + + def test_train_and_eval_return_the_bridge_itself(self) -> None: + """nn.Module convention: train()/eval() return self so callers can + chain (`bridge.eval().to(device)`, etc.).""" + bridge, _ = _make_stub_bridge() + assert bridge.train(False) is bridge + assert bridge.train(True) is bridge + assert bridge.eval() is bridge + + def test_direct_mode_on_original_model_stays_in_sync(self) -> None: + """Setting mode via the caller's own reference to the source model + still works -- both paths write the same underlying flags, so + neither clobbers the other.""" + bridge, model = _make_stub_bridge() + bridge.eval() + model.train() + assert model.training is True + bridge.eval() + assert model.training is False diff --git a/tests/unit/model_bridge/test_checkpoint_revision.py b/tests/unit/model_bridge/test_checkpoint_revision.py index dedcb8a99e..f24e428e66 100644 --- a/tests/unit/model_bridge/test_checkpoint_revision.py +++ b/tests/unit/model_bridge/test_checkpoint_revision.py @@ -13,7 +13,7 @@ class TestResolveCheckpointToRevision: def test_pythia_index_resolves_to_step_revision(self): labels = [0, 1000, 3000, 10000] with patch( - "transformer_lens.loading_from_pretrained.get_checkpoint_labels", + "transformer_lens.model_bridge.sources.transformers.helpers.get_checkpoint_labels", return_value=(labels, "step"), ): revision = _resolve_checkpoint_to_revision( @@ -24,7 +24,7 @@ def test_pythia_index_resolves_to_step_revision(self): def test_pythia_value_resolves_to_step_revision(self): labels = [0, 1000, 3000, 10000] with patch( - "transformer_lens.loading_from_pretrained.get_checkpoint_labels", + "transformer_lens.model_bridge.sources.transformers.helpers.get_checkpoint_labels", return_value=(labels, "step"), ): revision = _resolve_checkpoint_to_revision( @@ -35,7 +35,7 @@ def test_pythia_value_resolves_to_step_revision(self): def test_stanford_crfm_uses_checkpoint_prefix(self): labels = [100, 200, 400] with patch( - "transformer_lens.loading_from_pretrained.get_checkpoint_labels", + "transformer_lens.model_bridge.sources.transformers.helpers.get_checkpoint_labels", return_value=(labels, "step"), ): revision = _resolve_checkpoint_to_revision( @@ -50,7 +50,7 @@ def test_unknown_family_raises(self): def test_index_out_of_range_raises(self): labels = [0, 1000] with patch( - "transformer_lens.loading_from_pretrained.get_checkpoint_labels", + "transformer_lens.model_bridge.sources.transformers.helpers.get_checkpoint_labels", return_value=(labels, "step"), ): with pytest.raises(ValueError, match="out of range"): @@ -61,7 +61,7 @@ def test_index_out_of_range_raises(self): def test_unknown_value_raises(self): labels = [0, 1000] with patch( - "transformer_lens.loading_from_pretrained.get_checkpoint_labels", + "transformer_lens.model_bridge.sources.transformers.helpers.get_checkpoint_labels", return_value=(labels, "step"), ): with pytest.raises(ValueError, match="not in available checkpoints"): @@ -125,7 +125,7 @@ def test_revision_forwarded_to_model_load(self): def test_checkpoint_index_resolves_to_revision(self): labels = [0, 1000, 3000, 10000] with patch( - "transformer_lens.loading_from_pretrained.get_checkpoint_labels", + "transformer_lens.model_bridge.sources.transformers.helpers.get_checkpoint_labels", return_value=(labels, "step"), ): captured = self._patched_boot(checkpoint_index=2) diff --git a/tests/unit/test_tracr_conversion.py b/tests/unit/test_tracr_conversion.py index b8f00b3b6a..0b9eb34de8 100644 --- a/tests/unit/test_tracr_conversion.py +++ b/tests/unit/test_tracr_conversion.py @@ -74,9 +74,9 @@ def _fake_tracr_model() -> SimpleNamespace: "aggregate_1:2", "aggregate_1:3", "indices:0", - "reverse:1", - "reverse:2", - "reverse:3", + "reverse_1:1", + "reverse_1:2", + "reverse_1:3", "tokens:BOS", ], output_encoder=_CategoricalEncoder({1: 0, 2: 1, 3: 2}), @@ -94,7 +94,7 @@ def _fake_tracr_model() -> SimpleNamespace: def test_categorical_unembed_uses_named_output_basis(): model = _fake_tracr_model() - unembed = make_tracr_categorical_unembed(model, output_label="reverse") + unembed = make_tracr_categorical_unembed(model, output_label="reverse_1") assert unembed.shape == (8, 3) np.testing.assert_array_equal(unembed[:4], np.zeros((4, 3))) @@ -125,7 +125,7 @@ def test_bridge_state_dict_transposes_tracr_weights_and_reconstructs_unembed(): model = _fake_tracr_model() state_dict = make_tracr_transformer_bridge_state_dict( - model, output_label="reverse", dtype=torch.float64 + model, output_label="reverse_1", dtype=torch.float64 ) assert state_dict["tok_embed.weight"].shape == (5, 8) @@ -161,4 +161,4 @@ def test_bridge_state_dict_rejects_layer_norm_tracr_models(): model.model_config.layer_norm = True with pytest.raises(NotImplementedError, match="layer_norm=True"): - make_tracr_transformer_bridge_state_dict(model, output_label="reverse") + make_tracr_transformer_bridge_state_dict(model, output_label="reverse_1") diff --git a/tests/unit/tools/test_model_registry.py b/tests/unit/tools/test_model_registry.py index 8fbb12c014..4c8b66b883 100644 --- a/tests/unit/tools/test_model_registry.py +++ b/tests/unit/tools/test_model_registry.py @@ -807,6 +807,7 @@ class TestRegistrySyncedWithFactory: "NeelSoluOldForCausalLM", "GPT2LMHeadCustomModel", "TransformerLensNative", + "TransformerLensPretrain", # Group 2: factory-internal alias casings (HF emits the canonical name). "Gemma1ForCausalLM", # HF emits: GemmaForCausalLM "NeoForCausalLM", # HF emits: GPTNeoForCausalLM @@ -873,3 +874,114 @@ def test_canonical_authors_keys_have_a_factory_adapter(self): assert ( not orphaned ), f"CANONICAL_AUTHORS_BY_ARCH entries with no factory adapter: {orphaned}" + + +class TestModelAliases: + """Registry-canonical alias table (data/model_aliases.json).""" + + def test_alias_resolves_to_official_name(self): + from transformer_lens.tools.model_registry.registry_io import ( + resolve_model_alias, + ) + + assert resolve_model_alias("gpt2-small") == "gpt2" + + def test_official_name_and_unknown_return_none(self): + from transformer_lens.tools.model_registry.registry_io import ( + resolve_model_alias, + ) + + assert resolve_model_alias("gpt2") is None + assert resolve_model_alias("no-such-model-xyz") is None + + def test_no_alias_maps_to_two_officials(self): + """Duplicate aliases would make resolution depend on table order.""" + from transformer_lens.tools.model_registry.registry_io import load_model_aliases + + seen: dict[str, str] = {} + for official, aliases in load_model_aliases().items(): + assert aliases, f"{official} has an empty alias list" + for alias in aliases: + assert ( + alias not in seen + ), f"alias {alias!r} under both {seen[alias]!r} and {official!r}" + seen[alias] = official + + def test_legacy_loader_agrees_with_registry(self): + """HookedTransformer and the bridge must resolve aliases identically until 4.0.""" + from transformer_lens.loading_from_pretrained import get_official_model_name + from transformer_lens.tools.model_registry.registry_io import ( + resolve_model_alias, + ) + + assert get_official_model_name("gpt2-small") == resolve_model_alias("gpt2-small") + + +class TestCheckpointLabels: + """Registry-canonical checkpoint schedules (checkpoints.py).""" + + def test_pythia_routes_to_v1_schedule(self): + from transformer_lens.tools.model_registry.checkpoints import ( + PYTHIA_CHECKPOINTS, + get_checkpoint_labels, + ) + + labels, label_type = get_checkpoint_labels("EleutherAI/pythia-70m") + assert labels == PYTHIA_CHECKPOINTS + assert label_type == "step" + + def test_pythia_v0_routes_to_v0_schedule(self): + from transformer_lens.tools.model_registry.checkpoints import ( + PYTHIA_V0_CHECKPOINTS, + get_checkpoint_labels, + ) + + labels, _ = get_checkpoint_labels("EleutherAI/pythia-70m-v0") + assert labels == PYTHIA_V0_CHECKPOINTS + + def test_stanford_crfm_routes_to_crfm_schedule(self): + from transformer_lens.tools.model_registry.checkpoints import ( + STANFORD_CRFM_CHECKPOINTS, + get_checkpoint_labels, + ) + + labels, label_type = get_checkpoint_labels("stanford-crfm/alias-gpt2-small-x21") + assert labels == STANFORD_CRFM_CHECKPOINTS + assert label_type == "step" + + def test_alias_input_is_resolved(self): + from transformer_lens.tools.model_registry.checkpoints import ( + get_checkpoint_labels, + ) + + assert get_checkpoint_labels("pythia-70m") == get_checkpoint_labels("EleutherAI/pythia-70m") + + def test_non_checkpointed_model_raises(self): + from transformer_lens.tools.model_registry.checkpoints import ( + get_checkpoint_labels, + ) + + with pytest.raises(ValueError, match="not checkpointed"): + get_checkpoint_labels("gpt2") + + def test_matches_legacy_loader(self): + """Bridge checkpoint resolution must not drift from the legacy HT path until 4.0.""" + from transformer_lens.loading_from_pretrained import ( + get_checkpoint_labels as legacy_labels, + ) + from transformer_lens.tools.model_registry.checkpoints import ( + get_checkpoint_labels, + ) + + for name in ("EleutherAI/pythia-70m", "stanford-crfm/alias-gpt2-small-x21"): + assert get_checkpoint_labels(name) == legacy_labels(name) + + +class TestRemoteCodePrefixes: + def test_covers_legacy_remote_code_models(self): + """Registry tooling loads legacy-listed models too; prefixes must stay a superset until 4.0.""" + from transformer_lens.loading_from_pretrained import NEED_REMOTE_CODE_MODELS + from transformer_lens.tools.model_registry import REMOTE_CODE_MODEL_PREFIXES + + missing = set(NEED_REMOTE_CODE_MODELS) - set(REMOTE_CODE_MODEL_PREFIXES) + assert not missing, f"legacy remote-code prefixes not in registry list: {missing}" diff --git a/transformer_lens/ActivationCache.py b/transformer_lens/ActivationCache.py index 52c4c11c76..d4454e4c27 100644 --- a/transformer_lens/ActivationCache.py +++ b/transformer_lens/ActivationCache.py @@ -37,7 +37,7 @@ class first, including the examples, and then skimming the available methods. Yo from transformer_lens.utilities import Slice, SliceInput, warn_if_mps if TYPE_CHECKING: - from transformer_lens.HookedTransformer import HookedTransformer + from transformer_lens.model_protocol import TransformerLensModelWithWeights def _normalize_projection_to_2d( @@ -144,8 +144,9 @@ def __init__( has_batch_dim: bool = True, ): self.cache_dict = cache_dict - # Helper methods require HT-internal structure; bridge users only use cache_dict. - self.model = cast("HookedTransformer", model) + # Advanced helpers (LN folding, residual-direction projection) need the + # weight-processing surface; both HookedTransformer and TransformerBridge expose it. + self.model = cast("TransformerLensModelWithWeights", model) self.has_batch_dim = has_batch_dim self.has_embed = "hook_embed" in self.cache_dict self.has_pos_embed = "hook_pos_embed" in self.cache_dict diff --git a/transformer_lens/HookedAudioEncoder.py b/transformer_lens/HookedAudioEncoder.py index a1cbd47eb0..5b58351588 100644 --- a/transformer_lens/HookedAudioEncoder.py +++ b/transformer_lens/HookedAudioEncoder.py @@ -362,6 +362,17 @@ def from_pretrained( **from_pretrained_kwargs: Any, ) -> "HookedAudioEncoder": """Loads in the pretrained weights from huggingface. Currently supports loading weight from HuggingFace BertForMaskedLM. Unlike HookedTransformer, this does not yet do any preprocessing on the model.""" + import warnings + + warnings.warn( + "HookedAudioEncoder.from_pretrained is deprecated and will be removed in a " + "future major release. Use TransformerBridge.boot_transformers(...) instead — " + "HuBERT/Wav2Vec2 are supported via the bridge's audio adapter. See " + "docs/source/content/migrating_to_v3.md.", + DeprecationWarning, + stacklevel=2, + ) + logging.warning( "Support for HuBERT in TransformerLens is currently experimental, until such a time when it has feature " "parity with HookedTransformer and has been tested on real research tasks. Until then, backward " diff --git a/transformer_lens/HookedEncoderDecoder.py b/transformer_lens/HookedEncoderDecoder.py index df466aaa0e..aa08483914 100644 --- a/transformer_lens/HookedEncoderDecoder.py +++ b/transformer_lens/HookedEncoderDecoder.py @@ -551,6 +551,17 @@ def from_pretrained( **from_pretrained_kwargs: Any, ) -> T: """Loads in the pretrained weights from huggingface. Currently supports loading weight from HuggingFace BertForMaskedLM. Unlike HookedTransformer, this does not yet do any preprocessing on the model.""" + import warnings + + warnings.warn( + "HookedEncoderDecoder.from_pretrained is deprecated and will be removed in a " + "future major release. Use TransformerBridge.boot_transformers(...) instead — " + "the bridge supports T5-style encoder-decoder models. See " + "docs/source/content/migrating_to_v3.md.", + DeprecationWarning, + stacklevel=2, + ) + logging.warning( "Support for T5 in TransformerLens is currently experimental, until such a time when it has feature " "parity with HookedTransformer and has been tested on real research tasks. Until then, backward " diff --git a/transformer_lens/HookedTransformer.py b/transformer_lens/HookedTransformer.py index 8d96ce2e09..133eb5dae9 100644 --- a/transformer_lens/HookedTransformer.py +++ b/transformer_lens/HookedTransformer.py @@ -1313,6 +1313,17 @@ def from_pretrained( 3. Global default ("right") first_n_layers: If specified, only load the first n layers of the model. """ + import warnings + + warnings.warn( + "HookedTransformer.from_pretrained is deprecated and will be removed in a " + "future major release. Use TransformerBridge.boot_transformers(...) instead, " + "then call enable_compatibility_mode() for HookedTransformer-equivalent " + "numerics. See docs/source/content/migrating_to_v3.md.", + DeprecationWarning, + stacklevel=2, + ) + if checkpoint_value is not None and checkpoint_label is not None: raise ValueError( "Specify checkpoint_value or checkpoint_label, not both — they are aliases." diff --git a/transformer_lens/conversion_utils/hook_conversion_utils.py b/transformer_lens/conversion_utils/hook_conversion_utils.py index 5c1dd37bbc..d0469f061d 100644 --- a/transformer_lens/conversion_utils/hook_conversion_utils.py +++ b/transformer_lens/conversion_utils/hook_conversion_utils.py @@ -2,8 +2,6 @@ import torch -from transformer_lens.loading_from_pretrained import get_pretrained_model_config - def get_weight_conversion_field_set(weights: dict) -> str: """Creates a formatted string showing how weights are mapped between frameworks. @@ -72,5 +70,7 @@ def model_info(model_name): model_name (str): Name of the pretrained model to analyze (e.g., 'gpt2', 'bert-base-uncased', etc.) """ + from transformer_lens.loading_from_pretrained import get_pretrained_model_config + cfg = get_pretrained_model_config(model_name) model_info_cfg(cfg) diff --git a/transformer_lens/evals.py b/transformer_lens/evals.py index 69bbe6e2ed..4200ea8b94 100644 --- a/transformer_lens/evals.py +++ b/transformer_lens/evals.py @@ -446,7 +446,7 @@ def ioi_eval(model, dataset=None, batch_size=8, num_samples=1000, tokenizer=None """Evaluate the Model on the Indirect Object Identification Task. Args: - model: HookedTransformer model. + model: A HookedTransformer or TransformerBridge model. dataset: PyTorch Dataset that returns a dict with keys "prompt", "IO", and "S". batch_size: Batch size to use. num_samples: Number of samples to use. @@ -533,7 +533,7 @@ def mmlu_eval( Paper: https://arxiv.org/abs/2009.03300 Args: - model: HookedTransformer model to evaluate. + model: A HookedTransformer or TransformerBridge model to evaluate. tokenizer: Tokenizer to use. If None, uses model.tokenizer. subjects: Subject(s) to evaluate on. Can be None (all 57 subjects), a single subject string, or a list of subjects. See :const:`MMLU_SUBJECTS` for valid names. diff --git a/transformer_lens/factories/architecture_adapter_factory.py b/transformer_lens/factories/architecture_adapter_factory.py index 1514e9f870..1e1f96bd0e 100644 --- a/transformer_lens/factories/architecture_adapter_factory.py +++ b/transformer_lens/factories/architecture_adapter_factory.py @@ -74,6 +74,7 @@ Phi3ArchitectureAdapter, PhiArchitectureAdapter, PhiMoEArchitectureAdapter, + PretrainArchitectureAdapter, Qwen2ArchitectureAdapter, Qwen2MoeArchitectureAdapter, Qwen3_5ArchitectureAdapter, @@ -82,7 +83,9 @@ Qwen3MoeArchitectureAdapter, Qwen3NextArchitectureAdapter, QwenArchitectureAdapter, + RavenArchitectureAdapter, RecurrentGemmaArchitectureAdapter, + RWKV7ArchitectureAdapter, SmolLM3ArchitectureAdapter, StableLmArchitectureAdapter, T5ArchitectureAdapter, @@ -170,7 +173,9 @@ "Qwen3NextForCausalLM": Qwen3NextArchitectureAdapter, "Qwen3_5ForCausalLM": Qwen3_5ArchitectureAdapter, "Qwen3_5ForConditionalGeneration": Qwen3_5MultimodalArchitectureAdapter, + "RavenForCausalLM": RavenArchitectureAdapter, "RecurrentGemmaForCausalLM": RecurrentGemmaArchitectureAdapter, + "RWKV7ForCausalLM": RWKV7ArchitectureAdapter, "SmolLM3ForCausalLM": SmolLM3ArchitectureAdapter, "StableLmForCausalLM": StableLmArchitectureAdapter, "T5ForConditionalGeneration": T5ArchitectureAdapter, @@ -181,6 +186,7 @@ "Zamba2ForCausalLM": Zamba2ArchitectureAdapter, "NanoGPTForCausalLM": NanogptArchitectureAdapter, "TransformerLensNative": NativeArchitectureAdapter, + "TransformerLensPretrain": PretrainArchitectureAdapter, "MinGPTForCausalLM": MingptArchitectureAdapter, "GPTNeoForCausalLM": NeoArchitectureAdapter, "GPTNeoXForCausalLM": NeoxArchitectureAdapter, diff --git a/transformer_lens/head_detector.py b/transformer_lens/head_detector.py index e05ad64122..95d4fdc7df 100644 --- a/transformer_lens/head_detector.py +++ b/transformer_lens/head_detector.py @@ -12,7 +12,7 @@ from typing_extensions import Literal, get_args from transformer_lens.ActivationCache import ActivationCache -from transformer_lens.HookedTransformer import HookedTransformer +from transformer_lens.model_protocol import TransformerLensModel from transformer_lens.utilities import is_lower_triangular, is_square HeadName = Literal["previous_token_head", "duplicate_token_head", "induction_head"] @@ -32,7 +32,7 @@ def detect_head( - model: HookedTransformer, + model: TransformerLensModel, seq: Union[str, List[str]], detection_pattern: Union[torch.Tensor, HeadName], heads: Optional[Union[List[LayerHeadTuple], LayerToHead]] = None, diff --git a/transformer_lens/hook_points.py b/transformer_lens/hook_points.py index bbaf10f4d3..1d0c041f9d 100644 --- a/transformer_lens/hook_points.py +++ b/transformer_lens/hook_points.py @@ -440,7 +440,7 @@ def __getattr__(name: str): warnings.warn( "Importing HookedRootModule from transformer_lens.hook_points is " - "deprecated and will be removed in a future release. Import it from " + "deprecated and will be removed in TransformerLens 4.0. Import it from " "transformer_lens (preferred) or transformer_lens.HookedRootModule instead.", DeprecationWarning, stacklevel=2, diff --git a/transformer_lens/loading_from_pretrained.py b/transformer_lens/loading_from_pretrained.py index 0c5ee3ca63..606bd1ebaf 100644 --- a/transformer_lens/loading_from_pretrained.py +++ b/transformer_lens/loading_from_pretrained.py @@ -56,6 +56,11 @@ convert_t5_weights, ) from transformer_lens.supported_models import MODEL_ALIASES, OFFICIAL_MODEL_NAMES +from transformer_lens.tools.model_registry.checkpoints import ( + PYTHIA_CHECKPOINTS, + PYTHIA_V0_CHECKPOINTS, + STANFORD_CRFM_CHECKPOINTS, +) from transformer_lens.utilities.hf_utils import get_rotary_pct_from_config NON_HF_HOSTED_MODEL_NAMES = [ @@ -1756,21 +1761,9 @@ def get_num_params_of_pretrained(model_name: str) -> int: # %% Load checkpointed model state dicts -# The steps for which there are checkpoints in the stanford crfm models -STANFORD_CRFM_CHECKPOINTS = ( - list(range(0, 100, 10)) - + list(range(100, 2000, 50)) - + list(range(2000, 20000, 100)) - + list(range(20000, 400000 + 1, 1000)) -) - -# Linearly spaced checkpoints for Pythia models, taken every 1000 steps. -# Batch size 2,097,152 tokens, so checkpoints every 2.1B tokens -PYTHIA_CHECKPOINTS = [0, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512] + list( - range(1000, 143000 + 1, 1000) -) -# Pythia V1 has log-spaced early checkpoints (see line above), but V0 doesn't -PYTHIA_V0_CHECKPOINTS = list(range(1000, 143000 + 1, 1000)) +# Checkpoint schedules (STANFORD_CRFM_CHECKPOINTS, PYTHIA_CHECKPOINTS, +# PYTHIA_V0_CHECKPOINTS) are imported from tools/model_registry/checkpoints.py, +# their canonical home. def get_checkpoint_labels(model_name: str, **kwargs: Any) -> tuple[list[int], str]: diff --git a/transformer_lens/model_bridge/bridge_core.py b/transformer_lens/model_bridge/bridge_core.py index 9637cdadce..ee35b0a0a4 100644 --- a/transformer_lens/model_bridge/bridge_core.py +++ b/transformer_lens/model_bridge/bridge_core.py @@ -3,12 +3,13 @@ import re import warnings -from contextlib import contextmanager -from functools import lru_cache +from contextlib import contextmanager, nullcontext +from functools import lru_cache, partial from typing import ( Any, Callable, Dict, + Iterable, Iterator, List, Literal, @@ -16,6 +17,7 @@ Optional, Tuple, Union, + cast, overload, ) @@ -28,6 +30,7 @@ from transformer_lens.model_bridge.exceptions import StopAtLayerException from transformer_lens.utilities.aliases import resolve_alias from transformer_lens.utilities.lm_utils import lm_cross_entropy_loss +from transformer_lens.utilities.slice import Slice, SliceInput _BLOCK_PATTERN = re.compile("blocks\\.(\\d+)") @@ -121,6 +124,24 @@ def hook_dict(self) -> dict[str, HookPoint]: self._add_aliases_to_hooks(hooks) return hooks + @property + def mod_dict(self) -> Dict[str, Any]: + """Module/hook name -> object, HookedRootModule-compatible. + + Union of the named-module tree and the aliased hook view, so both + canonical (``blocks.0.mlp.hook_out``) and HT-style + (``blocks.0.hook_mlp_out``) names resolve to the same HookPoint. + """ + # BridgeCore is a mixin; concrete bridges (TransformerBridge/RemoteBridge) + # are nn.Modules, so named_modules() is always present at runtime. + mods: Dict[str, Any] = { + name: module + for name, module in cast(torch.nn.Module, self).named_modules() + if name != "" + } + mods.update(self.hook_dict) + return mods + # ---- alias registry ---- def _register_aliases(self) -> None: @@ -365,6 +386,19 @@ def _check_loss_supported(self, return_type: Optional[str]) -> None: # ---- hook lookup / mutation ---- + @staticmethod + def _is_embedding_stage_hook(name: str) -> bool: + """Hooks belonging to the pre-block token/positional embedding stage. + + Excluded from ``start_at_layer`` output: the caller's residual already + carries the embedding, so the embedding stage is logically skipped even + though HF still computes (and discards) it. ``unembed``/``hook_unembed`` + are the output stage and deliberately not matched. + """ + return name in ("hook_embed", "hook_pos_embed", "hook_tokens") or name.startswith( + ("embed.", "pos_embed.") + ) + def _check_hook_fireable(self, *names: str) -> None: """Fail loud when the driver declares it can't fire a requested hook — attaching anyway would yield a silently-unhooked forward / empty cache.""" @@ -404,6 +438,33 @@ def get_hook_point(self, hook_name: str) -> Optional[HookPoint]: pass return None + def check_hooks_to_add( + self, + hook_point: HookPoint, + hook_point_name: str, + hook: Callable, + dir: Literal["fwd", "bwd"] = "fwd", + is_permanent: bool = False, + prepend: bool = False, + ) -> None: + """Validate a hook before it is added; override to add checks. + + No-op by default (mirrors ``HookedRootModule.check_hooks_to_add``). + Driver-fireability is enforced separately in ``_check_hook_fireable``. + """ + + def _add_fn_to_hook_point( + self, + hook_point: HookPoint, + name: str, + hook_fn: Callable, + dir: Literal["fwd", "bwd"], + is_permanent: bool, + ) -> None: + """Run the extension-point check, then attach the hook function.""" + self.check_hooks_to_add(hook_point, name, hook_fn, dir=dir, is_permanent=is_permanent) + hook_point.add_hook(hook_fn, dir=dir, is_permanent=is_permanent) + def add_hook( self, name: Union[str, Callable[[str], bool]], @@ -431,7 +492,7 @@ def add_hook( if hook_id in seen_hooks: continue seen_hooks.add(hook_id) - hook_point.add_hook(hook_fn, dir=dir, is_permanent=is_permanent) + self._add_fn_to_hook_point(hook_point, hook_name, hook_fn, dir, is_permanent) return # Fast path: canonical registry names skip the alias-map build (hook_dict + @@ -439,7 +500,7 @@ def add_hook( registry_hp = self._hook_registry.get(name) if registry_hp is not None: self._check_hook_fireable(name) - registry_hp.add_hook(hook_fn, dir=dir, is_permanent=is_permanent) + self._add_fn_to_hook_point(registry_hp, name, hook_fn, dir, is_permanent) return # Same alias resolution run_with_hooks uses, so HT-style names work here too. canonical = build_alias_to_canonical_map(self.hook_dict).get(name, name) @@ -449,7 +510,7 @@ def add_hook( self._check_hook_fireable(name) registry_hp = self._hook_registry.get(canonical) if registry_hp is not None: - registry_hp.add_hook(hook_fn, dir=dir, is_permanent=is_permanent) + self._add_fn_to_hook_point(registry_hp, canonical, hook_fn, dir, is_permanent) return component: Any = self @@ -463,7 +524,7 @@ def add_hook( if hasattr(component, hook_name): hook_point = getattr(component, hook_name) if isinstance(hook_point, HookPoint): - hook_point.add_hook(hook_fn, dir=dir, is_permanent=is_permanent) + self._add_fn_to_hook_point(hook_point, name, hook_fn, dir, is_permanent) else: raise AttributeError( f"'{hook_name}' is not a hook point. Found object of type: {type(hook_point)} with value: {hook_point}" @@ -485,23 +546,177 @@ def add_perma_hook( """ self.add_hook(name, hook_fn, dir=dir, is_permanent=True) - def reset_hooks(self, clear_contexts: bool = True) -> None: - """Remove all hooks. Registry is canonical; nn.Module children walked additively.""" + def hook_points(self) -> Iterable[HookPoint]: + """All :class:`HookPoint` instances (registry is canonical and complete).""" + return self._hook_registry.values() + + def clear_contexts(self) -> None: + """Clear the stored ``ctx`` on every hook point.""" for hp in self._hook_registry.values(): - hp.remove_hooks() - if hasattr(self, "children"): - from transformer_lens.model_bridge.generalized_components.base import ( - GeneralizedComponent, - ) + hp.clear_context() + + def remove_all_hook_fns( + self, + direction: Literal["fwd", "bwd", "both"] = "both", + including_permanent: bool = False, + level: Optional[int] = None, + ) -> None: + """Remove hook functions from every hook point.""" + for hp in self._hook_registry.values(): + hp.remove_hooks(direction, including_permanent=including_permanent, level=level) + + def reset_hooks( + self, + clear_contexts: bool = True, + direction: Literal["fwd", "bwd", "both"] = "both", + including_permanent: bool = False, + level: Optional[int] = None, + ) -> None: + """Remove hooks from every hook point; mirrors ``HookedRootModule.reset_hooks``. + + The hook registry is canonical and complete (every component's HookPoint + is registered), so a single pass covers the whole model. + + Args: + clear_contexts: Also clear each hook point's stored ``ctx``. + direction: Which direction(s) to remove — ``"fwd"``, ``"bwd"``, or ``"both"``. + including_permanent: If True, also remove hooks added via ``add_perma_hook``. + level: If set, only remove hooks registered at this context level. + """ + if clear_contexts: + self.clear_contexts() + self.remove_all_hook_fns(direction, including_permanent=including_permanent, level=level) + + @staticmethod + def _normalize_names_filter( + names_filter: Optional[Union[str, List[str], Callable[[str], bool]]], + ) -> Callable[[str], bool]: + """Turn None / str / list / callable into a name predicate.""" + if names_filter is None: + return lambda name: True + if isinstance(names_filter, str): + return lambda name: name == names_filter + if isinstance(names_filter, list): + return lambda name: name in names_filter + if callable(names_filter): + return names_filter + raise ValueError("names_filter must be None, a string, a list of strings, or a callable") + + @staticmethod + def _pos_slice_dim(name: str) -> int: + """Position dimension for a hook's activation (see ``run_with_cache``).""" + return -2 if name.endswith(("hook_pattern", "hook_attn_scores")) else 1 + + def get_caching_hooks( + self, + names_filter: Optional[Union[str, List[str], Callable[[str], bool]]] = None, + incl_bwd: bool = False, + device: Any = None, + remove_batch_dim: bool = False, + cache: Optional[dict] = None, + pos_slice: Optional[Union[Slice, SliceInput]] = None, + ) -> Tuple[dict, list, list]: + """Build caching hooks without adding them. Mirrors ``HookedRootModule.get_caching_hooks``. + + Returns ``(cache, fwd_hooks, bwd_hooks)`` where each hook is a + ``(name, hook_fn)`` pair suitable for ``hooks()`` / ``run_with_hooks``. + Activations are keyed by the HookPoint's canonical name; backward hooks + append ``"_grad"``. ``bwd_hooks`` is empty unless ``incl_bwd``. + """ + if cache is None: + cache = {} + pos_slice_obj = Slice.unwrap(pos_slice) + filter_fn = self._normalize_names_filter(names_filter) + + def save_hook(tensor: Any, hook: Any, is_backward: bool = False) -> None: + assert hook.name is not None + key = hook.name + "_grad" if is_backward else hook.name + stored = tensor.detach().to(device) + if remove_batch_dim: + stored = stored[0] + if pos_slice_obj is not None and stored.dim() >= 2: + stored = pos_slice_obj.apply(stored, dim=self._pos_slice_dim(hook.name)) + cache[key] = stored + + fwd_hooks: list = [] + bwd_hooks: list = [] + seen: set = set() + for name, hook_point in self.hook_dict.items(): + if filter_fn(name) and id(hook_point) not in seen: + seen.add(id(hook_point)) + fwd_hooks.append((name, partial(save_hook, is_backward=False))) + if incl_bwd: + bwd_hooks.append((name, partial(save_hook, is_backward=True))) + return cache, fwd_hooks, bwd_hooks + + def add_caching_hooks( + self, + names_filter: Optional[Union[str, List[str], Callable[[str], bool]]] = None, + incl_bwd: bool = False, + device: Any = None, + remove_batch_dim: bool = False, + cache: Optional[dict] = None, + ) -> dict: + """Attach caching hooks to the model (does not run it). Returns the cache dict. + + Mirrors ``HookedRootModule.add_caching_hooks``. The hooks persist until + ``reset_hooks()``. + """ + cache, fwd_hooks, bwd_hooks = self.get_caching_hooks( + names_filter, + incl_bwd=incl_bwd, + device=device, + remove_batch_dim=remove_batch_dim, + cache=cache, + ) + for name, hook_fn in fwd_hooks: + self.add_hook(name, hook_fn, dir="fwd") + for name, hook_fn in bwd_hooks: + self.add_hook(name, hook_fn, dir="bwd") + return cache - def remove_hooks_recursive(module: Any) -> None: - if isinstance(module, GeneralizedComponent): - module.remove_hooks() - if hasattr(module, "children"): - for child in module.children(): - remove_hooks_recursive(child) + def cache_all( + self, + cache: Optional[dict], + incl_bwd: bool = False, + device: Any = None, + remove_batch_dim: bool = False, + ) -> None: + """Deprecated: cache every activation. Use ``run_with_cache`` / ``add_caching_hooks``.""" + warnings.warn( + "cache_all is deprecated; use run_with_cache or add_caching_hooks.", + DeprecationWarning, + stacklevel=2, + ) + self.add_caching_hooks( + names_filter=None, + cache=cache, + incl_bwd=incl_bwd, + device=device, + remove_batch_dim=remove_batch_dim, + ) - remove_hooks_recursive(self) + def cache_some( + self, + cache: Optional[dict], + names: Callable[[str], bool], + incl_bwd: bool = False, + device: Any = None, + remove_batch_dim: bool = False, + ) -> None: + """Deprecated: cache activations matching ``names``. Use ``run_with_cache``.""" + warnings.warn( + "cache_some is deprecated; use run_with_cache or add_caching_hooks.", + DeprecationWarning, + stacklevel=2, + ) + self.add_caching_hooks( + names_filter=names, + cache=cache, + incl_bwd=incl_bwd, + device=device, + remove_batch_dim=remove_batch_dim, + ) def hooks( self, @@ -527,6 +742,7 @@ def add_hook_to_point( name: str, dir: Literal["fwd", "bwd"] = "fwd", ) -> None: + self.check_hooks_to_add(hook_point, name, hook_fn, dir=dir) if self.compatibility_mode and name != hook_point.name: alias_names_list: list = [] if hook_point.name is not None: @@ -580,14 +796,17 @@ def run_with_hooks( clear_contexts: bool = False, return_type: Optional[str] = "logits", stop_at_layer: Optional[int] = None, + start_at_layer: Optional[int] = None, remove_batch_dim: bool = False, **kwargs: Any, ) -> Any: """Run the model with specified forward and backward hooks. ``stop_at_layer`` raises :class:`StopAtLayerException` to stop early - (KV cache cleaned up on stop). ``remove_batch_dim`` squeezes/unsqueezes - the batch dim around hook callbacks (batch_size==1 only). + (KV cache cleaned up on stop). ``start_at_layer`` treats ``input`` as the + residual entering block ``k`` (see :meth:`forward`); hooks on blocks below + ``k`` are skipped to match HookedTransformer. ``remove_batch_dim`` + squeezes/unsqueezes the batch dim around hook callbacks (batch_size==1 only). """ if "names_filter" in kwargs: # **kwargs would silently absorb it; fail loud. @@ -602,6 +821,11 @@ def run_with_hooks( effective_stop_layer = len(self.blocks) + stop_at_layer else: effective_stop_layer = stop_at_layer + effective_start_layer = None + if start_at_layer is not None and hasattr(self, "blocks"): + effective_start_layer = ( + len(self.blocks) + start_at_layer if start_at_layer < 0 else start_at_layer + ) def add_hook_to_point( hook_point: HookPoint, @@ -609,13 +833,19 @@ def add_hook_to_point( name: str, dir: Literal["fwd", "bwd"] = "fwd", ) -> None: - if effective_stop_layer is not None and name.startswith("blocks."): + if effective_start_layer is not None and self._is_embedding_stage_hook(name): + return + if name.startswith("blocks."): try: - layer_num = int(name.split(".")[1]) - if layer_num >= effective_stop_layer: - return + layer_num: Optional[int] = int(name.split(".")[1]) except (IndexError, ValueError): - pass + layer_num = None + if layer_num is not None: + if effective_stop_layer is not None and layer_num >= effective_stop_layer: + return + if effective_start_layer is not None and layer_num < effective_start_layer: + return + self.check_hooks_to_add(hook_point, name, hook_fn, dir=dir) if self.compatibility_mode and name != hook_point.name: alias_names_list: list = [] if hook_point.name is not None: @@ -681,6 +911,8 @@ def wrapped_hook_fn(tensor, hook, _orig_fn=original_hook_fn): try: apply_hooks(fwd_hooks, True) apply_hooks(bwd_hooks, False) + if start_at_layer is not None: + kwargs["start_at_layer"] = start_at_layer try: output = self.forward( input, return_type=return_type, stop_at_layer=stop_at_layer, **kwargs @@ -724,14 +956,26 @@ def run_with_cache( remove_batch_dim: bool = False, names_filter: Optional[Union[str, List[str], Callable[[str], bool]]] = None, stop_at_layer: Optional[int] = None, + start_at_layer: Optional[int] = None, + pos_slice: Optional[Union[Slice, SliceInput]] = None, + incl_bwd: bool = False, **kwargs, ) -> Tuple[Any, Union[ActivationCache, Dict[str, torch.Tensor]]]: """Run the model and cache activations. Returns ``(output, cache)``. ``stop_at_layer`` raises :class:`StopAtLayerException` to stop early. - ``device`` offloads cached activations (matches ``ActivationCache.to``); the - model and inputs stay where the caller put them. + ``start_at_layer`` treats ``input`` as the residual entering block ``k`` + (see :meth:`forward`); blocks below ``k`` are excluded from the cache to + match HookedTransformer. ``pos_slice`` slices each cached activation along + its position dimension (dim 1 for resid/per-head/token-id activations; the + query position ``-2`` for attention patterns/scores). ``incl_bwd`` also + caches gradients under ``"_grad"`` by running ``output.backward()``; + the caller must request a scalar output (``return_type="loss"``) and the + model must be on the gradients-capable transformers driver. ``device`` + offloads cached activations (matches ``ActivationCache.to``); the model and + inputs stay where the caller put them. """ + pos_slice_obj = Slice.unwrap(pos_slice) aliases = build_alias_to_canonical_map(self.hook_dict) def create_names_filter_fn(filter_input): @@ -768,21 +1012,30 @@ def create_names_filter_fn(filter_input): # None → no-op .to(None), tensors stay on their current device. cache_device = kwargs.pop("device", None) + def _store(name: str, value: torch.Tensor) -> None: + stored = value.detach().to(cache_device) + if pos_slice_obj is not None and stored.dim() >= 2: + # Position is dim 1 for every bridge activation (resid [b,p,d], per-head + # [b,p,h,d], token ids [b,p]) except attention patterns/scores, which + # slice the query position at -2 — see _pos_slice_dim. + stored = pos_slice_obj.apply(stored, dim=self._pos_slice_dim(name)) + cache[name] = stored + def make_cache_hook(name: str): def cache_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: if tensor is None: cache[name] = None elif isinstance(tensor, torch.Tensor): - cache[name] = tensor.detach().to(cache_device) + _store(name, tensor) elif isinstance(tensor, tuple): if len(tensor) > 0 and isinstance(tensor[0], torch.Tensor): - cache[name] = tensor[0].detach().to(cache_device) + _store(name, tensor[0]) else: pass else: try: if hasattr(tensor, "detach"): - cache[name] = tensor.detach().to(cache_device) + _store(name, tensor) except: pass return tensor @@ -796,18 +1049,30 @@ def cache_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: effective_stop_layer = len(self.blocks) + stop_at_layer else: effective_stop_layer = stop_at_layer + effective_start_layer = None + if start_at_layer is not None and hasattr(self, "blocks"): + effective_start_layer = ( + len(self.blocks) + start_at_layer if start_at_layer < 0 else start_at_layer + ) matched_any = False for hook_name, hook in hook_dict.items(): if names_filter_fn(hook_name): matched_any = True - if effective_stop_layer is not None: - if hook_name.startswith("blocks."): - try: - layer_num = int(hook_name.split(".")[1]) - if layer_num >= effective_stop_layer: - continue - except (IndexError, ValueError): - pass + if effective_start_layer is not None and self._is_embedding_stage_hook(hook_name): + continue + if hook_name.startswith("blocks."): + try: + layer_num = int(hook_name.split(".")[1]) + except (IndexError, ValueError): + layer_num = None + if layer_num is not None: + # stop/start bound the executed range; blocks outside it + # either don't run (stop) or run on discarded input (start), + # so their activations must not enter the cache. + if effective_stop_layer is not None and layer_num >= effective_stop_layer: + continue + if effective_start_layer is not None and layer_num < effective_start_layer: + continue hooks.append((hook, hook_name)) # Explicit string/list filters matching nothing must not return (logits, {}) silently. if not matched_any and names_filter and isinstance(names_filter, (str, list)): @@ -815,8 +1080,19 @@ def cache_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: f"names_filter {names_filter!r} matched no hook points on this model; " "check the name against model.hook_dict (this backend may not serve it)." ) + + def make_grad_cache_hook(name: str): + def grad_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: + if isinstance(tensor, torch.Tensor): + _store(name + "_grad", tensor) + return tensor + + return grad_hook + for hp, name in hooks: hp.add_hook(make_cache_hook(name)) + if incl_bwd: + hp.add_hook(make_grad_cache_hook(name), dir="bwd") processed_args = [input] # Driver-aware input placement: torch drivers move input_ids to the model's # device; remote drivers (no local parameters) leave them as-is. @@ -864,30 +1140,37 @@ def stop_hook(tensor: torch.Tensor, *, hook: Any) -> torch.Tensor: "will remain on their per-layer devices.", stacklevel=2, ) + if start_at_layer is not None: + filtered_kwargs["start_at_layer"] = start_at_layer try: if ( "output_attentions" not in filtered_kwargs and self.adapter.supports_hf_output_attentions ): filtered_kwargs["output_attentions"] = True - if processed_args: - output = self.forward(processed_args[0], **filtered_kwargs) - elif "input_ids" in filtered_kwargs: - output = self.forward( - filtered_kwargs["input_ids"], - **{k: v for k, v in filtered_kwargs.items() if k != "input_ids"}, - ) - else: - output = self.forward(**filtered_kwargs) - if hasattr(output, "logits"): - output = output.logits + # incl_bwd needs grad to build the graph and run backward while the + # bwd cache hooks are still attached (i.e. before the finally below). + with torch.enable_grad() if incl_bwd else nullcontext(): + if processed_args: + output = self.forward(processed_args[0], **filtered_kwargs) + elif "input_ids" in filtered_kwargs: + output = self.forward( + filtered_kwargs["input_ids"], + **{k: v for k, v in filtered_kwargs.items() if k != "input_ids"}, + ) + else: + output = self.forward(**filtered_kwargs) + if hasattr(output, "logits"): + output = output.logits + if incl_bwd: + output.backward() except StopAtLayerException as e: output = e.layer_output except Exception as e: raise e finally: for hp, _ in hooks: - hp.remove_hooks(dir="fwd") + hp.remove_hooks(dir="both" if incl_bwd else "fwd") if self.compatibility_mode == True: reverse_aliases = {} for old_name, new_name in aliases.items(): diff --git a/transformer_lens/model_bridge/generalized_components/__init__.py b/transformer_lens/model_bridge/generalized_components/__init__.py index 1ab4685eb5..5ed5826e2b 100644 --- a/transformer_lens/model_bridge/generalized_components/__init__.py +++ b/transformer_lens/model_bridge/generalized_components/__init__.py @@ -1,4 +1,11 @@ """Bridge components for transformer architectures.""" + +from transformer_lens.model_bridge.generalized_components.alibi_joint_qkv_attention import ( + ALiBiJointQKVAttentionBridge, +) +from transformer_lens.model_bridge.generalized_components.altup_block import ( + AltUpBlockBridge, +) from transformer_lens.model_bridge.generalized_components.attention import ( AttentionBridge, ) @@ -14,9 +21,6 @@ from transformer_lens.model_bridge.generalized_components.bloom_attention import ( BloomAttentionBridge, ) -from transformer_lens.model_bridge.generalized_components.codegen_attention import ( - CodeGenAttentionBridge, -) from transformer_lens.model_bridge.generalized_components.bloom_block import ( BloomBlockBridge, ) @@ -27,6 +31,9 @@ CLIPVisionEncoderBridge, CLIPVisionEncoderLayerBridge, ) +from transformer_lens.model_bridge.generalized_components.codegen_attention import ( + CodeGenAttentionBridge, +) from transformer_lens.model_bridge.generalized_components.conv1d import Conv1DBridge from transformer_lens.model_bridge.generalized_components.conv_pos_embed import ( ConvPosEmbedBridge, @@ -37,24 +44,18 @@ from transformer_lens.model_bridge.generalized_components.embedding import ( EmbeddingBridge, ) -from transformer_lens.model_bridge.generalized_components.alibi_joint_qkv_attention import ( - ALiBiJointQKVAttentionBridge, -) from transformer_lens.model_bridge.generalized_components.gated_delta_net import ( GatedDeltaNetBridge, ) -from transformer_lens.model_bridge.generalized_components.altup_block import ( - AltUpBlockBridge, -) from transformer_lens.model_bridge.generalized_components.gated_mlp import ( GatedMLPBridge, ) -from transformer_lens.model_bridge.generalized_components.glm_moe_dsa_attention import ( - GlmMoeDsaAttentionBridge, -) from transformer_lens.model_bridge.generalized_components.gated_rms_norm import ( GatedRMSNormBridge, ) +from transformer_lens.model_bridge.generalized_components.glm_moe_dsa_attention import ( + GlmMoeDsaAttentionBridge, +) from transformer_lens.model_bridge.generalized_components.joint_gate_up_mlp import ( JointGateUpMLPBridge, ) @@ -72,23 +73,26 @@ MLAAttentionBridge, ) from transformer_lens.model_bridge.generalized_components.mlp import MLPBridge +from transformer_lens.model_bridge.generalized_components.moe import MoEBridge from transformer_lens.model_bridge.generalized_components.mpt_alibi_attention import ( MPTALiBiAttentionBridge, ) -from transformer_lens.model_bridge.generalized_components.moe import MoEBridge from transformer_lens.model_bridge.generalized_components.normalization import ( NormalizationBridge, ) +from transformer_lens.model_bridge.generalized_components.opaque_block import ( + OpaqueBlockBridge, +) from transformer_lens.model_bridge.generalized_components.pos_embed import ( PosEmbedBridge, ) +from transformer_lens.model_bridge.generalized_components.position_embeddings_attention import ( + PositionEmbeddingsAttentionBridge, +) from transformer_lens.model_bridge.generalized_components.qwen3_5_vision_encoder import ( Qwen3_5VisionBlockBridge, Qwen3_5VisionEncoderBridge, ) -from transformer_lens.model_bridge.generalized_components.position_embeddings_attention import ( - PositionEmbeddingsAttentionBridge, -) from transformer_lens.model_bridge.generalized_components.rms_normalization import ( RMSNormalizationBridge, ) @@ -99,9 +103,15 @@ SiglipVisionEncoderBridge, SiglipVisionEncoderLayerBridge, ) -from transformer_lens.model_bridge.generalized_components.ssm2_mixer import SSM2MixerBridge -from transformer_lens.model_bridge.generalized_components.ssm_block import SSMBlockBridge -from transformer_lens.model_bridge.generalized_components.ssm_mixer import SSMMixerBridge +from transformer_lens.model_bridge.generalized_components.ssm2_mixer import ( + SSM2MixerBridge, +) +from transformer_lens.model_bridge.generalized_components.ssm_block import ( + SSMBlockBridge, +) +from transformer_lens.model_bridge.generalized_components.ssm_mixer import ( + SSMMixerBridge, +) from transformer_lens.model_bridge.generalized_components.ssm_protocol import ( SSMMixerProtocol, SSMStateHookMixin, @@ -109,15 +119,15 @@ ) from transformer_lens.model_bridge.generalized_components.symbolic import SymbolicBridge from transformer_lens.model_bridge.generalized_components.t5_block import T5BlockBridge -from transformer_lens.model_bridge.generalized_components.t5gemma_decoder_block import ( - T5GemmaDecoderBlockBridge, -) from transformer_lens.model_bridge.generalized_components.t5gemma2_decoder_block import ( T5Gemma2DecoderBlockBridge, ) from transformer_lens.model_bridge.generalized_components.t5gemma2_merged_attention import ( T5Gemma2MergedAttentionBridge, ) +from transformer_lens.model_bridge.generalized_components.t5gemma_decoder_block import ( + T5GemmaDecoderBlockBridge, +) from transformer_lens.model_bridge.generalized_components.unembedding import ( UnembeddingBridge, ) @@ -175,6 +185,7 @@ "SSMMixerProtocol", "SSMStateHookMixin", "find_ssm_mixer", + "OpaqueBlockBridge", "SSMBlockBridge", "SSMMixerBridge", "VisionProjectionBridge", diff --git a/transformer_lens/model_bridge/generalized_components/block.py b/transformer_lens/model_bridge/generalized_components/block.py index 00151fc0b3..b998c15836 100644 --- a/transformer_lens/model_bridge/generalized_components/block.py +++ b/transformer_lens/model_bridge/generalized_components/block.py @@ -194,6 +194,7 @@ def forward(self, *args: Any, **kwargs: Any) -> Any: ) self._maybe_wire_pre_ln_capture() + args, kwargs = self._maybe_inject_start_residual(args, kwargs) self._check_stop_at_layer(*args, **kwargs) args, kwargs = self._hook_input_hidden_states(args, kwargs) @@ -257,35 +258,57 @@ def _is_standalone_hidden_state_call(args: tuple, kwargs: dict) -> bool: and isinstance(kwargs["hidden_states"], torch.Tensor) ) + def _extract_layer_idx(self) -> Optional[int]: + """Parse this block's layer index from its name (TL/GPT-2/LLaMA patterns).""" + if self.name is None: + return None + match = ( + re.search(r"blocks\.(\d+)", self.name) + or re.search(r"\.h\.(\d+)", self.name) + or re.search(r"\.layers\.(\d+)", self.name) + ) + return int(match.group(1)) if match else None + def _check_stop_at_layer(self, *args: Any, **kwargs: Any) -> None: """Check if execution should stop before this block. Raises StopAtLayerException. The _stop_at_layer_idx attribute is set by the bridge's forward method. Supports TL/GPT-2/LLaMA naming patterns for layer index extraction. """ - if not (hasattr(self, "_stop_at_layer_idx") and self._stop_at_layer_idx is not None): + if getattr(self, "_stop_at_layer_idx", None) is None: return - if self.name is not None: - match = ( - re.search(r"blocks\.(\d+)", self.name) - or re.search(r"\.h\.(\d+)", self.name) - or re.search(r"\.layers\.(\d+)", self.name) - ) - else: - match = None - if match: - layer_idx = int(match.group(1)) - if layer_idx == self._stop_at_layer_idx: - if len(args) > 0 and isinstance(args[0], torch.Tensor): - input_tensor = args[0] - elif "hidden_states" in kwargs and isinstance( - kwargs["hidden_states"], torch.Tensor - ): - input_tensor = kwargs["hidden_states"] - else: - raise ValueError(f"Cannot find input tensor to stop at layer {layer_idx}") - input_tensor = self.hook_in(input_tensor) - raise StopAtLayerException(input_tensor) + layer_idx = self._extract_layer_idx() + if layer_idx is not None and layer_idx == self._stop_at_layer_idx: + if len(args) > 0 and isinstance(args[0], torch.Tensor): + input_tensor = args[0] + elif "hidden_states" in kwargs and isinstance(kwargs["hidden_states"], torch.Tensor): + input_tensor = kwargs["hidden_states"] + else: + raise ValueError(f"Cannot find input tensor to stop at layer {layer_idx}") + input_tensor = self.hook_in(input_tensor) + raise StopAtLayerException(input_tensor) + + def _maybe_inject_start_residual(self, args: tuple, kwargs: dict) -> tuple[tuple, dict]: + """If this is the start_at_layer block, swap in the caller's residual. + + Mirror of ``_check_stop_at_layer``: the bridge's forward stashes the + residual-stream input on the block via ``_start_residual`` and sets + ``_start_at_layer_idx``. This block replaces its incoming hidden states + with that residual; ``_hook_input_hidden_states`` then fires ``hook_in`` + on it, so ``hook_resid_pre`` reflects the injected value. + """ + if getattr(self, "_start_at_layer_idx", None) is None: + return args, kwargs + if self._extract_layer_idx() != self._start_at_layer_idx: + return args, kwargs + residual = getattr(self, "_start_residual", None) + if residual is None: + return args, kwargs + if len(args) > 0 and isinstance(args[0], torch.Tensor): + args = (residual,) + args[1:] + elif "hidden_states" in kwargs: + kwargs = {**kwargs, "hidden_states": residual} + return args, kwargs def _hook_input_hidden_states(self, args: tuple, kwargs: dict) -> tuple[tuple, dict]: """Apply hook_in to the hidden_states input, whether in args or kwargs.""" diff --git a/transformer_lens/model_bridge/generalized_components/moe.py b/transformer_lens/model_bridge/generalized_components/moe.py index 18bbdac7ad..67a677ffc6 100644 --- a/transformer_lens/model_bridge/generalized_components/moe.py +++ b/transformer_lens/model_bridge/generalized_components/moe.py @@ -2,6 +2,7 @@ This module contains the bridge component for Mixture of Experts layers. """ + from __future__ import annotations from typing import Any, Dict, Optional @@ -110,10 +111,24 @@ def forward(self, *args: Any, **kwargs: Any) -> Any: kwargs = {**kwargs, "hidden_states": hooked} output = self.original_component(*args, **kwargs) if isinstance(output, tuple): + if not output: + raise TypeError( + f"{self.name}: expected a non-empty tuple whose first " + "element is a torch.Tensor from the wrapped MoE component, " + "got an empty tuple." + ) + hidden_states = output[0] + if not isinstance(hidden_states, torch.Tensor): + raise TypeError( + f"{self.name}: expected the first tuple element from the " + f"wrapped MoE component to be a torch.Tensor, got " + f"{type(hidden_states).__name__}." + ) if len(output) > 1: router_scores = output[1] - self.hook_router_scores(router_scores) + if isinstance(router_scores, torch.Tensor): + self.hook_router_scores(router_scores) hidden_states = self.hook_out(hidden_states) return (hidden_states,) + output[1:] else: diff --git a/transformer_lens/model_bridge/generalized_components/opaque_block.py b/transformer_lens/model_bridge/generalized_components/opaque_block.py new file mode 100644 index 0000000000..2bfb990fc6 --- /dev/null +++ b/transformer_lens/model_bridge/generalized_components/opaque_block.py @@ -0,0 +1,112 @@ +"""Generic opaque block bridge for non-SSM, non-standard-transformer architectures.""" + +from __future__ import annotations + +import re +from typing import Any, Dict, Optional + +import torch + +from transformer_lens.model_bridge.exceptions import StopAtLayerException +from transformer_lens.model_bridge.generalized_components.base import ( + GeneralizedComponent, +) + + +class OpaqueBlockBridge(GeneralizedComponent): + """Generic block bridge that delegates a full block forward unchanged. + + Exposes ``hook_resid_pre`` / ``hook_resid_post`` (aliases of ``hook_in`` + / ``hook_out``) on the residual stream only — no assumptions are made + about the block's internal structure. + + Use this for architectures whose block internals do not follow a standard + SSM or transformer pre-norm flow, e.g.: + + - Post-residual (sandwich-norm) blocks like Raven / Huginn. + - Attention-free recurrent blocks like RWKV-7. + - Any custom block where ``hook_mixer_in`` / ``hook_mixer_out`` would be + semantically wrong or structurally absent. + + For SSM architectures (Mamba, Falcon-H1) use :class:`SSMBlockBridge`, + which extends this class and adds the ``hook_mixer_in`` / + ``hook_mixer_out`` aliases pointing at ``mixer.hook_in`` / + ``mixer.hook_out``. + """ + + is_list_item: bool = True + hook_out_is_single_residual_stream: bool = True + hook_aliases = { + "hook_resid_pre": "hook_in", + "hook_resid_post": "hook_out", + } + + def __init__( + self, + name: str, + config: Optional[Any] = None, + submodules: Optional[Dict[str, GeneralizedComponent]] = None, + hook_alias_overrides: Optional[Dict[str, str]] = None, + ): + super().__init__( + name, + config, + submodules=submodules if submodules is not None else {}, + hook_alias_overrides=hook_alias_overrides, + ) + + def forward(self, *args: Any, **kwargs: Any) -> Any: + """Delegate to the HF block with hook_in/hook_out wrapped around it.""" + if self.original_component is None: + raise RuntimeError( + f"Original component not set for {self.name}. " + "Call set_original_component() first." + ) + + self._check_stop_at_layer(*args, **kwargs) + args, kwargs = self._hook_input_hidden_states(args, kwargs) + output = self.original_component(*args, **kwargs) + return self._apply_output_hook(output) + + def _apply_output_hook(self, output: Any) -> Any: + """Hook the primary output tensor, preserving tuple structure if present.""" + if isinstance(output, tuple) and len(output) > 0: + first = output[0] + if isinstance(first, torch.Tensor): + first = self.hook_out(first) + return (first,) + output[1:] + return output + if isinstance(output, torch.Tensor): + return self.hook_out(output) + return output + + def _hook_input_hidden_states(self, args: tuple, kwargs: dict) -> tuple[tuple, dict]: + """Hook the hidden_states input whether it arrives positionally or by name.""" + if len(args) > 0 and isinstance(args[0], torch.Tensor): + hooked = self.hook_in(args[0]) + args = (hooked,) + args[1:] + elif "hidden_states" in kwargs and isinstance(kwargs["hidden_states"], torch.Tensor): + kwargs["hidden_states"] = self.hook_in(kwargs["hidden_states"]) + return args, kwargs + + def _check_stop_at_layer(self, *args: Any, **kwargs: Any) -> None: + """Raise StopAtLayerException when the configured stop index matches this block.""" + if not (hasattr(self, "_stop_at_layer_idx") and self._stop_at_layer_idx is not None): + return + if self.name is None: + return + # Mamba uses `.layers.{i}`; `blocks.{i}` is the fallback TL convention. + match = re.search(r"\.layers\.(\d+)", self.name) or re.search(r"blocks\.(\d+)", self.name) + if not match: + return + layer_idx = int(match.group(1)) + if layer_idx != self._stop_at_layer_idx: + return + if len(args) > 0 and isinstance(args[0], torch.Tensor): + input_tensor = args[0] + elif "hidden_states" in kwargs and isinstance(kwargs["hidden_states"], torch.Tensor): + input_tensor = kwargs["hidden_states"] + else: + raise ValueError(f"Cannot find input tensor to stop at layer {layer_idx}") + input_tensor = self.hook_in(input_tensor) + raise StopAtLayerException(input_tensor) diff --git a/transformer_lens/model_bridge/generalized_components/ssm_block.py b/transformer_lens/model_bridge/generalized_components/ssm_block.py index ad9d8c14f9..28d0720fd9 100644 --- a/transformer_lens/model_bridge/generalized_components/ssm_block.py +++ b/transformer_lens/model_bridge/generalized_components/ssm_block.py @@ -1,99 +1,27 @@ """Block container for State Space Model (Mamba) layers: norm → mixer → residual.""" + from __future__ import annotations -import re -from typing import Any, Dict, Optional +from transformer_lens.model_bridge.generalized_components.opaque_block import ( + OpaqueBlockBridge, +) -import torch -from transformer_lens.model_bridge.exceptions import StopAtLayerException -from transformer_lens.model_bridge.generalized_components.base import ( - GeneralizedComponent, -) +class SSMBlockBridge(OpaqueBlockBridge): + """Block bridge for SSM layers (Mamba, Falcon-H1, etc.). + Extends :class:`OpaqueBlockBridge` with SSM-specific hook aliases: -class SSMBlockBridge(GeneralizedComponent): - """Block bridge for SSM layers — direct GeneralizedComponent subclass. + - ``hook_mixer_in`` → ``mixer.hook_in`` + - ``hook_mixer_out`` → ``mixer.hook_out`` - Does not inherit from BlockBridge because BlockBridge's hook_aliases hardcode - transformer-specific names (hook_attn_*, hook_mlp_*, hook_resid_mid). + These aliases are only meaningful for architectures whose blocks contain a + ``mixer`` submodule (SSM or hybrid SSM/attention). For non-SSM architectures + use :class:`OpaqueBlockBridge` directly. """ - is_list_item: bool = True - hook_out_is_single_residual_stream: bool = True hook_aliases = { - "hook_resid_pre": "hook_in", - "hook_resid_post": "hook_out", + **OpaqueBlockBridge.hook_aliases, "hook_mixer_in": "mixer.hook_in", "hook_mixer_out": "mixer.hook_out", } - - def __init__( - self, - name: str, - config: Optional[Any] = None, - submodules: Optional[Dict[str, GeneralizedComponent]] = None, - hook_alias_overrides: Optional[Dict[str, str]] = None, - ): - super().__init__( - name, - config, - submodules=submodules if submodules is not None else {}, - hook_alias_overrides=hook_alias_overrides, - ) - - def forward(self, *args: Any, **kwargs: Any) -> Any: - """Delegate to the HF block with hook_in/hook_out wrapped around it.""" - if self.original_component is None: - raise RuntimeError( - f"Original component not set for {self.name}. " - "Call set_original_component() first." - ) - - self._check_stop_at_layer(*args, **kwargs) - args, kwargs = self._hook_input_hidden_states(args, kwargs) - output = self.original_component(*args, **kwargs) - return self._apply_output_hook(output) - - def _apply_output_hook(self, output: Any) -> Any: - """Hook the primary output tensor, preserving tuple structure if present.""" - if isinstance(output, tuple) and len(output) > 0: - first = output[0] - if isinstance(first, torch.Tensor): - first = self.hook_out(first) - return (first,) + output[1:] - return output - if isinstance(output, torch.Tensor): - return self.hook_out(output) - return output - - def _hook_input_hidden_states(self, args: tuple, kwargs: dict) -> tuple[tuple, dict]: - """Hook the hidden_states input whether it arrives positionally or by name.""" - if len(args) > 0 and isinstance(args[0], torch.Tensor): - hooked = self.hook_in(args[0]) - args = (hooked,) + args[1:] - elif "hidden_states" in kwargs and isinstance(kwargs["hidden_states"], torch.Tensor): - kwargs["hidden_states"] = self.hook_in(kwargs["hidden_states"]) - return args, kwargs - - def _check_stop_at_layer(self, *args: Any, **kwargs: Any) -> None: - """Raise StopAtLayerException when the configured stop index matches this block.""" - if not (hasattr(self, "_stop_at_layer_idx") and self._stop_at_layer_idx is not None): - return - if self.name is None: - return - # Mamba uses `.layers.{i}`; `blocks.{i}` is the fallback TL convention. - match = re.search(r"\.layers\.(\d+)", self.name) or re.search(r"blocks\.(\d+)", self.name) - if not match: - return - layer_idx = int(match.group(1)) - if layer_idx != self._stop_at_layer_idx: - return - if len(args) > 0 and isinstance(args[0], torch.Tensor): - input_tensor = args[0] - elif "hidden_states" in kwargs and isinstance(kwargs["hidden_states"], torch.Tensor): - input_tensor = kwargs["hidden_states"] - else: - raise ValueError(f"Cannot find input tensor to stop at layer {layer_idx}") - input_tensor = self.hook_in(input_tensor) - raise StopAtLayerException(input_tensor) diff --git a/transformer_lens/model_bridge/remote_bridge.py b/transformer_lens/model_bridge/remote_bridge.py index 6b9c5e8b83..50f56d3b5c 100644 --- a/transformer_lens/model_bridge/remote_bridge.py +++ b/transformer_lens/model_bridge/remote_bridge.py @@ -127,6 +127,7 @@ def run_with_hooks( clear_contexts: bool = False, return_type: Optional[str] = "logits", stop_at_layer: Optional[int] = None, + start_at_layer: Optional[int] = None, remove_batch_dim: bool = False, **kwargs: Any, ) -> Any: @@ -138,6 +139,7 @@ def run_with_hooks( "RemoteBridge has no backward pass; bwd_hooks are unsupported." ) self._reject_stop_at_layer(stop_at_layer) + self._reject_start_at_layer(start_at_layer) if fwd_hooks: warnings.warn( "RemoteBridge fwd_hooks fire on already-captured activations (read-only): " @@ -169,9 +171,20 @@ def _reject_stop_at_layer(stop_at_layer: Any) -> None: "always runs the full forward pass." ) + @staticmethod + def _reject_start_at_layer(start_at_layer: Any) -> None: + # Residual-stream injection needs a local `blocks` tree; the remote engine + # only runs a full forward from tokens. + if start_at_layer is not None: + raise NotImplementedError( + "RemoteBridge does not support start_at_layer: the remote engine " + "always runs the full forward pass from tokens." + ) + def run_with_cache(self, *args: Any, **kwargs: Any) -> Any: - """Cache via driver captures; stop_at_layer rejected (full remote forward).""" + """Cache via driver captures; stop/start_at_layer rejected (full remote forward).""" self._reject_stop_at_layer(kwargs.get("stop_at_layer")) + self._reject_start_at_layer(kwargs.get("start_at_layer")) return super().run_with_cache(*args, **kwargs) def to_tokens(self, input: Any, prepend_bos: bool | None = None, truncate: bool = True) -> Any: diff --git a/transformer_lens/model_bridge/sources/_bridge_builder.py b/transformer_lens/model_bridge/sources/_bridge_builder.py index d389bcd8b9..006d3cf07f 100644 --- a/transformer_lens/model_bridge/sources/_bridge_builder.py +++ b/transformer_lens/model_bridge/sources/_bridge_builder.py @@ -127,6 +127,28 @@ "index_head_dim", "index_topk", "q_lora_rank", + # Raven / Huginn (depth-recurrent) + "mean_recurrence", + "mean_backprop_depth", + "n_layers_in_prelude", + "n_layers_in_recurrent_block", + "n_layers_in_coda", + "injection_type", + "qk_bias", + # RWKV-7 (attention-free recurrent, generalized delta-rule time-mixing). + # head_dim is intentionally omitted: it is a read-only alias of d_head on + # TransformerBridgeConfig, so a passthrough setattr would raise. + "num_heads", + "value_dim", + "decay_low_rank_dim", + "gate_low_rank_dim", + "a_low_rank_dim", + "v_low_rank_dim", + "norm_first", + "norm_bias", + "fuse_norm", + "attn_mode", + "hidden_act", ] diff --git a/transformer_lens/model_bridge/sources/transformers/helpers.py b/transformer_lens/model_bridge/sources/transformers/helpers.py index 922c30f5f8..eb88f7d2b8 100644 --- a/transformer_lens/model_bridge/sources/transformers/helpers.py +++ b/transformer_lens/model_bridge/sources/transformers/helpers.py @@ -7,6 +7,8 @@ AutoModelForSeq2SeqLM, ) +from transformer_lens.tools.model_registry.checkpoints import get_checkpoint_labels + def get_hf_model_class_for_architecture(architecture: str): """Pick the correct HuggingFace ``AutoModel*`` class for the architecture.""" @@ -65,8 +67,6 @@ def _resolve_checkpoint_to_revision( f"families: {list(_CHECKPOINT_REVISION_FORMATS.keys())}." ) - from transformer_lens.loading_from_pretrained import get_checkpoint_labels - labels, _ = get_checkpoint_labels(model_name) if checkpoint_value is not None: if checkpoint_value not in labels: diff --git a/transformer_lens/model_bridge/sources/transformers/source.py b/transformer_lens/model_bridge/sources/transformers/source.py index bcfa47f1c8..19caeb45eb 100644 --- a/transformer_lens/model_bridge/sources/transformers/source.py +++ b/transformer_lens/model_bridge/sources/transformers/source.py @@ -23,7 +23,7 @@ determine_architecture_from_hf_config, setup_tokenizer, ) -from transformer_lens.supported_models import MODEL_ALIASES +from transformer_lens.tools.model_registry.registry_io import resolve_model_alias from transformer_lens.utilities import get_device from .helpers import ( @@ -108,13 +108,12 @@ def boot( Returns: The bridge to the loaded model. """ - for official_name, aliases in MODEL_ALIASES.items(): - if model_name in aliases: - logging.warning( - f"DEPRECATED: You are using a deprecated, model_name alias '{model_name}'. TransformerLens will now load the official transformers model name, '{official_name}' instead.\n Please update your code to use the official name by changing model_name from '{model_name}' to '{official_name}'.\nSince TransformerLens v3, all model names should be the official transformers model names.\nThe aliases will be removed in the next version of TransformerLens, so please do the update now." - ) - model_name = official_name - break + official_name = resolve_model_alias(model_name) + if official_name is not None: + logging.warning( + f"DEPRECATED: You are using a deprecated, model_name alias '{model_name}'. TransformerLens will now load the official transformers model name, '{official_name}' instead.\n Please update your code to use the official name by changing model_name from '{model_name}' to '{official_name}'.\nSince TransformerLens v3, all model names should be the official transformers model names.\nThe aliases will be removed in the next version of TransformerLens, so please do the update now." + ) + model_name = official_name if checkpoint_index is not None or checkpoint_value is not None: if revision is not None: raise ValueError( diff --git a/transformer_lens/model_bridge/supported_architectures/__init__.py b/transformer_lens/model_bridge/supported_architectures/__init__.py index aee5182f01..dcec4fdc68 100644 --- a/transformer_lens/model_bridge/supported_architectures/__init__.py +++ b/transformer_lens/model_bridge/supported_architectures/__init__.py @@ -193,6 +193,9 @@ from transformer_lens.model_bridge.supported_architectures.phimoe import ( PhiMoEArchitectureAdapter, ) +from transformer_lens.model_bridge.supported_architectures.pretrain import ( + PretrainArchitectureAdapter, +) from transformer_lens.model_bridge.supported_architectures.qwen import ( QwenArchitectureAdapter, ) @@ -217,9 +220,15 @@ from transformer_lens.model_bridge.supported_architectures.qwen3_next import ( Qwen3NextArchitectureAdapter, ) +from transformer_lens.model_bridge.supported_architectures.raven import ( + RavenArchitectureAdapter, +) from transformer_lens.model_bridge.supported_architectures.recurrent_gemma import ( RecurrentGemmaArchitectureAdapter, ) +from transformer_lens.model_bridge.supported_architectures.rwkv7 import ( + RWKV7ArchitectureAdapter, +) from transformer_lens.model_bridge.supported_architectures.smollm3 import ( SmolLM3ArchitectureAdapter, ) @@ -307,6 +316,7 @@ "PhiArchitectureAdapter", "Phi3ArchitectureAdapter", "PhiMoEArchitectureAdapter", + "PretrainArchitectureAdapter", "QwenArchitectureAdapter", "Qwen2ArchitectureAdapter", "Qwen2MoeArchitectureAdapter", @@ -315,7 +325,9 @@ "Qwen3NextArchitectureAdapter", "Qwen3_5ArchitectureAdapter", "Qwen3_5MultimodalArchitectureAdapter", + "RavenArchitectureAdapter", "RecurrentGemmaArchitectureAdapter", + "RWKV7ArchitectureAdapter", "SmolLM3ArchitectureAdapter", "StableLmArchitectureAdapter", "T5ArchitectureAdapter", diff --git a/transformer_lens/model_bridge/supported_architectures/pretrain.py b/transformer_lens/model_bridge/supported_architectures/pretrain.py new file mode 100644 index 0000000000..20091b65ff --- /dev/null +++ b/transformer_lens/model_bridge/supported_architectures/pretrain.py @@ -0,0 +1,442 @@ +"""Architecture adapter for a lightweight decoder-only pretraining model. + +Maps a decoder-only transformer using RoPE, RMSNorm, gated SwiGLU MLPs, and +optional sparse mixture-of-experts feed-forward layers into +TransformerBridge, by wrapping the source module and delegating to its own +`forward` rather than translating parameters into a second implementation. + +Usage: `build_pretrain_bridge(model, cfg)` -- the public entry point. +`PretrainModelContainer` and direct `build_bridge_from_module` use are +internal/advanced details (see `PretrainModelContainer`'s docstring). + +Scope: maps a live module into TransformerBridge. Does not load +checkpoints, merge tensor-parallel shards, or depend on a training +framework. + +Required module protocol -- "lightweight decoder-only pretraining models" +describes intent, not a generality guarantee. The wrapped model must +expose: + + model.embed (embedding lookup) + model.blocks[i].norm1 (pre-attention norm) + model.blocks[i].attn (called as attn(x, ...)) + model.blocks[i].norm2 (pre-MLP norm) + model.blocks[i].mlp (gate/up/down, or router/experts) + model.norm_f (final norm) + model.lm_head (unembedding) + +`gate`/`up`/`down` and `router`/`experts` name the supported protocol. +`DenseOrMoEFeedForwardBridge` checks these structurally -- attribute +presence plus basic type (each is a module, `experts` is a registered +module collection) -- and raises clearly on a mismatch, but that is +structural validation only: it does not and cannot validate that a +module satisfying the shape actually implements matching forward +semantics. Blocks must take more than the bare hidden state (this target +passes `cos`/`sin`) -- see `PretrainModelContainer`. +""" +from __future__ import annotations + +from typing import Any + +import torch + +from transformer_lens.config import TransformerBridgeConfig +from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter +from transformer_lens.model_bridge.bridge import TransformerBridge +from transformer_lens.model_bridge.generalized_components import ( + AttentionBridge, + DelegatedAttentionBlockBridge, + EmbeddingBridge, + GatedMLPBridge, + LinearBridge, + MoEBridge, + RMSNormalizationBridge, + UnembeddingBridge, +) +from transformer_lens.model_bridge.generalized_components.base import ( + GeneralizedComponent, +) + +ARCHITECTURE_NAME = "TransformerLensPretrain" + +# Reserved bridge kwargs removed before forwarding to the wrapped model. +# Only known bridge-compatibility kwargs are stripped so genuine caller +# mistakes (e.g. `target=` vs `targets=`) still raise naturally, and no +# signature introspection is needed to support an arbitrary forward. +_BRIDGE_COMPAT_KWARGS = frozenset({"output_attentions"}) + + +class DenseOrMoEFeedForwardBridge(GeneralizedComponent): + """Wraps either a dense SwiGLU MLP or a sparse MoE layer behind a + common interface. Dispatch is determined by structural inspection + (`router`/`experts` vs `gate`/`up`/`down`) rather than configuration, + so dense, MoE, and mixed architectures all use the same component + mapping. This identifies the supported protocol -- it does not + validate that a module merely sharing those attribute names actually + implements the matching forward behavior. + + `hasattr` alone would let a module with, say, both `router`/`experts` + and `gate`/`up`/`down` (or `router`/`experts` of the wrong types) win + the MoE branch by attribute-name coincidence and fail later with a + confusing error from deep inside `MoEBridge`, or not fail until + forward time. `set_original_component` therefore also checks the + basic shape of whichever protocol wins: `router`/`gate`/`up`/`down` + must themselves be modules, and `experts` must be a *registered* + module collection (`nn.ModuleList`/`nn.ModuleDict`) -- a plain Python + list/tuple of `nn.Module` experts is rejected even though every + element is itself a valid module, because modules held in an + ordinary list aren't registered as children and would silently drop + out of `parameters()`/`state_dict()`/`.to(...)`/`train()`/`eval()`, + contradicting this adapter's lifecycle guarantees. + """ + + def __init__(self, name: str, config: Any): + super().__init__(name, config=config, submodules={}) + self._delegate: GeneralizedComponent | None = None + + def set_original_component(self, component: torch.nn.Module) -> None: + super().set_original_component(component) + # This subclass's own __init__ takes `name: str` (non-optional), so + # self.name is always a str here -- but the base GeneralizedComponent + # attribute is typed `str | None`, which is all mypy sees without this + # narrowing. MoEBridge/GatedMLPBridge both require a plain `str` name. + assert self.name is not None + if hasattr(component, "router") and hasattr(component, "experts"): + if not isinstance(component.router, torch.nn.Module): + raise TypeError( + f"{type(component).__name__}.router must be an nn.Module; " + f"got {type(component.router).__name__}." + ) + if not isinstance(component.experts, (torch.nn.ModuleList, torch.nn.ModuleDict)): + raise TypeError( + f"{type(component).__name__}.experts must be a registered " + "module collection (nn.ModuleList or nn.ModuleDict); got " + f"{type(component.experts).__name__}." + ) + delegate: GeneralizedComponent = MoEBridge( + name=self.name, + config=self.config, + submodules={"gate": LinearBridge(name="router")}, + ) + elif hasattr(component, "gate") and hasattr(component, "up") and hasattr(component, "down"): + for field in ("gate", "up", "down"): + value = getattr(component, field) + if not isinstance(value, torch.nn.Module): + raise TypeError( + f"{type(component).__name__}.{field} must be an " + f"nn.Module; got {type(value).__name__}." + ) + delegate = GatedMLPBridge( + name=self.name, + config=self.config, + submodules={ + "gate": LinearBridge(name="gate"), + "in": LinearBridge(name="up"), + "out": LinearBridge(name="down"), + }, + ) + else: + raise ValueError( + f"Block.mlp is a {type(component).__name__} with neither " + "an MoE layer's (router, experts) nor a gated MLP's " + "(gate, up, down) attributes -- this adapter doesn't know " + "how to wrap it." + ) + delegate.set_original_component(component) + # Not `self._delegate = delegate`: normal registration would + # duplicate hook_in/hook_out under a nested `._delegate.` path, + # risking a broad hook selector firing twice. + # `_delegate` is an execution helper, absent from named_modules() + # -- safe since parameters/state_dict/dtype are read from the raw + # wrapped model (see PretrainModelContainer), not this tree. + object.__setattr__(self, "_delegate", delegate) + + def forward(self, *args: Any, **kwargs: Any) -> Any: + assert self._delegate is not None, f"{self.name}: original component not set" + if args: + args = (self.hook_in(args[0]),) + args[1:] + elif "hidden_states" in kwargs: + kwargs = {**kwargs, "hidden_states": self.hook_in(kwargs["hidden_states"])} + output = self._delegate(*args, **kwargs) + + if isinstance(output, tuple): + if len(output) == 0: + raise TypeError( + "DenseOrMoEFeedForwardBridge expected a non-empty tuple " + "whose first element is a torch.Tensor" + ) + + first = output[0] + + if not isinstance(first, torch.Tensor): + raise TypeError( + "DenseOrMoEFeedForwardBridge expected the first tuple element " + f"to be a torch.Tensor, got {type(first).__name__}" + ) + + hooked_first = self.hook_out(first) + + # Preserve every auxiliary element without sending it through HookPoint. + return (hooked_first, *output[1:]) + + if not isinstance(output, torch.Tensor): + raise TypeError( + "DenseOrMoEFeedForwardBridge expected a torch.Tensor or a tuple " + f"whose first element is a torch.Tensor, got {type(output).__name__}" + ) + + return self.hook_out(output) + + +class _LogitsAttrDict(dict): + """Makes a plain dict's keys accessible as attributes (`d.logits` reads + `d["logits"]`), so a source model's plain-dict forward output satisfies + the `hasattr(output, "logits")` contract `TransformerBridge` expects. + Behaves as a plain dict everywhere else (indexing, `.get`, `in`, ...). + """ + + def __getattr__(self, key: str) -> Any: + try: + return self[key] + except KeyError as e: + raise AttributeError(key) from e + + +class PretrainModelContainer(torch.nn.Module): + """Internal detail -- `build_pretrain_bridge` applies this + automatically. Three responsibilities, all in this container's own + `forward`: + + 1. Avoids a `TransformerBridge.__getattr__` collision: a source + model's own `self.embed`/`self.blocks` clashes with identically + named component_mapping keys. Wrapping one level deeper + (`container.inner.embed`) fixes this without touching the source + model. + 2. Normalizes the return value to the `.logits` contract + `TransformerBridge` expects: a plain `"logits"` dict (the target + architecture's actual shape) is wrapped in `_LogitsAttrDict`; a bare + tensor, tensor-first tuple, or object already exposing `.logits` + passes through after validating the tensor is present and is a + tensor; anything else raises immediately with a clear message. + 3. Strips `_BRIDGE_COMPAT_KWARGS` from kwargs before calling the + wrapped model (see that constant's comment). + + Also sidesteps a `BlockBridge` convention where a bare-tensor block + output gets wrapped in a 1-tuple for "standalone hidden_states calls": + since this target's blocks take `cos`/`sin` too, that path never + triggers, so the source forward loop needs no changes to be bridged. + + `self.inner` is a regular registered submodule, so + `container.train()`/`.eval()` already recurse into it via the normal + `nn.Module` traversal -- no override needed here. The propagation gap + lives one level up, at `TransformerBridge` itself (see + `build_pretrain_bridge`), whose `.train()`/`.eval()` do not walk down + to `original_model`. + """ + + def __init__(self, model: torch.nn.Module) -> None: + super().__init__() + self.inner = model + + def forward(self, *args: Any, **kwargs: Any) -> Any: + filtered = {k: v for k, v in kwargs.items() if k not in _BRIDGE_COMPAT_KWARGS} + output = self.inner(*args, **filtered) + + # Already-normalized or already-HF-style outputs pass through + # unchanged, but only after checking .logits is actually a tensor + # -- an object merely exposing the attribute isn't enough. + if isinstance(output, _LogitsAttrDict): + if "logits" not in output: + raise ValueError( + f"{type(self.inner).__name__}.forward returned a " + "_LogitsAttrDict without a 'logits' key." + ) + if not isinstance(output["logits"], torch.Tensor): + raise TypeError( + f"{type(self.inner).__name__}.forward returned a " + f"_LogitsAttrDict with a non-tensor 'logits' value: " + f"{type(output['logits']).__name__}." + ) + return output + + # try/except, not hasattr(): hasattr() would evaluate a + # property-backed .logits once, then a separate read would + # evaluate it again. This reads it exactly once. + try: + logits = output.logits + except AttributeError: + pass + else: + if not isinstance(logits, torch.Tensor): + raise TypeError( + f"{type(self.inner).__name__}.forward returned a " + f"{type(output).__name__} with a non-tensor .logits value: " + f"{type(logits).__name__}." + ) + return output + + if isinstance(output, torch.Tensor): + return output + + # TransformerBridge extracts output[0] as logits for tuple returns. + if isinstance(output, tuple): + if output and isinstance(output[0], torch.Tensor): + return output + raise TypeError( + f"{type(self.inner).__name__}.forward returned a tuple whose " + f"first element is a {type(output[0]).__name__ if output else 'empty tuple'}, " + "not a torch.Tensor -- TransformerBridge extracts output[0] as " + "logits for tuple returns, so it must be a tensor." + ) + + # The primary case: a plain dict, normalized into _LogitsAttrDict. + if isinstance(output, dict): + if "logits" not in output: + raise ValueError( + f"{type(self.inner).__name__}.forward returned a dict with keys " + # list(), not sorted(): sorted() raises TypeError on + # heterogeneous keys, which would mask this error. + f"{list(output.keys())}, but PretrainModelContainer requires a " + "'logits' key -- without it, TransformerBridge's own " + "hasattr(output, 'logits') check would silently fail the same " + "way this container exists to prevent." + ) + if not isinstance(output["logits"], torch.Tensor): + raise TypeError( + f"{type(self.inner).__name__}.forward returned a dict whose " + f"'logits' value is a {type(output['logits']).__name__}, not a " + "torch.Tensor." + ) + return _LogitsAttrDict(output) + + raise TypeError( + f"{type(self.inner).__name__}.forward must return a torch.Tensor, a " + "tuple whose first element is a tensor, a dict containing a " + "tensor-valued 'logits' key, or an object with a tensor-valued " + f".logits attribute; got {type(output).__name__}." + ) + + +class NativeForwardAttentionBridge(AttentionBridge): + """Opaque attention bridge that delegates to the source attention. + + This adapter intentionally exposes only input/output attention hooks. + It has no mapped Q/K/V/O projection components, so the standard + per-head aliases and weight aliases do not apply. + """ + + hook_aliases = {} + property_aliases = {} + supports_split_qkv_fork = False + + +class PretrainArchitectureAdapter(ArchitectureAdapter): + """Adapter for a decoder-only transformer using RoPE, RMSNorm, gated + SwiGLU MLPs, and optional sparse MoE feed-forward layers. + + Uses an opaque `NativeForwardAttentionBridge` with no attention + projection submodules, not `JointQKVAttentionBridge`/ + `PositionEmbeddingsAttentionBridge`: those reimplement RoPE via HF's + rotate-half convention, wrong for a source model using the + adjacent-pair convention. The opaque bridge delegates unchanged to + `Attention.forward`, so RoPE runs as written -- at the cost of no + per-head hooks, only block-level + `resid_pre`/`resid_mid`/`resid_post`. + + Blocks use `DelegatedAttentionBlockBridge` rather than plain + `BlockBridge`: that existing abstraction already exists for + architectures where attention is delegated wholesale and the + split-qkv-fork block-level aliases (`hook_attn_in`/`hook_q_input`/ + `hook_k_input`/`hook_v_input`) don't apply. It complements + `NativeForwardAttentionBridge.supports_split_qkv_fork = False` (which + prevents the split-QKV-fork machinery and its associated HookPoints + from being exposed for this attention component) by also removing the + now-dangling block-level aliases that would otherwise point at them. + `hook_attn_out` is untouched by either change, since the attention + component still fires its own `hook_out` normally. + + `self.cfg` is mutated in place, not copied (matches `nanogpt.py`'s + convention) -- callers holding another reference to the same config + will see these fields change. + + Bridges built through `build_pretrain_bridge` are given a + mode-propagating subclass so `.train()`/`.eval()` reach the wrapped + source model (see that function's docstring) -- this adapter class + itself has no lifecycle behavior of its own. + """ + + def __init__(self, cfg: Any) -> None: + super().__init__(cfg) + + self.cfg.normalization_type = "RMS" + self.cfg.positional_embedding_type = "rotary" + self.cfg.final_rms = True + self.cfg.gated_mlp = True + self.cfg.attn_only = False + + self.component_mapping = { + # "inner." because this adapter expects the source model to + # arrive wrapped in `PretrainModelContainer` -- see that + # class's docstring for why. + "embed": EmbeddingBridge(name="inner.embed"), + "blocks": DelegatedAttentionBlockBridge( + name="inner.blocks", + config=self.cfg, + submodules={ + "ln1": RMSNormalizationBridge(name="norm1", config=self.cfg), + "attn": NativeForwardAttentionBridge( + name="attn", + config=self.cfg, + submodules={}, # opaque wrap -- see class docstring + ), + "ln2": RMSNormalizationBridge(name="norm2", config=self.cfg), + "mlp": DenseOrMoEFeedForwardBridge(name="mlp", config=self.cfg), + }, + ), + "ln_final": RMSNormalizationBridge(name="inner.norm_f", config=self.cfg), + "unembed": UnembeddingBridge(name="inner.lm_head"), + } + + +def build_pretrain_bridge( + model: torch.nn.Module, + cfg: TransformerBridgeConfig, + *, + device: Any = None, + dtype: torch.dtype | None = None, + model_name: str | None = None, +) -> TransformerBridge: + """Public entry point: wraps `model` in `PretrainModelContainer` and + builds a `TransformerBridge` around it. Prefer this over calling + `build_bridge_from_module` directly -- the container is easy to forget. + + `device`/`dtype`/`model_name` forward to `build_bridge_from_module` + only when explicitly given. + + `bridge.train()`/`.eval()` propagate to `model` via + `TransformerBridge.train()` itself, which sets mode on + `original_model` in addition to the registered module tree + (`original_model` is deliberately not a registered submodule, so + `nn.Module.train()`'s own recursion never reaches it). This adapter + needs nothing extra for mode propagation. + + Setting mode on `model` directly still works too and stays in sync. + """ + from transformer_lens.model_bridge.sources._bridge_builder import ( + build_bridge_from_module, + ) + + kwargs: dict[str, Any] = {} + if device is not None: + kwargs["device"] = device + if dtype is not None: + kwargs["dtype"] = dtype + if model_name is not None: + kwargs["model_name"] = model_name + + return build_bridge_from_module( + PretrainModelContainer(model), + architecture=ARCHITECTURE_NAME, + tl_config=cfg, + **kwargs, + ) diff --git a/transformer_lens/model_bridge/supported_architectures/raven.py b/transformer_lens/model_bridge/supported_architectures/raven.py new file mode 100644 index 0000000000..28fc3836f1 --- /dev/null +++ b/transformer_lens/model_bridge/supported_architectures/raven.py @@ -0,0 +1,303 @@ +"""Raven / Huginn architecture adapter (RavenForCausalLM). + +Model family: tomg-group-umd/huginn-0125 ("Huginn"), a depth-recurrent +("latent reasoning") decoder from Geiping et al. Loaded via remote code +(``auto_map`` → ``raven_modeling_minimal.RavenForCausalLM``), so +``trust_remote_code=True`` is required. + +Architecture overview +--------------------- +Huginn is NOT a flat stack of transformer layers. Its forward has three +phases, all operating on the same residual width ``n_embd`` (5280): + + wte → prelude (P physical blocks) + → [ recurrent core: R physical blocks, applied N times ] + → coda (C physical blocks) → ln_f → lm_head + +with P = ``n_layers_in_prelude`` (2), R = ``n_layers_in_recurrent_block`` +(4), C = ``n_layers_in_coda`` (2). ``num_hidden_layers`` (8) counts the +*physical* blocks (2 + 4 + 2), stored as three separate ``ModuleList``s under +``model.transformer`` (``prelude`` / ``core_block`` / ``coda``), NOT one +``model.layers``. + +The recurrence is the defining feature. ``RavenForCausalLM.forward`` calls +``iterate_forward`` → ``core_block_forward``, which runs the SAME four +``core_block`` modules N times. Each step re-injects the prelude output: + + x = adapter(cat([latent_state, prelude_output], dim=-1)) # injection + for block in core_block: x = block(x) # 4 blocks + +``N`` (``num_steps``) is a RUNTIME argument to ``forward``, not a fixed +config value. At eval it defaults to ``config.mean_recurrence`` (32) via +``randomized_iteration_sampler``; a caller may pass any ``num_steps``. + +Each block is a ``SandwichBlock``: four RMSNorms with post-residual +normalisation (``x = norm_2(attn(norm_1(x)) + x)``; ``x = norm_4(mlp(norm_3 +(x)) + x)``) — the residual stream itself is renormalised after each add, +unlike a standard pre-norm transformer. Attention is MHA (55 heads == 55 kv +heads) with a COMBINED ``Wqkv`` projection plus a learned additive ``qk_bias`` +parameter and RoPE (base 50000). The MLP is a gated SiLU MLP with a combined +gate+up ``fc`` projection. Embeddings are scaled by ``√n_embd`` (≈72.66) in +the HF forward, and ``lm_head`` is tied to ``wte``. + +Key adapter decisions +--------------------- +1. Full delegation, like Ouro. The recurrence, the prelude re-injection, the + emb-scale, the sandwich norms and RoPE all live inside the remote-code + ``forward`` that the bridge delegates to, so a single forward pass is + numerically correct with no loop handling here. + +2. ``OpaqueBlockBridge`` for all three block lists (``prelude`` / ``core_block`` + / ``coda``): ``BlockBridge``'s hook aliases hardcode a standard pre-norm flow + (hook_resid_mid, ln1→attn→ln2→mlp) that the post-residual ``SandwichBlock`` + does not follow. ``OpaqueBlockBridge`` delegates the whole block and exposes + only ``hook_in`` / ``hook_out`` on the residual stream, which is correct + regardless of the internal norm placement. Each block's inner attn / mlp / + norms are still declared as submodules so they wrap the live HF modules and + their hooks fire. + +3. Recurrent core hooks. Because the core loop lives inside the HF forward, + the four ``core_block`` blocks' ``hook_in`` / ``hook_out`` fire once PER + recurrence step (N times per forward), and ``run_with_cache`` keeps the + final step. This is the load-bearing behavioural difference from a flat + decoder and mirrors Ouro's looped-depth semantics. Separately addressing + an individual recurrence step (e.g. logit-lens across steps) is NOT + expressible through the static ``core_block.{i}.hook_out`` names — it + requires the model's native ``iterate_one_step`` / ``predict_from_latents`` + interface. Deliberately not mapped: ``transformer.adapter`` (the injection + Linear) and ``HuginnDynamicCache``'s block-indexed slot layout. + +4. ``applicable_phases = []``. Huginn diverges too far from the transformer- + shaped ``verify_models`` phases to score meaningfully: post-residual + sandwich norms, a runtime recurrence count, combined QKV + ``qk_bias``, + and — decisively — a RANDOM initial latent state (``initialize_state`` + uses ``torch.randn_like``), which makes the forward non-deterministic + across calls unless the RNG is seeded or ``input_states`` is supplied. + Correctness is covered in the integration tests, where the seed is pinned + before both the bridge and HF calls (same decision spirit as nemotron_h / + mamba). ``ln_f`` is applied mid-network (after the recurrence, feeding the + coda) as well as at the end, so ``supports_fold_ln = False`` — folding it + into ``W_U`` would corrupt the coda input. + +Config propagation +------------------ +The recurrence-shape attributes (``mean_recurrence``, ``n_layers_in_prelude`` +/ ``_recurrent_block`` / ``_coda``, ``mean_backprop_depth``, ``injection_type``, +``qk_bias``) are surfaced on ``self.cfg`` here AND added to the two +``_HF_PASSTHROUGH_ATTRS`` lists so analysis tooling can read them off a booted +bridge. + +Remote-code loading (transformers v5) +------------------------------------ +Huginn's remote code targets transformers 4.44 and breaks under v5 (5.8.1) in +two ways that ``prepare_loading`` patches: (1) ``_tied_weights_keys`` is a list, +but v5's ``tie_weights`` expects a dict, so the model does not even construct; +(2) v5's meta-device load re-invokes ``PreTrainedModel._init_weights`` on +already-materialised modules and would re-randomise the checkpoint. See +``prepare_loading`` for both. (Huginn does not use ``ROPE_INIT_FUNCTIONS``; it +precomputes its own ``freqs_cis``, so no RoPE patch is needed.) + +Optional parameters (may be absent from a state_dict) +---------------------------------------------------- +Huginn has ``bias=False`` everywhere except the attention ``qk_bias`` +parameter; RMSNorm has no bias. Weight processing must tolerate missing +biases via ``ProcessWeights._safe_get_tensor()``. +""" + +import sys +from typing import Any + +from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter +from transformer_lens.model_bridge.generalized_components import ( + EmbeddingBridge, + LinearBridge, + MLPBridge, + OpaqueBlockBridge, + RMSNormalizationBridge, + UnembeddingBridge, +) +from transformer_lens.model_bridge.generalized_components.attention import ( + AttentionBridge, +) + + +class RavenArchitectureAdapter(ArchitectureAdapter): + """Architecture adapter for RavenForCausalLM (Huginn depth-recurrent decoder). + + Prelude / weight-tied recurrent core / coda phases over a shared residual + width. The recurrence and prelude re-injection live inside the remote-code + HF forward, which the bridge delegates to; see the module docstring for the + full set of adapter decisions. + """ + + # Huginn is off the transformer-shaped verify_models path: post-residual + # sandwich norms, runtime recurrence count, and a random initial latent + # state make the phases non-meaningful. Correctness lives in the + # integration tests (seed pinned before bridge and HF calls). + applicable_phases: list[int] = [] + + def __init__(self, cfg: Any) -> None: + """Initialize the Raven / Huginn architecture adapter.""" + super().__init__(cfg) + + # Standard weight-processing / norm flags. + self.cfg.normalization_type = "RMS" + self.cfg.uses_rms_norm = True + self.cfg.positional_embedding_type = "rotary" + self.cfg.final_rms = True + self.cfg.gated_mlp = True + self.cfg.attn_only = False + + # ln_f (transformer.ln_f) is applied after the recurrence (feeding the + # coda) AND at the very end, so it is not a final-only norm. Folding it + # into W_U would corrupt the coda's input. + self.supports_fold_ln = False + + # Surface the recurrence-shape attributes on cfg so they are present on + # both the HF-boot path (also via _HF_PASSTHROUGH_ATTRS) and the + # synthetic-config path used by the unit tests. + setattr(self.cfg, "mean_recurrence", getattr(cfg, "mean_recurrence", 32)) + setattr(self.cfg, "mean_backprop_depth", getattr(cfg, "mean_backprop_depth", 8)) + setattr(self.cfg, "n_layers_in_prelude", getattr(cfg, "n_layers_in_prelude", 2)) + setattr( + self.cfg, "n_layers_in_recurrent_block", getattr(cfg, "n_layers_in_recurrent_block", 4) + ) + setattr(self.cfg, "n_layers_in_coda", getattr(cfg, "n_layers_in_coda", 2)) + setattr(self.cfg, "injection_type", getattr(cfg, "injection_type", "linear")) + setattr(self.cfg, "qk_bias", getattr(cfg, "qk_bias", True)) + + # Full delegation to the HF forward — no HT-format weight reshaping. + self.weight_processing_conversions = {} + + self.component_mapping = { + "embed": EmbeddingBridge(name="transformer.wte"), + # Three separate physical block lists. Each uses OpaqueBlockBridge so + # the delegated SandwichBlock forward keeps its post-residual norm + # placement while hook_in / hook_out wrap the residual stream. Fresh + # submodule instances per list (they bind to distinct HF modules). + "prelude": OpaqueBlockBridge( + name="transformer.prelude", + submodules=self._sandwich_submodules(), + ), + "core_block": OpaqueBlockBridge( + name="transformer.core_block", + submodules=self._sandwich_submodules(), + ), + "coda": OpaqueBlockBridge( + name="transformer.coda", + submodules=self._sandwich_submodules(), + ), + "ln_final": RMSNormalizationBridge(name="transformer.ln_f", config=self.cfg), + "unembed": UnembeddingBridge(name="lm_head"), + } + + def _sandwich_submodules(self) -> dict[str, Any]: + """Build a fresh set of SandwichBlock submodule bridges. + + Returns new instances on every call so each of the three block lists + wraps its own live HF modules rather than sharing bridge objects. + + Submodule keys mirror the HF attribute names (``norm_1``..``norm_4``, + ``attn``, ``mlp``) so weight-key translation is identity. Attention is + native (combined ``Wqkv`` + ``qk_bias``, RoPE, custom SDPA path), so it + is delegated via ``maintain_native_attention``; only the combined + ``qkv`` and output ``o`` projections are exposed. The gated MLP is + likewise delegated with its combined gate+up ``fc`` and output ``proj``. + """ + attn = AttentionBridge( + name="attn", + config=self.cfg, + submodules={ + "qkv": LinearBridge(name="Wqkv"), + "o": LinearBridge(name="proj"), + }, + maintain_native_attention=True, + requires_attention_mask=True, + ) + # Raven uses a combined Wqkv projection; no separate q/k/v submodules exist. + # Strip the default hook_q/hook_k/hook_v aliases so they don't appear as + # dead (unresolvable) aliases in the hook-alias resolution audit. + attn.hook_aliases = { + k: v + for k, v in AttentionBridge.hook_aliases.items() + if k not in {"hook_q", "hook_k", "hook_v"} + } + return { + "norm_1": RMSNormalizationBridge(name="norm_1", config=self.cfg), + "attn": attn, + "norm_2": RMSNormalizationBridge(name="norm_2", config=self.cfg), + "norm_3": RMSNormalizationBridge(name="norm_3", config=self.cfg), + "mlp": MLPBridge( + name="mlp", + config=self.cfg, + submodules={ + "in": LinearBridge(name="fc"), + "out": LinearBridge(name="proj"), + }, + ), + "norm_4": RMSNormalizationBridge(name="norm_4", config=self.cfg), + } + + def prepare_loading(self, model_name: str, model_kwargs: dict) -> None: + """Patch Huginn's remote code for transformers v5 compatibility. + + Huginn's modeling code targets transformers 4.44; two things break under + v5 (5.8.1), so two patches: + + 1. Tied-weights format. ``RavenForCausalLM._tied_weights_keys`` is a list + (``["lm_head.weight"]``, the 4.x format), but v5's ``tie_weights`` -> + ``get_expanded_tied_weights_keys`` calls ``.keys()`` on it and raises + ``AttributeError``. The model does not even construct. Rewrite it to + the v5 dict form ``{"lm_head.weight": "transformer.wte.weight"}`` + (Huginn ties ``lm_head`` to ``transformer.wte``). + + 2. Weight re-init. Under v5's meta-device load-then-materialise flow, + ``PreTrainedModel._init_weights`` is invoked on modules that already + hold checkpoint weights, re-randomising them. Guard it to skip modules + whose parameters are already on a real (non-meta) device — the same + defensive patch openelm.py applies. + + Args: + model_name: The HuggingFace model name/path. + model_kwargs: The kwargs dict for from_pretrained(). + """ + # Force-import the modeling module so it appears in sys.modules to patch. + try: + from transformers.dynamic_module_utils import get_class_from_dynamic_module + + get_class_from_dynamic_module( + "raven_modeling_minimal.RavenForCausalLM", + model_name, + ) + except Exception: + return + + # Each checkpoint revision gets its own module in sys.modules; patch all. + for key in list(sys.modules.keys()): + if "raven" not in key.lower() or "modeling" not in key.lower(): + continue + module = sys.modules[key] + + # Patch 1: tied-weights keys list -> v5 dict form. + causal_lm_class = getattr(module, "RavenForCausalLM", None) + if causal_lm_class is not None and isinstance( + getattr(causal_lm_class, "_tied_weights_keys", None), list + ): + causal_lm_class._tied_weights_keys = {"lm_head.weight": "transformer.wte.weight"} + + # Patch 2: don't re-randomise already-loaded weights. + pretrained_class = getattr(module, "RavenPreTrainedModel", None) + if pretrained_class is None or getattr(pretrained_class, "_tl_patched", False): + continue + original_init_weights = pretrained_class._init_weights + + def safe_init_weights(self, mod, _original=original_init_weights): + # Only initialise modules still on meta device (pre-loading); + # never re-randomise weights already read from the checkpoint. + first_param = next(mod.parameters(), None) + if first_param is not None and first_param.device.type != "meta": + return + _original(self, mod) + + pretrained_class._init_weights = safe_init_weights + pretrained_class._tl_patched = True diff --git a/transformer_lens/model_bridge/supported_architectures/rwkv7.py b/transformer_lens/model_bridge/supported_architectures/rwkv7.py new file mode 100644 index 0000000000..f4186330a1 --- /dev/null +++ b/transformer_lens/model_bridge/supported_architectures/rwkv7.py @@ -0,0 +1,296 @@ +"""RWKV-7 ("Goose") architecture adapter (RWKV7ForCausalLM). + +Model family: ``fla-hub/rwkv7-*`` (e.g. ``fla-hub/rwkv7-0.1B-g1``), an +attention-free recurrent language model from the flash-linear-attention (``fla``) +library. Loaded via remote code (``trust_remote_code=True``); the modeling +classes live in ``fla.models.rwkv7``. + +Architecture overview +--------------------- +RWKV-7 is a flat stack of recurrent blocks over a shared residual width, wrapped +by standard (biased) LayerNorm rather than RMSNorm and with no positional +embeddings:: + + embeddings -> [ RWKV7Block x N ] -> norm -> lm_head + +Each ``RWKV7Block`` is a pre-norm pair of a time-mixing and a channel-mixing +sublayer:: + + x = x + attn(attn_norm(x)) # time-mixing (RWKV7Attention) + x = x + ffn(ffn_norm(x)) # channel-mixing (RWKV7FeedForward) + +with an extra ``pre_norm`` LayerNorm on layer 0 only (``config.norm_first``). +The time-mixing sublayer is the RWKV-7 "generalized delta rule": token-shifted +lerp coefficients (``x_r``..``x_g``), receptance / key / value / output +projections (``r_proj`` / ``k_proj`` / ``v_proj`` / ``o_proj``), low-rank LoRAs +for the log-space decay / value blend / a-coefficient / gate, and a GroupNorm. +The channel-mixing sublayer is a token-shifted squared-ReLU MLP (``key`` up, +``value`` down). + +Cross-block ``v_first`` threading: ``RWKV7Model.forward`` initialises +``v_first = torch.zeros_like(hidden_states)`` and threads it through every block +(layer 0 fills it; later layers blend their value with it). This is managed +entirely by the HF model-level forward, so the bridge — which delegates each +block's forward to HF — gets it for free. + +Key adapter decisions +--------------------- +1. Full delegation. The recurrence, the token shift, the LoRAs, the GroupNorm, + and the ``v_first`` threading all live inside the ``fla`` remote-code forward + that the bridge delegates to, so a single forward pass is numerically correct + with no scan handling here. ``weight_processing_conversions = {}``. + +2. ``OpaqueBlockBridge`` for the block list, for the same reason nemotron_h / raven + use it: ``BlockBridge``'s hook aliases hardcode a standard pre-norm attention + flow (``hook_resid_mid``, ``ln1 -> attn -> ln2 -> mlp``) that a recurrent + RWKV-7 block does not follow. ``OpaqueBlockBridge`` delegates the whole block and + exposes only ``hook_in`` / ``hook_out`` on the residual stream, which is + correct regardless of the internal mixing. The block's inner norms / mixers + are still declared as submodules so they wrap the live HF modules and fire. + +3. ``GeneralizedComponent`` for both the time-mixing (``attn``) and channel-mixing + (``ffn``) sublayers. Its forward is a generic I/O-hooked passthrough that + delegates to the live HF module and hooks the primary output while preserving + the rest of the tuple. ``attn`` returns a 4-tuple ``(hidden_states, attentions, + past_key_values, v_first)`` and ``ffn`` returns a 2-tuple + ``(hidden_states, state)``; the generic passthrough handles both. The + ``r_proj`` / ``k_proj`` / ``v_proj`` / ``o_proj`` (time-mixing) and ``key`` / + ``value`` (channel-mixing) projections are exposed as ``LinearBridge`` + submodules so their hooks fire; the LoRAs, GroupNorm, and per-channel lerp + parameters are deliberately left inside the delegated forward. + +4. Norms. RWKV-7 uses standard biased LayerNorm, so ``normalization_type = "LN"`` + and the norms are ``NormalizationBridge`` (not ``RMSNormalizationBridge``). + ``attn_norm`` and the final ``model.norm`` are ordinary single-input norms. + ``ffn_norm`` is the exception: under ``config.fuse_norm`` (default ``True``) + the block calls it as ``ffn_norm(hidden_states, residual, True)`` — a fused + add-and-norm returning ``(normed, new_residual)`` — which the reimplementing + ``NormalizationBridge`` cannot express. It is therefore wrapped with a plain + delegating ``GeneralizedComponent`` (generic I/O-hooked passthrough) that + forwards any signature to the live HF module and hooks the primary output. + +5. ``applicable_phases = []``. RWKV-7 is attention-free and recurrent, off the + transformer-shaped ``verify_models`` phases; correctness is covered by the + integration tests (bridge-vs-HF logit parity). ``ln_final`` (``model.norm``) + feeds only ``lm_head``, so folding is unnecessary and ``supports_fold_ln`` is + left at its default. + +6. ``is_stateful = False``. ``fla`` manages recurrent decode state through its + own ``Cache`` object, not the ``cache_params`` convention the stateful-cache + path expects, so the adapter does not advertise a stateful cache. + +Config propagation +------------------ +The RWKV-7 shape attributes (``head_dim``, ``num_heads``, ``value_dim``, the four +LoRA low-rank dims, ``norm_first`` / ``norm_bias`` / ``fuse_norm``, ``attn_mode``, +``hidden_act``) are surfaced on ``self.cfg`` here AND added to the +``_HF_PASSTHROUGH_ATTRS`` list so analysis tooling can read them off a booted +bridge; the synthetic-config unit tests read the same names. + +Remote-code loading (transformers v5) +------------------------------------ +``fla``'s modeling code predates transformers v5; ``prepare_loading`` applies the +same two defensive patches raven does: (1) rewrite ``_tied_weights_keys`` from the +4.x list form to the v5 dict form so ``get_expanded_tied_weights_keys`` does not +call ``.keys()`` on a list (only reached on tied checkpoints — RWKV-7 defaults +``tie_word_embeddings=False`` — but harmless when untied); (2) guard +``_init_weights`` so v5's meta-device materialise pass does not re-randomise +weights already read from the checkpoint. +""" + +import sys +from typing import Any + +from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter +from transformer_lens.model_bridge.generalized_components import ( + EmbeddingBridge, + LinearBridge, + NormalizationBridge, + OpaqueBlockBridge, + UnembeddingBridge, +) +from transformer_lens.model_bridge.generalized_components.base import ( + GeneralizedComponent, +) + + +class RWKV7ArchitectureAdapter(ArchitectureAdapter): + """Architecture adapter for RWKV7ForCausalLM (RWKV-7 "Goose"). + + Attention-free recurrent decoder: a flat stack of pre-norm blocks, each a + generalized-delta-rule time-mixing sublayer plus a token-shifted squared-ReLU + channel-mixing sublayer, wrapped by standard biased LayerNorm. The recurrence + and the cross-block ``v_first`` threading live inside the ``fla`` remote-code + forward, which the bridge delegates to; see the module docstring for the full + set of adapter decisions. + """ + + # Attention-free and recurrent — off the transformer-shaped verify_models + # path; correctness lives in the integration tests (bridge-vs-HF parity). + applicable_phases: list[int] = [] + + def __init__(self, cfg: Any) -> None: + """Initialize the RWKV-7 architecture adapter.""" + super().__init__(cfg) + + # Standard biased LayerNorm, no positional embeddings, ungated FFN. + self.cfg.normalization_type = "LN" + self.cfg.uses_rms_norm = False + self.cfg.positional_embedding_type = "none" + self.cfg.final_rms = False + self.cfg.attn_only = False + self.cfg.gated_mlp = False + + # fla drives recurrent decode state via its own Cache, not the + # cache_params convention, so the adapter does not advertise a cache. + setattr(self.cfg, "is_stateful", False) + + # Surface the RWKV-7 shape attributes on cfg so they are present on both + # the HF-boot path (also via _HF_PASSTHROUGH_ATTRS) and the synthetic + # config path used by the unit tests. getattr-with-default keeps a bare + # TransformerBridgeConfig from raising. + # head_dim is a read-only property on TransformerBridgeConfig (aliases + # d_head), so it is read but never assigned. num_heads defaults to + # d_model // head_dim. + head_dim = getattr(cfg, "head_dim", 64) or 64 + num_heads = getattr(cfg, "num_heads", None) or max(1, self.cfg.d_model // head_dim) + value_dim = getattr(cfg, "value_dim", None) or [self.cfg.d_model] * self.cfg.n_layers + # setattr (not direct assignment) for the RWKV-7-specific names so mypy + # does not flag them as undeclared on TransformerBridgeConfig. + setattr(self.cfg, "num_heads", num_heads) + setattr(self.cfg, "value_dim", value_dim) + setattr(self.cfg, "decay_low_rank_dim", getattr(cfg, "decay_low_rank_dim", 64)) + setattr(self.cfg, "gate_low_rank_dim", getattr(cfg, "gate_low_rank_dim", 128)) + setattr(self.cfg, "a_low_rank_dim", getattr(cfg, "a_low_rank_dim", 64)) + setattr(self.cfg, "v_low_rank_dim", getattr(cfg, "v_low_rank_dim", 16)) + setattr(self.cfg, "norm_first", getattr(cfg, "norm_first", True)) + setattr(self.cfg, "norm_bias", getattr(cfg, "norm_bias", True)) + setattr(self.cfg, "fuse_norm", getattr(cfg, "fuse_norm", True)) + setattr(self.cfg, "attn_mode", getattr(cfg, "attn_mode", "chunk")) + setattr(self.cfg, "hidden_act", getattr(cfg, "hidden_act", "sqrelu")) + # LayerNorm epsilon for the reimplementing NormalizationBridge path (eps + # is a real config field, so direct assignment is fine). + self.cfg.eps = getattr(cfg, "norm_eps", getattr(cfg, "eps", 1e-5)) + + # Full delegation to the fla forward — no HT-format weight reshaping. + self.weight_processing_conversions = {} + + self.component_mapping = { + "embed": EmbeddingBridge(name="model.embeddings"), + "blocks": OpaqueBlockBridge( + name="model.layers", + submodules={ + # Pre-norm before time-mixing (standard single-input LayerNorm). + "attn_norm": NormalizationBridge( + name="attn_norm", config=self.cfg, uses_rms_norm=False + ), + # Time-mixing: generalized delta rule. Delegated passthrough; + # only the four projections are exposed (LoRAs / GroupNorm / + # lerp params stay inside the fla forward). + "attn": GeneralizedComponent( + name="attn", + config=self.cfg, + submodules={ + "r_proj": LinearBridge(name="r_proj"), + "k_proj": LinearBridge(name="k_proj"), + "v_proj": LinearBridge(name="v_proj"), + "o_proj": LinearBridge(name="o_proj"), + }, + ), + # Pre-norm before channel-mixing. Under config.fuse_norm the + # block calls this as ffn_norm(x, residual, True) -> (normed, + # residual), a fused signature the reimplementing + # NormalizationBridge can't express, so delegate any-signature + # to the live HF module with I/O hooks. + "ffn_norm": GeneralizedComponent(name="ffn_norm", config=self.cfg), + # Channel-mixing: token-shifted squared-ReLU MLP. Delegated + # passthrough exposing the up ("key") and down ("value") + # projections. HF confusingly names the output proj "value". + "ffn": GeneralizedComponent( + name="ffn", + config=self.cfg, + submodules={ + "key": LinearBridge(name="key"), + "value": LinearBridge(name="value"), + }, + ), + }, + ), + "ln_final": NormalizationBridge( + name="model.norm", config=self.cfg, uses_rms_norm=False + ), + "unembed": UnembeddingBridge(name="lm_head"), + } + + def prepare_loading(self, model_name: str, model_kwargs: dict) -> None: + """Patch fla's RWKV-7 remote code for transformers v5 compatibility. + + Two defensive patches, mirroring raven: + + 1. Tied-weights format. ``RWKV7ForCausalLM._tied_weights_keys`` is a list + (``["lm_head.weight"]``, the 4.x form). v5's ``tie_weights`` -> + ``get_expanded_tied_weights_keys`` calls ``.keys()`` on the mapping, + which raises ``AttributeError`` on a list. Rewrite it to the v5 dict + form ``{"lm_head.weight": "model.embeddings.weight"}``. RWKV-7 defaults + ``tie_word_embeddings=False`` (so the list path is usually short- + circuited before ``.keys()``), but the rewrite is harmless when untied + and prevents the crash on any tied checkpoint. + + 2. Weight re-init. Under v5's meta-device load-then-materialise flow, + ``PreTrainedModel._init_weights`` is invoked on modules that already + hold checkpoint weights, re-randomising them. Guard it to skip modules + whose parameters are already on a real (non-meta) device — the same + defensive patch openelm.py / raven.py apply. + + Args: + model_name: The HuggingFace model name/path. + model_kwargs: The kwargs dict for from_pretrained(). + """ + # Force-import the fla RWKV-7 modeling module so its classes appear in + # sys.modules to patch. fla is normally a pip package; fall back to the + # dynamic-module route for genuinely bundled remote code. + try: + import fla.models.rwkv7.modeling_rwkv7 # noqa: F401 + except Exception: + try: + from transformers.dynamic_module_utils import ( + get_class_from_dynamic_module, + ) + + get_class_from_dynamic_module( + "modeling_rwkv7.RWKV7ForCausalLM", + model_name, + ) + except Exception: + return + + # Patch every loaded RWKV-7 modeling module (each remote revision gets its + # own module object in sys.modules). + for key in list(sys.modules.keys()): + if "rwkv7" not in key.lower() or "modeling" not in key.lower(): + continue + module = sys.modules[key] + + # Patch 1: tied-weights keys list -> v5 dict form. + causal_lm_class = getattr(module, "RWKV7ForCausalLM", None) + if causal_lm_class is not None and isinstance( + getattr(causal_lm_class, "_tied_weights_keys", None), list + ): + causal_lm_class._tied_weights_keys = {"lm_head.weight": "model.embeddings.weight"} + + # Patch 2: don't re-randomise already-loaded weights. + pretrained_class = getattr(module, "RWKV7PreTrainedModel", None) + if pretrained_class is None or getattr(pretrained_class, "_tl_patched", False): + continue + original_init_weights = pretrained_class._init_weights + + def safe_init_weights(self, mod, _original=original_init_weights): + # Only initialise modules still on meta device (pre-loading); + # never re-randomise weights already read from the checkpoint. + first_param = next(mod.parameters(), None) + if first_param is not None and first_param.device.type != "meta": + return + _original(self, mod) + + pretrained_class._init_weights = safe_init_weights + pretrained_class._tl_patched = True diff --git a/transformer_lens/model_bridge/transformer_bridge.py b/transformer_lens/model_bridge/transformer_bridge.py index cd874f9f93..aff4229f4b 100644 --- a/transformer_lens/model_bridge/transformer_bridge.py +++ b/transformer_lens/model_bridge/transformer_bridge.py @@ -282,8 +282,12 @@ def __setattr__(self, name: str, value: Any) -> None: def _scan_existing_hooks(self, module: nn.Module, prefix: str = "") -> None: """Scan existing modules for hooks and add them to registry.""" visited = set() - # Protect canonical HookPoint names from alias overwrites - named_hook_ids: set = set() + # Protect canonical HookPoint names from alias overwrites. Seeded with + # already-registered hooks so the post-alias-registration re-scan adds + # alias registry entries without renaming shared HookPoint instances + # (dir() sorts alphabetically, so block-level alias attributes like + # hook_mlp_out are visited before the mlp child they point into). + named_hook_ids: set = {id(hp) for hp in self._hook_registry.values() if hp.name is not None} def scan_module(mod: nn.Module, path: str = "") -> None: obj_id = id(mod) @@ -1295,6 +1299,40 @@ def tl_named_parameters(self) -> Iterator[tuple[str, torch.Tensor]]: """ return iter(self.get_params().items()) + def input_to_embed( + self, + input: Union[str, List[str], torch.Tensor], + prepend_bos: Optional[bool] = None, + padding_side: Optional[str] = None, + attention_mask: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor, None, Optional[torch.Tensor]]: + """Convert input to the residual stream entering block 0 (``resid_pre[0]``). + + Bridge analog of :meth:`HookedTransformer.input_to_embed`. Returns + ``(residual, tokens, shortformer_pos_embed, attention_mask)``; feed the + residual to ``forward(..., start_at_layer=0)`` to resume the pass. + + ``shortformer_pos_embed`` is always ``None``: for the models the bridge + supports the residual already carries positional information, so there is + no separate positional stream to return. + """ + if isinstance(input, (str, list)): + assert self.tokenizer is not None, "Must provide a tokenizer for string input." + tokens = self.to_tokens(input, prepend_bos=prepend_bos, padding_side=padding_side) + else: + tokens = input + if isinstance(tokens, torch.Tensor) and tokens.ndim == 1: + tokens = tokens.unsqueeze(0) + if ( + attention_mask is None + and self.tokenizer is not None + and self.tokenizer.padding_side == "left" + ): + _prepend = self.cfg.default_prepend_bos if prepend_bos is None else prepend_bos + attention_mask = utils.get_attention_mask(self.tokenizer, tokens, _prepend) + residual = self.forward(tokens, stop_at_layer=0, attention_mask=attention_mask) + return residual, tokens, None, attention_mask + def forward( self, input: Union[str, List[str], torch.Tensor], @@ -1307,21 +1345,32 @@ def forward( stop_at_layer: Optional[int] = None, pixel_values: Optional[torch.Tensor] = None, input_values: Optional[torch.Tensor] = None, + past_key_values: Optional[Any] = None, **kwargs, ) -> Any: """Forward pass through the model. Args: input: Input to the model - return_type: Type of output to return ('logits', 'loss', 'both', 'predictions', None) + return_type: Type of output to return ('logits', 'loss', 'both', 'predictions', + 'logits_and_cache', None). 'logits_and_cache' returns + ``(logits, past_key_values)`` — the HuggingFace cache after this step, to + feed back on the next call for incremental decoding. loss_per_token: Whether to return loss per token prepend_bos: Whether to prepend BOS token padding_side: Which side to pad on - start_at_layer: Not implemented in TransformerBridge. The bridge delegates - to HuggingFace's model.forward() which owns the layer iteration loop, - making start_at_layer infeasible without monkey-patching HF internals - (fragile across HF versions) or exception-based layer skipping (corrupts - model state). Raises NotImplementedError if a non-None value is passed. + past_key_values: HuggingFace KV cache from a prior ``use_cache=True`` step + (e.g. the second element of a ``return_type='logits_and_cache'`` return). + When provided, KV caching is enabled automatically and only the new + tokens' keys/values are computed; HF derives the position offset from the + cache length. This is the bridge's manual KV-cache entry point — it uses + HF's native cache object rather than a TransformerLens cache. + start_at_layer: Resume the forward from block ``k``, treating ``input`` as + the residual-stream tensor ``[batch, pos, d_model]`` entering that block + (mirrors HookedTransformer). Blocks 0..k-1 still execute internally (their + output is discarded when block k swaps in the residual) but are excluded + from ``run_with_cache`` output. Requires an HF model that accepts + ``inputs_embeds``; only supported on the standard ``blocks`` stack. stop_at_layer: Layer to stop forward pass at pixel_values: Optional image tensor for multimodal models (e.g., LLaVA, Gemma3). The tensor is passed directly to the underlying HuggingFace model. @@ -1344,12 +1393,7 @@ def forward( ) if start_at_layer is not None: - raise NotImplementedError( - "start_at_layer is not supported in TransformerBridge. " - "The bridge delegates to HuggingFace's model.forward() which controls " - "the layer iteration loop. See the TransformerBridge review plan for a " - "detailed analysis of implementation approaches and their tradeoffs." - ) + input = self._setup_start_at_layer(input, start_at_layer) # Set stop_at_layer flag on all blocks if requested if stop_at_layer is not None: @@ -1452,6 +1496,11 @@ def forward( if attention_mask is not None: kwargs["attention_mask"] = attention_mask + if past_key_values is not None: + # Manual KV-cache injection: hand HF its own cache back and let it + # extend it, computing only the new tokens' keys/values. + kwargs["past_key_values"] = past_key_values + kwargs["use_cache"] = True if kwargs.pop("use_past_kv_cache", False) or kwargs.get("use_cache", False): kwargs["use_cache"] = True # Auto-generate decoder_input_ids for encoder-decoder models @@ -1563,6 +1612,53 @@ def forward( if hasattr(self, "_last_hf_cache"): del self._last_hf_cache + if start_at_layer is not None: + self._teardown_start_at_layer() + + def _setup_start_at_layer(self, input: Any, start_at_layer: int) -> torch.Tensor: + """Arm residual-stream injection at block ``start_at_layer``. + + ``input`` is the residual entering that block. It is returned (batch-promoted) + to drive the HF forward as ``inputs_embeds`` so position ids / attention mask + are computed for the right sequence length; block ``start_at_layer`` then swaps + it back in for its own input, discarding whatever blocks 0..k-1 produced. + """ + for alt in ("encoder_blocks", "decoder_blocks", "L_blocks", "H_blocks"): + if hasattr(self, alt): + raise NotImplementedError( + "start_at_layer is only supported on the standard 'blocks' stack, " + f"not {alt!r}." + ) + if not hasattr(self, "blocks"): + raise NotImplementedError("start_at_layer requires a 'blocks' stack.") + if not (isinstance(input, torch.Tensor) and input.is_floating_point()): + raise ValueError( + "start_at_layer requires a residual-stream tensor [batch, pos, d_model]; " + f"got {type(input).__name__}. Capture it from a prior run_with_cache " + "(e.g. cache['blocks.k.hook_in'])." + ) + residual = input if input.ndim == 3 else input.unsqueeze(0) + n_blocks = len(self.blocks) + if start_at_layer < 0: + start_at_layer += n_blocks + if not 0 <= start_at_layer < n_blocks: + raise ValueError(f"start_at_layer={start_at_layer} out of range [0, {n_blocks}).") + # The returned tensor is fed to HF as inputs_embeds; stash a clone so an + # architecture that mutates inputs_embeds in place can't corrupt the value + # block ``start_at_layer`` swaps back in. + injected = residual.clone() + for block in self.blocks: + block._start_at_layer_idx = start_at_layer + block._start_residual = injected + return residual + + def _teardown_start_at_layer(self) -> None: + """Clear the residual-injection state set by ``_setup_start_at_layer``.""" + if hasattr(self, "blocks"): + for block in self.blocks: + block._start_at_layer_idx = None + block._start_residual = None + # loss_fn inherited from BridgeCore def _resolve_stopping_criteria( @@ -2879,6 +2975,19 @@ def mps(self) -> "TransformerBridge": """ return self.to(torch.device("mps")) + def train(self, mode: bool = True) -> "TransformerBridge": + """Set training mode, propagating to the wrapped source model. + + ``original_model`` lives in ``__dict__`` rather than the registered + module tree, so the inherited ``nn.Module.train()`` recursion does not + reach it. + """ + super().train(mode) + original = getattr(self, "original_model", None) + if isinstance(original, torch.nn.Module): + original.train(mode) + return self + def set_use_attn_result(self, use_attn_result: bool): """Toggle whether to explicitly calculate and expose the result for each attention head. diff --git a/transformer_lens/model_protocol.py b/transformer_lens/model_protocol.py new file mode 100644 index 0000000000..1ebb7b8292 --- /dev/null +++ b/transformer_lens/model_protocol.py @@ -0,0 +1,65 @@ +"""Structural type shared by :class:`HookedTransformer` and :class:`TransformerBridge`. + +The interpretability utilities (``patching``, ``head_detector``, ``ActivationCache``) +are model-agnostic at runtime — they only need ``cfg`` plus the ``run_with_*`` / +tokenization surface and a few weight-processing helpers. Historically their +signatures said ``HookedTransformer``, which made a ``TransformerBridge`` fail to +type-check even though it works. Typing the model parameter as this Protocol accepts +either (and any future structural match, e.g. ``RemoteBridge``). + +Members are typed loosely on purpose: this is a compatibility shim over two concrete +classes whose method signatures differ in detail but agree in use. +""" +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, Protocol, runtime_checkable + +if TYPE_CHECKING: + from transformer_lens.config import TransformerLensConfig + + +# runtime_checkable so the interp utilities' beartype-decorated signatures can +# isinstance-check the parameter at runtime (presence-only; both models qualify). +@runtime_checkable +class TransformerLensModel(Protocol): + """Minimal structural interface common to HookedTransformer and TransformerBridge.""" + + # Read-only property (not a bare attribute) so it is covariant: a concrete model + # whose cfg is a TransformerLensConfig *subclass* (HookedTransformerConfig / + # TransformerBridgeConfig) still conforms. A mutable attribute would be invariant. + @property + def cfg(self) -> "TransformerLensConfig": + ... + + def run_with_cache(self, *args: Any, **kwargs: Any) -> Any: + ... + + def run_with_hooks(self, *args: Any, **kwargs: Any) -> Any: + ... + + def to_tokens(self, *args: Any, **kwargs: Any) -> Any: + ... + + +@runtime_checkable +class TransformerLensModelWithWeights(TransformerLensModel, Protocol): + """Adds the weight-processing surface that ``ActivationCache``'s advanced helpers + (LayerNorm folding, residual-direction projection) reach for. Both concrete models + expose these; the bridge builds them from its adapter.""" + + @property + def blocks(self) -> Any: + ... + + @property + def ln_final(self) -> Any: + ... + + def accumulated_bias(self, *args: Any, **kwargs: Any) -> Any: + ... + + def to_single_token(self, *args: Any, **kwargs: Any) -> Any: + ... + + def tokens_to_residual_directions(self, *args: Any, **kwargs: Any) -> Any: + ... diff --git a/transformer_lens/patching.py b/transformer_lens/patching.py index 868b1f2156..24a50ed126 100644 --- a/transformer_lens/patching.py +++ b/transformer_lens/patching.py @@ -62,7 +62,7 @@ import transformer_lens.utilities as utils from transformer_lens.ActivationCache import ActivationCache -from transformer_lens.HookedTransformer import HookedTransformer +from transformer_lens.model_protocol import TransformerLensModel # %% Logits = torch.Tensor @@ -90,7 +90,7 @@ def make_df_from_ranges( @overload def generic_activation_patch( - model: HookedTransformer, + model: TransformerLensModel, corrupted_tokens: Int[torch.Tensor, "batch pos"], clean_cache: ActivationCache, patching_metric: Callable[[Float[torch.Tensor, "batch pos d_vocab"]], Float[torch.Tensor, ""]], @@ -107,7 +107,7 @@ def generic_activation_patch( @overload def generic_activation_patch( - model: HookedTransformer, + model: TransformerLensModel, corrupted_tokens: Int[torch.Tensor, "batch pos"], clean_cache: ActivationCache, patching_metric: Callable[[Float[torch.Tensor, "batch pos d_vocab"]], Float[torch.Tensor, ""]], @@ -123,7 +123,7 @@ def generic_activation_patch( def generic_activation_patch( - model: HookedTransformer, + model: TransformerLensModel, corrupted_tokens: Int[torch.Tensor, "batch pos"], clean_cache: ActivationCache, patching_metric: Callable[[Float[torch.Tensor, "batch pos d_vocab"]], Float[torch.Tensor, ""]], diff --git a/transformer_lens/supported_models.py b/transformer_lens/supported_models.py index ab6b2f2d8a..bd670c20f8 100644 --- a/transformer_lens/supported_models.py +++ b/transformer_lens/supported_models.py @@ -1,6 +1,8 @@ # HookedTransformers model registry. If you are looking for TransformerBridge's supported models # see transformer_lens/tools/model_registry/data/supported_models.json or the docs page at # https://transformerlensorg.github.io/TransformerLens/generated/transformer_bridge_models.html +from transformer_lens.tools.model_registry.registry_io import load_model_aliases + OFFICIAL_MODEL_NAMES: list[str] = [ "01-ai/Yi-34B", "01-ai/Yi-34B-Chat", @@ -255,400 +257,10 @@ """Official model names for models on HuggingFace.""" # Model Aliases: -MODEL_ALIASES: dict[str, list[str]] = { - "01-ai/Yi-34B": ["yi-34b", "Yi-34B"], - "01-ai/Yi-34B-Chat": ["yi-34b-chat", "Yi-34B-Chat"], - "01-ai/Yi-6B": ["yi-6b", "Yi-6B"], - "01-ai/Yi-6B-Chat": ["yi-6b-chat", "Yi-6B-Chat"], - "ai-forever/mGPT": ["mGPT"], - "allenai/OLMo-1B-hf": ["olmo-1b"], - "allenai/OLMo-2-0425-1B": ["olmo-2-1b"], - "allenai/OLMo-2-1124-7B": ["olmo-2-7b"], - "allenai/Olmo-3-1025-7B": ["olmo-3-1025-7b"], - "allenai/Olmo-3-1125-32B": ["olmo-3-1125-32b"], - "allenai/Olmo-3-32B-Think": ["olmo-3-32b-think"], - "allenai/Olmo-3-7B-Instruct": ["olmo-3-7b-instruct"], - "allenai/Olmo-3-7B-Think": ["olmo-3-7b-think"], - "allenai/Olmo-3.1-32B-Instruct": ["olmo-3.1-32b-instruct"], - "allenai/Olmo-3.1-32B-Think": ["olmo-3.1-32b-think"], - "allenai/OLMo-7B-hf": ["olmo-7b"], - "allenai/OLMoE-1B-7B-0924": ["olmoe"], - "ArthurConmy/redwood_attn_2l": ["redwood_attn_2l"], - "Baidicoot/Othello-GPT-Transformer-Lens": ["othello-gpt"], - "bigcode/santacoder": ["santacoder"], - "bigscience/bloom-1b1": ["bloom-1b1"], - "bigscience/bloom-1b7": ["bloom-1b7"], - "bigscience/bloom-3b": ["bloom-3b"], - "bigscience/bloom-560m": ["bloom-560m"], - "bigscience/bloom-7b1": ["bloom-7b1"], - "codellama/CodeLlama-7b-hf": ["CodeLlamallama-2-7b"], - "codellama/CodeLlama-7b-Instruct-hf": ["CodeLlama-7b-instruct"], - "codellama/CodeLlama-7b-Python-hf": ["CodeLlama-7b-python"], - "distilgpt2": ["distillgpt2", "distill-gpt2", "distil-gpt2", "gpt2-xs"], - "EleutherAI/gpt-j-6B": ["gpt-j-6B", "gpt-j", "gptj"], - "EleutherAI/gpt-neo-1.3B": ["gpt-neo-1.3B", "gpt-neo-medium", "neo-medium"], - "EleutherAI/gpt-neo-125M": ["gpt-neo-125M", "gpt-neo-small", "neo-small", "neo"], - "EleutherAI/gpt-neo-2.7B": ["gpt-neo-2.7B", "gpt-neo-large", "neo-large"], - "EleutherAI/gpt-neox-20b": ["gpt-neox-20b", "gpt-neox", "neox"], - "EleutherAI/pythia-1.4b": ["pythia-1.4b", "EleutherAI/pythia-1.3b", "pythia-1.3b"], - "EleutherAI/pythia-1.4b-deduped": [ - "pythia-1.4b-deduped", - "EleutherAI/pythia-1.3b-deduped", - "pythia-1.3b-deduped", - ], - "EleutherAI/pythia-1.4b-deduped-v0": [ - "pythia-1.4b-deduped-v0", - "EleutherAI/pythia-1.3b-deduped-v0", - "pythia-1.3b-deduped-v0", - ], - "EleutherAI/pythia-1.4b-v0": ["pythia-1.4b-v0", "EleutherAI/pythia-1.3b-v0", "pythia-1.3b-v0"], - "EleutherAI/pythia-12b": ["pythia-12b", "EleutherAI/pythia-13b", "pythia-13b"], - "EleutherAI/pythia-12b-deduped": [ - "pythia-12b-deduped", - "EleutherAI/pythia-13b-deduped", - "pythia-13b-deduped", - ], - "EleutherAI/pythia-12b-deduped-v0": [ - "pythia-12b-deduped-v0", - "EleutherAI/pythia-13b-deduped-v0", - "pythia-13b-deduped-v0", - ], - "EleutherAI/pythia-12b-v0": ["pythia-12b-v0", "EleutherAI/pythia-13b-v0", "pythia-13b-v0"], - "EleutherAI/pythia-14m": ["pythia-14m"], - "EleutherAI/pythia-160m": ["pythia-160m", "EleutherAI/pythia-125m", "pythia-125m"], - "EleutherAI/pythia-160m-deduped": [ - "pythia-160m-deduped", - "EleutherAI/pythia-125m-deduped", - "pythia-125m-deduped", - ], - "EleutherAI/pythia-160m-deduped-v0": [ - "pythia-160m-deduped-v0", - "EleutherAI/pythia-125m-deduped-v0", - "pythia-125m-deduped-v0", - ], - "EleutherAI/pythia-160m-seed1": [ - "pythia-160m-seed1", - "EleutherAI/pythia-125m-seed1", - "pythia-125m-seed1", - ], - "EleutherAI/pythia-160m-seed2": [ - "pythia-160m-seed2", - "EleutherAI/pythia-125m-seed2", - "pythia-125m-seed2", - ], - "EleutherAI/pythia-160m-seed3": [ - "pythia-160m-seed3", - "EleutherAI/pythia-125m-seed3", - "pythia-125m-seed3", - ], - "EleutherAI/pythia-160m-v0": ["pythia-160m-v0", "EleutherAI/pythia-125m-v0", "pythia-125m-v0"], - "EleutherAI/pythia-1b": ["pythia-1b", "EleutherAI/pythia-800m", "pythia-800m"], - "EleutherAI/pythia-1b-deduped": [ - "pythia-1b-deduped", - "EleutherAI/pythia-800m-deduped", - "pythia-800m-deduped", - ], - "EleutherAI/pythia-1b-deduped-v0": [ - "pythia-1b-deduped-v0", - "EleutherAI/pythia-800m-deduped-v0", - "pythia-800m-deduped-v0", - ], - "EleutherAI/pythia-1b-v0": ["pythia-1b-v0", "EleutherAI/pythia-800m-v0", "pythia-800m-v0"], - "EleutherAI/pythia-2.8b": ["pythia-2.8b", "EleutherAI/pythia-2.7b", "pythia-2.7b"], - "EleutherAI/pythia-2.8b-deduped": [ - "pythia-2.8b-deduped", - "EleutherAI/pythia-2.7b-deduped", - "pythia-2.7b-deduped", - ], - "EleutherAI/pythia-2.8b-deduped-v0": [ - "pythia-2.8b-deduped-v0", - "EleutherAI/pythia-2.7b-deduped-v0", - "pythia-2.7b-deduped-v0", - ], - "EleutherAI/pythia-2.8b-v0": ["pythia-2.8b-v0", "EleutherAI/pythia-2.7b-v0", "pythia-2.7b-v0"], - "EleutherAI/pythia-31m": ["pythia-31m"], - "EleutherAI/pythia-410m": ["pythia-410m", "EleutherAI/pythia-350m", "pythia-350m"], - "EleutherAI/pythia-410m-deduped": [ - "pythia-410m-deduped", - "EleutherAI/pythia-350m-deduped", - "pythia-350m-deduped", - ], - "EleutherAI/pythia-410m-deduped-v0": [ - "pythia-410m-deduped-v0", - "EleutherAI/pythia-350m-deduped-v0", - "pythia-350m-deduped-v0", - ], - "EleutherAI/pythia-410m-v0": ["pythia-410m-v0", "EleutherAI/pythia-350m-v0", "pythia-350m-v0"], - "EleutherAI/pythia-6.9b": ["pythia-6.9b", "EleutherAI/pythia-6.7b", "pythia-6.7b"], - "EleutherAI/pythia-6.9b-deduped": [ - "pythia-6.9b-deduped", - "EleutherAI/pythia-6.7b-deduped", - "pythia-6.7b-deduped", - ], - "EleutherAI/pythia-6.9b-deduped-v0": [ - "pythia-6.9b-deduped-v0", - "EleutherAI/pythia-6.7b-deduped-v0", - "pythia-6.7b-deduped-v0", - ], - "EleutherAI/pythia-6.9b-v0": ["pythia-6.9b-v0", "EleutherAI/pythia-6.7b-v0", "pythia-6.7b-v0"], - "EleutherAI/pythia-70m": ["pythia-70m", "pythia", "EleutherAI/pythia-19m", "pythia-19m"], - "EleutherAI/pythia-70m-deduped": [ - "pythia-70m-deduped", - "EleutherAI/pythia-19m-deduped", - "pythia-19m-deduped", - ], - "EleutherAI/pythia-70m-deduped-v0": [ - "pythia-70m-deduped-v0", - "EleutherAI/pythia-19m-deduped-v0", - "pythia-19m-deduped-v0", - ], - "EleutherAI/pythia-70m-v0": [ - "pythia-70m-v0", - "pythia-v0", - "EleutherAI/pythia-19m-v0", - "pythia-19m-v0", - ], - "facebook/hubert-base-ls960": ["hubert-base-ls960"], - "facebook/opt-1.3b": ["opt-1.3b", "opt-medium"], - "facebook/opt-125m": ["opt-125m", "opt-small", "opt"], - "facebook/opt-13b": ["opt-13b", "opt-xxl"], - "facebook/opt-2.7b": ["opt-2.7b", "opt-large"], - "facebook/opt-30b": ["opt-30b", "opt-xxxl"], - "facebook/opt-6.7b": ["opt-6.7b", "opt-xl"], - "facebook/opt-66b": ["opt-66b", "opt-xxxxl"], - "facebook/wav2vec2-base": ["wav2vec2-base", "w2v2-base"], - "facebook/wav2vec2-large": ["wav2vec2-large", "w2v2-large"], - "google-bert/bert-base-cased": ["bert-base-cased"], - "google-bert/bert-base-uncased": ["bert-base-uncased"], - "google-bert/bert-large-cased": ["bert-large-cased"], - "google-bert/bert-large-uncased": ["bert-large-uncased"], - "google-t5/t5-base": ["t5-base"], - "google-t5/t5-large": ["t5-large"], - "google-t5/t5-small": ["t5-small"], - "google/gemma-2-27b": ["gemma-2-27b"], - "google/gemma-2-27b-it": ["gemma-2-27b-it"], - "google/gemma-2-2b": ["gemma-2-2b"], - "google/gemma-2-2b-it": ["gemma-2-2b-it"], - "google/gemma-2-9b": ["gemma-2-9b"], - "google/gemma-2-9b-it": ["gemma-2-9b-it"], - "google/gemma-2b": ["gemma-2b"], - "google/gemma-2b-it": ["gemma-2b-it"], - "google/gemma-3-12b-it": ["gemma-3-12b-it"], - "google/gemma-3-12b-pt": ["gemma-3-12b-pt"], - "google/gemma-3-1b-it": ["gemma-3-1b-it"], - "google/gemma-3-1b-pt": ["gemma-3-1b-pt"], - "google/gemma-3-270m": ["gemma-3-270m"], - "google/gemma-3-270m-it": ["gemma-3-270m-it"], - "google/gemma-3-27b-it": ["gemma-3-27b-it"], - "google/gemma-3-27b-pt": ["gemma-3-27b-pt"], - "google/gemma-3-4b-it": ["gemma-3-4b-it"], - "google/gemma-3-4b-pt": ["gemma-3-4b-pt"], - "google/gemma-7b": ["gemma-7b"], - "google/gemma-7b-it": ["gemma-7b-it"], - "google/medgemma-27b-it": ["medgemma-27b-it"], - "google/medgemma-27b-text-it": ["medgemma-27b-text-it"], - "google/medgemma-4b-it": ["medgemma-4b-it"], - "google/medgemma-4b-pt": ["medgemma-4b-pt"], - "gpt2": ["gpt2-small"], - "llama-13b-hf": ["llama-13b"], - "llama-30b-hf": ["llama-30b"], - "llama-65b-hf": ["llama-65b"], - "llama-7b-hf": ["llama-7b"], - "meta-llama/Llama-2-13b-chat-hf": ["Llama-2-13b-chat"], - "meta-llama/Llama-2-13b-hf": ["Llama-2-13b"], - "meta-llama/Llama-2-70b-chat-hf": ["Llama-2-70b-chat", "meta-llama-2-70b-chat-hf"], - "meta-llama/Llama-2-7b-chat-hf": ["Llama-2-7b-chat"], - "meta-llama/Llama-2-7b-hf": ["Llama-2-7b"], - "microsoft/phi-1": ["phi-1"], - "microsoft/phi-1_5": ["phi-1_5"], - "microsoft/phi-2": ["phi-2"], - "microsoft/Phi-3-mini-4k-instruct": ["phi-3"], - "microsoft/phi-4": ["phi-4"], - "mistralai/Mistral-7B-Instruct-v0.1": ["mistral-7b-instruct"], - "mistralai/Mistral-7B-v0.1": ["mistral-7b"], - "mistralai/Mistral-Nemo-Base-2407": ["mistral-nemo-base-2407"], - "mistralai/Mixtral-8x7B-Instruct-v0.1": ["mixtral-instruct", "mixtral-8x7b-instruct"], - "mistralai/Mixtral-8x7B-v0.1": ["mixtral", "mixtral-8x7b"], - "NeelNanda/Attn-Only-2L512W-Shortformer-6B-big-lr": [ - "attn-only-2l-demo", - "attn-only-2l-shortformer-6b-big-lr", - "attn-only-2l-induction-demo", - "attn-only-demo", - ], - "NeelNanda/Attn_Only_1L512W_C4_Code": [ - "attn-only-1l", - "attn-only-1l-new", - "attn-only-1l-c4-code", - ], - "NeelNanda/Attn_Only_2L512W_C4_Code": [ - "attn-only-2l", - "attn-only-2l-new", - "attn-only-2l-c4-code", - ], - "NeelNanda/Attn_Only_3L512W_C4_Code": [ - "attn-only-3l", - "attn-only-3l-new", - "attn-only-3l-c4-code", - ], - "NeelNanda/Attn_Only_4L512W_C4_Code": [ - "attn-only-4l", - "attn-only-4l-new", - "attn-only-4l-c4-code", - ], - "NeelNanda/GELU_1L512W_C4_Code": ["gelu-1l", "gelu-1l-new", "gelu-1l-c4-code"], - "NeelNanda/GELU_2L512W_C4_Code": ["gelu-2l", "gelu-2l-new", "gelu-2l-c4-code"], - "NeelNanda/GELU_3L512W_C4_Code": ["gelu-3l", "gelu-3l-new", "gelu-3l-c4-code"], - "NeelNanda/GELU_4L512W_C4_Code": ["gelu-4l", "gelu-4l-new", "gelu-4l-c4-code"], - "NeelNanda/SoLU_10L1280W_C4_Code": ["solu-10l", "solu-10l-new", "solu-10l-c4-code"], - "NeelNanda/SoLU_10L_v22_old": ["solu-10l-pile", "solu-10l-old"], - "NeelNanda/SoLU_12L1536W_C4_Code": ["solu-12l", "solu-12l-new", "solu-12l-c4-code"], - "NeelNanda/SoLU_12L_v23_old": ["solu-12l-pile", "solu-12l-old"], - "NeelNanda/SoLU_1L512W_C4_Code": ["solu-1l", "solu-1l-new", "solu-1l-c4-code"], - "NeelNanda/SoLU_1L512W_Wiki_Finetune": [ - "solu-1l-wiki", - "solu-1l-wiki-finetune", - "solu-1l-finetune", - ], - "NeelNanda/SoLU_1L_v9_old": ["solu-1l-pile", "solu-1l-old"], - "NeelNanda/SoLU_2L512W_C4_Code": ["solu-2l", "solu-2l-new", "solu-2l-c4-code"], - "NeelNanda/SoLU_2L_v10_old": ["solu-2l-pile", "solu-2l-old"], - "NeelNanda/SoLU_3L512W_C4_Code": ["solu-3l", "solu-3l-new", "solu-3l-c4-code"], - "NeelNanda/SoLU_4L512W_C4_Code": ["solu-4l", "solu-4l-new", "solu-4l-c4-code"], - "NeelNanda/SoLU_4L512W_Wiki_Finetune": [ - "solu-4l-wiki", - "solu-4l-wiki-finetune", - "solu-4l-finetune", - ], - "NeelNanda/SoLU_4L_v11_old": ["solu-4l-pile", "solu-4l-old"], - "NeelNanda/SoLU_6L768W_C4_Code": ["solu-6l", "solu-6l-new", "solu-6l-c4-code"], - "NeelNanda/SoLU_6L_v13_old": ["solu-6l-pile", "solu-6l-old"], - "NeelNanda/SoLU_8L1024W_C4_Code": ["solu-8l", "solu-8l-new", "solu-8l-c4-code"], - "NeelNanda/SoLU_8L_v21_old": ["solu-8l-pile", "solu-8l-old"], - "openai/gpt-oss-20b": ["gpt-oss-20b", "gpt-oss"], - "Qwen/Qwen-14B": ["qwen-14b"], - "Qwen/Qwen-14B-Chat": ["qwen-14b-chat"], - "Qwen/Qwen-1_8B": ["qwen-1.8b"], - "Qwen/Qwen-1_8B-Chat": ["qwen-1.8b-chat"], - "Qwen/Qwen-7B": ["qwen-7b"], - "Qwen/Qwen-7B-Chat": ["qwen-7b-chat"], - "Qwen/Qwen1.5-0.5B": ["qwen1.5-0.5b"], - "Qwen/Qwen1.5-0.5B-Chat": ["qwen1.5-0.5b-chat"], - "Qwen/Qwen1.5-1.8B": ["qwen1.5-1.8b"], - "Qwen/Qwen1.5-1.8B-Chat": ["qwen1.5-1.8b-chat"], - "Qwen/Qwen1.5-14B": ["qwen1.5-14b"], - "Qwen/Qwen1.5-14B-Chat": ["qwen1.5-14b-chat"], - "Qwen/Qwen1.5-4B": ["qwen1.5-4b"], - "Qwen/Qwen1.5-4B-Chat": ["qwen1.5-4b-chat"], - "Qwen/Qwen1.5-7B": ["qwen1.5-7b"], - "Qwen/Qwen1.5-7B-Chat": ["qwen1.5-7b-chat"], - "Qwen/Qwen2-0.5B": ["qwen2-0.5b"], - "Qwen/Qwen2-0.5B-Instruct": ["qwen2-0.5b-instruct"], - "Qwen/Qwen2-1.5B": ["qwen2-1.5b"], - "Qwen/Qwen2-1.5B-Instruct": ["qwen2-1.5b-instruct"], - "Qwen/Qwen2-7B": ["qwen2-7b"], - "Qwen/Qwen2-7B-Instruct": ["qwen2-7b-instruct"], - "Qwen/Qwen2.5-0.5B": ["qwen2.5-0.5b"], - "Qwen/Qwen2.5-0.5B-Instruct": ["qwen2.5-0.5b-instruct"], - "Qwen/Qwen2.5-1.5B": ["qwen2.5-1.5b"], - "Qwen/Qwen2.5-1.5B-Instruct": ["qwen2.5-1.5b-instruct"], - "Qwen/Qwen2.5-14B": ["qwen2.5-14b"], - "Qwen/Qwen2.5-14B-Instruct": ["qwen2.5-14b-instruct"], - "Qwen/Qwen2.5-32B": ["qwen2.5-32b"], - "Qwen/Qwen2.5-32B-Instruct": ["qwen2.5-32b-instruct"], - "Qwen/Qwen2.5-3B": ["qwen2.5-3b"], - "Qwen/Qwen2.5-3B-Instruct": ["qwen2.5-3b-instruct"], - "Qwen/Qwen2.5-72B": ["qwen2.5-72b"], - "Qwen/Qwen2.5-72B-Instruct": ["qwen2.5-72b-instruct"], - "Qwen/Qwen2.5-7B": ["qwen2.5-7b"], - "Qwen/Qwen2.5-7B-Instruct": ["qwen2.5-7b-instruct"], - "Qwen/Qwen3-0.6B": ["qwen3-0.6b"], - "Qwen/Qwen3-0.6B-Base": ["qwen3-0.6b-base"], - "Qwen/Qwen3-1.7B": ["qwen3-1.7b"], - "Qwen/Qwen3-14B": ["qwen3-14b"], - "Qwen/Qwen3-4B": ["qwen3-4b"], - "Qwen/Qwen3-8B": ["qwen3-8b"], - "Qwen/QwQ-32B-Preview": ["qwen-32b-preview"], - "roneneldan/TinyStories-1Layer-21M": ["tiny-stories-1L-21M"], - "roneneldan/TinyStories-1M": ["tiny-stories-1M"], - "roneneldan/TinyStories-28M": ["tiny-stories-28M"], - "roneneldan/TinyStories-2Layers-33M": ["tiny-stories-2L-33M"], - "roneneldan/TinyStories-33M": ["tiny-stories-33M"], - "roneneldan/TinyStories-3M": ["tiny-stories-3M"], - "roneneldan/TinyStories-8M": ["tiny-stories-8M"], - "roneneldan/TinyStories-Instruct-1M": ["tiny-stories-instruct-1M"], - "roneneldan/TinyStories-Instruct-28M": ["tiny-stories-instruct-28M"], - "roneneldan/TinyStories-Instruct-2Layers-33M": ["tiny-stories-instruct-2L-33M"], - "roneneldan/TinyStories-Instruct-33M": ["tiny-stories-instruct-33M"], - "roneneldan/TinyStories-Instruct-3M": ["tiny-stories-instruct-3M"], - "roneneldan/TinyStories-Instruct-8M": ["tiny-stories-instruct-8M"], - "roneneldan/TinyStories-Instuct-1Layer-21M": ["tiny-stories-instruct-1L-21M"], - "stabilityai/stablelm-base-alpha-3b": ["stablelm-base-alpha-3b", "stablelm-base-3b"], - "stabilityai/stablelm-base-alpha-7b": ["stablelm-base-alpha-7b", "stablelm-base-7b"], - "stabilityai/stablelm-tuned-alpha-3b": ["stablelm-tuned-alpha-3b", "stablelm-tuned-3b"], - "stabilityai/stablelm-tuned-alpha-7b": ["stablelm-tuned-alpha-7b", "stablelm-tuned-7b"], - "stanford-crfm/alias-gpt2-small-x21": [ - "stanford-gpt2-small-a", - "alias-gpt2-small-x21", - "gpt2-mistral-small-a", - "gpt2-stanford-small-a", - ], - "stanford-crfm/arwen-gpt2-medium-x21": [ - "stanford-gpt2-medium-a", - "arwen-gpt2-medium-x21", - "gpt2-medium-small-a", - "gpt2-stanford-medium-a", - ], - "stanford-crfm/battlestar-gpt2-small-x49": [ - "stanford-gpt2-small-b", - "battlestar-gpt2-small-x49", - "gpt2-mistral-small-b", - "gpt2-mistral-small-b", - ], - "stanford-crfm/beren-gpt2-medium-x49": [ - "stanford-gpt2-medium-b", - "beren-gpt2-medium-x49", - "gpt2-medium-small-b", - "gpt2-stanford-medium-b", - ], - "stanford-crfm/caprica-gpt2-small-x81": [ - "stanford-gpt2-small-c", - "caprica-gpt2-small-x81", - "gpt2-mistral-small-c", - "gpt2-stanford-small-c", - ], - "stanford-crfm/celebrimbor-gpt2-medium-x81": [ - "stanford-gpt2-medium-c", - "celebrimbor-gpt2-medium-x81", - "gpt2-medium-small-c", - "gpt2-medium-small-c", - ], - "stanford-crfm/darkmatter-gpt2-small-x343": [ - "stanford-gpt2-small-d", - "darkmatter-gpt2-small-x343", - "gpt2-mistral-small-d", - "gpt2-mistral-small-d", - ], - "stanford-crfm/durin-gpt2-medium-x343": [ - "stanford-gpt2-medium-d", - "durin-gpt2-medium-x343", - "gpt2-medium-small-d", - "gpt2-stanford-medium-d", - ], - "stanford-crfm/eowyn-gpt2-medium-x777": [ - "stanford-gpt2-medium-e", - "eowyn-gpt2-medium-x777", - "gpt2-medium-small-e", - "gpt2-stanford-medium-e", - ], - "stanford-crfm/expanse-gpt2-small-x777": [ - "stanford-gpt2-small-e", - "expanse-gpt2-small-x777", - "gpt2-mistral-small-e", - "gpt2-mistral-small-e", - ], - "swiss-ai/Apertus-8B-2509": ["apertus-8b", "apertus"], - "swiss-ai/Apertus-8B-Instruct-2509": ["apertus-8b-instruct", "apertus-instruct"], -} +# Canonical data lives in the bridge model registry +# (tools/model_registry/data/model_aliases.json); this re-export serves the +# legacy HookedTransformer loaders until their removal at 4.0. +MODEL_ALIASES: dict[str, list[str]] = load_model_aliases() """Model aliases for models on HuggingFace.""" diff --git a/transformer_lens/tools/__init__.py b/transformer_lens/tools/__init__.py index 2b78fa84ec..98af80819d 100644 --- a/transformer_lens/tools/__init__.py +++ b/transformer_lens/tools/__init__.py @@ -6,8 +6,25 @@ Subpackages: - analysis: High-level interpretability analyses (e.g. Direct Logit Attribution) - model_registry: Tools for discovering and documenting supported models + +Subpackages load lazily (PEP 562): ``analysis`` imports HookedTransformer, so an +eager import here would create a cycle for core modules that consume +``model_registry`` data at import time. """ -from . import analysis, model_registry +import importlib +from typing import Any + +_SUBMODULES = ("analysis", "model_registry") __all__ = ["analysis", "model_registry"] + + +def __getattr__(name: str) -> Any: + if name in _SUBMODULES: + return importlib.import_module(f".{name}", __name__) + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") + + +def __dir__() -> list[str]: + return sorted(set(globals()) | set(_SUBMODULES)) diff --git a/transformer_lens/tools/model_registry/__init__.py b/transformer_lens/tools/model_registry/__init__.py index b04c5bb7ab..30a3839267 100644 --- a/transformer_lens/tools/model_registry/__init__.py +++ b/transformer_lens/tools/model_registry/__init__.py @@ -115,7 +115,9 @@ "Qwen3NextForCausalLM", "Qwen3_5ForCausalLM", "Qwen3_5ForConditionalGeneration", + "RavenForCausalLM", "RecurrentGemmaForCausalLM", + "RWKV7ForCausalLM", "SmolLM3ForCausalLM", "StableLmForCausalLM", "T5ForConditionalGeneration", @@ -200,7 +202,9 @@ "Qwen3_5ForCausalLM": ["Qwen"], "Qwen3_5ForConditionalGeneration": ["Qwen"], "QwenForCausalLM": ["Qwen"], + "RavenForCausalLM": ["tomg-group-umd"], "RecurrentGemmaForCausalLM": ["google"], + "RWKV7ForCausalLM": ["fla-hub"], "SmolLM3ForCausalLM": ["HuggingFaceTB"], "StableLmForCausalLM": ["stabilityai"], "T5ForConditionalGeneration": ["google-t5", "google", "Salesforce", "MBZUAI"], @@ -210,10 +214,29 @@ "Zamba2ForCausalLM": ["Zyphra"], } +# Model-name prefixes that require trust_remote_code=True to load (custom +# modeling code on the HF Hub). +REMOTE_CODE_MODEL_PREFIXES: tuple[str, ...] = ( + "bigcode/santacoder", + "Qwen/Qwen-", + "Qwen/Qwen3-", + "microsoft/phi-2", + "microsoft/phi-4", + "apple/OpenELM", + "openai/gpt-oss-", + "swiss-ai/Apertus-", + "baichuan-inc/", # BaichuanForCausalLM — ships own modeling_baichuan.py + "ByteDance/Ouro-", # OuroForCausalLM — ships own modeling_ouro.py + "internlm/", # InternLM2ForCausalLM — ships own modeling_internlm2.py + "GSAI-ML/LLaDA", # LLaDAModelLM — ships configuration_llada.py/modeling_llada.py + "kuleshov-group/", # BD3LM — ships own custom modeling_d_dit.py +) + __all__ = [ # Constants "HF_SUPPORTED_ARCHITECTURES", "CANONICAL_AUTHORS_BY_ARCH", + "REMOTE_CODE_MODEL_PREFIXES", # Exceptions "ModelRegistryError", "ModelNotFoundError", diff --git a/transformer_lens/tools/model_registry/checkpoints.py b/transformer_lens/tools/model_registry/checkpoints.py new file mode 100644 index 0000000000..fbcab44d9f --- /dev/null +++ b/transformer_lens/tools/model_registry/checkpoints.py @@ -0,0 +1,45 @@ +"""Training-checkpoint label data for checkpointed model families. + +Canonical home for the checkpoint schedules previously defined in +``transformer_lens/loading_from_pretrained.py``. The schedules are frozen +historical artifacts of the published training runs. +""" + +import logging + +from .registry_io import resolve_model_alias + +# The steps for which there are checkpoints in the stanford crfm models +STANFORD_CRFM_CHECKPOINTS: list[int] = ( + list(range(0, 100, 10)) + + list(range(100, 2000, 50)) + + list(range(2000, 20000, 100)) + + list(range(20000, 400000 + 1, 1000)) +) + +# Linearly spaced checkpoints for Pythia models, taken every 1000 steps. +# Batch size 2,097,152 tokens, so checkpoints every 2.1B tokens +PYTHIA_CHECKPOINTS: list[int] = [0, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512] + list( + range(1000, 143000 + 1, 1000) +) +# Pythia V1 has log-spaced early checkpoints (see line above), but V0 doesn't +PYTHIA_V0_CHECKPOINTS: list[int] = list(range(1000, 143000 + 1, 1000)) + + +def get_checkpoint_labels(model_name: str) -> tuple[list[int], str]: + """Return (checkpoint labels, label type) for a checkpointed model family. + + Covers the HF-revision-checkpointed families (Pythia, stanford-crfm). + Raises ValueError for models without published checkpoint schedules. + """ + official_name = resolve_model_alias(model_name) or model_name + if official_name.startswith("stanford-crfm/"): + return STANFORD_CRFM_CHECKPOINTS, "step" + if official_name.startswith("EleutherAI/pythia"): + if "v0" in official_name: + return PYTHIA_V0_CHECKPOINTS, "step" + logging.warning( + "Pythia models on HF were updated on 4/3/23! add '-v0' to model name to access the old models." + ) + return PYTHIA_CHECKPOINTS, "step" + raise ValueError(f"Model {official_name} is not checkpointed.") diff --git a/transformer_lens/tools/model_registry/data/model_aliases.json b/transformer_lens/tools/model_registry/data/model_aliases.json new file mode 100644 index 0000000000..60124aab0c --- /dev/null +++ b/transformer_lens/tools/model_registry/data/model_aliases.json @@ -0,0 +1,883 @@ +{ + "description": "Canonical model-name alias table. Maps official HuggingFace model names to deprecated TransformerLens short aliases. Aliases are frozen: they warn on use and will be removed in TransformerLens 4.0.", + "aliases": { + "01-ai/Yi-34B": [ + "yi-34b", + "Yi-34B" + ], + "01-ai/Yi-34B-Chat": [ + "yi-34b-chat", + "Yi-34B-Chat" + ], + "01-ai/Yi-6B": [ + "yi-6b", + "Yi-6B" + ], + "01-ai/Yi-6B-Chat": [ + "yi-6b-chat", + "Yi-6B-Chat" + ], + "ai-forever/mGPT": [ + "mGPT" + ], + "allenai/OLMo-1B-hf": [ + "olmo-1b" + ], + "allenai/OLMo-2-0425-1B": [ + "olmo-2-1b" + ], + "allenai/OLMo-2-1124-7B": [ + "olmo-2-7b" + ], + "allenai/Olmo-3-1025-7B": [ + "olmo-3-1025-7b" + ], + "allenai/Olmo-3-1125-32B": [ + "olmo-3-1125-32b" + ], + "allenai/Olmo-3-32B-Think": [ + "olmo-3-32b-think" + ], + "allenai/Olmo-3-7B-Instruct": [ + "olmo-3-7b-instruct" + ], + "allenai/Olmo-3-7B-Think": [ + "olmo-3-7b-think" + ], + "allenai/Olmo-3.1-32B-Instruct": [ + "olmo-3.1-32b-instruct" + ], + "allenai/Olmo-3.1-32B-Think": [ + "olmo-3.1-32b-think" + ], + "allenai/OLMo-7B-hf": [ + "olmo-7b" + ], + "allenai/OLMoE-1B-7B-0924": [ + "olmoe" + ], + "ArthurConmy/redwood_attn_2l": [ + "redwood_attn_2l" + ], + "Baidicoot/Othello-GPT-Transformer-Lens": [ + "othello-gpt" + ], + "bigcode/santacoder": [ + "santacoder" + ], + "bigscience/bloom-1b1": [ + "bloom-1b1" + ], + "bigscience/bloom-1b7": [ + "bloom-1b7" + ], + "bigscience/bloom-3b": [ + "bloom-3b" + ], + "bigscience/bloom-560m": [ + "bloom-560m" + ], + "bigscience/bloom-7b1": [ + "bloom-7b1" + ], + "codellama/CodeLlama-7b-hf": [ + "CodeLlamallama-2-7b" + ], + "codellama/CodeLlama-7b-Instruct-hf": [ + "CodeLlama-7b-instruct" + ], + "codellama/CodeLlama-7b-Python-hf": [ + "CodeLlama-7b-python" + ], + "distilgpt2": [ + "distillgpt2", + "distill-gpt2", + "distil-gpt2", + "gpt2-xs" + ], + "EleutherAI/gpt-j-6B": [ + "gpt-j-6B", + "gpt-j", + "gptj" + ], + "EleutherAI/gpt-neo-1.3B": [ + "gpt-neo-1.3B", + "gpt-neo-medium", + "neo-medium" + ], + "EleutherAI/gpt-neo-125M": [ + "gpt-neo-125M", + "gpt-neo-small", + "neo-small", + "neo" + ], + "EleutherAI/gpt-neo-2.7B": [ + "gpt-neo-2.7B", + 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"stanford-crfm/arwen-gpt2-medium-x21": [ + "stanford-gpt2-medium-a", + "arwen-gpt2-medium-x21", + "gpt2-medium-small-a", + "gpt2-stanford-medium-a" + ], + "stanford-crfm/battlestar-gpt2-small-x49": [ + "stanford-gpt2-small-b", + "battlestar-gpt2-small-x49", + "gpt2-mistral-small-b" + ], + "stanford-crfm/beren-gpt2-medium-x49": [ + "stanford-gpt2-medium-b", + "beren-gpt2-medium-x49", + "gpt2-medium-small-b", + "gpt2-stanford-medium-b" + ], + "stanford-crfm/caprica-gpt2-small-x81": [ + "stanford-gpt2-small-c", + "caprica-gpt2-small-x81", + "gpt2-mistral-small-c", + "gpt2-stanford-small-c" + ], + "stanford-crfm/celebrimbor-gpt2-medium-x81": [ + "stanford-gpt2-medium-c", + "celebrimbor-gpt2-medium-x81", + "gpt2-medium-small-c" + ], + "stanford-crfm/darkmatter-gpt2-small-x343": [ + "stanford-gpt2-small-d", + "darkmatter-gpt2-small-x343", + "gpt2-mistral-small-d" + ], + "stanford-crfm/durin-gpt2-medium-x343": [ + "stanford-gpt2-medium-d", + "durin-gpt2-medium-x343", + "gpt2-medium-small-d", + "gpt2-stanford-medium-d" + ], + "stanford-crfm/eowyn-gpt2-medium-x777": [ + "stanford-gpt2-medium-e", + "eowyn-gpt2-medium-x777", + "gpt2-medium-small-e", + "gpt2-stanford-medium-e" + ], + "stanford-crfm/expanse-gpt2-small-x777": [ + "stanford-gpt2-small-e", + "expanse-gpt2-small-x777", + "gpt2-mistral-small-e" + ], + "swiss-ai/Apertus-8B-2509": [ + "apertus-8b", + "apertus" + ], + "swiss-ai/Apertus-8B-Instruct-2509": [ + "apertus-8b-instruct", + "apertus-instruct" + ] + } +} diff --git a/transformer_lens/tools/model_registry/data/supported_models.json b/transformer_lens/tools/model_registry/data/supported_models.json index 0a377103fe..713cb2da50 100644 --- a/transformer_lens/tools/model_registry/data/supported_models.json +++ b/transformer_lens/tools/model_registry/data/supported_models.json @@ -182721,6 +182721,20 @@ "phase7_score": null, "phase8_score": null }, + { + "architecture_id": "RavenForCausalLM", + "model_id": "tomg-group-umd/huginn-0125", + "status": 0, + "verified_date": null, + "metadata": null, + "note": null, + "phase1_score": null, + "phase2_score": null, + "phase3_score": null, + "phase4_score": null, + "phase7_score": null, + "phase8_score": null + }, { "architecture_id": "HrmTextForCausalLM", "model_id": "sapientinc/HRM-Text-1B", diff --git a/transformer_lens/tools/model_registry/generate_report.py b/transformer_lens/tools/model_registry/generate_report.py index 2478116e07..0a4f8cb05e 100644 --- a/transformer_lens/tools/model_registry/generate_report.py +++ b/transformer_lens/tools/model_registry/generate_report.py @@ -72,6 +72,8 @@ "HunYuanDenseV1ForCausalLM": "Tencent's open source decoder models", "Cohere2ForCausalLM": "Cohere's Command-A architecture with interleaved sliding-window RoPE and full-attention NoPE layers", "OuroForCausalLM": "ByteDance's Ouro looped language model (LoopLM) with weight-shared iterated depth", + "RavenForCausalLM": "tomg-group-umd's Huginn depth-recurrent decoder (prelude / weight-tied recurrent core / coda) with runtime recurrence count", + "RWKV7ForCausalLM": 'fla-hub\'s RWKV-7 ("Goose") attention-free recurrent decoder with generalized-delta-rule time-mixing and squared-ReLU channel-mixing', # Unsupported architectures "BertModel": "Google's BERT bidirectional encoder for understanding tasks", "BertForMaskedLM": "BERT with masked language modeling head", diff --git a/transformer_lens/tools/model_registry/registry_io.py b/transformer_lens/tools/model_registry/registry_io.py index 29d31ad988..a29079c245 100644 --- a/transformer_lens/tools/model_registry/registry_io.py +++ b/transformer_lens/tools/model_registry/registry_io.py @@ -8,6 +8,7 @@ import json import logging from datetime import date +from functools import lru_cache from pathlib import Path from typing import Callable, Optional @@ -18,6 +19,7 @@ _DATA_DIR = Path(__file__).parent / "data" _SUPPORTED_MODELS_PATH = _DATA_DIR / "supported_models.json" _VERIFICATION_HISTORY_PATH = _DATA_DIR / "verification_history.json" +_MODEL_ALIASES_PATH = _DATA_DIR / "model_aliases.json" # Status codes STATUS_UNVERIFIED = 0 @@ -136,6 +138,21 @@ def is_quantized_model(model_id: str) -> bool: return is_incompatible_quantized(model_id) +@lru_cache(maxsize=1) +def load_model_aliases() -> dict[str, list[str]]: + """Load the canonical alias table: official HF model name -> deprecated short aliases.""" + with open(_MODEL_ALIASES_PATH) as f: + return json.load(f)["aliases"] + + +def resolve_model_alias(model_name: str) -> Optional[str]: + """Return the official HF name if ``model_name`` is a deprecated alias, else None.""" + for official_name, aliases in load_model_aliases().items(): + if model_name in aliases: + return official_name + return None + + def load_supported_models_raw() -> dict: """Load supported_models.json as a raw dict.""" with open(_SUPPORTED_MODELS_PATH) as f: diff --git a/transformer_lens/tools/model_registry/verify_models.py b/transformer_lens/tools/model_registry/verify_models.py index ed7ad23075..fcfb713dcb 100644 --- a/transformer_lens/tools/model_registry/verify_models.py +++ b/transformer_lens/tools/model_registry/verify_models.py @@ -43,6 +43,7 @@ # between models without corrupting state. _interrupt_requested = False +from . import REMOTE_CODE_MODEL_PREFIXES from .registry_io import ( QUANTIZED_NOTE, STATUS_FAILED, @@ -58,16 +59,6 @@ logger = logging.getLogger(__name__) -# Architectures added via the TransformerBridge system that need trust_remote_code=True. -# These are not in the legacy NEED_REMOTE_CODE_MODELS tuple (loading_from_pretrained.py). -_BRIDGE_REMOTE_CODE_PREFIXES: tuple[str, ...] = ( - "baichuan-inc/", # BaichuanForCausalLM — ships own modeling_baichuan.py - "ByteDance/Ouro-", # OuroForCausalLM — ships own modeling_ouro.py - "internlm/", # InternLM2ForCausalLM — ships own modeling_internlm2.py - "GSAI-ML/LLaDA", # LLaDAModelLM — ships configuration_llada.py/modeling_llada.py - "kuleshov-group/", # BD3LM — ships own custom modeling_d_dit.py -) - # Data directory for registry files _DATA_DIR = Path(__file__).parent / "data" _CHECKPOINT_PATH = _DATA_DIR / "verification_checkpoint.json" @@ -199,10 +190,7 @@ def estimate_model_params(model_id: str) -> int: """ from transformers import AutoConfig - from transformer_lens.loading_from_pretrained import NEED_REMOTE_CODE_MODELS - - _all_remote_prefixes = NEED_REMOTE_CODE_MODELS + _BRIDGE_REMOTE_CODE_PREFIXES - trust_remote_code = any(model_id.startswith(prefix) for prefix in _all_remote_prefixes) + trust_remote_code = any(model_id.startswith(prefix) for prefix in REMOTE_CODE_MODEL_PREFIXES) from transformer_lens.utilities.hf_utils import get_hf_token config = AutoConfig.from_pretrained( @@ -889,10 +877,9 @@ def verify_models( all_results: list = [] error_msg: Optional[str] = None - from transformer_lens.loading_from_pretrained import NEED_REMOTE_CODE_MODELS - - _all_remote_prefixes = NEED_REMOTE_CODE_MODELS + _BRIDGE_REMOTE_CODE_PREFIXES - needs_remote_code = any(model_id.startswith(prefix) for prefix in _all_remote_prefixes) + needs_remote_code = any( + model_id.startswith(prefix) for prefix in REMOTE_CODE_MODEL_PREFIXES + ) # Convert string dtype to torch.dtype for benchmark suite import torch diff --git a/transformer_lens/utilities/tracr.py b/transformer_lens/utilities/tracr.py index a8db6b579c..fdfa74c9dd 100644 --- a/transformer_lens/utilities/tracr.py +++ b/transformer_lens/utilities/tracr.py @@ -19,7 +19,7 @@ def infer_tracr_output_label(model: Any) -> str: """Infer the RASP output label used in ``model.residual_labels``. - Tracr stores residual basis labels as strings like ``"reverse:3"`` while + Tracr stores residual basis labels as strings like ``"reverse_1:3"`` while its categorical output encoder stores only values and output-column ids. The output label is the unique residual-label prefix whose value set exactly matches the output encoder's categorical value set. From 596e584846982c940b1418bc165d61d7c58e9eca Mon Sep 17 00:00:00 2001 From: Jonah Larson Date: Fri, 24 Jul 2026 18:04:54 -0500 Subject: [PATCH 15/43] Comment cleanup jul 26 (#1541) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add dedicated GraniteMoe adapter tests (#1302) (#1524) granite_moe had substantive test coverage, but embedded inside test_granite_adapter.py rather than in its own file — inconsistent with every other architecture listed in #1302 (including granite_moe_hybrid), which each get a dedicated test__adapter.py. Moves that coverage into test_granite_moe_adapter.py and adds the one piece that was missing: an explicit config-flag test class (GraniteMoe doesn't override config setup, so it inherits dense Granite's flags, but nothing asserted that directly before). test_granite_adapter.py goes back to covering only GraniteArchitectureAdapter, matching its own docstring. * Updating documentation for future deprecation (#1523) * feat: add RWKV-7 (Goose) TransformerBridge adapter (RWKV7ForCausalLM) (#1521) * feat: add RWKV-7 (Goose) TransformerBridge adapter (RWKV7ForCausalLM) Signed-off-by: Mukund Pandey * refactor: use OpaqueBlockBridge for RWKV-7; introduce OpaqueBlockBridge base class Signed-off-by: Mukund Pandey * added hook_out_is_single_residual_stream default to OpaqueBlockBridge * style: apply black formatting to opaque_block, ssm_block, __init__ Signed-off-by: Mukund Pandey * style: fix import order in generalized_components/__init__.py (isort) Signed-off-by: Mukund Pandey * style: fix import order in generalized_components/__init__.py (isort) Signed-off-by: Mukund Pandey * style: fix import order in generalized_components/__init__.py (isort) Signed-off-by: Mukund Pandey * refactor: replace SSM2MixerBridge with GeneralizedComponent for RWKV-7 attn/ffn sublayers Signed-off-by: Mukund Pandey * style: fix black formatting in generate_report.py Signed-off-by: Mukund Pandey --------- Signed-off-by: Mukund Pandey Co-authored-by: jlarson4 * feat: add RavenForCausalLM (Huginn) depth-recurrent adapter (#1520) * Add RavenForCausalLM (Huginn) TransformerBridge adapter Adds a TransformerBridge architecture adapter for RavenForCausalLM (tomg-group-umd/huginn-0125), a depth-recurrent decoder with a prelude / weight-tied recurrent core / coda structure, resolving #1469. - raven.py delegates the recurrence to the remote-code HF forward and maps the three physical block lists (prelude / core_block / coda) via SSMBlockBridge, with combined-QKV native attention and a gated MLP. Sets applicable_phases=[] (a random initial latent state and post-residual sandwich norms diverge from the verify_models phases) and supports_fold_ln=False (ln_f is reused mid-network). prepare_loading patches Huginn's remote code for transformers v5 (tied-weights-keys dict form + a weight re-init guard). - Registers in the adapter factory, the model registry (canonical author and description) and supported_models.json; surfaces the recurrence-shape config via both _HF_PASSTHROUGH_ATTRS lists. - Adds synthetic-config unit tests and CI-gated integration tests. * fix: pass num_steps as int to iterate_forward (0-d tensor has no len) * fix: remove unused torch import and use setattr for dynamic cfg attrs * style: apply black formatting to raven.py Signed-off-by: Mukund Pandey * refactor: introduce OpaqueBlockBridge; use it for Raven instead of SSMBlockBridge Signed-off-by: Mukund Pandey * style: apply black formatting to opaque_block, ssm_block, __init__ Signed-off-by: Mukund Pandey * style: fix import order in generalized_components/__init__.py (isort) Signed-off-by: Mukund Pandey * fix: strip dead hook_q/k/v aliases from Raven AttentionBridge Raven uses a combined Wqkv projection (no separate q/k/v submodules), so AttentionBridge's default hook_q/hook_k/hook_v aliases (which target q.hook_out / k.hook_out / v.hook_out) are unresolvable. The upstream test_every_hook_alias_resolves_to_hookpoint audit catches these as 9 dead aliases across prelude / core_block / coda. Strip them by setting an instance-level hook_aliases that omits those three keys, leaving only the aliases that have real HookPoint targets. Signed-off-by: Mukund Pandey * chore: merge upstream/dev — add RWKV-7 passthrough attrs and registry entry Brings in the RWKV-7 additions from #1521 that landed in dev: - _bridge_builder.py: added RWKV-7 passthrough attrs (num_heads, value_dim, decay/gate/a/v_low_rank_dim, norm_first, norm_bias, fuse_norm, attn_mode, hidden_act) - generate_report.py: added RWKV7ForCausalLM description entry Signed-off-by: Mukund Pandey --------- Signed-off-by: Mukund Pandey * Fix Tracr demo output_label: compiled labels are auto-numbered (#1525) The Tracr demo passes output_label="reverse", but Tracr builds labels as f"{name}_{unique_id}", so the compiled expression's residual labels are reverse_1:* and the demo raises ValueError at the state-dict cell. Pass reverse.label instead of hardcoding the name, and correct the same wrong label shape in the unit-test fixture (real Tracr never emits "reverse:1") and in the infer_tracr_output_label docstring example. Co-authored-by: Claude Fable 5 * Add TransformerBridge adapter for lightweight decoder-only pretraining models (#1519) * Add TransformerLens pretrain bridge adapter * Add TransformerLens pretrain bridge adapter * Setup registry for hookedtransformer deprecation * bug fixes and rebase to 4.x * Hook management improvements * implemented `stop_at_layer` on TransformerBridge * input_to_embed and pos_slice added to bridge * prevent corruption, check_hooks_to_add placed in all locations that need it * Adjusted head detector to be HookedTransformer agnostic, added key value injection to bridge * Add specific migration differences section * Attempting to let pytest run in parallel * Attempting to fix the OOM on the multi-device Full Coverage run * Big comment sweep * Comment fix --------- Signed-off-by: Mukund Pandey Co-authored-by: Delaida Muminovic Co-authored-by: Mukund Pandey Co-authored-by: Joseph Quevedo <41499530+dewstend@users.noreply.github.com> Co-authored-by: Claude Fable 5 Co-authored-by: Cacapice --- docs/make_docs.py | 2 +- docs/source/conf.py | 1 - .../compatibility/test_hook_completeness.py | 6 +- ...test_legacy_hooked_transformer_coverage.py | 2 +- .../model_bridge/test_n_ctx_override.py | 18 ++-- tests/acceptance/test_activation_cache.py | 5 - .../acceptance/test_hooked_encoder_decoder.py | 1 - tests/acceptance/test_hooked_transformer.py | 11 --- .../test_bridge_generate_return_cache.py | 2 +- .../model_bridge/test_bridge_stop_at_layer.py | 3 - .../model_bridge/test_falcon_h1_adapter.py | 2 +- .../test_granite_moe_hybrid_adapter.py | 8 +- ..._joint_qkv_attention_hook_compatibility.py | 1 - .../model_bridge/test_mamba2_adapter.py | 14 +-- .../model_bridge/test_mamba_adapter.py | 19 ++-- .../model_bridge/test_pretrain_adapter.py | 9 +- .../model_bridge/test_qwen3_moe_bridge.py | 2 +- .../test_ssm_effective_attention_dispatch.py | 6 +- .../test_centralized_weight_processing.py | 2 +- .../test_fold_layer_integration.py | 1 - .../test_grouped_query_attention.py | 7 +- .../test_main_demo_pattern_hooks.py | 1 - tests/integration/test_start_at_layer.py | 3 +- .../test_next_sentence_prediction.py | 4 - .../test_codegen_attention_bridge.py | 4 +- .../test_joint_qkv_attention.py | 12 --- .../test_bd3lm_adapter.py | 1 - .../test_gemma3n_adapter.py | 2 +- .../test_hunyuan_v1_dense_adapter.py | 2 +- .../test_lfm2_adapter.py | 2 +- .../test_mpt_adapter.py | 4 +- .../test_pretrain_model_container.py | 30 +----- .../test_stablelm_adapter.py | 4 +- .../model_bridge/test_component_hooks_fire.py | 6 +- .../test_hook_alias_resolution.py | 2 +- .../test_inspect_vllm_provider.py | 2 +- .../unit/model_bridge/test_native_features.py | 3 +- .../test_optimizer_compatibility.py | 2 - tests/unit/test_hook_points.py | 4 +- tests/unit/test_hooked_transformer_config.py | 2 - tests/unit/test_key_value_cache_entry.py | 35 +------ tests/unit/test_supported_models.py | 3 - tests/unit/test_svd_interpreter.py | 2 +- tests/unit/test_utils.py | 4 +- tests/unit/test_weight_processing.py | 2 - tests/unit/utilities/test_devices.py | 1 - .../test_base_tensor_conversion.py | 4 - .../test_callable_tensor_conversion.py | 3 - .../test_rearrange_tensor_conversion.py | 6 +- .../test_repeat_tensor_conversion.py | 2 - .../test_terenary_tensor_conversion.py | 10 +- transformer_lens/HookedAudioEncoder.py | 1 - transformer_lens/HookedEncoder.py | 1 - transformer_lens/HookedEncoderDecoder.py | 3 - transformer_lens/HookedTransformer.py | 22 ----- transformer_lens/benchmarks/forward_pass.py | 3 +- transformer_lens/benchmarks/main_benchmark.py | 2 +- .../components/abstract_attention.py | 6 -- transformer_lens/components/attention.py | 1 - transformer_lens/components/t5_block.py | 4 +- .../components/transformer_block.py | 1 - .../config/hooked_transformer_config.py | 1 - .../config/transformer_lens_config.py | 8 -- .../conversion_steps/tensor_conversion_set.py | 1 - .../helpers/merge_quantiziation_fields.py | 2 - .../conversion_utils/hook_conversion_utils.py | 6 -- transformer_lens/evals.py | 2 - transformer_lens/head_detector.py | 2 +- transformer_lens/loading_from_pretrained.py | 4 - .../model_bridge/architecture_adapter.py | 3 +- .../generalized_components/base.py | 2 - .../generalized_components/block.py | 7 +- .../generalized_components/bloom_block.py | 5 - .../generalized_components/bloom_mlp.py | 1 - .../clip_vision_encoder.py | 1 - .../generalized_components/linear.py | 1 - .../position_embeddings_attention.py | 3 - .../rotary_embedding.py | 1 - .../siglip_vision_encoder.py | 1 - .../vision_projection.py | 3 - .../sources/transformers/source.py | 3 +- .../supported_architectures/gemma1.py | 1 - .../supported_architectures/gemma2.py | 2 - .../gemma3_multimodal.py | 1 - .../gpt2_lm_head_custom.py | 2 +- .../hunyuan_v1_dense.py | 2 - .../supported_architectures/llama.py | 1 - .../supported_architectures/llava.py | 1 - .../supported_architectures/olmo2.py | 1 - .../supported_architectures/olmoe.py | 1 - .../supported_architectures/pretrain.py | 96 ++++--------------- .../supported_architectures/qwen2.py | 2 - .../supported_architectures/qwen3.py | 3 +- .../supported_architectures/qwen3_moe.py | 1 - .../model_bridge/transformer_bridge.py | 3 - transformer_lens/patching.py | 3 - .../pretrained/weight_conversions/hubert.py | 1 - .../pretrained/weight_conversions/olmoe.py | 1 - .../pretrained/weight_conversions/qwen3.py | 1 - transformer_lens/utilities/aliases.py | 1 - transformer_lens/utilities/attribute_utils.py | 1 - .../utilities/bridge_components.py | 3 - transformer_lens/utilities/hf_utils.py | 3 - transformer_lens/utilities/matrix.py | 1 - transformer_lens/utilities/tokenize_utils.py | 2 +- transformer_lens/weight_processing.py | 1 - 106 files changed, 97 insertions(+), 423 deletions(-) diff --git a/docs/make_docs.py b/docs/make_docs.py index 242ce043e0..5f96d36405 100644 --- a/docs/make_docs.py +++ b/docs/make_docs.py @@ -217,7 +217,7 @@ def tokenizer_vocab_hash(tokenizer: PreTrainedTokenizer) -> str: # convert to base64 return base64.b64encode( hash_obj.digest(), - altchars=b"-_", # - and _ as altchars + altchars=b"-_", ).decode("UTF-8") diff --git a/docs/source/conf.py b/docs/source/conf.py index 1960b0c4ac..eea59e26a8 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -184,4 +184,3 @@ def setup(app): """Sphinx setup overrides.""" # Connect functions to run when watch detects a file change app.connect("builder-inited", run_apidoc) - # app.connect("builder-inited", copy_demos) # Don't run as too slow diff --git a/tests/acceptance/model_bridge/compatibility/test_hook_completeness.py b/tests/acceptance/model_bridge/compatibility/test_hook_completeness.py index 84c6a2d223..fdcfcdca28 100644 --- a/tests/acceptance/model_bridge/compatibility/test_hook_completeness.py +++ b/tests/acceptance/model_bridge/compatibility/test_hook_completeness.py @@ -19,13 +19,13 @@ # Diverse architectures for hook completeness testing. # Constraint: these tests compare bridge vs legacy HookedTransformer, so each -# entry must be in HookedTransformer's OFFICIAL_MODEL_NAMES. Tiny C1-affected -# families (Llama/Qwen/Gemma under ~150M) aren't registered with HT; for those, +# entry must be in HookedTransformer's OFFICIAL_MODEL_NAMES. Tiny Llama/Qwen/Gemma +# families (under ~150M) aren't registered with HT; for those, # tests/unit/model_bridge/test_component_hooks_fire.py (Tier 2) provides # direct per-adapter hook-firing coverage without needing an HT counterpart. MODELS_TO_TEST = [ "gpt2", # JointQKVAttentionBridge (standard decoder-only) - "EleutherAI/pythia-14m", # ParallelBlockBridge (C15 regression guard) + "EleutherAI/pythia-14m", # ParallelBlockBridge regression guard ] # Gemma2: local only (too large for CI) diff --git a/tests/acceptance/model_bridge/compatibility/test_legacy_hooked_transformer_coverage.py b/tests/acceptance/model_bridge/compatibility/test_legacy_hooked_transformer_coverage.py index ad2b53874a..e8d82544d2 100644 --- a/tests/acceptance/model_bridge/compatibility/test_legacy_hooked_transformer_coverage.py +++ b/tests/acceptance/model_bridge/compatibility/test_legacy_hooked_transformer_coverage.py @@ -229,7 +229,7 @@ def test_memory_efficiency(self, bridge_model): # Run multiple forward passes for _ in range(5): output = bridge_model(prompt) - del output # Explicitly delete + del output # release tensor so the leak probe below is meaningful # Clean up gc.collect() diff --git a/tests/acceptance/model_bridge/test_n_ctx_override.py b/tests/acceptance/model_bridge/test_n_ctx_override.py index be35474e68..54b2a49be1 100644 --- a/tests/acceptance/model_bridge/test_n_ctx_override.py +++ b/tests/acceptance/model_bridge/test_n_ctx_override.py @@ -51,24 +51,20 @@ def test_n_ctx_combined_with_hf_config_overrides(): assert bridge.cfg.n_ctx == 256 -# --- Coverage for code-review items #2, #4, #5, #7 --- - - def test_n_ctx_zero_raises_value_error(): - """#2: n_ctx must be positive; zero should raise ValueError.""" + """n_ctx must be positive; zero should raise ValueError.""" with pytest.raises(ValueError, match="positive integer"): TransformerBridge.boot_transformers("gpt2", device="cpu", n_ctx=0, load_weights=False) def test_n_ctx_negative_raises_value_error(): - """#2: n_ctx must be positive; negative should raise ValueError.""" + """n_ctx must be positive; negative should raise ValueError.""" with pytest.raises(ValueError, match="positive integer"): TransformerBridge.boot_transformers("gpt2", device="cpu", n_ctx=-1, load_weights=False) def test_n_ctx_conflict_with_hf_config_overrides_warns(caplog): - """#4: When both n_ctx and the same hf_config_overrides field are set with different values, - a warning should be emitted explaining that n_ctx wins.""" + """When both n_ctx and the same hf_config_overrides field are set with different values, a warning should be emitted explaining that n_ctx wins.""" with caplog.at_level(logging.WARNING): TransformerBridge.boot_transformers( "gpt2", @@ -85,7 +81,7 @@ def test_n_ctx_conflict_with_hf_config_overrides_warns(caplog): def test_n_ctx_no_conflict_when_values_match(caplog): - """#4: If n_ctx and hf_config_overrides agree on the value, no conflict warning is emitted.""" + """If n_ctx and hf_config_overrides agree on the value, no conflict warning is emitted.""" with caplog.at_level(logging.WARNING): TransformerBridge.boot_transformers( "gpt2", @@ -98,8 +94,7 @@ def test_n_ctx_no_conflict_when_values_match(caplog): def test_n_ctx_shrink_with_load_weights_gives_clear_error(): - """#5: Shrinking a learned-pos-embed model's n_ctx at weight-load time should raise - with a message explaining the cause and suggesting alternatives.""" + """Shrinking a learned-pos-embed model's n_ctx at weight-load time should raise with a message explaining the cause and suggesting alternatives.""" with pytest.raises(RuntimeError) as exc_info: TransformerBridge.boot_transformers("gpt2", device="cpu", n_ctx=256, load_weights=True) err = str(exc_info.value) @@ -108,8 +103,7 @@ def test_n_ctx_shrink_with_load_weights_gives_clear_error(): def test_n_ctx_override_verified_on_loaded_model(): - """#7: After load, the override should be visible on hf_model.config so users - can trust that the longer/shorter context is actually in effect.""" + """After load, the override should be visible on hf_model.config so users can trust that the longer/shorter context is actually in effect.""" bridge = TransformerBridge.boot_transformers( "gpt2", device="cpu", n_ctx=2048, load_weights=False ) diff --git a/tests/acceptance/test_activation_cache.py b/tests/acceptance/test_activation_cache.py index 68f6fd0b39..60875620f7 100644 --- a/tests/acceptance/test_activation_cache.py +++ b/tests/acceptance/test_activation_cache.py @@ -23,7 +23,6 @@ def get_ioi_tokens_and_answer_tokens(model): - # List of prompts prompts = [] # List of answers, in the format (correct, incorrect) answers = [] @@ -62,15 +61,12 @@ def load_model(name): @torch.no_grad def test_logit_attrs_matches_reference_code(): - # Load solu-2l model = load_model("solu-2l") tokens, answer_tokens = get_ioi_tokens_and_answer_tokens(model) - # Run the model and cache all activations _, cache = model.run_with_cache(tokens) - # Get accumulated resid accumulated_residual = cache.accumulated_resid(layer=-1, incl_mid=True, pos_slice=-1) # Get ref ave logit diffs (cribbed notebook code) @@ -745,7 +741,6 @@ def test_compute_test_head_results_does_not_compute_results_twice(): cache.compute_head_results() assert "blocks.0.attn.hook_result" in cache.cache_dict - # set infinity to the first element of the head results assert cache.cache_dict["blocks.0.attn.hook_result"][0, 0, 0, 0] != float("inf") cache.cache_dict["blocks.0.attn.hook_result"][0, 0, 0, 0] = float("inf") cache.compute_head_results() diff --git a/tests/acceptance/test_hooked_encoder_decoder.py b/tests/acceptance/test_hooked_encoder_decoder.py index d3628e7afb..2be657c479 100644 --- a/tests/acceptance/test_hooked_encoder_decoder.py +++ b/tests/acceptance/test_hooked_encoder_decoder.py @@ -229,7 +229,6 @@ def test_cross_attention_layer(our_model, huggingface_model, hello_world_tokens, hf_layer = huggingface_model.decoder.block[0].layer[1] our_layer = our_model.decoder[0] - # assert ln weights are the same assert_close(hf_layer.layer_norm.weight, our_layer.ln2.w) our_cross_attn_out = ( diff --git a/tests/acceptance/test_hooked_transformer.py b/tests/acceptance/test_hooked_transformer.py index 8020c19237..a2d472e497 100644 --- a/tests/acceptance/test_hooked_transformer.py +++ b/tests/acceptance/test_hooked_transformer.py @@ -73,16 +73,6 @@ text = "Hello world!" -""" -# Code to regenerate loss store -store = {} -for name in model_names: - model = HookedTransformer.from_pretrained(name, device='cuda') - loss = model(text,return_type="loss") - store[name] = loss.item() -print(store) -""" - # Loss values for minimal testing SMALL_LOSS_STORE = { "gpt2-small": 5.331855773925781, @@ -385,7 +375,6 @@ def benchmark_model_options( device ) - # hf_model = hf_model.to(device) hf_logits = hf_model(tokens).logits.detach() hf_logits = hf_logits.to("cpu") diff --git a/tests/integration/model_bridge/test_bridge_generate_return_cache.py b/tests/integration/model_bridge/test_bridge_generate_return_cache.py index c6323818be..3b74f2226b 100644 --- a/tests/integration/model_bridge/test_bridge_generate_return_cache.py +++ b/tests/integration/model_bridge/test_bridge_generate_return_cache.py @@ -78,7 +78,7 @@ def test_cache_matches_run_with_cache_over_full_sequence(self, bridge): assert torch.allclose(g, r, atol=1e-5, rtol=1e-4), f"{name} differs from run_with_cache" def test_includes_attention_patterns(self, bridge): - """The cache includes full [batch, heads, q, k] attention patterns (an Option B benefit).""" + """The cache includes full [batch, heads, q, k] attention patterns.""" tokens = bridge.to_tokens("The quick brown") with torch.no_grad(): out, cache = bridge.generate( diff --git a/tests/integration/model_bridge/test_bridge_stop_at_layer.py b/tests/integration/model_bridge/test_bridge_stop_at_layer.py index 20e6e2298e..8e2c7e5ba2 100644 --- a/tests/integration/model_bridge/test_bridge_stop_at_layer.py +++ b/tests/integration/model_bridge/test_bridge_stop_at_layer.py @@ -200,7 +200,6 @@ def test_stop_at_embed_processed(bridge_with_processed_weights): """Test stop_at_layer=0 with processed weights.""" rand_input = torch.randint(0, 100, (2, 10)) - # Run with stop_at_layer=0 output, cache = bridge_with_processed_weights.run_with_cache(rand_input, stop_at_layer=0) # Verify output shape @@ -245,7 +244,6 @@ def test_no_stop_processed(bridge_with_processed_weights): """Test stop_at_layer=None with processed weights (full forward pass).""" rand_input = torch.randint(0, 100, (2, 10)) - # Run with stop_at_layer=None output, cache = bridge_with_processed_weights.run_with_cache(rand_input, stop_at_layer=None) # Verify output shape is correct (logits) @@ -328,7 +326,6 @@ def test_stop_at_final_compat_no_processing(bridge_with_compat_no_processing): """Test stop_at_layer=-1 with compatibility mode (no processing).""" rand_input = torch.randint(0, 100, (2, 10)) - # Run with stop_at_layer=-1 output, cache = bridge_with_compat_no_processing.run_with_cache(rand_input, stop_at_layer=-1) # Verify output shape diff --git a/tests/integration/model_bridge/test_falcon_h1_adapter.py b/tests/integration/model_bridge/test_falcon_h1_adapter.py index cd38eecc03..51c82af218 100644 --- a/tests/integration/model_bridge/test_falcon_h1_adapter.py +++ b/tests/integration/model_bridge/test_falcon_h1_adapter.py @@ -2,7 +2,7 @@ Verifies wrap-don't-reimplement behaviour against Falcon-H1 checkpoints: -- ``tiiuae/Falcon-H1-Tiny-90M-Instruct`` — smallest variant (issue #1403 verify-first). +- ``tiiuae/Falcon-H1-Tiny-90M-Instruct`` — smallest variant. - ``tiiuae/Falcon-H1-0.5B-Base`` — primary parity suite. - Forward-pass logits match HF **exactly** (the bridge delegates the whole block to HF, which applies Falcon-H1's ~12 scalar multipliers natively). diff --git a/tests/integration/model_bridge/test_granite_moe_hybrid_adapter.py b/tests/integration/model_bridge/test_granite_moe_hybrid_adapter.py index f363e545b1..105677d34a 100644 --- a/tests/integration/model_bridge/test_granite_moe_hybrid_adapter.py +++ b/tests/integration/model_bridge/test_granite_moe_hybrid_adapter.py @@ -10,7 +10,7 @@ access or weight downloads (mirrors tests/unit/model_bridge/test_gpt_oss_moe.py and test_qwen3_moe_bridge.py for the from_config + direct-constructor pattern). -Coverage focus (Phase 0, SSM mixer access normalization): +Coverage focus (SSM mixer access normalization): - The Mamba-2 mixer is reachable at the canonical ``.mixer`` slot on SSM layers. - compute_effective_attention runs on a Granite SSM layer and reconstructs the SSM output (proves dims are sourced from the wrapped HF mixer, not the shared @@ -168,7 +168,7 @@ def test_shared_mlp_and_moe_hooks_fire(self, cache) -> None: # --------------------------------------------------------------------------- -# Effective attention: the Phase 0 acceptance surface +# Effective attention # --------------------------------------------------------------------------- @@ -351,7 +351,7 @@ def _available_devices(): class TestGraniteMoeHybridProcessedParity: - """Phase-3 parity guard: processed (compat-mode) bridge vs raw HF via log_softmax. + """Parity guard: processed (compat-mode) bridge vs raw HF via log_softmax. The forward test only checks UNPROCESSED delegation (==0.0); this pins the PROCESSED path so a compat-mode regression is caught in CI without the full-size checkpoint. @@ -376,7 +376,7 @@ def test_compat_mode_logits_match_raw_hf(self): class TestGraniteMoeHybridEagerScanIntervention: - """Phase 4 eager-scan intervention on Granite's Mamba-2 mixer layers (hybrid): + """Eager-scan intervention on Granite's Mamba-2 mixer layers (hybrid): hooks fire on the Mamba layers only, interventions propagate to logits, and the default path is untouched. Eager scan needs use_cache=False (prefill).""" diff --git a/tests/integration/model_bridge/test_joint_qkv_attention_hook_compatibility.py b/tests/integration/model_bridge/test_joint_qkv_attention_hook_compatibility.py index b7b0461556..d602978ecc 100644 --- a/tests/integration/model_bridge/test_joint_qkv_attention_hook_compatibility.py +++ b/tests/integration/model_bridge/test_joint_qkv_attention_hook_compatibility.py @@ -13,7 +13,6 @@ def test_v_hook_out_equals_blocks_attn_hook_v(self): # Load DistilGPT-2 in TransformerBridge (faster for testing) bridge = TransformerBridge.boot_transformers("distilgpt2", device="cpu") - # Turn on compatibility mode bridge.enable_compatibility_mode(disable_warnings=True) # Create test input diff --git a/tests/integration/model_bridge/test_mamba2_adapter.py b/tests/integration/model_bridge/test_mamba2_adapter.py index 511baea59c..b503921b9e 100644 --- a/tests/integration/model_bridge/test_mamba2_adapter.py +++ b/tests/integration/model_bridge/test_mamba2_adapter.py @@ -10,10 +10,10 @@ - Generation via is_stateful fallback delegates to hf_generate - d_mlp == 0 assertion: in_proj output features match the 3-way split formula -Cache clone safety: Same guarantee as Phase 1 — the wrap-don't-reimplement -design keeps Mamba2Mixer opaque, so `ssm_states` is never hooked. The only -hooked tensors are projection inputs/outputs, which are per-step and not -mutated by the cache machinery. +Cache clone safety: the wrap-don't-reimplement design keeps Mamba2Mixer +opaque, so `ssm_states` is never hooked. The only hooked tensors are +projection inputs/outputs, which are per-step and not mutated by the cache +machinery. """ import contextlib @@ -81,7 +81,7 @@ def test_no_x_proj_or_dt_proj(self, mamba2_bridge): assert not hasattr(hf_mixer, "dt_proj") def test_d_mlp_is_zero(self, mamba2_bridge): - """Plan's assertion: projection_size = intermediate + conv_dim + num_heads. + """projection_size = intermediate + conv_dim + num_heads. If a future HF release introduces non-zero d_mlp, this test will fail, signaling that the 3-way in_proj split assumption no longer holds. @@ -462,7 +462,7 @@ def test_d_shape(self, mamba2_bridge): class TestMamba2StatefulGeneration: - """Phase 3: bridge.generate() runs a dedicated stateful loop with + """bridge.generate() runs a dedicated stateful loop with Mamba2Cache. Tokens match HF's native generate() exactly, and projection hooks fire on every step so interventions are possible. """ @@ -683,7 +683,7 @@ def _eager_scan(bridge): class TestMamba2EagerScanIntervention: - """Phase 4: opt-in eager-scan path exposes hook_ssm_write / hook_ssm_state for + """opt-in eager-scan path exposes hook_ssm_write / hook_ssm_state for interventions (write-knockout, state-patch) that propagate to logits, while the default run_with_cache path is untouched. Eager scan requires use_cache=False (so cache_params is None — prefill only).""" diff --git a/tests/integration/model_bridge/test_mamba_adapter.py b/tests/integration/model_bridge/test_mamba_adapter.py index 32cae35dd3..e3710438e6 100644 --- a/tests/integration/model_bridge/test_mamba_adapter.py +++ b/tests/integration/model_bridge/test_mamba_adapter.py @@ -7,15 +7,8 @@ - SSM blocks correctly exclude transformer-specific hook_resid_mid - Parameter access via __getattr__ fallback (A_log, D) -Note on cache clone safety: The Mamba adapter plan flagged in-place MambaCache -mutation as a risk — hooks that capture `ssm_states` would see corrupted values -on subsequent decode steps because HF mutates the cache in place. Phase 1 avoids -this risk entirely by design: the wrap-don't-reimplement approach keeps the -MambaMixer opaque, so `ssm_states` is never exposed through a hook. Phase 1 -hooks only observe projection inputs/outputs (in_proj, conv1d, x_proj, dt_proj, -out_proj), which are per-step tensors and are never mutated by the cache -machinery. If a future phase adds per-step SSM state hooks (compatibility mode), -those hooks MUST `.clone()` captured state tensors. +Cache-clone safety: hooks never expose HF's in-place-mutated `ssm_states`; any +future per-step SSM-state hook MUST `.clone()` captured state. """ import contextlib @@ -109,7 +102,7 @@ def test_mixer_submodule_hooks_fire(self, cache): assert f"blocks.{i}.mixer.{submod}.hook_out" in cache def test_projection_shapes(self, cache, mamba_bridge): - """Hook tensor shapes match the plan's Step 1.5 shape summary.""" + """Hook tensor shapes match the expected projection shapes.""" d_model = mamba_bridge.cfg.d_model # 768 intermediate = mamba_bridge.cfg.intermediate_size # 1536 conv_kernel = mamba_bridge.cfg.conv_kernel # 4 @@ -219,7 +212,7 @@ def test_full_forward_works_after_stop(self, mamba_bridge): class TestMambaStatefulGeneration: - """Phase 3: bridge.generate() runs a proper stateful loop instead of + """bridge.generate() runs a proper stateful loop instead of delegating to hf_generate(). This gives hook integration during generation. """ @@ -449,7 +442,7 @@ def test_raises_on_empty_cache(self, mamba_bridge): class TestMamba1SSMState: - """Mamba-1 recurrent-state reconstruction (Phase 4.5): read-parity with Mamba-2. + """Mamba-1 recurrent-state reconstruction: read-parity with Mamba-2. The vectorized state must match a naive fp64 step-by-step S6 scan, and ``y = C·S + D·x`` reconstructed through gate + out_proj must match HF's cached @@ -566,7 +559,7 @@ def _eager_scan(bridge): class TestMamba1EagerScanIntervention: - """Phase 4 (Mamba-1): opt-in eager S6 scan exposes hook_ssm_write / hook_ssm_state + """Opt-in eager S6 scan exposes hook_ssm_write / hook_ssm_state for interventions that propagate to logits, while the default path is untouched. Eager scan needs use_cache=False (prefill; cache_params is None).""" diff --git a/tests/integration/model_bridge/test_pretrain_adapter.py b/tests/integration/model_bridge/test_pretrain_adapter.py index 5878532e38..18f7e8b937 100644 --- a/tests/integration/model_bridge/test_pretrain_adapter.py +++ b/tests/integration/model_bridge/test_pretrain_adapter.py @@ -15,13 +15,8 @@ cross-checked against an independent way of expressing the same edit; and float64 construction-time preservation. -Note on scope: since the adapter delegates the whole forward to the -source model rather than reimplementing attention/RoPE, exact logit -parity mainly demonstrates that wrapping and output normalization don't -alter an unhooked forward, not that the RoPE convention itself is -correct (the same implementation runs on both sides of the comparison). -`TestResidualStreamDecomposition` and `TestHookIntervention` are the -stronger evidence for correct intervention points. +Scope: parity only checks that wrapping doesn't alter an unhooked forward; +`TestResidualStreamDecomposition`/`TestHookIntervention` verify intervention points. """ from __future__ import annotations diff --git a/tests/integration/model_bridge/test_qwen3_moe_bridge.py b/tests/integration/model_bridge/test_qwen3_moe_bridge.py index 4cb9604faf..62aeac3f8b 100644 --- a/tests/integration/model_bridge/test_qwen3_moe_bridge.py +++ b/tests/integration/model_bridge/test_qwen3_moe_bridge.py @@ -136,7 +136,7 @@ def test_block_attn_has_q_norm_k_norm(self, tiny_qwen3moe_bridge) -> None: # Forward-pass tests require real weights — meta-device tensor ops raise -# NotImplementedError. Run these manually during Step 3 verification. +# NotImplementedError. Run these manually during model verification. @pytest.mark.skip(reason="Requires real weights — run manually during verification") diff --git a/tests/integration/model_bridge/test_ssm_effective_attention_dispatch.py b/tests/integration/model_bridge/test_ssm_effective_attention_dispatch.py index 11ed879001..43d308e2a1 100644 --- a/tests/integration/model_bridge/test_ssm_effective_attention_dispatch.py +++ b/tests/integration/model_bridge/test_ssm_effective_attention_dispatch.py @@ -1,4 +1,4 @@ -"""Phase 3: family-agnostic SSM effective-attention dispatch + canonical hook vocabulary. +"""Family-agnostic SSM effective-attention dispatch + canonical hook vocabulary. Builds tiny synthetic models (no Hub access) for each SSM family and verifies: - SSMMixerProtocol conformance (Mamba-1, Mamba-2, gated-delta-net; not attention), @@ -330,7 +330,7 @@ def test_qwen35_dict_and_include_dt_scaling_unsupported(self, qwen35_bridge): # --------------------------------------------------------------------------- -# Canonical hook vocabulary (3b) — survives the hybrid alias resolution +# Canonical hook vocabulary — survives the hybrid alias resolution # --------------------------------------------------------------------------- @@ -364,7 +364,7 @@ def test_mamba1_hook_ssm_dt_alias(self, mamba1_bridge): # --------------------------------------------------------------------------- -# cache.compute_ssm_state (Phase 4.5) — family-agnostic over Mamba-1 / Mamba-2 +# cache.compute_ssm_state — family-agnostic over Mamba-1 / Mamba-2 # and gated-delta-net (each reconstructs its own recurrent state). # --------------------------------------------------------------------------- diff --git a/tests/integration/test_centralized_weight_processing.py b/tests/integration/test_centralized_weight_processing.py index b4636f262b..ee278b21ad 100644 --- a/tests/integration/test_centralized_weight_processing.py +++ b/tests/integration/test_centralized_weight_processing.py @@ -53,7 +53,7 @@ def test_processing_without_architecture_adapter( processed_without_adapter = ProcessWeights.process_weights( state_dict=raw_state_dict, cfg=cfg, - adapter=None, # No adapter + adapter=None, fold_ln=False, center_writing_weights=False, center_unembed=False, diff --git a/tests/integration/test_fold_layer_integration.py b/tests/integration/test_fold_layer_integration.py index c45067bc5a..6871ae2b72 100644 --- a/tests/integration/test_fold_layer_integration.py +++ b/tests/integration/test_fold_layer_integration.py @@ -39,7 +39,6 @@ def gpt2_model_and_config(self): tl_model = HookedTransformer.from_pretrained(model_name, device=device) # Create architecture adapter - # Convert HookedTransformerConfig to TransformerBridgeConfig bridge_config = TransformerBridgeConfig.from_dict(tl_model.cfg.__dict__) bridge_config.architecture = "gpt2" adapter = GPT2ArchitectureAdapter(bridge_config) diff --git a/tests/integration/test_grouped_query_attention.py b/tests/integration/test_grouped_query_attention.py index a8cf6f3972..7d560adc3c 100644 --- a/tests/integration/test_grouped_query_attention.py +++ b/tests/integration/test_grouped_query_attention.py @@ -92,12 +92,7 @@ def test_grouped_query_attention_output_is_correct(): split_query_input, split_key_input, split_value_input ) - # Use both relative and absolute tolerances for numerical stability - # Different code paths (split vs non-split) can have tiny floating point differences - # rtol=5e-5 allows 0.005% relative error, atol=0.5 handles absolute differences - # CI shows max absolute diff: ~0.39, max relative diff: ~1.3e-5 (0.0013%) - # Local shows max absolute diff: ~0.008, max relative diff: ~2e-7 - # Variation due to different hardware/compiler optimizations - relative error is what matters + # split vs non-split paths differ in fp; loose atol/rtol needed across hardware # Calculate differences for debugging abs_diff = torch.abs(regular_attn_output - split_grouped_query_attn_output) diff --git a/tests/integration/test_main_demo_pattern_hooks.py b/tests/integration/test_main_demo_pattern_hooks.py index 4539cb42a1..97ad674ffb 100644 --- a/tests/integration/test_main_demo_pattern_hooks.py +++ b/tests/integration/test_main_demo_pattern_hooks.py @@ -50,7 +50,6 @@ def induction_score_hook(pattern, hook): induction_stripe, "batch head_index position -> head_index", "mean" ) - # Store the result induction_score_store[hook.layer(), :] = induction_score return pattern diff --git a/tests/integration/test_start_at_layer.py b/tests/integration/test_start_at_layer.py index f1d007829d..a849887d47 100644 --- a/tests/integration/test_start_at_layer.py +++ b/tests/integration/test_start_at_layer.py @@ -59,8 +59,7 @@ def count_hook(activation, hook): ("blocks.0.attn.hook_k", count_hook), ("blocks.1.mlp.hook_pre", count_hook), ("blocks.2.attn.hook_k", count_hook), - ("blocks.2.mlp.hook_pre", count_hook) - # ("blocks.2.mlp.hook_mid", count_hook), + ("blocks.2.mlp.hook_pre", count_hook), ], ) diff --git a/tests/unit/model_bridge/compatibility/test_next_sentence_prediction.py b/tests/unit/model_bridge/compatibility/test_next_sentence_prediction.py index 59fa5c3722..b7999127fa 100644 --- a/tests/unit/model_bridge/compatibility/test_next_sentence_prediction.py +++ b/tests/unit/model_bridge/compatibility/test_next_sentence_prediction.py @@ -67,7 +67,6 @@ def test_tokenizer_integration(bert_nsp, mock_transformer_bridge): } mock_transformer_bridge.tokenizer.return_value = mock_encodings - # Call to_tokens tokens, type_ids, mask = bert_nsp.to_tokens(input_sentences) # Verify tokenizer was called correctly @@ -101,14 +100,12 @@ def test_device_handling_to_tokens(bert_nsp, mock_transformer_bridge): } mock_transformer_bridge.tokenizer.return_value = mock_encodings - # Call to_tokens with move_to_device=True tokens, type_ids, mask = bert_nsp.to_tokens(input_data, move_to_device=True) # Verify each tensor was moved to the correct device for tensor in [tokens, type_ids, mask]: assert tensor.device.type == mock_transformer_bridge.cfg.device - # Call with move_to_device=False tokens, type_ids, mask = bert_nsp.to_tokens(input_data, move_to_device=False) # Verify tensors remained on CPU @@ -177,7 +174,6 @@ def test_run_with_cache(bert_nsp, mock_transformer_bridge): input_data = ["First sentence.", "Second sentence."] - # Run with cache output, cache = bert_nsp.run_with_cache( input_data, return_type="logits", return_cache_object=True ) diff --git a/tests/unit/model_bridge/generalized_components/test_codegen_attention_bridge.py b/tests/unit/model_bridge/generalized_components/test_codegen_attention_bridge.py index df2c2b004e..9e8418d609 100644 --- a/tests/unit/model_bridge/generalized_components/test_codegen_attention_bridge.py +++ b/tests/unit/model_bridge/generalized_components/test_codegen_attention_bridge.py @@ -467,9 +467,7 @@ def capture_q_after_rope(tensor, hook): q_after_rope.append(tensor.clone()) return tensor - # We patch _reconstruct_attention to intercept Q after RoPE. - # Simpler: capture attn_scores and back-compute is complex. - # Instead, we patch the module-level function with a wrapper. + # Patch the module-level _apply_rotary_pos_emb to capture Q/K post-RoPE. import transformer_lens.model_bridge.generalized_components.codegen_attention as codegen_attn_mod original_fn = codegen_attn_mod._apply_rotary_pos_emb diff --git a/tests/unit/model_bridge/generalized_components/test_joint_qkv_attention.py b/tests/unit/model_bridge/generalized_components/test_joint_qkv_attention.py index 31064a5d17..d7191fc106 100644 --- a/tests/unit/model_bridge/generalized_components/test_joint_qkv_attention.py +++ b/tests/unit/model_bridge/generalized_components/test_joint_qkv_attention.py @@ -143,13 +143,11 @@ def forward(self, input): def q_hook_id_fn(q_output, hook): return q_output - # Add the hook to q.hook_out qkv_bridge.q.hook_out.add_hook(q_hook_id_fn) # Run forward pass with identity hook to get baseline baseline_output, _ = qkv_bridge(test_input) - # Remove the identity hook qkv_bridge.q.hook_out.remove_hooks() # Add a hook to q.hook_out that modifies the output @@ -159,10 +157,8 @@ def q_hook_id_fn(q_output, hook): def q_hook_fn(q_output, hook): nonlocal q_mutation_applied q_mutation_applied = True - # Modify the q output by adding a distinct value return q_output + q_mutated_value - # Add the hook to q.hook_out qkv_bridge.q.hook_out.add_hook(q_hook_fn) # Run forward pass with hook @@ -230,13 +226,11 @@ def forward(self, input): def k_hook_id_fn(k_output, hook): return k_output - # Add the hook to k.hook_out qkv_bridge.k.hook_out.add_hook(k_hook_id_fn) # Run forward pass with identity hook to get baseline baseline_output, _ = qkv_bridge(test_input) - # Remove the identity hook qkv_bridge.k.hook_out.remove_hooks() # Add a hook to k.hook_out that modifies the output @@ -246,10 +240,8 @@ def k_hook_id_fn(k_output, hook): def k_hook_fn(k_output, hook): nonlocal k_mutation_applied k_mutation_applied = True - # Modify the k output by adding a distinct value return k_output + k_mutated_value - # Add the hook to k_hook_out qkv_bridge.k.hook_out.add_hook(k_hook_fn) # Run forward pass with hook @@ -317,13 +309,11 @@ def forward(self, input): def v_hook_id_fn(v_output, hook): return v_output - # Add the hook to v.hook_out qkv_bridge.v.hook_out.add_hook(v_hook_id_fn) # Run forward pass with identity hook to get baseline baseline_output, _ = qkv_bridge(test_input) - # Remove the identity hook qkv_bridge.v.hook_out.remove_hooks() # Add a hook to v.hook_out that modifies the output @@ -333,10 +323,8 @@ def v_hook_id_fn(v_output, hook): def v_hook_fn(v_output, hook): nonlocal v_mutation_applied v_mutation_applied = True - # Modify the v output by adding a distinct value return v_output + v_mutated_value - # Add the hook to v.hook_out qkv_bridge.v.hook_out.add_hook(v_hook_fn) # Run forward pass with hook diff --git a/tests/unit/model_bridge/supported_architectures/test_bd3lm_adapter.py b/tests/unit/model_bridge/supported_architectures/test_bd3lm_adapter.py index 01873d72fc..3626f607e1 100644 --- a/tests/unit/model_bridge/supported_architectures/test_bd3lm_adapter.py +++ b/tests/unit/model_bridge/supported_architectures/test_bd3lm_adapter.py @@ -92,7 +92,6 @@ def test_final_rms_false(self, adapter: BD3LMArchitectureAdapter) -> None: assert adapter.cfg.final_rms is False def test_applicable_phases(self, adapter: BD3LMArchitectureAdapter) -> None: - # BD3LM now supports all phases correctly assert adapter.applicable_phases == [1, 2, 3] def test_supports_generation(self, adapter: BD3LMArchitectureAdapter) -> None: diff --git a/tests/unit/model_bridge/supported_architectures/test_gemma3n_adapter.py b/tests/unit/model_bridge/supported_architectures/test_gemma3n_adapter.py index 458fa9e719..87c42b11d7 100644 --- a/tests/unit/model_bridge/supported_architectures/test_gemma3n_adapter.py +++ b/tests/unit/model_bridge/supported_architectures/test_gemma3n_adapter.py @@ -75,7 +75,7 @@ def test_text_path_nested_under_language_model(): assert isinstance(m["embed"], EmbeddingBridge) assert isinstance(m["blocks"], AltUpBlockBridge) assert isinstance(m["unembed"], UnembeddingBridge) - # Vision/audio are referenced-but-unbridged (Route D groundwork). + # Vision/audio are referenced but not bridged (text-only adapter for now). assert "vision_encoder" not in m and "audio_encoder" not in m diff --git a/tests/unit/model_bridge/supported_architectures/test_hunyuan_v1_dense_adapter.py b/tests/unit/model_bridge/supported_architectures/test_hunyuan_v1_dense_adapter.py index d0511b0104..bb5f120874 100644 --- a/tests/unit/model_bridge/supported_architectures/test_hunyuan_v1_dense_adapter.py +++ b/tests/unit/model_bridge/supported_architectures/test_hunyuan_v1_dense_adapter.py @@ -123,7 +123,7 @@ class TestHunYuanDenseV1AdapterConfig: """Adapter must set all required config flags to the values HunYuanDenseV1 expects.""" def test_attn_implementation_is_eager(self, adapter: HunYuanDenseV1ArchitectureAdapter) -> None: - """Set to eager.""" + """Adapter forces eager attention so component-level activations are hookable.""" assert adapter.cfg.attn_implementation == "eager" diff --git a/tests/unit/model_bridge/supported_architectures/test_lfm2_adapter.py b/tests/unit/model_bridge/supported_architectures/test_lfm2_adapter.py index b80a67cdd8..4acadc33ae 100644 --- a/tests/unit/model_bridge/supported_architectures/test_lfm2_adapter.py +++ b/tests/unit/model_bridge/supported_architectures/test_lfm2_adapter.py @@ -136,7 +136,7 @@ class TestLfm2AdapterConfig: """Adapter must set all required config flags to the values Lfm2 expects.""" def test_attn_implementation_is_eager(self, adapter: Lfm2ArchitectureAdapter) -> None: - """Set to eager.""" + """Adapter forces eager attention so component-level activations are hookable.""" assert adapter.cfg.attn_implementation == "eager" diff --git a/tests/unit/model_bridge/supported_architectures/test_mpt_adapter.py b/tests/unit/model_bridge/supported_architectures/test_mpt_adapter.py index 36333ecac1..d5a1ba7d39 100644 --- a/tests/unit/model_bridge/supported_architectures/test_mpt_adapter.py +++ b/tests/unit/model_bridge/supported_architectures/test_mpt_adapter.py @@ -66,7 +66,7 @@ def test_no_mlp_conversion_keys(self, adapter: MPTArchitectureAdapter) -> None: # --------------------------------------------------------------------------- -# Component mapping structure tests (Phase B-1) +# Component mapping structure tests # --------------------------------------------------------------------------- @@ -100,7 +100,7 @@ def test_mlp_submodule_keys(self, adapter: MPTArchitectureAdapter) -> None: # --------------------------------------------------------------------------- -# _split_mpt_qkv tests (Phase B-1) +# _split_mpt_qkv tests # --------------------------------------------------------------------------- diff --git a/tests/unit/model_bridge/supported_architectures/test_pretrain_model_container.py b/tests/unit/model_bridge/supported_architectures/test_pretrain_model_container.py index 43c40901ea..8ccb981b0d 100644 --- a/tests/unit/model_bridge/supported_architectures/test_pretrain_model_container.py +++ b/tests/unit/model_bridge/supported_architectures/test_pretrain_model_container.py @@ -197,33 +197,13 @@ def test_train_and_eval_return_the_bridge_itself(self) -> None: assert bridge.eval() is bridge def test_bridge_can_be_reconstructed_from_supported_state(self) -> None: - """The adapter's actual persistence contract is: rebuild the - bridge from the source model class + cfg + the source model's - *raw* state_dict, then confirm behavior (outputs, train/eval - propagation) survives the round trip. + """Rebuild the bridge from the source model's *raw* state_dict and + confirm behavior survives the round trip. The state_dict must be snapshotted BEFORE `build_pretrain_bridge` - is called. Building the bridge wraps the model's submodules in - place (each one gains an `_original_component` wrapper for - hooking), so `model.state_dict()` taken *after* wrapping has - keys like `embed._original_component.weight` instead of - `embed.weight` -- a fresh, unwrapped `TinyPretrainModel` can't - load that. Capturing the state_dict first, then wrapping a - second fresh model and loading the pre-wrap snapshot into it, - avoids that trap. - - Only the source model's state_dict is used -- not - `bridge.state_dict()` -- since the bridge doesn't own an - independent set of learned weights; it exposes the wrapped - source model's parameters under bridge-shaped names. - - This is deliberately weaker than whole-object `pickle.dumps`, - which would also require every nested `TransformerBridge` - implementation detail (e.g. `AttentionBridge`'s hook - conversions) to be picklable -- a guarantee unrelated to this - adapter and not part of what it needs to promise. If a stronger - whole-object-pickle guarantee is later required, it belongs in a - `TransformerBridge`-level test, not here. + wraps the model's submodules in place; taken after, keys become + `embed._original_component.weight` and a fresh unwrapped model + can't load them. """ cfg = _make_cfg(n_layers=1) model = TinyPretrainModel(d_model=16, n_heads=2, n_layers=1, d_ff=32, vocab_size=64) diff --git a/tests/unit/model_bridge/supported_architectures/test_stablelm_adapter.py b/tests/unit/model_bridge/supported_architectures/test_stablelm_adapter.py index 9d49b92cdf..6767848a6d 100644 --- a/tests/unit/model_bridge/supported_architectures/test_stablelm_adapter.py +++ b/tests/unit/model_bridge/supported_architectures/test_stablelm_adapter.py @@ -387,9 +387,7 @@ def parallel_adapter(self) -> StableLmArchitectureAdapter: def test_parallel_block_submodule_keys( self, parallel_adapter: StableLmArchitectureAdapter ) -> None: - """Container is ParallelBlockBridge so the no-ln2 layout is the supported shape. - BlockBridge's C15 guard rejects `attn + mlp` without `ln2`, which is exactly the - regression #1386 was filed for.""" + """Container is ParallelBlockBridge so the no-ln2 layout (attn + mlp without ln2) is the supported shape that BlockBridge would otherwise reject.""" block = _mapping(parallel_adapter)["blocks"] assert isinstance(block, ParallelBlockBridge) assert set(block.submodules.keys()) == {"ln1", "attn", "mlp"} diff --git a/tests/unit/model_bridge/test_component_hooks_fire.py b/tests/unit/model_bridge/test_component_hooks_fire.py index 8c9ce751b5..3cd1e1e54b 100644 --- a/tests/unit/model_bridge/test_component_hooks_fire.py +++ b/tests/unit/model_bridge/test_component_hooks_fire.py @@ -1,7 +1,7 @@ -"""Regression test: attention hooks fire on forward (C1 guard). +"""Regression test: attention hooks fire on forward. Complements the alias-resolution test: aliases can resolve yet the HookPoint -may never fire if forward bypasses the LinearBridge (the original C1 bug). +may never fire if forward bypasses the LinearBridge (the original bug). Isolates blocks.0.attn per PositionEmbeddingsAttentionBridge adapter with a synthetic HF module and asserts hook_q/k/v/z all fire. """ @@ -121,7 +121,7 @@ def _wire_attention_submodules( @pytest.mark.parametrize("architecture", _position_embeddings_adapters()) def test_attention_critical_hooks_fire_on_forward(architecture: str) -> None: - """Assert hook_q/k/v/z fire during attention forward (C1 regression guard).""" + """Assert hook_q/k/v/z fire during attention forward (regression guard).""" adapter_cls = SUPPORTED_ARCHITECTURES[architecture] adapter = adapter_cls(_stub_cfg(architecture)) attn = adapter.component_mapping["blocks"].submodules["attn"] diff --git a/tests/unit/model_bridge/test_hook_alias_resolution.py b/tests/unit/model_bridge/test_hook_alias_resolution.py index fd5b8d85b0..f3ad05cd5c 100644 --- a/tests/unit/model_bridge/test_hook_alias_resolution.py +++ b/tests/unit/model_bridge/test_hook_alias_resolution.py @@ -66,7 +66,7 @@ def _resolve(component: GeneralizedComponent, target: str) -> Any: # xfail(strict=True) so future fixes XPASS and force the marker to be removed. -# Each entry maps to a specific audit finding deferred from the C1+C15 PR. +# Each entry marks an adapter with known-dead hook aliases; xfail until fixed. _KNOWN_DEAD_ALIASES = { "GPT2LMHeadCustomModel": "audit H27 — stale adapter, delete candidate", "NanoGPTForCausalLM": "audit H28 — broken weight conversion, delete candidate", diff --git a/tests/unit/model_bridge/test_inspect_vllm_provider.py b/tests/unit/model_bridge/test_inspect_vllm_provider.py index 2d9b1e81fd..e9a4478dda 100644 --- a/tests/unit/model_bridge/test_inspect_vllm_provider.py +++ b/tests/unit/model_bridge/test_inspect_vllm_provider.py @@ -183,7 +183,7 @@ def test_passes_locked_kwargs_to_llm(self, monkeypatch): assert kwargs["tensor_parallel_size"] == 1 assert kwargs["skip_tokenizer_init"] is True assert kwargs["disable_log_stats"] is True - # Inc 2: capture wiring requires worker_extension_cls + full-vocab logprobs. + # Capture wiring requires worker_extension_cls + full-vocab logprobs. assert kwargs["worker_extension_cls"].endswith("TLWorkerExtension") assert kwargs["max_logprobs"] == 128 # _fake_hf_config().vocab_size assert kwargs["max_num_batched_tokens"] == 2048 # default diff --git a/tests/unit/model_bridge/test_native_features.py b/tests/unit/model_bridge/test_native_features.py index 85f6b5ad46..b658f2c06d 100644 --- a/tests/unit/model_bridge/test_native_features.py +++ b/tests/unit/model_bridge/test_native_features.py @@ -173,8 +173,7 @@ def test_output_logits_soft_cap_bounds_logits(): """Logits must be bounded by ±cap when output_logits_soft_cap > 0.""" cap = 5.0 cfg = _cfg(output_logits_soft_cap=cap, seed=0) - # Force-large outputs by skipping the soft-cap → then re-enabling. Easier: - # just pick a cap and assert |logits| <= cap. tanh-cap math guarantees it. + # tanh soft-cap guarantees |logits| <= cap. bridge = TransformerBridge.boot_native(cfg) logits = _forward(bridge) assert logits.abs().max().item() <= cap + 1e-5 diff --git a/tests/unit/model_bridge/test_optimizer_compatibility.py b/tests/unit/model_bridge/test_optimizer_compatibility.py index 8342dbff1a..2d948d769a 100644 --- a/tests/unit/model_bridge/test_optimizer_compatibility.py +++ b/tests/unit/model_bridge/test_optimizer_compatibility.py @@ -44,7 +44,6 @@ def test_tl_parameters_provides_tl_style_names(self, small_bridge_model): For optimization, use parameters() which returns only leaf tensors. """ - # Get TL-style parameters tl_params = small_bridge_model.tl_parameters() # Check that we have TL-style names (blocks.X.attn.W_Y format) @@ -63,7 +62,6 @@ def test_tl_named_parameters_provides_iterator(self, small_bridge_model): This method provides the same content as tl_parameters() but as an iterator, maintaining consistency with PyTorch's named_parameters() API pattern. """ - # Get TL-style parameters as iterator tl_named_params = list(small_bridge_model.tl_named_parameters()) tl_params_dict = small_bridge_model.tl_parameters() diff --git a/tests/unit/test_hook_points.py b/tests/unit/test_hook_points.py index df85849f06..51947a9cc2 100644 --- a/tests/unit/test_hook_points.py +++ b/tests/unit/test_hook_points.py @@ -128,10 +128,10 @@ def test_reshape_functionality_integration(): # Create a test hook conversion class TestHookConversion(BaseTensorConversion): def handle_conversion(self, input_value, *full_context): - return input_value * 2 # Double the input + return input_value * 2 def revert(self, input_value, *full_context): - return input_value + 10 # Add 10 to the output + return input_value + 10 # Create a simple test module that uses HookPoint class TestModule(torch.nn.Module): diff --git a/tests/unit/test_hooked_transformer_config.py b/tests/unit/test_hooked_transformer_config.py index c9a2d13694..e44e7409f8 100644 --- a/tests/unit/test_hooked_transformer_config.py +++ b/tests/unit/test_hooked_transformer_config.py @@ -10,7 +10,6 @@ def test_hooked_transformer_config_object(): n_layers=2, d_vocab=100, d_model=6, n_ctx=5, d_head=2, attn_only=True ) result = HookedTransformerConfig.unwrap(hooked_transformer_config) - # Assert that the same object was returned assert result is hooked_transformer_config @@ -24,7 +23,6 @@ def test_hooked_transformer_config_dict(): "attn_only": True, } result = HookedTransformerConfig.unwrap(hooked_transformer_config_dict) - # Assert that the new returned value has been transformed into a config object assert isinstance(result, HookedTransformerConfig) diff --git a/tests/unit/test_key_value_cache_entry.py b/tests/unit/test_key_value_cache_entry.py index e4b300017e..e17b718719 100644 --- a/tests/unit/test_key_value_cache_entry.py +++ b/tests/unit/test_key_value_cache_entry.py @@ -1,19 +1,4 @@ -"""Tests for TransformerLensKeyValueCacheEntry.init_cache_entry dtype behaviour. - -The buggy pre-fix code used ``torch.get_default_dtype()`` to initialise -``past_keys`` and ``past_values``. PyTorch's default is ``torch.float32``, -so the bug silently produced the correct dtype for fp32 models but the -wrong dtype (fp32 instead of fp16/bf16) for reduced-precision ones. Of -the tests below, ``test_init_cache_entry_uses_cfg_dtype_float32`` is -therefore a baseline sanity check that passes against both the buggy -and fixed code — it verifies the common case works, not that the bug is -absent. The real regression guards are -``test_init_cache_entry_uses_cfg_dtype_float16``, -``..._bfloat16``, ``..._dtype_independent_of_global_default``, and -``test_append_preserves_cfg_dtype``, which all fail against the buggy -code (the fp16 cache was getting promoted to fp32 by the bug, breaking -the downstream attention-score matmul). -""" +"""Tests that KeyValueCacheEntry.init_cache_entry buffers follow cfg.dtype, not torch's global default.""" import torch @@ -36,15 +21,7 @@ def _make_cfg(dtype: torch.dtype, n_heads: int = 4, d_head: int = 8, n_key_value def test_init_cache_entry_uses_cfg_dtype_float32(): - """Baseline: cfg.dtype=float32 produces fp32 buffers. - - Note: this test passes against both the buggy and fixed implementations - because torch's default dtype is also float32. It is a sanity check - that the common case works, not a regression guard for the specific - bug this module was added to prevent. See module docstring and - ``test_init_cache_entry_dtype_independent_of_global_default`` for the - tests that discriminate fix vs bug. - """ + """Baseline: cfg.dtype=float32 produces fp32 buffers.""" cfg = _make_cfg(dtype=torch.float32) entry = TransformerLensKeyValueCacheEntry.init_cache_entry(cfg, device="cpu") assert entry.past_keys.dtype == torch.float32 @@ -66,13 +43,7 @@ def test_init_cache_entry_uses_cfg_dtype_bfloat16(): def test_init_cache_entry_dtype_independent_of_global_default(): - """Regression guard: cache dtype follows cfg.dtype, not the global default. - - Also covers the fp32 case indirectly: if someone reintroduces the old - ``torch.get_default_dtype()`` behaviour, this test plus the fp16 / - bfloat16 / append / GQA tests catch it; the fp32-only baseline above - would not, since fp32 happens to be torch's global default. - """ + """Regression guard: cache dtype follows cfg.dtype, not the global default.""" cfg = _make_cfg(dtype=torch.float16) original_default = torch.get_default_dtype() try: diff --git a/tests/unit/test_supported_models.py b/tests/unit/test_supported_models.py index 2571907c31..56a59d2054 100644 --- a/tests/unit/test_supported_models.py +++ b/tests/unit/test_supported_models.py @@ -14,13 +14,10 @@ def test_official_model_names_is_alphabetical(): def test_model_aliases_is_alphabetical(): - # Extract the keys as they appear in the dictionary actual_keys = list(MODEL_ALIASES.keys()) - # Create a sorted version, ignoring case expected_keys = sorted(actual_keys, key=str.casefold) - # Compare the actual insertion order to the expected alphabetical order assert actual_keys == expected_keys, "MODEL_ALIASES keys are not in alphabetical order. " diff --git a/tests/unit/test_svd_interpreter.py b/tests/unit/test_svd_interpreter.py index dfe22444cb..e08e98b4b4 100644 --- a/tests/unit/test_svd_interpreter.py +++ b/tests/unit/test_svd_interpreter.py @@ -12,7 +12,7 @@ MODEL = "solu-2l" VECTOR_TYPES = ["OV", "w_in", "w_out"] -ATOL = 2e-4 # Absolute tolerance - how far does a float have to be before we consider it no longer equal? +ATOL = 2e-4 # absolute tolerance for float comparison @pytest.fixture(scope="module") diff --git a/tests/unit/test_utils.py b/tests/unit/test_utils.py index 78050e503b..3f73ae1421 100644 --- a/tests/unit/test_utils.py +++ b/tests/unit/test_utils.py @@ -263,9 +263,7 @@ def test_get_attention_mask(self, model, padding_side, prepend_bos, prompts_with def test_calc_fan_in_fan_out(): - """ - Test for the calc_fan_in_and_fan_out function in the utils module. - """ + """Verifies fan_in/fan_out for 1D/2D/3D tensors and raises ValueError for 0D/4D.""" # Test for the case when the tensor is 1D tensor_1d = torch.tensor([1, 2, 3, 4, 5]) fan_in, fan_out = utils.calc_fan_in_and_fan_out(tensor_1d) diff --git a/tests/unit/test_weight_processing.py b/tests/unit/test_weight_processing.py index 6735de1ea2..d5d7f59e2e 100644 --- a/tests/unit/test_weight_processing.py +++ b/tests/unit/test_weight_processing.py @@ -14,8 +14,6 @@ from transformer_lens.config.transformer_lens_config import TransformerLensConfig from transformer_lens.weight_processing import ProcessWeights -# from typing import Dict # Unused import - def deep_copy_state_dict(state_dict): """Create a deep copy of a state dict with cloned tensors. diff --git a/tests/unit/utilities/test_devices.py b/tests/unit/utilities/test_devices.py index 5834486a89..ca2bf7ae26 100644 --- a/tests/unit/utilities/test_devices.py +++ b/tests/unit/utilities/test_devices.py @@ -28,7 +28,6 @@ def state_dict(self): return self._parameters def to(self, device_or_dtype): - # Mock the to method if isinstance(device_or_dtype, torch.device): self.cfg.device = device_or_dtype.type elif isinstance(device_or_dtype, str): diff --git a/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_base_tensor_conversion.py b/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_base_tensor_conversion.py index adffa3704f..538f6dd1fc 100644 --- a/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_base_tensor_conversion.py +++ b/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_base_tensor_conversion.py @@ -26,12 +26,8 @@ def test_mock_weight_conversion_adds_five(): output_tensor = weight_conversion.convert(input_tensor) expected_tensor = torch.full((1, 4), 5.0, dtype=torch.float32) - # Option 1: simple equality check assert torch.equal(output_tensor, expected_tensor) - # Option 2: more robust approximate check - # torch.testing.assert_close(output_tensor, expected_tensor) - @pytest.mark.parametrize("shape", [(1, 4), (2, 2), (3,)]) def test_mock_weight_conversion_various_shapes(shape): diff --git a/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_callable_tensor_conversion.py b/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_callable_tensor_conversion.py index 977d02e68b..c86a2045f6 100644 --- a/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_callable_tensor_conversion.py +++ b/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_callable_tensor_conversion.py @@ -12,7 +12,6 @@ def test_callable_tensor_conversion_basic(): """ def my_callable(tensor_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: - # Example transformation: add 1 to each tensor value new_dict = {} for k, v in tensor_dict.items(): new_dict[k] = v + 1 @@ -52,7 +51,6 @@ def my_callable(tensor_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor] return new_dict def my_input_filter(tensor_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: - # Multiply each tensor by 2 filtered_dict = {} for k, v in tensor_dict.items(): filtered_dict[k] = v * 2 @@ -86,7 +84,6 @@ def my_callable(tensor_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor] return new_dict def my_output_filter(tensor_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: - # Subtract 5 from each element filtered_dict = {} for k, v in tensor_dict.items(): filtered_dict[k] = v - 5 diff --git a/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_rearrange_tensor_conversion.py b/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_rearrange_tensor_conversion.py index 43f74014f0..92232b627e 100644 --- a/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_rearrange_tensor_conversion.py +++ b/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_rearrange_tensor_conversion.py @@ -41,7 +41,6 @@ def test_rearrange_tensor_conversion_input_filter(): """ def input_filter(tensor): - # E.g., multiply by 2 return tensor * 2 conversion = RearrangeTensorConversion("(n h) m->n m h", input_filter=input_filter, n=8) @@ -67,7 +66,6 @@ def test_rearrange_tensor_conversion_output_filter(): """ def output_filter(tensor): - # E.g., add 10 return tensor + 10 conversion = RearrangeTensorConversion("(n h) m->n m h", output_filter=output_filter, n=8) @@ -96,10 +94,10 @@ def test_rearrange_tensor_conversion_input_output_filters(): """ def input_filter(tensor): - return tensor * 2 # Double + return tensor * 2 def output_filter(tensor): - return tensor + 3 # Then add 3 + return tensor + 3 conversion = RearrangeTensorConversion( "(n h) m->n m h", input_filter=input_filter, output_filter=output_filter, n=8 diff --git a/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_repeat_tensor_conversion.py b/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_repeat_tensor_conversion.py index d99f402017..e210b9aae1 100644 --- a/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_repeat_tensor_conversion.py +++ b/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_repeat_tensor_conversion.py @@ -13,8 +13,6 @@ def test_repeat_tensor_conversion_basic(): Example pattern (h w -> h 2 w): We repeat along the second dimension. """ - # Pattern says: "h w -> h repeat1 w" - # let's do: pattern="h w -> h 2 w", so we define repeat2=2 for the new axis conversion = RepeatTensorConversion("h w -> h 2 w", h=3, w=4) # Start with a known shape: [3,4], fill with arange to track values diff --git a/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_terenary_tensor_conversion.py b/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_terenary_tensor_conversion.py index 16b2ef495d..76716f0764 100644 --- a/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_terenary_tensor_conversion.py +++ b/tests/unit/weight_conversion/conversion_utils/conversion_steps/test_terenary_tensor_conversion.py @@ -153,18 +153,12 @@ def handle_conversion(self, input_value: torch.Tensor, *unused) -> torch.Tensor: def test_ternary_tensor_conversion_find_context_field_failure(): """ - If fallback is a str, but the context doesn't contain that key, - find_context_field returns None, and so fallback is effectively None => - This might cause an error or we accept returning None. - We'll see how your code handles that scenario. + If fallback is a str missing from the context, find_context_field returns None and convert returns None. """ ternary = TernaryTensorConversion(fallback_conversion="missing_key", primary_conversion=None) # No context => won't find 'missing_key' output = ternary.convert(None, *{}) - # According to your code, if nothing is found, we return None from find_context_field. - # handle_fallback_conversion will return that None from the function. - # => This might break if the calling code expects a tensor. - # We'll just check it's None. + # missing key -> find_context_field returns None, so convert returns None assert output is None, "Expected None if the fallback str wasn't found in the provided context." diff --git a/transformer_lens/HookedAudioEncoder.py b/transformer_lens/HookedAudioEncoder.py index 5b58351588..5b5d6747a2 100644 --- a/transformer_lens/HookedAudioEncoder.py +++ b/transformer_lens/HookedAudioEncoder.py @@ -264,7 +264,6 @@ def forward( # ---------- 1) Normalize input: get (frames, frame_mask) ---------- frames = None frame_mask = None # one_zero_attention_mask: 1 = valid, 0 = padding - # print(type(inputs)) # If user passed (frames, mask) tuple if isinstance(inputs, tuple) and len(inputs) == 2 and isinstance(inputs[0], torch.Tensor): frames, frame_mask = inputs diff --git a/transformer_lens/HookedEncoder.py b/transformer_lens/HookedEncoder.py index f751fafbfe..64cb09b141 100644 --- a/transformer_lens/HookedEncoder.py +++ b/transformer_lens/HookedEncoder.py @@ -300,7 +300,6 @@ def forward( # If input was a list of strings, split predictions into a list if " " in predictions: - # Split along space predictions = predictions.split(" ") predictions = [f"Prediction {i}: {p}" for i, p in enumerate(predictions)] return predictions diff --git a/transformer_lens/HookedEncoderDecoder.py b/transformer_lens/HookedEncoderDecoder.py index aa08483914..2961190183 100644 --- a/transformer_lens/HookedEncoderDecoder.py +++ b/transformer_lens/HookedEncoderDecoder.py @@ -112,7 +112,6 @@ def __init__( ] ) self.decoder_final_ln = RMSNorm(self.cfg) - # self.lm_head = nn.Linear(self.cfg.d_model, self.cfg.d_vocab_out) self.unembed = Unembed(self.cfg) self.hook_embed = HookPoint() @@ -470,7 +469,6 @@ def generate( ) ) - # Append new token to the decoder input decoder_input = torch.cat([decoder_input, sampled_tokens.unsqueeze(-1)], dim=-1) if stop_at_eos and finished_sequences.all(): @@ -478,7 +476,6 @@ def generate( if return_type == "str": assert self.tokenizer is not None - # Convert tokens to string return cast(str, self.tokenizer.decode(decoder_input[0], skip_special_tokens=True)) else: diff --git a/transformer_lens/HookedTransformer.py b/transformer_lens/HookedTransformer.py index 133eb5dae9..fbaaec11a0 100644 --- a/transformer_lens/HookedTransformer.py +++ b/transformer_lens/HookedTransformer.py @@ -1508,27 +1508,6 @@ def init_weights(self): weights! Note that this function assumes that weight names being with `W_`. Set seed here to ensure determinism. - - This does NOT follow the PyTorch scheme, which as far as I can tell is super out of date but - no one has gotten round to updating it? https://github.com/pytorch/pytorch/issues/18182 - - The default PyTorch scheme is the following: all linear layers use uniform(-1/sqrt(fan_in), - 1/sqrt(fan_in)) for weights, and uniform(-1/sqrt(fan_in), 1/sqrt(fan_in)) for biases. For - biases, fan_in is computed using the fan_in for the weight matrix of the linear layer. Note - that it *does not actually* use Kaiming initialization, despite the fact that it calls the - function. - - However, for Transformer blocks, it instead initializes biases to zero and weights using Xavier uniform, that - is: uniform(-sqrt(6 / (fan_in + fan_out)), sqrt(6 / (fan_in + fan_out))) for weights. - - PyTorch Transformers are especially bad - TransformerEncoder initializes all layers to the - exact same weights?! https://github.com/pytorch/pytorch/issues/72253. - - The best paper I've found on transformer initialization is the muP paper, but haven't - integrated those ideas yet: https://arxiv.org/abs/2203.03466 - - We split off the initialization into separate functions because muP initialization handles - different parts of the model differently. """ if self.cfg.seed is not None: @@ -2257,7 +2236,6 @@ def generate( def _logits_to_tuple(logits_list: list[torch.Tensor]) -> tuple[torch.Tensor, ...]: assert logits_list is not None - # Convert to tuple of tensors return tuple(logits_list) try: diff --git a/transformer_lens/benchmarks/forward_pass.py b/transformer_lens/benchmarks/forward_pass.py index a4940b2a05..559224cfc9 100644 --- a/transformer_lens/benchmarks/forward_pass.py +++ b/transformer_lens/benchmarks/forward_pass.py @@ -131,8 +131,7 @@ def benchmark_forward_pass( else: reference_output = hf_output.last_hidden_state else: - # HuggingFace model (reference_model is guaranteed non-None here - # because we returned early at line 80 when both are None) + # reference_model is non-None here: the both-None case returns early above assert reference_model is not None assert isinstance(test_input, str), "Text model requires string input" tokens = bridge.to_tokens(test_input) diff --git a/transformer_lens/benchmarks/main_benchmark.py b/transformer_lens/benchmarks/main_benchmark.py index d2ccbfa707..d8894dc577 100644 --- a/transformer_lens/benchmarks/main_benchmark.py +++ b/transformer_lens/benchmarks/main_benchmark.py @@ -880,7 +880,7 @@ def cleanup_model(model, model_name_str: str): print(f"✓ Detected attn_implementation={attn_implementation}") # Clean up config-only bridge immediately to free memory del bridge_config_only - gc.collect() # Force garbage collection immediately + gc.collect() except Exception as e: if verbose: print(f"⚠ Could not detect config (will use defaults): {str(e)}") diff --git a/transformer_lens/components/abstract_attention.py b/transformer_lens/components/abstract_attention.py index 69b6667300..855ea35aa8 100644 --- a/transformer_lens/components/abstract_attention.py +++ b/transformer_lens/components/abstract_attention.py @@ -867,7 +867,6 @@ def create_alibi_slope( # Use broadcasting to create the desired lower triangular part of the matrix slope_matrix = rows - cols - # Use the clamp method to set all positive values (upper right triangle) to return slope_matrix.clamp(max=0).to(torch.float32) @staticmethod @@ -898,10 +897,8 @@ def create_alibi_multipliers( Returns: A tensor of shape (n_heads,) containing the scalar multiplier for each head. """ - # Calculate the starting value start = 2 ** (-8 / n_heads) - # Generate the indices [0, 1, ..., n_heads-1] indices = torch.arange(n_heads, device=device) # Compute the multipliers, with the starting value being the same as the ratio @@ -943,12 +940,10 @@ def create_alibi_bias( Returns: The ALiBi bias that should be added to the attention scores before the softmax. """ - # Create the slope matrix slope: Float[torch.Tensor, "query key"] = AbstractAttention.create_alibi_slope( n_ctx, device ) - # Create the scalar multiplier for each head. multipliers: Float[torch.Tensor, "head_idx"] = AbstractAttention.create_alibi_multipliers( n_heads, device ) @@ -957,7 +952,6 @@ def create_alibi_bias( slope = einops.rearrange(slope, "query key -> 1 query key") multipliers = einops.rearrange(multipliers, "head_idx -> head_idx 1 1") - # Element-wise multiplication of the slope and multipliers alibi_bias = multipliers * slope return alibi_bias diff --git a/transformer_lens/components/attention.py b/transformer_lens/components/attention.py index cfce395d4e..11d94cb4ac 100644 --- a/transformer_lens/components/attention.py +++ b/transformer_lens/components/attention.py @@ -16,7 +16,6 @@ from bitsandbytes.nn.modules import Params4bit -# Attention class Attention(AbstractAttention): def __init__( self, diff --git a/transformer_lens/components/t5_block.py b/transformer_lens/components/t5_block.py index e93b70e7f6..d3b54210e5 100644 --- a/transformer_lens/components/t5_block.py +++ b/transformer_lens/components/t5_block.py @@ -102,9 +102,7 @@ def forward( value_input = attn_in attn_out = self.hook_attn_out( - # hook the residual stream states that are used to calculate the - # queries, keys and values, independently. - # Then take the layer norm of these inputs, and pass these to the attention module. + # ln1 applied per Q/K/V input so split-QKV hooks stay independent self.attn( query_input=self.ln1(query_input), key_input=self.ln1(key_input), diff --git a/transformer_lens/components/transformer_block.py b/transformer_lens/components/transformer_block.py index 92ac7484ad..b80289ae6d 100644 --- a/transformer_lens/components/transformer_block.py +++ b/transformer_lens/components/transformer_block.py @@ -27,7 +27,6 @@ from transformer_lens.utilities import repeat_along_head_dimension -# Transformer Block class TransformerBlock(nn.Module): ln1: nn.Module ln2: nn.Module diff --git a/transformer_lens/config/hooked_transformer_config.py b/transformer_lens/config/hooked_transformer_config.py index 0e4a757ef6..b726b60662 100644 --- a/transformer_lens/config/hooked_transformer_config.py +++ b/transformer_lens/config/hooked_transformer_config.py @@ -299,7 +299,6 @@ class HookedTransformerConfig(TransformerLensConfig): norm_topk_prob: bool = False def __post_init__(self): - # Call parent's post_init first super().__post_init__() if self.seed is not None: diff --git a/transformer_lens/config/transformer_lens_config.py b/transformer_lens/config/transformer_lens_config.py index fb1f5f0459..dacae23a16 100644 --- a/transformer_lens/config/transformer_lens_config.py +++ b/transformer_lens/config/transformer_lens_config.py @@ -74,10 +74,8 @@ class TransformerLensConfig: # GQA configuration n_key_value_heads: Optional[int] = None - # Attention only model attn_only: bool = False - # Gated MLP gated_mlp: bool = False # Normalization configuration @@ -86,22 +84,16 @@ class TransformerLensConfig: # Epsilon for normalization eps: float = 1e-5 - # Layer norm folding activated layer_norm_folding: bool = False - # Activation function act_fn: str = "relu" - # Normalization type normalization_type: Optional[str] = "LN" - # Number of experts num_experts: Optional[int] = None - # Number of experts per token experts_per_token: Optional[int] = None - # Final RMS norm final_rms: bool = False # Model dtype for LayerNormPre compatibility diff --git a/transformer_lens/conversion_utils/conversion_steps/tensor_conversion_set.py b/transformer_lens/conversion_utils/conversion_steps/tensor_conversion_set.py index c5bd804c6e..ec486d3877 100644 --- a/transformer_lens/conversion_utils/conversion_steps/tensor_conversion_set.py +++ b/transformer_lens/conversion_utils/conversion_steps/tensor_conversion_set.py @@ -41,7 +41,6 @@ def get_component(self, model: Any, name: str) -> Any: else: field_name, conversion_step = field_info - # Get the component from the model component = find_property(field_name, model) # Apply conversion step if specified diff --git a/transformer_lens/conversion_utils/helpers/merge_quantiziation_fields.py b/transformer_lens/conversion_utils/helpers/merge_quantiziation_fields.py index e32ed8e6c7..0a69ff7e76 100644 --- a/transformer_lens/conversion_utils/helpers/merge_quantiziation_fields.py +++ b/transformer_lens/conversion_utils/helpers/merge_quantiziation_fields.py @@ -16,7 +16,6 @@ def merge_quantization_fields(field_set: Any, quantization_fields: dict[str, Any Returns: The merged field set (same object, modified in-place). """ - # Merge the quantization fields into the existing field_set for field_name, new_field_value in quantization_fields.items(): existing_field = field_set.fields.get(field_name) @@ -52,7 +51,6 @@ def merge_quantization_fields(field_set: Any, quantization_fields: dict[str, Any ) else: # new_field_value is a simple value (like torch.Tensor) - # Simply overwrite the existing field field_set.fields[field_name] = new_field_value return field_set diff --git a/transformer_lens/conversion_utils/hook_conversion_utils.py b/transformer_lens/conversion_utils/hook_conversion_utils.py index d0469f061d..2712c7f242 100644 --- a/transformer_lens/conversion_utils/hook_conversion_utils.py +++ b/transformer_lens/conversion_utils/hook_conversion_utils.py @@ -52,13 +52,7 @@ def model_info_cfg(cfg): """ # TODO: WeightConversionFactory import needs to be updated or removed - # from transformer_lens.factories.weight_conversion_factory import ( - # WeightConversionFactory, - # ) - - # weight_conversion = WeightConversionFactory.select_weight_conversion_config(cfg) print(f"Hook conversion details for architecture {cfg.original_architecture}:") - # print(weight_conversion.__repr__()) print("Hook conversion factory not yet implemented") diff --git a/transformer_lens/evals.py b/transformer_lens/evals.py index 4200ea8b94..8423f301e4 100644 --- a/transformer_lens/evals.py +++ b/transformer_lens/evals.py @@ -609,7 +609,6 @@ def mmlu_eval( # Tokenize the prompt tokens = tokenizer.encode(prompt, return_tensors="pt").to(model.cfg.device) - # Get logits logits = model(tokens, return_type="logits") # Get log probabilities at the last position (predicting the answer letter) @@ -624,7 +623,6 @@ def mmlu_eval( # Select the choice with highest log probability predicted_answer = choice_log_probs.index(max(choice_log_probs)) - # Check if correct is_correct = predicted_answer == correct_answer num_correct += int(is_correct) num_total += 1 diff --git a/transformer_lens/head_detector.py b/transformer_lens/head_detector.py index 95d4fdc7df..5e32c56183 100644 --- a/transformer_lens/head_detector.py +++ b/transformer_lens/head_detector.py @@ -220,7 +220,7 @@ def get_induction_head_detection_pattern( def get_supported_heads() -> None: - """Returns a list of supported heads.""" + """Print the supported head names.""" print(f"Supported heads: {HEAD_NAMES}") diff --git a/transformer_lens/loading_from_pretrained.py b/transformer_lens/loading_from_pretrained.py index 606bd1ebaf..492078fa04 100644 --- a/transformer_lens/loading_from_pretrained.py +++ b/transformer_lens/loading_from_pretrained.py @@ -2037,11 +2037,8 @@ def fill_missing_keys( Returns: dict: State dict with missing keys filled in """ - # Get the default state dict default_state_dict = model.state_dict() - # Get the keys that are missing from the pretrained model missing_keys = set(default_state_dict.keys()) - set(state_dict.keys()) - # Fill in the missing keys with the default initialization for key in missing_keys: if "hf_model" in key: # Skip keys that are from the HuggingFace model, if loading from HF. @@ -2070,7 +2067,6 @@ class Config: n_layers: int = 12 -# Returns the configuration parameters of the model as a basic Config dataclass def get_basic_config(model_name: str, **kwargs: Any) -> Config: """Returns the configuration parameters of the model as a basic Config dataclass.""" return Config( diff --git a/transformer_lens/model_bridge/architecture_adapter.py b/transformer_lens/model_bridge/architecture_adapter.py index d69524543f..95e9bd55f2 100644 --- a/transformer_lens/model_bridge/architecture_adapter.py +++ b/transformer_lens/model_bridge/architecture_adapter.py @@ -41,8 +41,7 @@ class ArchitectureAdapter: # in specific phases (e.g. SSMs don't have the transformer-shaped hooks/ # weights the benchmark phases assume) should override. An empty list # means "skip verify_models entirely; verification lives in integration - # tests." The full refactor that would make SSM phases meaningful is - # documented in ~/.claude/plans/ssm-verification-compatibility.md. + # tests." applicable_phases: list[int] = [1, 2, 3, 4] # Whether this architecture supports text generation via generate(). diff --git a/transformer_lens/model_bridge/generalized_components/base.py b/transformer_lens/model_bridge/generalized_components/base.py index 65768857e3..2dcc13c95d 100644 --- a/transformer_lens/model_bridge/generalized_components/base.py +++ b/transformer_lens/model_bridge/generalized_components/base.py @@ -70,7 +70,6 @@ def __init__( # Copy class-level hook_aliases and apply any overrides if hook_alias_overrides is not None: - # Make a copy of class-level aliases and update with overrides self.hook_aliases = self.__class__.hook_aliases.copy() self.hook_aliases.update(hook_alias_overrides) @@ -234,7 +233,6 @@ def set_processed_weights( if hasattr(self.original_component, key): param = getattr(self.original_component, key) if param is not None and isinstance(param, torch.nn.Parameter): - # Check that shapes match if param.shape != weight_tensor.shape: raise ValueError( f"Shape mismatch when setting weight '{key}' in {type(self.original_component).__name__}: " diff --git a/transformer_lens/model_bridge/generalized_components/block.py b/transformer_lens/model_bridge/generalized_components/block.py index b998c15836..89d0347a59 100644 --- a/transformer_lens/model_bridge/generalized_components/block.py +++ b/transformer_lens/model_bridge/generalized_components/block.py @@ -79,9 +79,9 @@ def __init__( auto_overrides["hook_mlp_out"] = "ln2_post.hook_out" merged_overrides = {**auto_overrides, **(hook_alias_overrides or {})} - # Guard against the C15 bug class: sequential transformer block (attn + - # mlp) with no ln2 would silently point hook_resid_mid at the wrong - # tensor. Use ParallelBlockBridge for parallel-residual architectures. + # Guard against the bug where a sequential block (attn + mlp) with no ln2 + # silently points hook_resid_mid at the wrong tensor. Use + # ParallelBlockBridge for parallel-residual architectures. # Skip the check on generic-container / attn-only uses (no mlp). has_attn_like = submodules is not None and any( k in submodules for k in _VARIANT_SUBMODULE_SET @@ -95,7 +95,6 @@ def __init__( f"parallel-residual architecture." ) - # Call parent with merged overrides super().__init__( name, config, diff --git a/transformer_lens/model_bridge/generalized_components/bloom_block.py b/transformer_lens/model_bridge/generalized_components/bloom_block.py index e233880348..d3a2f9a020 100644 --- a/transformer_lens/model_bridge/generalized_components/bloom_block.py +++ b/transformer_lens/model_bridge/generalized_components/bloom_block.py @@ -74,9 +74,6 @@ def forward(self, *args: Any, **kwargs: Any) -> Any: Returns: Output from the original BLOOM block """ - # Debug: Check if alibi is being passed - # print(f"BloomBlockBridge.forward() called with kwargs keys: {list(kwargs.keys())}") - if self.original_component is None: raise RuntimeError( f"Original component not set for {self.name}. Call set_original_component() first." @@ -131,10 +128,8 @@ def forward(self, *args: Any, **kwargs: Any) -> Any: alibi = self.build_alibi_tensor(attention_mask_2d, num_heads, dtype) alibi = alibi.reshape(batch_size * num_heads, 1, seq_length) - # Add alibi to kwargs kwargs["alibi"] = alibi # else: alibi is already present from HF, don't overwrite it! - # Call original component output = self.original_component(*args, **kwargs) return self._apply_output_hook(output, wrap_single_element=False) diff --git a/transformer_lens/model_bridge/generalized_components/bloom_mlp.py b/transformer_lens/model_bridge/generalized_components/bloom_mlp.py index eb45dada1f..54791b4e41 100644 --- a/transformer_lens/model_bridge/generalized_components/bloom_mlp.py +++ b/transformer_lens/model_bridge/generalized_components/bloom_mlp.py @@ -68,7 +68,6 @@ def forward(self, *args: Any, **kwargs: Any) -> Any: # The original BLOOM block passes it, so we just pass everything through # No need to validate since the original component will handle it - # Call the original BLOOM MLP component with all arguments output = self.original_component(*args, **kwargs) # Apply hook_out diff --git a/transformer_lens/model_bridge/generalized_components/clip_vision_encoder.py b/transformer_lens/model_bridge/generalized_components/clip_vision_encoder.py index e0debb530e..d7d81481b8 100644 --- a/transformer_lens/model_bridge/generalized_components/clip_vision_encoder.py +++ b/transformer_lens/model_bridge/generalized_components/clip_vision_encoder.py @@ -159,7 +159,6 @@ def forward( f"Original component not set for {self.name}. Call set_original_component() first." ) - # Apply input hook to pixel values pixel_values = self.hook_in(pixel_values) # Forward through the vision tower diff --git a/transformer_lens/model_bridge/generalized_components/linear.py b/transformer_lens/model_bridge/generalized_components/linear.py index 391f5a7640..9356b8031e 100644 --- a/transformer_lens/model_bridge/generalized_components/linear.py +++ b/transformer_lens/model_bridge/generalized_components/linear.py @@ -39,7 +39,6 @@ def forward(self, input: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tenso return output def __repr__(self) -> str: - """String representation of the LinearBridge.""" if self.original_component is not None: try: in_features = self.original_component.in_features diff --git a/transformer_lens/model_bridge/generalized_components/position_embeddings_attention.py b/transformer_lens/model_bridge/generalized_components/position_embeddings_attention.py index 5e43abf4e2..5af6599404 100644 --- a/transformer_lens/model_bridge/generalized_components/position_embeddings_attention.py +++ b/transformer_lens/model_bridge/generalized_components/position_embeddings_attention.py @@ -48,7 +48,6 @@ def _setup_eager_attention_hook_wrapper() -> None: if _EAGER_ATTENTION_WRAPPED: return - # Store the original function _ORIGINAL_EAGER_ATTENTION_FORWARD = gemma2_module.eager_attention_forward def hooked_eager_attention_forward( @@ -82,7 +81,6 @@ def hooked_eager_attention_forward( if hasattr(bridge, "hook_rot_k"): key = bridge.hook_rot_k(key) - # Call the original function assert _ORIGINAL_EAGER_ATTENTION_FORWARD is not None return _ORIGINAL_EAGER_ATTENTION_FORWARD( module, query, key, value, attention_mask, **kwargs @@ -271,7 +269,6 @@ def forward(self, *args: Any, **kwargs: Any) -> Any: position_embeddings = kwargs.pop("position_embeddings", None) attention_mask = kwargs.pop("attention_mask", None) - # Apply input hook hidden_states = self.hook_in(hidden_states) # Match dtype of HF module diff --git a/transformer_lens/model_bridge/generalized_components/rotary_embedding.py b/transformer_lens/model_bridge/generalized_components/rotary_embedding.py index c560fff960..d1cfd22b28 100644 --- a/transformer_lens/model_bridge/generalized_components/rotary_embedding.py +++ b/transformer_lens/model_bridge/generalized_components/rotary_embedding.py @@ -133,7 +133,6 @@ def forward( # Apply hooks to match HookedTransformer's rotary_cos/rotary_sin pattern cos = self.hook_cos(cos) sin = self.hook_sin(sin) - # Return the hooked cos and sin as a tuple # Note: Don't pass tuple through hook_out as it expects a tensor return (cos, sin) else: diff --git a/transformer_lens/model_bridge/generalized_components/siglip_vision_encoder.py b/transformer_lens/model_bridge/generalized_components/siglip_vision_encoder.py index f81f8c8f67..8da7c748dd 100644 --- a/transformer_lens/model_bridge/generalized_components/siglip_vision_encoder.py +++ b/transformer_lens/model_bridge/generalized_components/siglip_vision_encoder.py @@ -155,7 +155,6 @@ def forward( f"Original component not set for {self.name}. Call set_original_component() first." ) - # Apply input hook to pixel values pixel_values = self.hook_in(pixel_values) # Forward through the vision tower diff --git a/transformer_lens/model_bridge/generalized_components/vision_projection.py b/transformer_lens/model_bridge/generalized_components/vision_projection.py index e79652228b..b33489bdbd 100644 --- a/transformer_lens/model_bridge/generalized_components/vision_projection.py +++ b/transformer_lens/model_bridge/generalized_components/vision_projection.py @@ -66,13 +66,10 @@ def forward( f"Original component not set for {self.name}. Call set_original_component() first." ) - # Apply input hook vision_features = self.hook_in(vision_features) - # Forward through the projection layer output = self.original_component(vision_features, **kwargs) - # Apply output hook if isinstance(output, tuple): output = (self.hook_out(output[0]),) + output[1:] else: diff --git a/transformer_lens/model_bridge/sources/transformers/source.py b/transformer_lens/model_bridge/sources/transformers/source.py index 19caeb45eb..3c4fc3c3c8 100644 --- a/transformer_lens/model_bridge/sources/transformers/source.py +++ b/transformer_lens/model_bridge/sources/transformers/source.py @@ -51,8 +51,7 @@ def boot( revision: str | None = None, checkpoint_index: int | None = None, checkpoint_value: int | None = None, - # Multi-device placement (accelerate-dispatched). GPU-validated 2026-07-16: - # tests/acceptance/model_bridge/test_bridge_multigpu*.py + scripts/bridge_multi_device_parity.py. + # Multi-device placement (accelerate-dispatched). Mutually exclusive with device. device_map: str | dict[str, str | int] | None = None, n_devices: int | None = None, max_memory: dict[str | int, str | int] | None = None, diff --git a/transformer_lens/model_bridge/supported_architectures/gemma1.py b/transformer_lens/model_bridge/supported_architectures/gemma1.py index 63bdab124d..a85220c6fd 100644 --- a/transformer_lens/model_bridge/supported_architectures/gemma1.py +++ b/transformer_lens/model_bridge/supported_architectures/gemma1.py @@ -129,7 +129,6 @@ def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> No hf_model: The HuggingFace Gemma1 model instance bridge_model: The TransformerBridge model (if available, set rotary_emb on actual instances) """ - # Get rotary embedding instance from the model rotary_emb = hf_model.model.rotary_emb # Force HF model to use "eager" attention to match bridge implementation diff --git a/transformer_lens/model_bridge/supported_architectures/gemma2.py b/transformer_lens/model_bridge/supported_architectures/gemma2.py index 719ead0e5f..674732848e 100644 --- a/transformer_lens/model_bridge/supported_architectures/gemma2.py +++ b/transformer_lens/model_bridge/supported_architectures/gemma2.py @@ -40,8 +40,6 @@ def __init__(self, cfg: Any) -> None: self.cfg.gated_mlp = True self.cfg.attn_only = False - # Gemma models were not trained with BOS tokens - # self.cfg.default_prepend_bos = False self.cfg.uses_rms_norm = True # Gemma models use (1.0 + weight) in RMSNorm instead of just weight # See: https://github.com/huggingface/transformers/pull/29402 diff --git a/transformer_lens/model_bridge/supported_architectures/gemma3_multimodal.py b/transformer_lens/model_bridge/supported_architectures/gemma3_multimodal.py index a0c6d09ee3..d30061ef7a 100644 --- a/transformer_lens/model_bridge/supported_architectures/gemma3_multimodal.py +++ b/transformer_lens/model_bridge/supported_architectures/gemma3_multimodal.py @@ -51,7 +51,6 @@ def __init__(self, cfg: Any) -> None: """Initialize the Gemma3 multimodal architecture adapter.""" super().__init__(cfg) - # Mark this as a multimodal model self.cfg.is_multimodal = True # Language model configuration (same as text-only Gemma 3) diff --git a/transformer_lens/model_bridge/supported_architectures/gpt2_lm_head_custom.py b/transformer_lens/model_bridge/supported_architectures/gpt2_lm_head_custom.py index 4d5f53386c..f0557e09fa 100644 --- a/transformer_lens/model_bridge/supported_architectures/gpt2_lm_head_custom.py +++ b/transformer_lens/model_bridge/supported_architectures/gpt2_lm_head_custom.py @@ -62,7 +62,7 @@ def __init__(self, cfg: Any) -> None: ), source_key="transformer.h.{i}.attn.c_proj.weight", ), - # "unembed.b_U": "lm_head.bias", # gpt2 has no unembed bias + # no unembed.b_U mapping: gpt2 has no unembed bias } # Set up component mapping diff --git a/transformer_lens/model_bridge/supported_architectures/hunyuan_v1_dense.py b/transformer_lens/model_bridge/supported_architectures/hunyuan_v1_dense.py index 8629bfb2c6..a5ab20a1b6 100644 --- a/transformer_lens/model_bridge/supported_architectures/hunyuan_v1_dense.py +++ b/transformer_lens/model_bridge/supported_architectures/hunyuan_v1_dense.py @@ -19,7 +19,6 @@ class HunYuanDenseV1ArchitectureAdapter(ArchitectureAdapter): """Architecture adapter for HunYuanDenseV1 models.""" def __init__(self, cfg: Any) -> None: - """Initialize the HunYuanDenseV1 architecture adapter.""" super().__init__(cfg) self.cfg.normalization_type = "RMS" @@ -85,7 +84,6 @@ def __init__(self, cfg: Any) -> None: def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> None: """Set up model-specific references for component testing.""" - # Get rotary embedding instance from the HF model rotary_emb = hf_model.model.rotary_emb # Set attention implementation on HF model to eager (vs sdpa default) diff --git a/transformer_lens/model_bridge/supported_architectures/llama.py b/transformer_lens/model_bridge/supported_architectures/llama.py index b7e731a393..98eb195e19 100644 --- a/transformer_lens/model_bridge/supported_architectures/llama.py +++ b/transformer_lens/model_bridge/supported_architectures/llama.py @@ -113,7 +113,6 @@ def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> No hf_model: The HuggingFace Llama model instance bridge_model: The TransformerBridge model (if available, set rotary_emb on actual instances) """ - # Get rotary embedding instance from the model rotary_emb = hf_model.model.rotary_emb # Set rotary_emb on actual bridge instances in bridge_model if available diff --git a/transformer_lens/model_bridge/supported_architectures/llava.py b/transformer_lens/model_bridge/supported_architectures/llava.py index 4993ffdb55..b77a5a7a9b 100644 --- a/transformer_lens/model_bridge/supported_architectures/llava.py +++ b/transformer_lens/model_bridge/supported_architectures/llava.py @@ -51,7 +51,6 @@ def __init__(self, cfg: Any) -> None: """Initialize the LLava architecture adapter.""" super().__init__(cfg) - # Mark this as a multimodal model self.cfg.is_multimodal = True # Language model configuration (same as LLaMA) diff --git a/transformer_lens/model_bridge/supported_architectures/olmo2.py b/transformer_lens/model_bridge/supported_architectures/olmo2.py index 80b51a13e0..6bc5f1fc75 100644 --- a/transformer_lens/model_bridge/supported_architectures/olmo2.py +++ b/transformer_lens/model_bridge/supported_architectures/olmo2.py @@ -139,7 +139,6 @@ def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> No hf_model: The HuggingFace OLMo 2 model instance bridge_model: The TransformerBridge model (if available) """ - # Get rotary embedding instance from the model rotary_emb = hf_model.model.rotary_emb # Force HF model to use "eager" attention to match bridge implementation diff --git a/transformer_lens/model_bridge/supported_architectures/olmoe.py b/transformer_lens/model_bridge/supported_architectures/olmoe.py index f8fc8e3440..912680a9b0 100644 --- a/transformer_lens/model_bridge/supported_architectures/olmoe.py +++ b/transformer_lens/model_bridge/supported_architectures/olmoe.py @@ -156,7 +156,6 @@ def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> No hf_model: The HuggingFace OLMoE model instance bridge_model: The TransformerBridge model (if available) """ - # Get rotary embedding instance from the model rotary_emb = hf_model.model.rotary_emb # Force HF model to use "eager" attention to match bridge implementation diff --git a/transformer_lens/model_bridge/supported_architectures/pretrain.py b/transformer_lens/model_bridge/supported_architectures/pretrain.py index 20091b65ff..ee468ec2f1 100644 --- a/transformer_lens/model_bridge/supported_architectures/pretrain.py +++ b/transformer_lens/model_bridge/supported_architectures/pretrain.py @@ -67,27 +67,10 @@ class DenseOrMoEFeedForwardBridge(GeneralizedComponent): - """Wraps either a dense SwiGLU MLP or a sparse MoE layer behind a - common interface. Dispatch is determined by structural inspection - (`router`/`experts` vs `gate`/`up`/`down`) rather than configuration, - so dense, MoE, and mixed architectures all use the same component - mapping. This identifies the supported protocol -- it does not - validate that a module merely sharing those attribute names actually - implements the matching forward behavior. - - `hasattr` alone would let a module with, say, both `router`/`experts` - and `gate`/`up`/`down` (or `router`/`experts` of the wrong types) win - the MoE branch by attribute-name coincidence and fail later with a - confusing error from deep inside `MoEBridge`, or not fail until - forward time. `set_original_component` therefore also checks the - basic shape of whichever protocol wins: `router`/`gate`/`up`/`down` - must themselves be modules, and `experts` must be a *registered* - module collection (`nn.ModuleList`/`nn.ModuleDict`) -- a plain Python - list/tuple of `nn.Module` experts is rejected even though every - element is itself a valid module, because modules held in an - ordinary list aren't registered as children and would silently drop - out of `parameters()`/`state_dict()`/`.to(...)`/`train()`/`eval()`, - contradicting this adapter's lifecycle guarantees. + """Wraps a dense SwiGLU MLP or sparse MoE layer behind one interface. + Dispatch is by structural inspection (`router`/`experts` vs + `gate`/`up`/`down`), not config, so dense/MoE/mixed architectures all + share the same component mapping. """ def __init__(self, name: str, config: Any): @@ -101,6 +84,10 @@ def set_original_component(self, component: torch.nn.Module) -> None: # attribute is typed `str | None`, which is all mypy sees without this # narrowing. MoEBridge/GatedMLPBridge both require a plain `str` name. assert self.name is not None + # hasattr can match by attribute-name coincidence, so verify the + # protocol's shape too: router/gate/up/down must be modules, and + # experts a *registered* ModuleList/ModuleDict -- a plain list of + # experts drops out of parameters()/state_dict()/.to()/train()/eval(). if hasattr(component, "router") and hasattr(component, "experts"): if not isinstance(component.router, torch.nn.Module): raise TypeError( @@ -203,35 +190,11 @@ def __getattr__(self, key: str) -> Any: class PretrainModelContainer(torch.nn.Module): - """Internal detail -- `build_pretrain_bridge` applies this - automatically. Three responsibilities, all in this container's own - `forward`: - - 1. Avoids a `TransformerBridge.__getattr__` collision: a source - model's own `self.embed`/`self.blocks` clashes with identically - named component_mapping keys. Wrapping one level deeper - (`container.inner.embed`) fixes this without touching the source - model. - 2. Normalizes the return value to the `.logits` contract - `TransformerBridge` expects: a plain `"logits"` dict (the target - architecture's actual shape) is wrapped in `_LogitsAttrDict`; a bare - tensor, tensor-first tuple, or object already exposing `.logits` - passes through after validating the tensor is present and is a - tensor; anything else raises immediately with a clear message. - 3. Strips `_BRIDGE_COMPAT_KWARGS` from kwargs before calling the - wrapped model (see that constant's comment). - - Also sidesteps a `BlockBridge` convention where a bare-tensor block - output gets wrapped in a 1-tuple for "standalone hidden_states calls": - since this target's blocks take `cos`/`sin` too, that path never - triggers, so the source forward loop needs no changes to be bridged. - - `self.inner` is a regular registered submodule, so - `container.train()`/`.eval()` already recurse into it via the normal - `nn.Module` traversal -- no override needed here. The propagation gap - lives one level up, at `TransformerBridge` itself (see - `build_pretrain_bridge`), whose `.train()`/`.eval()` do not walk down - to `original_model`. + """Wraps the source model one level deeper (`container.inner`) so its + own `embed`/`blocks` attrs don't collide with `TransformerBridge` + component_mapping keys, normalizes the forward return to the `.logits` + contract, and strips `_BRIDGE_COMPAT_KWARGS`. Applied automatically by + `build_pretrain_bridge`. """ def __init__(self, model: torch.nn.Module) -> None: @@ -334,35 +297,10 @@ class PretrainArchitectureAdapter(ArchitectureAdapter): """Adapter for a decoder-only transformer using RoPE, RMSNorm, gated SwiGLU MLPs, and optional sparse MoE feed-forward layers. - Uses an opaque `NativeForwardAttentionBridge` with no attention - projection submodules, not `JointQKVAttentionBridge`/ - `PositionEmbeddingsAttentionBridge`: those reimplement RoPE via HF's - rotate-half convention, wrong for a source model using the - adjacent-pair convention. The opaque bridge delegates unchanged to - `Attention.forward`, so RoPE runs as written -- at the cost of no - per-head hooks, only block-level - `resid_pre`/`resid_mid`/`resid_post`. - - Blocks use `DelegatedAttentionBlockBridge` rather than plain - `BlockBridge`: that existing abstraction already exists for - architectures where attention is delegated wholesale and the - split-qkv-fork block-level aliases (`hook_attn_in`/`hook_q_input`/ - `hook_k_input`/`hook_v_input`) don't apply. It complements - `NativeForwardAttentionBridge.supports_split_qkv_fork = False` (which - prevents the split-QKV-fork machinery and its associated HookPoints - from being exposed for this attention component) by also removing the - now-dangling block-level aliases that would otherwise point at them. - `hook_attn_out` is untouched by either change, since the attention - component still fires its own `hook_out` normally. - - `self.cfg` is mutated in place, not copied (matches `nanogpt.py`'s - convention) -- callers holding another reference to the same config - will see these fields change. - - Bridges built through `build_pretrain_bridge` are given a - mode-propagating subclass so `.train()`/`.eval()` reach the wrapped - source model (see that function's docstring) -- this adapter class - itself has no lifecycle behavior of its own. + Uses an opaque `NativeForwardAttentionBridge` (delegates to + `Attention.forward`) so RoPE runs under the source's adjacent-pair + convention rather than HF's rotate-half -- at the cost of only + block-level hooks, no per-head hooks. """ def __init__(self, cfg: Any) -> None: diff --git a/transformer_lens/model_bridge/supported_architectures/qwen2.py b/transformer_lens/model_bridge/supported_architectures/qwen2.py index 7326201edf..ee400a11a5 100644 --- a/transformer_lens/model_bridge/supported_architectures/qwen2.py +++ b/transformer_lens/model_bridge/supported_architectures/qwen2.py @@ -100,12 +100,10 @@ def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> No hf_model: The HuggingFace Qwen2 model instance bridge_model: The TransformerBridge model (if available, set rotary_emb on actual instances) """ - # Get rotary embedding instance from the model rotary_emb = hf_model.model.rotary_emb # Set rotary_emb on actual bridge instances in bridge_model if available if bridge_model is not None and hasattr(bridge_model, "blocks"): - # Set on each layer's actual attention bridge instance for block in bridge_model.blocks: if hasattr(block, "attn"): block.attn.set_rotary_emb(rotary_emb) diff --git a/transformer_lens/model_bridge/supported_architectures/qwen3.py b/transformer_lens/model_bridge/supported_architectures/qwen3.py index d8117b7530..d2a8356f41 100644 --- a/transformer_lens/model_bridge/supported_architectures/qwen3.py +++ b/transformer_lens/model_bridge/supported_architectures/qwen3.py @@ -132,8 +132,7 @@ def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> No if "attn" in block._modules: block.attn.set_rotary_emb(rotary_emb) - # Set on template for get_generalized_component() calls - # Set on template — may not exist in hybrid adapters + # Set on template for get_generalized_component(); may be absent in hybrid adapters mapping = self.component_mapping or {} blocks_template = mapping.get("blocks") if isinstance(mapping, dict) else None if blocks_template and "attn" in getattr(blocks_template, "submodules", {}): diff --git a/transformer_lens/model_bridge/supported_architectures/qwen3_moe.py b/transformer_lens/model_bridge/supported_architectures/qwen3_moe.py index 643892cee1..58bb51db07 100644 --- a/transformer_lens/model_bridge/supported_architectures/qwen3_moe.py +++ b/transformer_lens/model_bridge/supported_architectures/qwen3_moe.py @@ -121,7 +121,6 @@ def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> No hf_model: The HuggingFace Qwen3MoE model instance bridge_model: The TransformerBridge model (if available) """ - # Get rotary embedding instance from the model rotary_emb = hf_model.model.rotary_emb # Force HF model to use "eager" attention to match bridge implementation diff --git a/transformer_lens/model_bridge/transformer_bridge.py b/transformer_lens/model_bridge/transformer_bridge.py index aff4229f4b..aee061f39d 100644 --- a/transformer_lens/model_bridge/transformer_bridge.py +++ b/transformer_lens/model_bridge/transformer_bridge.py @@ -2372,7 +2372,6 @@ def generate( if hasattr(self, "_last_hf_cache"): del self._last_hf_cache - # Concatenate all sampled tokens sampled_tokens = torch.cat(sampled_tokens_list, dim=1) if is_encoder_decoder: # Reconstruct full decoder sequence: start token + generated tokens @@ -2796,7 +2795,6 @@ def hf_generate( if any_flag_set: generation_kwargs.setdefault("return_dict_in_generate", True) - # Generate using the original HuggingFace model with torch.no_grad(): outputs = self.original_model.generate(input_ids, **generation_kwargs) # type: ignore[operator] @@ -3149,7 +3147,6 @@ def _is_valid_bridge_path(self, hf_path: str) -> bool: Returns: True if the path is valid, False if it contains nested HF components """ - # Split the path into parts parts = hf_path.split(".") # Get the component mapping for validation diff --git a/transformer_lens/patching.py b/transformer_lens/patching.py index 24a50ed126..1e802d6ddf 100644 --- a/transformer_lens/patching.py +++ b/transformer_lens/patching.py @@ -206,7 +206,6 @@ def patching_hook(corrupted_activation, hook, index, clean_activation): corrupted_activation = corrupted_activation.clone() return patch_setter(corrupted_activation, index, clean_activation) - # Iterate over every list of indices, and make the appropriate patch! for c, index_row in enumerate(tqdm((list(index_df.iterrows())))): index = index_row[1].to_list() @@ -220,12 +219,10 @@ def patching_hook(corrupted_activation, hook, index, clean_activation): clean_activation=clean_cache[current_activation_name], ) - # Run the model with the patching hook and get the logits! patched_logits = model.run_with_hooks( corrupted_tokens, fwd_hooks=[(current_activation_name, current_hook)] ) - # Calculate the patching metric and store if flattened_output: patched_metric_output[c] = patching_metric(patched_logits).item() else: diff --git a/transformer_lens/pretrained/weight_conversions/hubert.py b/transformer_lens/pretrained/weight_conversions/hubert.py index 54e0e8567d..64f4940aa0 100644 --- a/transformer_lens/pretrained/weight_conversions/hubert.py +++ b/transformer_lens/pretrained/weight_conversions/hubert.py @@ -26,7 +26,6 @@ def convert_hubert_weights(hf_model, cfg: HookedTransformerConfig): # Use cfg dims for reshaping d_model = cfg.d_model n_heads = cfg.n_heads - # d_head = d_model // n_heads # implicit if needed for l, layer in enumerate(encoder_layers): # --- Attention module --- diff --git a/transformer_lens/pretrained/weight_conversions/olmoe.py b/transformer_lens/pretrained/weight_conversions/olmoe.py index a38ea758f5..7528a1a4a5 100644 --- a/transformer_lens/pretrained/weight_conversions/olmoe.py +++ b/transformer_lens/pretrained/weight_conversions/olmoe.py @@ -56,7 +56,6 @@ def convert_olmoe_weights(olmoe, cfg: HookedTransformerConfig): down = experts.down_proj # [num_experts, d_model, d_mlp] for e in range(cfg.num_experts): - # Split fused gate_up into gate and up projections state_dict[f"blocks.{l}.mlp.experts.{e}.W_gate.weight"] = gate_up[e, : cfg.d_mlp, :] state_dict[f"blocks.{l}.mlp.experts.{e}.W_in.weight"] = gate_up[e, cfg.d_mlp :, :] state_dict[f"blocks.{l}.mlp.experts.{e}.W_out.weight"] = down[e] diff --git a/transformer_lens/pretrained/weight_conversions/qwen3.py b/transformer_lens/pretrained/weight_conversions/qwen3.py index c2848213c2..ec5a8fd522 100644 --- a/transformer_lens/pretrained/weight_conversions/qwen3.py +++ b/transformer_lens/pretrained/weight_conversions/qwen3.py @@ -35,7 +35,6 @@ def convert_qwen3_weights(qwen: Any, cfg: HookedTransformerConfig): state_dict[f"blocks.{l}.attn.{gqa_uscore}W_K"] = W_K state_dict[f"blocks.{l}.attn.{gqa_uscore}W_V"] = W_V - # Load weights into RMSNorm modules state_dict[f"blocks.{l}.attn.q_norm.w"] = qwen.model.layers[l].self_attn.q_norm.weight state_dict[f"blocks.{l}.attn.k_norm.w"] = qwen.model.layers[l].self_attn.k_norm.weight diff --git a/transformer_lens/utilities/aliases.py b/transformer_lens/utilities/aliases.py index 5e618c4cbc..2d8980e8c2 100644 --- a/transformer_lens/utilities/aliases.py +++ b/transformer_lens/utilities/aliases.py @@ -57,7 +57,6 @@ def _resolve_single_target(target_name: str) -> Any: raise AttributeError( f"'{type(target_object).__name__}' object has no attribute '{target_name}'" ) - # Return the target hook return getattr(target_object, target_name) # if the target_name is a list, we check all elements diff --git a/transformer_lens/utilities/attribute_utils.py b/transformer_lens/utilities/attribute_utils.py index 2ecbc9bdc3..d133b12966 100644 --- a/transformer_lens/utilities/attribute_utils.py +++ b/transformer_lens/utilities/attribute_utils.py @@ -42,5 +42,4 @@ def set_nested_attr(obj, attr_str, value): for attr in attrs[:-1]: obj = getattr(obj, attr) - # Set the nested attribute's value setattr(obj, attrs[-1], value) diff --git a/transformer_lens/utilities/bridge_components.py b/transformer_lens/utilities/bridge_components.py index 8c0418fb6d..a9d525aa3d 100644 --- a/transformer_lens/utilities/bridge_components.py +++ b/transformer_lens/utilities/bridge_components.py @@ -30,11 +30,9 @@ def collect_all_submodules_of_component( if component_submodule.name is not None: submodules[block_prefix + component_submodule.name] = component_submodule - # If the component is a list item, we need to collect all submodules of the block bridge if component_submodule.is_list_item: submodules = collect_components_of_block_bridge(model, component_submodule, submodules) - # If the component has submodules, we need to collect them recursively if component_submodule.submodules: submodules = collect_all_submodules_of_component( model, component_submodule, submodules, block_prefix @@ -93,7 +91,6 @@ def collect_all_components(model: TransformerBridge, components: dict) -> dict: Dictionary mapping component names to their respective components """ - # Iterate through all components in component mapping for component in model.adapter.get_component_mapping().values(): components[component.name] = component components = collect_all_submodules_of_component(model, component, components) diff --git a/transformer_lens/utilities/hf_utils.py b/transformer_lens/utilities/hf_utils.py index 4b405770dc..cfaaf4ee68 100644 --- a/transformer_lens/utilities/hf_utils.py +++ b/transformer_lens/utilities/hf_utils.py @@ -190,8 +190,6 @@ def clear_huggingface_cache(): """ Deletes the Hugging Face cache directory and all its contents. - This function deletes the Hugging Face cache directory, which is used to store downloaded models and their associated files. Deleting the cache directory will remove all the downloaded models and their files, so you will need to download them again if you want to use them in your code. - This function is safe to call in parallel test execution - it will handle race conditions where multiple workers might try to delete the same directory. @@ -204,7 +202,6 @@ def clear_huggingface_cache(): print("Deleting Hugging Face cache directory and all its contents.") - # Check if cache directory exists if not os.path.exists(CACHE_DIR): return diff --git a/transformer_lens/utilities/matrix.py b/transformer_lens/utilities/matrix.py index 601d1d964e..2d768fdaef 100644 --- a/transformer_lens/utilities/matrix.py +++ b/transformer_lens/utilities/matrix.py @@ -42,7 +42,6 @@ def composition_scores( def get_matrix_corner(matrix: FactoredMatrix, n=3): - # Prints the top left corner of the tensor result = get_corner(matrix[tuple(slice(n) for _ in range(matrix.ndim))]) return result.AB diff --git a/transformer_lens/utilities/tokenize_utils.py b/transformer_lens/utilities/tokenize_utils.py index 0ba674febc..2b6c729e11 100644 --- a/transformer_lens/utilities/tokenize_utils.py +++ b/transformer_lens/utilities/tokenize_utils.py @@ -245,7 +245,7 @@ def get_attention_mask( torch.Tensor: The attention mask for the input. """ - # Initialize the attention mask with ones (indicating all tokens should be attended to) + # Default to attending every token; pad positions are zeroed below. attention_mask = torch.ones_like(tokens) if tokenizer is None: return attention_mask diff --git a/transformer_lens/weight_processing.py b/transformer_lens/weight_processing.py index 2db0e2b883..7d1ea647b6 100644 --- a/transformer_lens/weight_processing.py +++ b/transformer_lens/weight_processing.py @@ -1778,7 +1778,6 @@ def refactor_factored_attn_matrices( # Add singleton dimension for broadcasting b_V_expanded = einops.rearrange(b_V, "head_index d_head -> head_index d_head 1") - # Element-wise multiplication of b_V and W_O b_V_times_W_O = b_V_expanded * W_O # Sum over d_head and head_index dimensions From 1b2eaa416ad0862714b783175f6640900798c059 Mon Sep 17 00:00:00 2001 From: Jonah Larson Date: Wed, 29 Jul 2026 16:38:40 -0500 Subject: [PATCH 16/43] Release cleanup 3.6.0 Release port cleanup for 4.x (#1550) * Fixing tests that were broken by the migration from dev to 4.x * Updating docs for latest information, adding support for vision benchmarking * Clean up dead code in architecture adapters, properly documenting all our slow tests, fixing some test code configs * Test and architecture DRY cleanup * more DRY tests * Refactoring repeated architecture functions into helpers * Reduced duplication across adapters in component mapping and other setup components --- .github/workflows/checks.yml | 2 - .github/workflows/oracle-parity.yml | 16 +- AGENTS.md | 6 +- README.md | 4 +- docs/make_docs.py | 34 +- .../content/adapter_development/hf-scraper.md | 5 +- docs/source/content/compatibility_mode.md | 2 +- docs/source/content/contributing.md | 2 +- docs/source/content/drivers.md | 12 +- docs/source/content/getting_started.md | 2 +- docs/source/content/jacobian_lens_fitting.md | 6 +- docs/source/content/migrating_to_v3.md | 2 +- makefile | 2 +- scripts/audit_quarantines.py | 89 ++ tests/AGENTS.md | 6 +- tests/QUARANTINES.md | 49 +- tests/acceptance/model_bridge/conftest.py | 56 - tests/conftest.py | 38 + tests/integration/model_bridge/conftest.py | 56 +- tests/integration/model_bridge/helpers.py | 36 +- .../model_bridge/test_afmoe_adapter.py | 39 +- .../test_attention_weight_accessors.py | 6 +- .../model_bridge/test_bamba_adapter.py | 50 +- .../model_bridge/test_bitnet_adapter.py | 24 +- .../test_bridge_creation_modes.py | 34 +- .../model_bridge/test_bridge_integration.py | 64 +- .../test_bridge_start_at_layer.py | 18 + .../model_bridge/test_ernie4_5_adapter.py | 28 +- .../model_bridge/test_ernie4_5_moe_adapter.py | 24 +- .../model_bridge/test_exaone4_adapter.py | 44 +- .../model_bridge/test_exaone_adapter.py | 44 +- .../model_bridge/test_falcon_mamba_adapter.py | 44 +- .../model_bridge/test_gemma4_text_adapter.py | 19 +- .../model_bridge/test_glm4_adapter.py | 31 +- .../test_glm4_moe_lite_adapter.py | 44 +- .../model_bridge/test_glm4v_adapter.py | 31 +- .../model_bridge/test_glm_adapter.py | 26 +- .../model_bridge/test_glm_asr_adapter.py | 27 +- .../model_bridge/test_hrm_text_adapter.py | 23 +- .../model_bridge/test_idefics3_adapter.py | 38 +- .../model_bridge/test_jais2_adapter.py | 21 +- .../model_bridge/test_laguna_adapter.py | 21 +- .../model_bridge/test_llada2_moe_adapter.py | 21 +- .../model_bridge/test_llama4_adapter.py | 45 +- .../test_llama4_multimodal_adapter.py | 27 +- .../model_bridge/test_marian_adapter.py | 25 +- .../model_bridge/test_minimax_m2_adapter.py | 52 +- .../model_bridge/test_mistral3_adapter.py | 55 +- .../model_bridge/test_mixtral_adapter.py | 21 +- .../model_bridge/test_mpt_adapter.py | 31 +- .../model_bridge/test_nemotron_adapter.py | 30 +- .../model_bridge/test_olmo_hybrid_adapter.py | 21 +- .../model_bridge/test_openai_gpt_adapter.py | 30 +- .../model_bridge/test_qwen2_5_vl_adapter.py | 31 +- .../model_bridge/test_qwen2_audio_adapter.py | 36 +- .../model_bridge/test_qwen3_5_moe_adapter.py | 50 +- .../model_bridge/test_qwen3_moe_bridge.py | 29 +- .../model_bridge/test_qwen3_vl_adapter.py | 35 +- .../model_bridge/test_qwen3_vl_moe_adapter.py | 31 +- .../model_bridge/test_seed_oss_adapter.py | 42 +- .../model_bridge/test_stablelm_adapter.py | 21 +- .../model_bridge/test_starcoder2_adapter.py | 30 +- .../model_bridge/test_vaultgemma_adapter.py | 21 +- tests/mocks/mock_logits_bridge.py | 7 +- tests/mocks/vllm_boot.py | 122 ++ .../unit/benchmarks/test_vision_benchmarks.py | 285 +++++ tests/unit/model_bridge/sources/__init__.py | 0 .../test_boot_architecture_resolution.py | 213 ++++ .../test_inspect_provider_model_class.py | 104 ++ .../sources/test_modality_helpers.py | 248 ++++ .../supported_architectures/helpers.py | 39 + .../test_dream_adapter.py | 18 +- .../test_exaone_adapter.py | 4 +- .../test_gidd_adapter.py | 11 +- .../test_llada2_moe_adapter.py | 12 +- .../test_neox_adapter.py | 21 + .../test_pretrain_adapter.py | 37 + .../test_qwen3_5_adapter.py | 6 +- .../test_thin_subclass_adapters.py | 60 +- .../model_bridge/test_bridge_generate_bos.py | 56 +- .../test_bridge_generate_kv_cache.py | 28 +- .../model_bridge/test_checkpoint_revision.py | 6 +- .../model_bridge/test_diffusion_generate.py | 91 +- .../test_hook_alias_resolution.py | 18 +- .../model_bridge/test_remote_code_compat.py | 257 ++++ tests/unit/model_bridge/test_vllm_boot.py | 111 +- tests/unit/test_make_docs.py | 23 + .../unit/test_verify_models_phase_defaults.py | 21 +- .../test_registry_io_helpers.py | 124 ++ .../test_update_model_registry.py | 148 +++ .../tools/model_registry/test_verify_gates.py | 91 +- tests/unit/tools/test_model_registry.py | 86 +- .../utilities/test_attn_implementation.py | 126 ++ transformer_lens/benchmarks/audio.py | 9 +- transformer_lens/benchmarks/main_benchmark.py | 168 ++- transformer_lens/benchmarks/vision.py | 189 +++ transformer_lens/config/AGENTS.md | 4 +- .../model_bridge/architecture_adapter.py | 74 +- .../model_bridge/remote_bridge.py | 1 + .../model_bridge/sources/_bridge_builder.py | 6 + .../model_bridge/sources/_hf_format.py | 3 + .../model_bridge/sources/inspect/source.py | 15 +- .../sources/inspect/transformers_provider.py | 27 +- .../sources/inspect/vllm_provider.py | 15 +- .../sources/transformers/helpers.py | 76 +- .../sources/transformers/source.py | 111 +- .../model_bridge/sources/vllm/source.py | 19 +- .../supported_architectures/AGENTS.md | 2 +- .../supported_architectures/__init__.py | 1 + .../_remote_code_compat.py | 159 +++ .../supported_architectures/apertus.py | 17 +- .../supported_architectures/arcee.py | 17 +- .../supported_architectures/baichuan.py | 47 +- .../supported_architectures/bd3lm.py | 16 +- .../supported_architectures/cohere.py | 3 + .../supported_architectures/deepseek_v2.py | 148 ++- .../supported_architectures/deepseek_v3.py | 87 +- .../supported_architectures/deepseek_v4.py | 9 +- .../supported_architectures/dream.py | 52 +- .../supported_architectures/gemma3.py | 27 +- .../gemma3_multimodal.py | 13 +- .../supported_architectures/gidd.py | 50 +- .../supported_architectures/glm4_moe.py | 6 +- .../supported_architectures/glm4_moe_lite.py | 91 +- .../supported_architectures/glm_moe_dsa.py | 74 +- .../supported_architectures/gpt_oss.py | 6 - .../supported_architectures/granite.py | 21 +- .../supported_architectures/hrm_text.py | 30 +- .../supported_architectures/internlm2.py | 53 +- .../supported_architectures/jamba.py | 10 +- .../supported_architectures/jetmoe.py | 6 +- .../supported_architectures/laguna.py | 6 +- .../supported_architectures/lfm2.py | 27 +- .../supported_architectures/llada2_moe.py | 8 +- .../supported_architectures/llama.py | 19 - .../supported_architectures/mamba2.py | 2 +- .../supported_architectures/minimax_m2.py | 3 + .../supported_architectures/mixtral.py | 6 - .../modernbert_decoder.py | 6 +- .../supported_architectures/nanochat.py | 7 +- .../supported_architectures/neox.py | 26 +- .../supported_architectures/olmo.py | 8 +- .../supported_architectures/olmoe.py | 11 +- .../supported_architectures/openelm.py | 99 +- .../supported_architectures/ouro.py | 126 +- .../supported_architectures/pretrain.py | 11 +- .../supported_architectures/qwen2.py | 19 - .../supported_architectures/qwen3.py | 20 +- .../supported_architectures/qwen3_5.py | 30 +- .../supported_architectures/qwen3_5_moe.py | 44 +- .../supported_architectures/qwen3_moe.py | 3 + .../supported_architectures/raven.py | 203 +--- .../supported_architectures/rwkv7.py | 104 +- .../supported_architectures/t5gemma.py | 4 +- .../supported_architectures/t5gemma2.py | 4 +- .../model_bridge/transformer_bridge.py | 5 + .../tools/model_registry/__init__.py | 13 +- transformer_lens/tools/model_registry/api.py | 4 + .../model_registry/data/supported_models.json | 1062 ++++++++++++++++- .../tools/model_registry/generate_report.py | 1 - .../tools/model_registry/hf_scraper.py | 62 +- .../tools/model_registry/registry_io.py | 97 +- .../tools/model_registry/schemas.py | 4 + .../tools/model_registry/validate.py | 10 +- .../tools/model_registry/verify_models.py | 137 ++- transformer_lens/utilities/architectures.py | 12 +- .../utilities/attn_implementation.py | 59 + 167 files changed, 5482 insertions(+), 2624 deletions(-) create mode 100644 scripts/audit_quarantines.py delete mode 100644 tests/acceptance/model_bridge/conftest.py create mode 100644 tests/mocks/vllm_boot.py create mode 100644 tests/unit/benchmarks/test_vision_benchmarks.py create mode 100644 tests/unit/model_bridge/sources/__init__.py create mode 100644 tests/unit/model_bridge/sources/test_boot_architecture_resolution.py create mode 100644 tests/unit/model_bridge/sources/test_inspect_provider_model_class.py create mode 100644 tests/unit/model_bridge/sources/test_modality_helpers.py create mode 100644 tests/unit/model_bridge/test_remote_code_compat.py create mode 100644 tests/unit/tools/model_registry/test_registry_io_helpers.py create mode 100644 tests/unit/tools/model_registry/test_update_model_registry.py create mode 100644 tests/unit/utilities/test_attn_implementation.py create mode 100644 transformer_lens/benchmarks/vision.py create mode 100644 transformer_lens/model_bridge/supported_architectures/_remote_code_compat.py create mode 100644 transformer_lens/utilities/attn_implementation.py diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml index 4318eb942e..2802597bb2 100644 --- a/.github/workflows/checks.yml +++ b/.github/workflows/checks.yml @@ -159,9 +159,7 @@ jobs: --ignore=tests/unit/model_bridge/test_optimizer_compatibility.py --ignore=tests/unit/model_bridge/test_gpt_oss_moe.py --ignore=tests/unit/model_bridge/test_component_inspection.py - --ignore=tests/unit/model_bridge/test_key_analysis.py --ignore=tests/unit/model_bridge/test_benchmark_gated_hooks_fire.py - --ignore=tests/unit/model_bridge/test_weight_processing_adapter_paths.py --ignore=tests/unit/model_bridge/test_bridge_generate_kv_cache.py --ignore=tests/unit/model_bridge/test_bridge_vs_hooked_transformer_patching.py --ignore=tests/unit/model_bridge/compatibility/ diff --git a/.github/workflows/oracle-parity.yml b/.github/workflows/oracle-parity.yml index 540729e327..388a48ded0 100644 --- a/.github/workflows/oracle-parity.yml +++ b/.github/workflows/oracle-parity.yml @@ -1,17 +1,11 @@ name: Oracle Parity # Runs the @pytest.mark.slow oracle parity suite for JacobianLens. -# Not part of the standard PR gate — requires ~4 GB VRAM and network access. -# Triggered manually or automatically when the test file or this workflow changes. +# Not part of the standard PR gate — requires ~4 GB VRAM (or a long CPU run) +# and network access, so it is manual-trigger only. Run it after changing +# tests/integration/test_jacobian_lens_oracle_parity.py or this workflow. on: workflow_dispatch: - push: - branches: - - main - - dev* - paths: - - "tests/integration/test_jacobian_lens_oracle_parity.py" - - ".github/workflows/oracle-parity.yml" permissions: contents: read @@ -21,6 +15,8 @@ jobs: name: Oracle Parity Checks runs-on: ubuntu-latest timeout-minutes: 60 + env: + HAS_HF_TOKEN: ${{ secrets.HF_TOKEN != '' }} steps: - uses: actions/checkout@v4 @@ -46,7 +42,7 @@ jobs: uv pip install "git+https://github.com/anthropics/jacobian-lens.git@${ORACLE_COMMIT}" - name: Authenticate HuggingFace - if: env.HF_TOKEN != '' + if: ${{ env.HAS_HF_TOKEN == 'true' }} run: uv run python -c "import os; from huggingface_hub import login; login(token=os.environ['HF_TOKEN'])" env: HF_TOKEN: ${{ secrets.HF_TOKEN }} diff --git a/AGENTS.md b/AGENTS.md index 12f81bdb8b..3d053dbbce 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -22,7 +22,7 @@ Sub-folder rules: [tests/AGENTS.md](tests/AGENTS.md) · [supported_architectures ## 1. What this repo is -**TransformerLens** — mechanistic-interpretability library. Loads 9,000+ models across 50+ architecture families (see [supported_models.json](transformer_lens/tools/model_registry/data/supported_models.json)) and exposes internal activations through a hook system for caching, editing, and ablating intermediate state. Built on HuggingFace `transformers`. +**TransformerLens** — mechanistic-interpretability library. Loads 15,000+ models across 140+ architecture families (see [supported_models.json](transformer_lens/tools/model_registry/data/supported_models.json)) and exposes internal activations through a hook system for caching, editing, and ablating intermediate state. Built on HuggingFace `transformers`. ## 2. Two systems live in this repo @@ -33,7 +33,7 @@ Sub-folder rules: [tests/AGENTS.md](tests/AGENTS.md) · [supported_architectures > ⚠ The **HookedTransformer acceptance suite is quarantined** ([test_hooked_transformer.py](tests/acceptance/test_hooked_transformer.py), [test_hooked_encoder.py](tests/acceptance/test_hooked_encoder.py), [test_hooked_encoder_decoder.py](tests/acceptance/test_hooked_encoder_decoder.py); see [QUARANTINES.md](tests/QUARANTINES.md)). HT changes land untested at the acceptance level — extra manual care required. -Bridge architecture-adapter pattern: each HF architecture has one file in [supported_architectures/](transformer_lens/model_bridge/supported_architectures/) mapping HF module paths to canonical names. Bridge hooks are architecture-native (e.g. `blocks.{i}.hook_out`); HT-style aliases live in [bridge.py](transformer_lens/model_bridge/bridge.py). +Bridge architecture-adapter pattern: each HF architecture has one file in [supported_architectures/](transformer_lens/model_bridge/supported_architectures/) mapping HF module paths to canonical names. Bridge hooks are architecture-native (e.g. `blocks.{i}.hook_out`); HT-style aliases live in [bridge_core.py](transformer_lens/model_bridge/bridge_core.py). **Mirroring rule:** if you change `HookedTransformer` behaviour that has a `TransformerBridge` counterpart, update both in the same PR. [supported_models.py](transformer_lens/supported_models.py) is HT-only — Bridge-only models go in the Bridge registry data file. @@ -107,7 +107,7 @@ Python: **>=3.10, <4.0**. CI tests 3.10, 3.11, 3.12. Format/type/docstring check ## 5. Hook naming — HT vs Bridge - **HT canonical**: uniform across architectures — `hook_embed`, `blocks.{i}.hook_resid_pre`, `blocks.{i}.attn.hook_q`, `blocks.{i}.hook_resid_post`. -- **Bridge-native**: architecture-shaped — `blocks.{i}.hook_out`, `blocks.{i}.attn.q.hook_out`. HT aliases registered via `build_alias_to_canonical_map()` in [bridge.py](transformer_lens/model_bridge/bridge.py). +- **Bridge-native**: architecture-shaped — `blocks.{i}.hook_out`, `blocks.{i}.attn.q.hook_out`. HT aliases registered via `build_alias_to_canonical_map()` in [bridge_core.py](transformer_lens/model_bridge/bridge_core.py). Prefer Bridge-native names in new code. Raw-HF-forward drivers comparing against `boot_transformers` must match its load configuration (fp32, eager attention) and probe for optional features like `resid_mid` rather than assume. diff --git a/README.md b/README.md index c76c8fe91e..2ad7365c1c 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ interpretability](https://distill.pub/2020/circuits/zoom-in/) of GPT-2 Style lan goal of mechanistic interpretability is to take a trained model and reverse engineer the algorithms the model learned during training from its weights. -TransformerLens lets you load in 9,000+ open source language models across 50+ architecture families, +TransformerLens lets you load in 15,000+ open source language models across 140+ architecture families, and exposes the internal activations of the model to you. You can cache any internal activation in the model, and add in functions to edit, remove or replace these activations as the model runs. @@ -52,7 +52,7 @@ logits, activations = bridge.run_with_cache("Hello World") > Gated models (Llama, Mistral, Gemma, ...) require `HF_TOKEN` in your environment. See [Environment Variables](https://TransformerLensOrg.github.io/TransformerLens/content/getting_started.html#environment-variables) for the full list. -`TransformerBridge` is the recommended 3.0 path and supports 9,000+ models across 50+ architecture families (see [`supported_models.json`](transformer_lens/tools/model_registry/data/supported_models.json) for the full inventory). By default it preserves raw HuggingFace weights – logits and activations match HF, *not* legacy `HookedTransformer` (which folds LayerNorm and centers weights by default). Call `bridge.enable_compatibility_mode()` after booting for HookedTransformer-equivalent numerics. The legacy `HookedTransformer.from_pretrained` API is still available but deprecated — see the [Migrating to TransformerLens 3](https://TransformerLensOrg.github.io/TransformerLens/content/migrating_to_v3.html) guide. +`TransformerBridge` is the recommended 3.0 path and supports 15,000+ models across 140+ architecture families (see [`supported_models.json`](transformer_lens/tools/model_registry/data/supported_models.json) for the full inventory). By default it preserves raw HuggingFace weights – logits and activations match HF, *not* legacy `HookedTransformer` (which folds LayerNorm and centers weights by default). Call `bridge.enable_compatibility_mode()` after booting for HookedTransformer-equivalent numerics. The legacy `HookedTransformer.from_pretrained` API is still available but deprecated — see the [Migrating to TransformerLens 3](https://TransformerLensOrg.github.io/TransformerLens/content/migrating_to_v3.html) guide. ## Key Tutorials diff --git a/docs/make_docs.py b/docs/make_docs.py index 9ad61ed16d..1810514c14 100644 --- a/docs/make_docs.py +++ b/docs/make_docs.py @@ -821,9 +821,10 @@ def copy_demos(_app: Optional[Any] = None): #bt-root tbody td a:hover { text-decoration: underline; } #bt-root .bt-rn { color: var(--color-foreground-muted, #999); font-size: 12px; width: 36px; text-align: right; } #bt-root .bt-badge { display: inline-block; padding: 2px 8px; border-radius: 10px; font-size: 11px; font-weight: 600; } +/* No .bt-s2: status-2 (skipped) rows deliberately render with the .bt-s0 + "Unverified" badge — see the status cell's class mapping. */ #bt-root .bt-s0 { background: #e8e8e8; color: #666; } #bt-root .bt-s1 { background: #d4edda; color: #155724; } -#bt-root .bt-s2 { background: #fff3cd; color: #856404; } #bt-root .bt-s3 { background: #f8d7da; color: #721c24; } #bt-root .bt-s4 { background: #d1ecf1; color: #0c5460; } #bt-root .bt-muted { color: var(--color-foreground-muted, #999); } @@ -902,7 +903,7 @@ def copy_demos(_app: Optional[Any] = None): - + @@ -928,7 +929,8 @@ def copy_demos(_app: Optional[Any] = None):