diff --git a/demos/Exploratory_Analysis_Demo.ipynb b/demos/Exploratory_Analysis_Demo.ipynb index 48caba76c..c2c86cea8 100644 --- a/demos/Exploratory_Analysis_Demo.ipynb +++ b/demos/Exploratory_Analysis_Demo.ipynb @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -86,8 +86,8 @@ "if not IN_COLAB:\n", " from IPython import get_ipython\n", " ip = get_ipython()\n", - " if 'autoreload' not in ip.extension_manager.loaded:\n", - " ip.extension_manager.load_extension('autoreload')\n", + " if not ip.extension_manager.loaded:\n", + " ip.extension_manager.load('autoreload')\n", " %autoreload 2\n" ] }, @@ -100,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -118,7 +118,8 @@ "from jaxtyping import Float\n", "\n", "import transformer_lens.utils as utils\n", - "from transformer_lens import ActivationCache, HookedTransformer" + "from transformer_lens import ActivationCache\n", + "from transformer_lens.model_bridge import TransformerBridge" ] }, { @@ -137,17 +138,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Disabled automatic differentiation\n" - ] - } - ], + "outputs": [], "source": [ "torch.set_grad_enabled(False)\n", "print(\"Disabled automatic differentiation\")" @@ -169,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -245,33 +238,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The first step is to load in our model, GPT-2 Small, a 12 layer and 80M parameter transformer with `HookedTransformer.from_pretrained`. The various flags are simplifications that preserve the model's output but simplify its internals." + "The first step is to load in our model, GPT-2 Small, a 12 layer and 80M parameter transformer with `TransformerBridge.boot_transformers`. The various flags are simplifications that preserve the model's output but simplify its internals." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using pad_token, but it is not set yet.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded pretrained model gpt2-small into HookedTransformer\n" - ] - } - ], + "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", - "model = HookedTransformer.from_pretrained(\n", - " \"gpt2-small\",\n", + "model = TransformerBridge.boot_transformers(\"gpt2\")\n", + "model.enable_compatibility_mode(\n", " center_unembed=True,\n", " center_writing_weights=True,\n", " fold_ln=True,\n", @@ -298,62 +276,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tokenized prompt: ['<|endoftext|>', 'After', ' John', ' and', ' Mary', ' went', ' to', ' the', ' store', ',', ' John', ' gave', ' a', ' bottle', ' of', ' milk', ' to']\n", - "Tokenized answer: [' Mary']\n" - ] - }, - { - "data": { - "text/html": [ - "
Performance on answer token:\n",
-       "Rank: 0        Logit: 18.09 Prob: 70.07% Token: | Mary|\n",
-       "
\n" - ], - "text/plain": [ - "Performance on answer token:\n", - "\u001b[1mRank: \u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m Logit: \u001b[0m\u001b[1;36m18.09\u001b[0m\u001b[1m Prob: \u001b[0m\u001b[1;36m70.07\u001b[0m\u001b[1m% Token: | Mary|\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Top 0th token. Logit: 18.09 Prob: 70.07% Token: | Mary|\n", - "Top 1th token. Logit: 15.38 Prob: 4.67% Token: | the|\n", - "Top 2th token. Logit: 15.35 Prob: 4.54% Token: | John|\n", - "Top 3th token. Logit: 15.25 Prob: 4.11% Token: | them|\n", - "Top 4th token. Logit: 14.84 Prob: 2.73% Token: | his|\n", - "Top 5th token. Logit: 14.06 Prob: 1.24% Token: | her|\n", - "Top 6th token. Logit: 13.54 Prob: 0.74% Token: | a|\n", - "Top 7th token. Logit: 13.52 Prob: 0.73% Token: | their|\n", - "Top 8th token. Logit: 13.13 Prob: 0.49% Token: | Jesus|\n", - "Top 9th token. Logit: 12.97 Prob: 0.42% Token: | him|\n" - ] - }, - { - "data": { - "text/html": [ - "
Ranks of the answer tokens: [(' Mary', 0)]\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1mRanks of the answer tokens:\u001b[0m \u001b[1m[\u001b[0m\u001b[1m(\u001b[0m\u001b[32m' Mary'\u001b[0m, \u001b[1;36m0\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "example_prompt = \"After John and Mary went to the store, John gave a bottle of milk to\"\n", "example_answer = \" Mary\"\n", @@ -372,25 +297,16 @@ "\n", "We want models that can take in arbitrary text, but models need to have a fixed vocabulary. So the solution is to define a vocabulary of **tokens** and to deterministically break up arbitrary text into tokens. Tokens are, essentially, subwords, and are determined by finding the most frequent substrings - this means that tokens vary a lot in length and frequency! \n", "\n", - "Tokens are a *massive* headache and are one of the most annoying things about reverse engineering language models... Different names will be different numbers of tokens, different prompts will have the relevant tokens at different positions, different prompts will have different total numbers of tokens, etc. Language models often devote significant amounts of parameters in early layers to convert inputs from tokens to a more sensible internal format (and do the reverse in later layers). You really, really want to avoid needing to think about tokenization wherever possible when doing exploratory analysis (though, of course, it's relevant later when trying to flesh out your analysis and make it rigorous!). HookedTransformer comes with several helper methods to deal with tokens: `to_tokens, to_string, to_str_tokens, to_single_token, get_token_position`\n", + "Tokens are a *massive* headache and are one of the most annoying things about reverse engineering language models... Different names will be different numbers of tokens, different prompts will have the relevant tokens at different positions, different prompts will have different total numbers of tokens, etc. Language models often devote significant amounts of parameters in early layers to convert inputs from tokens to a more sensible internal format (and do the reverse in later layers). You really, really want to avoid needing to think about tokenization wherever possible when doing exploratory analysis (though, of course, it's relevant later when trying to flesh out your analysis and make it rigorous!). TransformerBridge comes with several helper methods to deal with tokens: `to_tokens, to_string, to_str_tokens, to_single_token, get_token_position`\n", "\n", "**Exercise:** I recommend using `model.to_str_tokens` to explore how the model tokenizes different strings. In particular, try adding or removing spaces at the start, or changing capitalization - these change tokenization!" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['When John and Mary went to the shops, John gave the bag to', 'When John and Mary went to the shops, Mary gave the bag to', 'When Tom and James went to the park, James gave the ball to', 'When Tom and James went to the park, Tom gave the ball to', 'When Dan and Sid went to the shops, Sid gave an apple to', 'When Dan and Sid went to the shops, Dan gave an apple to', 'After Martin and Amy went to the park, Amy gave a drink to', 'After Martin and Amy went to the park, Martin gave a drink to']\n", - "[(' Mary', ' John'), (' John', ' Mary'), (' Tom', ' James'), (' James', ' Tom'), (' Dan', ' Sid'), (' Sid', ' Dan'), (' Martin', ' Amy'), (' Amy', ' Martin')]\n" - ] - } - ], + "outputs": [], "source": [ "prompt_format = [\n", " \"When John and Mary went to the shops,{} gave the bag to\",\n", @@ -440,32 +356,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prompt length: 15\n", - "Prompt as tokens: ['<|endoftext|>', 'When', ' John', ' and', ' Mary', ' went', ' to', ' the', ' shops', ',', ' John', ' gave', ' the', ' bag', ' to']\n", - "Prompt length: 15\n", - "Prompt as tokens: ['<|endoftext|>', 'When', ' John', ' and', ' Mary', ' went', ' to', ' the', ' shops', ',', ' Mary', ' gave', ' the', ' bag', ' to']\n", - "Prompt length: 15\n", - "Prompt as tokens: ['<|endoftext|>', 'When', ' Tom', ' and', ' James', ' went', ' to', ' the', ' park', ',', ' James', ' gave', ' the', ' ball', ' to']\n", - "Prompt length: 15\n", - "Prompt as tokens: ['<|endoftext|>', 'When', ' Tom', ' and', ' James', ' went', ' to', ' the', ' park', ',', ' Tom', ' gave', ' the', ' ball', ' to']\n", - "Prompt length: 15\n", - "Prompt as tokens: ['<|endoftext|>', 'When', ' Dan', ' and', ' Sid', ' went', ' to', ' the', ' shops', ',', ' Sid', ' gave', ' an', ' apple', ' to']\n", - "Prompt length: 15\n", - "Prompt as tokens: ['<|endoftext|>', 'When', ' Dan', ' and', ' Sid', ' went', ' to', ' the', ' shops', ',', ' Dan', ' gave', ' an', ' apple', ' to']\n", - "Prompt length: 15\n", - "Prompt as tokens: ['<|endoftext|>', 'After', ' Martin', ' and', ' Amy', ' went', ' to', ' the', ' park', ',', ' Amy', ' gave', ' a', ' drink', ' to']\n", - "Prompt length: 15\n", - "Prompt as tokens: ['<|endoftext|>', 'After', ' Martin', ' and', ' Amy', ' went', ' to', ' the', ' park', ',', ' Martin', ' gave', ' a', ' drink', ' to']\n" - ] - } - ], + "outputs": [], "source": [ "for prompt in prompts:\n", " str_tokens = model.to_str_tokens(prompt)\n", @@ -482,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -501,18 +394,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Per prompt logit difference: tensor([3.3370, 3.2020, 2.7090, 3.7970, 1.7200, 5.2810, 2.6010, 5.7670])\n", - "Average logit difference: 3.552\n" - ] - } - ], + "outputs": [], "source": [ "def logits_to_ave_logit_diff(logits, answer_tokens, per_prompt=False):\n", " # Only the final logits are relevant for the answer\n", @@ -638,25 +522,22 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Answer residual directions shape: torch.Size([8, 2, 768])\n", - "Logit difference directions shape: torch.Size([8, 768])\n" - ] - } - ], + "outputs": [], "source": [ - "answer_residual_directions = model.tokens_to_residual_directions(answer_tokens)\n", + "# TransformerBridge doesn't have tokens_to_residual_directions yet,\n", + "# so we implement it inline using model.unembed.W_U\n", + "W_U = model.unembed.W_U # [d_model, d_vocab]\n", + "answer_residual_directions = W_U[:, answer_tokens]\n", + "answer_residual_directions = einops.rearrange(\n", + " answer_residual_directions, \"d_model batch correct_incorrect -> batch correct_incorrect d_model\"\n", + ")\n", "print(\"Answer residual directions shape:\", answer_residual_directions.shape)\n", "logit_diff_directions = (\n", " answer_residual_directions[:, 0] - answer_residual_directions[:, 1]\n", ")\n", - "print(\"Logit difference directions shape:\", logit_diff_directions.shape)" + "print(\"Logit difference directions shape:\", logit_diff_directions.shape)\n" ] }, { @@ -682,19 +563,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final residual stream shape: torch.Size([8, 15, 768])\n", - "Calculated average logit diff: 3.552\n", - "Original logit difference: 3.552\n" - ] - } - ], + "outputs": [], "source": [ "# cache syntax - resid_post is the residual stream at the end of the layer, -1 gets the final layer. The general syntax is [activation_name, layer_index, sub_layer_type].\n", "final_residual_stream = cache[\"resid_post\", -1]\n", @@ -731,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -769,966 +640,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "%{hovertext}

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"autorange": "reversed", - "constrain": "domain", - "domain": [ - 0, - 1 - ], - "title": { - "text": "Layer" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "per_head_residual, labels = cache.stack_head_results(\n", " layer=-1, pos_slice=-1, return_labels=True\n", @@ -3893,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3908,6 +769,11 @@ " if isinstance(heads, int):\n", " heads = [heads]\n", "\n", + " # Handle empty head list\n", + " if len(heads) == 0:\n", + " title_html = f\"

{title}


\"\n", + " return f\"
{title_html}

No heads in this group

\"\n", + "\n", " # Create the plotting data\n", " labels: List[str] = []\n", " patterns: List[Float[torch.Tensor, \"dest_pos src_pos\"]] = []\n", @@ -3954,39 +820,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

Top 3 Positive Logit Attribution Heads


\n", - "

Top 3 Negative Logit Attribution Heads


\n", - "
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "top_k = 3\n", "\n", @@ -4098,18 +934,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Corrupted Average Logit Diff -3.55\n", - "Clean Average Logit Diff 3.55\n" - ] - } - ], + "outputs": [], "source": [ "corrupted_prompts = []\n", "for i in range(0, len(prompts), 2):\n", @@ -4126,27 +953,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['<|endoftext|>When John and Mary went to the shops, Mary gave the bag to',\n", - " '<|endoftext|>When John and Mary went to the shops, John gave the bag to',\n", - " '<|endoftext|>When Tom and James went to the park, Tom gave the ball to',\n", - " '<|endoftext|>When Tom and James went to the park, James gave the ball to',\n", - " '<|endoftext|>When Dan and Sid went to the shops, Dan gave an apple to',\n", - " '<|endoftext|>When Dan and Sid went to the shops, Sid gave an apple to',\n", - " '<|endoftext|>After Martin and Amy went to the park, Martin gave a drink to',\n", - " '<|endoftext|>After Martin and Amy went to the park, Amy gave a drink to']" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.to_string(corrupted_tokens)" ] @@ -4163,7 +972,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4173,7 +982,9 @@ " pos,\n", " clean_cache,\n", "):\n", - " corrupted_residual_component[:, pos, :] = clean_cache[hook.name][:, pos, :]\n", + " clean_value = clean_cache[hook.name][:, pos, :].clone()\n", + " corrupted_residual_component = corrupted_residual_component.clone()\n", + " corrupted_residual_component[:, pos : pos + 1, :] = clean_value.unsqueeze(1)\n", " return corrupted_residual_component\n", "\n", "\n", @@ -4217,1148 +1028,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "coloraxis": "coloraxis", - "hovertemplate": "Position: %{x}
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-0.5, - 0.5 - ], - "title": { - "text": "Output Patch" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "head_labels = [\n", " f\"L{l}H{h}\" for l in range(model.cfg.n_layers) for h in range(model.cfg.n_heads)\n", @@ -11619,7 +1329,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -11629,9 +1339,11 @@ " head_index,\n", " clean_cache,\n", "):\n", - " corrupted_head_pattern[:, head_index, :, :] = clean_cache[hook.name][\n", + " clean_value = clean_cache[hook.name][\n", " :, head_index, :, :\n", - " ]\n", + " ].clone()\n", + " corrupted_head_pattern = corrupted_head_pattern.clone()\n", + " corrupted_head_pattern[:, head_index : head_index + 1, :, :] = clean_value.unsqueeze(1)\n", " return corrupted_head_pattern\n", "\n", "\n", @@ -11655,2405 +1367,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - 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"outputs": [ - { - "data": { - "text/html": [ - "

Top Early Heads


\n", - "

Top Middle Heads


\n", - "

Top Late Heads


\n", - "
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "top_k = 10\n", "top_heads_by_output_patch = torch.topk(\n", @@ -14239,7 +1517,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -14254,31 +1532,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

Induction Heads


\n", - "
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} - ], + "outputs": [], "source": [ "imshow(\n", " prev_token_scores, labels={\"x\": \"Head\", \"y\": \"Layer\"}, title=\"Previous Token Scores\"\n", @@ -17763,21 +1747,9 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Top Name Mover to ablate: L9H9\n", - "Original logit diff: 3.55\n", - "Post ablation logit diff: 2.92\n", - "Direct Logit Attribution of top name mover head: 2.99\n", - "Naive prediction of post ablation logit diff: 0.57\n" - ] - } - ], + "outputs": [], "source": [ "top_name_mover = per_head_logit_diffs.flatten().argmax().item()\n", "top_name_mover_layer = top_name_mover // model.cfg.n_heads\n", @@ -17786,6 +1758,7 @@ "\n", "\n", "def ablate_top_head_hook(z: Float[torch.Tensor, \"batch pos head_index d_head\"], hook):\n", + " z = z.clone()\n", " z[:, -1, top_name_mover_head, :] = 0\n", " return z\n", "\n", @@ -17818,2420 +1791,9 @@ }, { "cell_type": "code", - 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"automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Original vs Post-Ablation Direct Logit Attribution of Heads" - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "range": [ - -3, - 3 - ], - "title": { - "text": "Ablated" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "range": [ - -3, - 3 - ], - "title": { - "text": "Original" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "per_head_ablated_residual, labels = ablated_cache.stack_head_results(\n", " layer=-1, pos_slice=-1, return_labels=True\n", @@ -20265,33 +1827,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average LN scaling ratio: 1.042\n", - "Ablation LN scale tensor([[18.5200],\n", - " [17.4700],\n", - " [17.8200],\n", - " [17.5100],\n", - " [17.2600],\n", - " [18.2500],\n", - " [16.1800],\n", - " [17.4300]])\n", - "Original LN scale tensor([[19.5700],\n", - " [18.3500],\n", - " [18.2900],\n", - " [18.6800],\n", - " [17.4900],\n", - " [18.8700],\n", - " [16.4200],\n", - " [18.6800]])\n" - ] - } - ], + "outputs": [], "source": [ "print(\n", " \"Average LN scaling ratio:\",\n", @@ -20326,7 +1864,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "transformer-lens", "language": "python", "name": "python3" }, @@ -20340,14 +1878,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" - }, - "vscode": { - "interpreter": { - "hash": "eb812820b5094695c8a581672e17220e30dd2c15d704c018326e3cc2e1a566f1" - } + "version": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/transformer_lens/ActivationCache.py b/transformer_lens/ActivationCache.py index 55cdbf34a..cf93fb8ec 100644 --- a/transformer_lens/ActivationCache.py +++ b/transformer_lens/ActivationCache.py @@ -677,9 +677,25 @@ def compute_head_results( Intended use is to enable use_attn_results when running and caching the model, but this can be useful if you forget. """ - if "blocks.0.attn.hook_result" in self.cache_dict: - logging.warning("Tried to compute head results when they were already cached") - return + # If valid 4D per-head results already exist (from forward pass with + # use_attn_result=True, or from a prior compute_head_results() call), + # return early to preserve idempotency. + # + # TransformerBridge may populate hook_result with a 3D combined-output + # tensor (from the hook_result → hook_out alias). We detect these + # wrong-shape entries by checking ndim and remove them before + # recomputing the correct 4D per-head results from z and W_O. + first_key = "blocks.0.attn.hook_result" + if first_key in self.cache_dict: + val = self.cache_dict[first_key] + if isinstance(val, torch.Tensor) and val.ndim >= 4: + logging.warning("Tried to compute head results when they were already cached") + return + # Stale 3D entries exist — remove them before recomputing + for layer in range(self.model.cfg.n_layers): + key = f"blocks.{layer}.attn.hook_result" + if key in self.cache_dict: + del self.cache_dict[key] for layer in range(self.model.cfg.n_layers): # Note that we haven't enabled set item on this object so we need to edit the underlying # cache_dict directly. @@ -734,11 +750,11 @@ def stack_head_results( # Default to the residual stream immediately pre unembed layer = self.model.cfg.n_layers - if "blocks.0.attn.hook_result" not in self.cache_dict: - print( - "Tried to stack head results when they weren't cached. Computing head results now" - ) - self.compute_head_results() + # Always call compute_head_results() – it handles idempotency + # (returns early for valid 4D data) and also cleans up any stale 3D + # entries that TransformerBridge's hook_result alias may have placed + # in the cache. + self.compute_head_results() components: Any = [] labels = [] diff --git a/transformer_lens/model_bridge/bridge.py b/transformer_lens/model_bridge/bridge.py index 6e738bcd1..6d7337bd8 100644 --- a/transformer_lens/model_bridge/bridge.py +++ b/transformer_lens/model_bridge/bridge.py @@ -206,8 +206,6 @@ def _register_aliases(self) -> None: for part in single_target.split("."): target_obj = getattr(target_obj, part) object.__setattr__(self, alias_name, target_obj) - if isinstance(target_obj, HookPoint): - target_obj.name = alias_name break except AttributeError: continue @@ -216,8 +214,6 @@ def _register_aliases(self) -> None: for part in target_path.split("."): target_obj = getattr(target_obj, part) object.__setattr__(self, alias_name, target_obj) - if isinstance(target_obj, HookPoint): - target_obj.name = alias_name except AttributeError: pass @@ -405,6 +401,11 @@ def _add_aliases_to_hooks(self, hooks: Dict[str, HookPoint]) -> None: def _scan_existing_hooks(self, module: nn.Module, prefix: str = "") -> None: """Scan existing modules for hooks and add them to registry.""" visited = set() + # Track which HookPoint objects have already been named so that + # alias entries (from get_hooks() in compatibility mode) do not + # overwrite the canonical name. get_hooks() returns canonical + # entries first, so the first name assigned is always canonical. + named_hook_ids: set = set() def scan_module(mod: nn.Module, path: str = "") -> None: obj_id = id(mod) @@ -417,7 +418,10 @@ def scan_module(mod: nn.Module, path: str = "") -> None: 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.name = full_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("_"): @@ -448,7 +452,10 @@ def scan_module(mod: nn.Module, path: str = "") -> None: continue name = f"{path}.{attr_name}" if path else attr_name if isinstance(attr, HookPoint): - attr.name = name + 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 (