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wip b4 new intervention

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wassname
2023-12-16 12:06:35 +08:00
parent 0d6bd5dd03
commit 9d176e3905
7 changed files with 464 additions and 347 deletions
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mjc_notes.md
+11
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@@ -2100,6 +2100,8 @@ TODO:
- [ ] maybe I can use my intervention as an importance matrix for the VAE loss?
- [ ] maybe I can do that codebook VAE? How big?
Wait I'm calibrating on the positive vs negative.... so yes my intervention will flip it. But it's not on truth or lie or deception!
OK I can't load all my hs into mem, that's not ideal....
and I tried the mean diff intervention since eluther like it, but the truthfull llama one seems broken
@@ -2183,7 +2185,14 @@ git fetch upstream
git rebase upstream/main
git push --force-with-lease
```
ok I wasn't even applying the activation right? I was adding, mean to multiply...
Definitly time to simplify
plan
- So delete rep_readers
- intervention_meta_fn2
- activations
- src.datasets.intervene create_cache_interventions
# Phi-2
@@ -2196,3 +2205,5 @@ maybe for padding use 50256? Rather than 0?
"torch_dtype": "float16",
"transformers_version": "4.37.0.dev0",
notebooks/104_check_intervention.ipynb
+441 -52
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@@ -120,78 +120,99 @@
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ExtractConfig(datasets=('amazon_polarity', 'super_glue:boolq', 'glue:qnli', 'imdb'), model='wassname/phi-2-GPTQ_w_hidden_states', batch_size=5, pad_token_id=50256, prompt_format='phi', data_dirs=(), max_examples=(400, 400), num_shots=2, num_variants=-1, seed=42, template_path=None, max_length=1000, disable_ds_cache=False, intervention_direction_method='cluster_mean', intervention_fit_examples=160, intervention_layer_name_template='transformer.h.{}')\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2023-12-16 10:14:43.517\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m24\u001b[0m - \u001b[1mchanging use_cache from True to False\u001b[0m\n",
"2023-12-16T10:14:43.517976+0800 INFO changing use_cache from True to False\n",
"\u001b[32m2023-12-16 11:51:16.540\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m24\u001b[0m - \u001b[1mchanging use_cache from True to False\u001b[0m\n",
"2023-12-16T11:51:16.540903+0800 INFO changing use_cache from True to False\n",
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
"\u001b[32m2023-12-16 10:14:43.853\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m24\u001b[0m - \u001b[1mchanging pad_token_id from None to 50256\u001b[0m\n",
"2023-12-16T10:14:43.853595+0800 INFO changing pad_token_id from None to 50256\n",
"\u001b[32m2023-12-16 10:14:43.854\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m24\u001b[0m - \u001b[1mchanging padding_side from right to left\u001b[0m\n",
"2023-12-16T10:14:43.854275+0800 INFO changing padding_side from right to left\n",
"\u001b[32m2023-12-16 10:14:43.854\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m24\u001b[0m - \u001b[1mchanging truncation_side from right to left\u001b[0m\n",
"2023-12-16T10:14:43.854771+0800 INFO changing truncation_side from right to left\n",
"Generating train split: 0 examples [00:00, ? examples/s]"
"\u001b[32m2023-12-16 11:51:16.902\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m24\u001b[0m - \u001b[1mchanging pad_token_id from None to 50256\u001b[0m\n",
"2023-12-16T11:51:16.902047+0800 INFO changing pad_token_id from None to 50256\n",
"\u001b[32m2023-12-16 11:51:16.902\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m24\u001b[0m - \u001b[1mchanging padding_side from right to left\u001b[0m\n",
"2023-12-16T11:51:16.902868+0800 INFO changing padding_side from right to left\n",
"\u001b[32m2023-12-16 11:51:16.903\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m24\u001b[0m - \u001b[1mchanging truncation_side from right to left\u001b[0m\n",
"2023-12-16T11:51:16.903473+0800 INFO changing truncation_side from right to left\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting 11 variants of each prompt\n"
"PhiForCausalLM(\n",
" (transformer): PhiModel(\n",
" (embd): Embedding(\n",
" (wte): Embedding(51200, 2560)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (h): ModuleList(\n",
" (0-31): 32 x ParallelBlock(\n",
" (ln): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" (mixer): MHA(\n",
" (rotary_emb): RotaryEmbedding()\n",
" (inner_attn): SelfAttention(\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (inner_cross_attn): CrossAttention(\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (Wqkv): QuantLinear()\n",
" (out_proj): QuantLinear()\n",
" )\n",
" (mlp): MLP(\n",
" (act): NewGELUActivation()\n",
" (fc1): QuantLinear()\n",
" (fc2): QuantLinear()\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (lm_head): CausalLMHead(\n",
" (ln): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
" (linear): Linear(in_features=2560, out_features=51200, bias=True)\n",
" )\n",
" (loss): CausalLMLoss(\n",
" (loss_fct): CrossEntropyLoss()\n",
" )\n",
")\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Generating train split: 242 examples [00:40, 5.91 examples/s]\n",
"format_prompt: 100%|██████████| 242/242 [00:00<00:00, 7027.58 examples/s]\n",
"tokenize: 100%|██████████| 242/242 [00:00<00:00, 1284.35 examples/s]\n",
"truncated: 100%|██████████| 242/242 [00:00<00:00, 2526.02 examples/s]\n",
"truncated: 100%|██████████| 242/242 [00:00<00:00, 2476.92 examples/s]\n",
"prompt_truncated: 100%|██████████| 242/242 [00:00<00:00, 307.85 examples/s]\n",
"choice_ids: 100%|██████████| 242/242 [00:00<00:00, 6967.33 examples/s]\n",
"\u001b[32m2023-12-16 10:15:28.371\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.prompts.prompt_loading\u001b[0m:\u001b[36mload_preproc_dataset\u001b[0m:\u001b[36m368\u001b[0m - \u001b[1mtruncation rate: 0.0 on amazon_polarity\u001b[0m\n",
"2023-12-16T10:15:28.371476+0800 INFO truncation rate: 0.0 on amazon_polarity\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"median token length: 440.0 for amazon_polarity. max_length=1000\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Filter: 100%|██████████| 242/242 [00:00<00:00, 2223.43 examples/s]\n",
"Filter: 100%|██████████| 242/242 [00:00<00:00, 2118.60 examples/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"num_rows (after filtering out truncated rows) 242=>242\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
"Generating train split: 0 examples [00:00, ? examples/s]\u001b[32m2023-12-16 11:51:22.526\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.prompts.prompt_loading\u001b[0m:\u001b[36mload_prompts\u001b[0m:\u001b[36m120\u001b[0m - \u001b[1mExtracting 11 variants of each prompt\u001b[0m\n",
"2023-12-16T11:51:22.526803+0800 INFO Extracting 11 variants of each prompt\n",
"Generating train split: 2402 examples [05:52, 6.82 examples/s]\n",
"format_prompt: 100%|██████████| 2402/2402 [00:00<00:00, 7939.78 examples/s]\n",
"tokenize: 100%|██████████| 2402/2402 [00:01<00:00, 1312.50 examples/s]\n",
"truncated: 100%|██████████| 2402/2402 [00:00<00:00, 2617.70 examples/s]\n",
"truncated: 100%|██████████| 2402/2402 [00:00<00:00, 2537.01 examples/s]\n",
"prompt_truncated: 100%|██████████| 2402/2402 [00:07<00:00, 322.85 examples/s]\n",
"choice_ids: 100%|██████████| 2402/2402 [00:00<00:00, 4870.83 examples/s]\n",
"\u001b[32m2023-12-16 11:57:23.152\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.prompts.prompt_loading\u001b[0m:\u001b[36mload_preproc_dataset\u001b[0m:\u001b[36m364\u001b[0m - \u001b[1mmedian token length: 433.0 for amazon_polarity. max_length=1000\u001b[0m\n",
"2023-12-16T11:57:23.152948+0800 INFO median token length: 433.0 for amazon_polarity. max_length=1000\n",
"\u001b[32m2023-12-16 11:57:23.154\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.prompts.prompt_loading\u001b[0m:\u001b[36mload_preproc_dataset\u001b[0m:\u001b[36m368\u001b[0m - \u001b[1mtruncation rate: 0.00% on amazon_polarity\u001b[0m\n",
"2023-12-16T11:57:23.154377+0800 INFO truncation rate: 0.00% on amazon_polarity\n",
"Filter: 100%|██████████| 2402/2402 [00:01<00:00, 2283.72 examples/s]\n",
"Filter: 100%|██████████| 2402/2402 [00:01<00:00, 2201.41 examples/s]\n",
"\u001b[32m2023-12-16 11:57:25.313\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.prompts.prompt_loading\u001b[0m:\u001b[36mload_preproc_dataset\u001b[0m:\u001b[36m377\u001b[0m - \u001b[1mnum_rows (after filtering out truncated rows) 2402=>2402\u001b[0m\n",
"2023-12-16T11:57:25.313993+0800 INFO num_rows (after filtering out truncated rows) 2402=>2402\n"
]
}
],
"source": [
"ds_name='amazon_polarity'\n",
"cfg = ExtractConfig(max_examples=(40, 40),\n",
" intervention_fit_examples=10,\n",
"cfg = ExtractConfig(max_examples=(400, 400),\n",
" intervention_fit_examples=160,\n",
" )\n",
"print(cfg)\n",
"batch_size = cfg.batch_size\n",
@@ -211,20 +232,308 @@
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Intervention"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2023-12-16 11:57:25.647\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.datasets.intervene\u001b[0m:\u001b[36mcreate_cache_interventions\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mLoaded interventions from /media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/data/interventions/wassname-phi-2-GPTQ_w_hidden_states_+_cluster_mean_481.pkl\u001b[0m\n",
"2023-12-16T11:57:25.647973+0800 INFO Loaded interventions from /media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/data/interventions/wassname-phi-2-GPTQ_w_hidden_states_+_cluster_mean_481.pkl\n"
]
},
{
"data": {
"text/plain": [
"<src.repe.rep_readers.ClusterMeanRepReader at 0x7fdcf2279810>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"honesty_rep_reader = create_cache_interventions(model, tokenizer, cfg)\n",
"honesty_rep_reader\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<src.repe.rep_readers.ClusterMeanRepReader at 0x7fdcf2279810>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"honesty_rep_reader\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.1623942 , -0.05915324, 0.08666992, ..., 0.04361978,\n",
" 0.13239542, 0.11653644]], dtype=float32)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"layer = 6\n",
"honesty_rep_reader.directions[layer]\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{6: [-1],\n",
" 7: [-1],\n",
" 8: [-1],\n",
" 9: [-1],\n",
" 10: [-1],\n",
" 11: [-1],\n",
" 12: [-1],\n",
" 13: [-1],\n",
" 14: [-1],\n",
" 15: [-1],\n",
" 16: [-1],\n",
" 17: [-1],\n",
" 18: [-1],\n",
" 19: [-1],\n",
" 20: [-1],\n",
" 21: [-1],\n",
" 22: [-1],\n",
" 23: [-1],\n",
" 24: [-1],\n",
" 25: [-1],\n",
" 26: [-1],\n",
" 27: [-1],\n",
" 28: [-1],\n",
" 29: [-1],\n",
" 30: [-1],\n",
" 31: [-1]}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"honesty_rep_reader.direction_signs\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate answers\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"rep-reading is already registered. Overwriting pipeline for task rep-reading...\n",
"rep-control2 is already registered. Overwriting pipeline for task rep-control2...\n",
"\u001b[32m2023-12-16 11:57:25.760\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36msrc.datasets.intervene\u001b[0m:\u001b[36mcreate_cache_interventions\u001b[0m:\u001b[36m138\u001b[0m - \u001b[1mLoaded interventions from /media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/data/interventions/wassname-phi-2-GPTQ_w_hidden_states_+_cluster_mean_481.pkl\u001b[0m\n",
"2023-12-16T11:57:25.760677+0800 INFO Loaded interventions from /media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/data/interventions/wassname-phi-2-GPTQ_w_hidden_states_+_cluster_mean_481.pkl\n"
]
},
{
"data": {
"text/plain": [
"<src.repe.rep_control_pipeline_baukit.RepControlPipeline2 at 0x7fdbd7545a20>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"from src.repe import repe_pipeline_registry\n",
"from transformers import pipeline\n",
"# from src.datasets.intervene import test_intervention_quality, intervention_metrics\n",
"repe_pipeline_registry()\n",
"\n",
"honesty_rep_reader = create_cache_interventions(model, tokenizer, cfg)\n",
"hidden_layers = sorted(honesty_rep_reader.directions.keys())\n",
"coeff=1.\n",
"\n",
"activations = {}\n",
"for layer in hidden_layers:\n",
" activations[layer] = torch.tensor(coeff * honesty_rep_reader.directions[layer] * honesty_rep_reader.direction_signs[layer]).to(model.device).half()\n",
"assert torch.isfinite(torch.concat(list(activations.values()))).all()\n",
"\n",
"activations_neg_i = {k:-v for k,v in activations.items()}\n",
"activations_neut = {k:v*0 for k,v in activations.items()}\n",
"\n",
"rep_control_pipeline2 = pipeline(\n",
" \"rep-control2\", \n",
" model=model, \n",
" tokenizer=tokenizer, \n",
" layers=hidden_layers, \n",
" max_length=cfg.max_length, layer_name_tmpl=cfg.intervention_layer_name_template)\n",
"rep_control_pipeline2\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def top_toke_probs(o, N=20):\n",
" data = {}\n",
" for i in range(o['end_logits'].shape[1]):\n",
" probs = torch.softmax(o['end_logits'][:, i], -1)\n",
" top = probs.argsort(0, descending=True)\n",
" top_probs = probs[top]\n",
" tokens_top20 = tokenizer.batch_decode(top[:N], skip_special_tokens=False , clean_up_tokenization_spaces=False)\n",
" tokens_top20 = [f\"`{t}`\" for t in tokens_top20] \n",
" data.update({f'prob_{i}':top_probs[:N], f'tokens_{i}':tokens_top20, f'id_{i}':top[:N]})\n",
" return pd.DataFrame(data)\n",
"\n",
"from IPython.display import display, HTML\n",
"\n",
"def print_pipeline_r(o):\n",
" choices = [tokenizer.batch_decode(cc) for cc in o['choice_ids']]\n",
" index = [o[0] for o in choices]\n",
" d = pd.DataFrame(o['choice_probs'].numpy(), columns=['edit=None', 'edit=+'], index=index)\n",
" print('choice probs')\n",
" display(d)\n",
"\n",
" d1 = top_toke_probs(o)\n",
" print('top token probs')\n",
" display(d1)\n",
" top1 = o['end_logits'][:, 0].argsort(0, descending=True)[:10]\n",
" top2 = o['end_logits'][:, 1].argsort(0, descending=True)[:10]\n",
"\n",
" max_prob1 = torch.softmax(o['end_logits'][:, 0], -1).max()\n",
" max_prob2 = torch.softmax(o['end_logits'][:, 1], -1).max()\n",
" print(top1)\n",
" print(top2)\n",
" print('top choices no intervention', tokenizer.batch_decode(top1, skip_special_tokens=False , clean_up_tokenization_spaces=False))\n",
" print('top choices pos intervention', tokenizer.batch_decode(top2))\n",
" \n",
" mean_prob = o['choice_probs'].sum(0)\n",
" print(f\"\\tchoice_cov=\\t{mean_prob[0]:2.2%} max_prob={max_prob1} (no edit) - Our choices accounted for a mean probability of this\")\n",
" print(f\"\\tchoice_cov=\\t{mean_prob[1]:2.2%} max_prob={max_prob2} (+ edit) - Our choices accounted for a mean probability of this\")\n",
" \n",
" print('choices', choices)\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "1D tensors expected, but got 3D and 1D tensors",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/notebooks/104_check_intervention.ipynb Cell 18\u001b[0m line \u001b[0;36m5\n\u001b[1;32m <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/notebooks/104_check_intervention.ipynb#Y102sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m ds \u001b[39m=\u001b[39m ds_tokens\u001b[39m.\u001b[39mselect(\u001b[39mrange\u001b[39m(\u001b[39m3\u001b[39m))\u001b[39m.\u001b[39mto_iterable_dataset()\n\u001b[1;32m <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/notebooks/104_check_intervention.ipynb#Y102sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m r1 \u001b[39m=\u001b[39m rep_control_pipeline2(model_inputs\u001b[39m=\u001b[39mds,\n\u001b[1;32m <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/notebooks/104_check_intervention.ipynb#Y102sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m activations\u001b[39m=\u001b[39mactivations_neg_i,\n\u001b[1;32m <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/notebooks/104_check_intervention.ipynb#Y102sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m batch_size\u001b[39m=\u001b[39mbatch_size,)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/notebooks/104_check_intervention.ipynb#Y102sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m r \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39;49m(r1)\n\u001b[1;32m <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/notebooks/104_check_intervention.ipynb#Y102sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m o \u001b[39m=\u001b[39m r[\u001b[39m0\u001b[39m]\n\u001b[1;32m <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/notebooks/104_check_intervention.ipynb#Y102sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m print_pipeline_r(o)\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/.venv/lib/python3.10/site-packages/transformers/pipelines/pt_utils.py:124\u001b[0m, in \u001b[0;36mPipelineIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloader_batch_item()\n\u001b[1;32m 123\u001b[0m \u001b[39m# We're out of items within a batch\u001b[39;00m\n\u001b[0;32m--> 124\u001b[0m item \u001b[39m=\u001b[39m \u001b[39mnext\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49miterator)\n\u001b[1;32m 125\u001b[0m processed \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minfer(item, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mparams)\n\u001b[1;32m 126\u001b[0m \u001b[39m# We now have a batch of \"inferred things\".\u001b[39;00m\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/.venv/lib/python3.10/site-packages/transformers/pipelines/pt_utils.py:125\u001b[0m, in \u001b[0;36mPipelineIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[39m# We're out of items within a batch\u001b[39;00m\n\u001b[1;32m 124\u001b[0m item \u001b[39m=\u001b[39m \u001b[39mnext\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39miterator)\n\u001b[0;32m--> 125\u001b[0m processed \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minfer(item, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mparams)\n\u001b[1;32m 126\u001b[0m \u001b[39m# We now have a batch of \"inferred things\".\u001b[39;00m\n\u001b[1;32m 127\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloader_batch_size \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 128\u001b[0m \u001b[39m# Try to infer the size of the batch\u001b[39;00m\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/.venv/lib/python3.10/site-packages/transformers/pipelines/base.py:1046\u001b[0m, in \u001b[0;36mPipeline.forward\u001b[0;34m(self, model_inputs, **forward_params)\u001b[0m\n\u001b[1;32m 1044\u001b[0m \u001b[39mwith\u001b[39;00m inference_context():\n\u001b[1;32m 1045\u001b[0m model_inputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ensure_tensor_on_device(model_inputs, device\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdevice)\n\u001b[0;32m-> 1046\u001b[0m model_outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_forward(model_inputs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mforward_params)\n\u001b[1;32m 1047\u001b[0m model_outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ensure_tensor_on_device(model_outputs, device\u001b[39m=\u001b[39mtorch\u001b[39m.\u001b[39mdevice(\u001b[39m\"\u001b[39m\u001b[39mcpu\u001b[39m\u001b[39m\"\u001b[39m))\n\u001b[1;32m 1048\u001b[0m \u001b[39melse\u001b[39;00m:\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/src/repe/rep_control_pipeline_baukit.py:124\u001b[0m, in \u001b[0;36mRepControlPipeline2._forward\u001b[0;34m(self, inputs, activations)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[1;32m 121\u001b[0m \u001b[39mwith\u001b[39;00m TraceDict(\n\u001b[1;32m 122\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel, layers_names, detach\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, edit_output\u001b[39m=\u001b[39medit_fn_pos\n\u001b[1;32m 123\u001b[0m ) \u001b[39mas\u001b[39;00m ret:\n\u001b[0;32m--> 124\u001b[0m outputs_pos \u001b[39m=\u001b[39m transform_model_output(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mmodel_in))\n\u001b[1;32m 126\u001b[0m \u001b[39mwith\u001b[39;00m TraceDict(\n\u001b[1;32m 127\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel, layers_names, detach\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, edit_output\u001b[39m=\u001b[39medit_fn_neu\n\u001b[1;32m 128\u001b[0m ) \u001b[39mas\u001b[39;00m ret:\n\u001b[1;32m 129\u001b[0m outputs_neg \u001b[39m=\u001b[39m transform_model_output(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mmodel_in))\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/.venv/lib/python3.10/site-packages/accelerate/hooks.py:165\u001b[0m, in \u001b[0;36madd_hook_to_module.<locals>.new_forward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 163\u001b[0m output \u001b[39m=\u001b[39m old_forward(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m 164\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 165\u001b[0m output \u001b[39m=\u001b[39m old_forward(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 166\u001b[0m \u001b[39mreturn\u001b[39;00m module\u001b[39m.\u001b[39m_hf_hook\u001b[39m.\u001b[39mpost_forward(module, output)\n",
"File \u001b[0;32m~/.cache/huggingface/modules/transformers_modules/wassname/phi-2-GPTQ_w_hidden_states/e39f09ffdefdb011e32b1cad1175c952a5a0f88d/modeling_phi.py:957\u001b[0m, in \u001b[0;36mPhiForCausalLM.forward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, labels, **kwargs)\u001b[0m\n\u001b[1;32m 949\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\n\u001b[1;32m 950\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[1;32m 951\u001b[0m input_ids: torch\u001b[39m.\u001b[39mLongTensor,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 955\u001b[0m \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m 956\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m CausalLMOutputWithPast:\n\u001b[0;32m--> 957\u001b[0m outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtransformer(input_ids, past_key_values\u001b[39m=\u001b[39;49mpast_key_values, attention_mask\u001b[39m=\u001b[39;49mattention_mask)\n\u001b[1;32m 958\u001b[0m lm_logits \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlm_head(outputs\u001b[39m.\u001b[39mlast_hidden_state)\n\u001b[1;32m 960\u001b[0m loss \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
"File \u001b[0;32m~/.cache/huggingface/modules/transformers_modules/wassname/phi-2-GPTQ_w_hidden_states/e39f09ffdefdb011e32b1cad1175c952a5a0f88d/modeling_phi.py:917\u001b[0m, in \u001b[0;36mPhiModel.forward\u001b[0;34m(self, input_ids, past_key_values, attention_mask)\u001b[0m\n\u001b[1;32m 915\u001b[0m all_hidden_states \u001b[39m=\u001b[39m [hidden_states]\n\u001b[1;32m 916\u001b[0m \u001b[39mfor\u001b[39;00m layer \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mh:\n\u001b[0;32m--> 917\u001b[0m hidden_states, attn_outputs \u001b[39m=\u001b[39m layer_outputs \u001b[39m=\u001b[39m layer(\n\u001b[1;32m 918\u001b[0m hidden_states,\n\u001b[1;32m 919\u001b[0m past_key_values\u001b[39m=\u001b[39;49mpast_key_values,\n\u001b[1;32m 920\u001b[0m attention_mask\u001b[39m=\u001b[39;49mattention_mask,\n\u001b[1;32m 921\u001b[0m )\n\u001b[1;32m 922\u001b[0m all_hidden_states\u001b[39m.\u001b[39mappend(hidden_states)\n\u001b[1;32m 923\u001b[0m all_self_attns\u001b[39m.\u001b[39mappend(attn_outputs)\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1581\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1579\u001b[0m hook_result \u001b[39m=\u001b[39m hook(\u001b[39mself\u001b[39m, args, kwargs, result)\n\u001b[1;32m 1580\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1581\u001b[0m hook_result \u001b[39m=\u001b[39m hook(\u001b[39mself\u001b[39;49m, args, result)\n\u001b[1;32m 1583\u001b[0m \u001b[39mif\u001b[39;00m hook_result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 1584\u001b[0m result \u001b[39m=\u001b[39m hook_result\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/.venv/lib/python3.10/site-packages/baukit/nethook.py:73\u001b[0m, in \u001b[0;36mTrace.__init__.<locals>.retain_hook\u001b[0;34m(m, inputs, output)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mretain_hook\u001b[39m(m, inputs, output):\n\u001b[1;32m 72\u001b[0m \u001b[39mif\u001b[39;00m edit_output:\n\u001b[0;32m---> 73\u001b[0m output \u001b[39m=\u001b[39m invoke_with_optional_args(\n\u001b[1;32m 74\u001b[0m edit_output, output\u001b[39m=\u001b[39;49moutput, layer\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlayer, inputs\u001b[39m=\u001b[39;49minputs\n\u001b[1;32m 75\u001b[0m )\n\u001b[1;32m 76\u001b[0m \u001b[39mif\u001b[39;00m retain_input:\n\u001b[1;32m 77\u001b[0m retainer\u001b[39m.\u001b[39minput \u001b[39m=\u001b[39m recursive_copy(\n\u001b[1;32m 78\u001b[0m inputs[\u001b[39m0\u001b[39m] \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(inputs) \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m \u001b[39melse\u001b[39;00m inputs,\n\u001b[1;32m 79\u001b[0m clone\u001b[39m=\u001b[39mclone,\n\u001b[1;32m 80\u001b[0m detach\u001b[39m=\u001b[39mdetach,\n\u001b[1;32m 81\u001b[0m retain_grad\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m,\n\u001b[1;32m 82\u001b[0m ) \u001b[39m# retain_grad applies to output only.\u001b[39;00m\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/.venv/lib/python3.10/site-packages/baukit/nethook.py:471\u001b[0m, in \u001b[0;36minvoke_with_optional_args\u001b[0;34m(fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 469\u001b[0m \u001b[39mif\u001b[39;00m argspec\u001b[39m.\u001b[39mvarargs \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 470\u001b[0m pass_args \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(args[used_pos:])\n\u001b[0;32m--> 471\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49mpass_args, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mpass_kw)\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/src/datasets/intervene.py:52\u001b[0m, in \u001b[0;36mintervention_meta_fn2\u001b[0;34m(outputs, layer_name, activations)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"see\u001b[39;00m\n\u001b[1;32m 42\u001b[0m \u001b[39m- honest_llama: https://github.com/likenneth/honest_llama/blob/e010f82bfbeaa4326cef8493b0dd5b8b14c6da67/validation/validate_2fold.py#L114\u001b[39;00m\n\u001b[1;32m 43\u001b[0m \u001b[39m- baukit: https://github.com/davidbau/baukit/blob/main/baukit/nethook.py#L42C1-L45C56\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[39m ...\u001b[39;00m\n\u001b[1;32m 49\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 50\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mtype\u001b[39m(outputs) \u001b[39mis\u001b[39;00m \u001b[39mtuple\u001b[39m:\n\u001b[1;32m 51\u001b[0m \u001b[39m# just edit the first one, and put it back in the tuple\u001b[39;00m\n\u001b[0;32m---> 52\u001b[0m output0 \u001b[39m=\u001b[39m intervene(outputs[\u001b[39m0\u001b[39;49m], activations[layer_name])\n\u001b[1;32m 53\u001b[0m \u001b[39mreturn\u001b[39;00m (output0, \u001b[39m*\u001b[39moutputs[\u001b[39m1\u001b[39m:])\n\u001b[1;32m 54\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mtype\u001b[39m(outputs) \u001b[39mis\u001b[39;00m torch\u001b[39m.\u001b[39mTensor:\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/src/datasets/intervene.py:33\u001b[0m, in \u001b[0;36mintervene\u001b[0;34m(output, activation)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[39m# assert torch.isfinite(output).all(), 'model output nan'\u001b[39;00m\n\u001b[1;32m 31\u001b[0m output2 \u001b[39m=\u001b[39m output \u001b[39m+\u001b[39m activation\u001b[39m.\u001b[39mto(output\u001b[39m.\u001b[39mdevice)[\u001b[39mNone\u001b[39;00m, :]\n\u001b[0;32m---> 33\u001b[0m output2 \u001b[39m=\u001b[39m project_onto_direction(output, activation)\n\u001b[1;32m 34\u001b[0m \u001b[39m# assert torch.isfinite(output2).all(), 'intervention lead to nan'\u001b[39;00m\n\u001b[1;32m 35\u001b[0m \u001b[39mreturn\u001b[39;00m output2\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/elk/discovering_latent_knowledge/src/repe/rep_readers.py:15\u001b[0m, in \u001b[0;36mproject_onto_direction\u001b[0;34m(H, direction)\u001b[0m\n\u001b[1;32m 13\u001b[0m mag \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mlinalg\u001b[39m.\u001b[39mnorm(direction)\u001b[39m.\u001b[39mto(H\u001b[39m.\u001b[39mdevice)\n\u001b[1;32m 14\u001b[0m \u001b[39massert\u001b[39;00m torch\u001b[39m.\u001b[39misfinite(mag)\n\u001b[0;32m---> 15\u001b[0m \u001b[39mreturn\u001b[39;00m H\u001b[39m.\u001b[39;49mdot(direction) \u001b[39m/\u001b[39m mag\n",
"\u001b[0;31mRuntimeError\u001b[0m: 1D tensors expected, but got 3D and 1D tensors"
]
}
],
"source": [
"ds = ds_tokens.select(range(3)).to_iterable_dataset()\n",
"r1 = rep_control_pipeline2(model_inputs=ds,\n",
" activations=activations_neg_i,\n",
" batch_size=batch_size,)\n",
"r = list(r1)\n",
"o = r[0]\n",
"print_pipeline_r(o)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"honesty_rep_reader = create_cache_interventions(model, tokenizer, cfg)\n"
"ds = ds_tokens.select(range(3)).to_iterable_dataset()\n",
"r1 = rep_control_pipeline2(model_inputs=ds,\n",
" activations=activations,\n",
" batch_size=batch_size,)\n",
"r = list(r1)\n",
"print_pipeline_r(r[0])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate\n"
"## Scratch choice ids\n"
]
},
{
@@ -232,6 +541,86 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from src.datasets.scores import choice2id\n",
"\n",
"choice2id(tokenizer, 'Positive')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate long form with and without intervention"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get a row\n",
"bi = 20\n",
"inputs = ds_tokens.with_format('torch')[bi]\n",
"\n",
"# tokenize if needed\n",
"if 'input_ids' not in inputs:\n",
" model_inputs = self.tokenizer(inputs['question'], return_tensors=True, return_attention_mask=True, add_special_tokens=True, truncation=True, padding=\"max_length\", max_length=cfg.max_length, **tokenize_kwargs)\n",
" inputs = {**inputs, **model_inputs}\n",
"\n",
"inputs.keys()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# generate\n",
"# https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/text_generation#transformers.GenerationConfig\n",
"# model = exllama_set_max_input_length(model, 4096)\n",
"@torch.no_grad()\n",
"def gen(model):\n",
" s = model.generate(inputs['input_ids'][None, :], attention_mask=inputs['attention_mask'][None, :], use_cache=False, max_new_tokens=20, min_new_tokens=20, do_sample=False, early_stopping=False)\n",
" input_l = inputs['input_ids'].shape[0]\n",
" old = tokenizer.decode(s[0, :input_l], clean_up_tokenization_spaces=False, skip_special_tokens=False)\n",
" new = tokenizer.decode(s[0, input_l:], clean_up_tokenization_spaces=False, skip_special_tokens=False)\n",
" display(HTML(f\"<pre>{old}</pre><b><pre>{new}</pre></b>\"))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gen(model)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from baukit.nethook import Trace, TraceDict, recursive_copy\n",
"from functools import partial\n",
"from src.repe.rep_control_pipeline_baukit import intervention_meta_fn2, Activations\n",
"layers_names = [rep_control_pipeline2.layer_name_tmpl.format(i) for i in activations.keys()] \n",
"activations_pos_i = Activations({rep_control_pipeline2.layer_name_tmpl.format(k):1. * v for k,v in activations.items()})\n",
"edit_fn_pos = partial(intervention_meta_fn2, activations=activations_pos_i)\n",
"with torch.no_grad():\n",
" with TraceDict(\n",
" model, layers_names, detach=True, edit_output=edit_fn_pos\n",
" ) as ret:\n",
" gen(model)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
src/datasets/batch.py
-68
View File
@@ -1,68 +0,0 @@
# from tqdm.auto import tqdm
# import torch
# from torch.utils.data import DataLoader
# from datasets.arrow_dataset import Dataset
# import hashlib
# import pickle
# import numpy as np
# from typing import List, Dict, Any, Union, NewType, Optional
# from src.datasets.hs import ExtractHiddenStates
# from src.helpers.typing import float_to_int16, int16_to_float
# from src.helpers.ds import ds_keep_cols, clear_mem
# from src.datasets.intervene import InterventionDict
# def batch_hidden_states(model, tokenizer, intervention_dicts: Optional[InterventionDict], data: Dataset, batch_size=2, layer_padding=3, layer_stride=4):
# """
# Given an encoder-decoder model, a list of data, computes the contrast hidden states on n random examples.
# Returns numpy arrays of shape (n, hidden_dim) for each candidate label, along with a boolean numpy array of shape (n,)
# with the ground truth labels
# This is deliberately simple so that it's easy to understand, rather than being optimized for efficiency
# """
# ehs = ExtractHiddenStates(model, tokenizer, intervention_dicts=intervention_dicts, layer_stride=layer_stride, layer_padding=layer_padding)
# torch_cols = ['input_ids', 'attention_mask', 'choice_ids']
# ds_t_subset = ds_keep_cols(data, torch_cols)
# ds_t_subset.set_format(type='torch')
# ds_p_subset = data.remove_columns(torch_cols)
# dl = DataLoader(ds_t_subset, batch_size=batch_size, shuffle=False)
# for i, batch in enumerate(tqdm(dl, desc='get hidden states')):
# input_ids, attention_mask, choice_ids = batch["input_ids"], batch["attention_mask"], batch["choice_ids"]
# nn = len(input_ids)
# index = i*batch_size+np.arange(nn)
# # different due to dropout
# hsl = ehs.get_batch_of_hidden_states(input_ids=input_ids, attention_mask=attention_mask, choice_ids=choice_ids)
# for j in range(nn):
# # let's add the non torch metadata like label, prompt, lie, etc
# k = i*batch_size + j
# info = ds_p_subset[k]
# large_arrays_keys = [k for k,v in hsl.items() if isinstance(v, torch.Tensor) and v.ndim>2]
# # TODO deal with multiple lists of hs in hs0
# large_arrays = {k:hsl[k][j] for k in large_arrays_keys}
# yield dict(
# # large_arrays_keys=large_arrays_keys,
# scores0=hsl["scores"][j],
# # layer_names=hsl["layers"][j] if k==0 else [], # just in the first one, to save space
# ds_index=index[j],
# # int16 makes our storage much smaller
# **large_arrays,
# **info
# )
# info = large_arrays = hsl = None
# clear_mem()
src/datasets/hs.py
-220
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@@ -1,220 +0,0 @@
# from dataclasses import dataclass
# import lightning as pl
# from loguru import logger
# from transformers import (
# AutoTokenizer,
# AutoModelForSeq2SeqLM,
# AutoModelForMaskedLM,
# AutoModelForCausalLM,
# AutoConfig,
# AutoModel,
# PreTrainedTokenizer,
# PreTrainedModel
# )
# from typing import Optional, List, Tuple, Dict, NewType
# from transformers import LogitsProcessorList
# import functools
# from src.helpers.torch import to_numpy
# from src.datasets.dropout import enable_dropout
# import re
# from tqdm.auto import tqdm
# # from src.datasets.hs import ExtractHiddenStates
# from torch.utils.data import DataLoader
# from datasets import Dataset
# import numpy as np
# import torch
# import torch.nn.functional as F
# from baukit.nethook import Trace, TraceDict, recursive_copy
# from einops import rearrange, reduce, repeat
# from src.datasets.scores import choice2id, choice2ids
# from src.helpers.torch import clear_mem, detachcpu
# from collections import defaultdict
# from dataclasses import field
# from src.datasets.intervene import InterventionDict, intervention_meta_fn
# from functools import partial
# # def noise_for_embeds(inputs_embeds, seed=42, std = 2e-2):
# # B, S, embed_dim = inputs_embeds.shape
# # with torch.random.fork_rng(devices=[inputs_embeds.device.index]):
# # torch.manual_seed(seed)
# # noise = torch.normal(0., std, (embed_dim, ))
# # noise = repeat(noise, 't -> b s t', b=B, s=S).to(inputs_embeds.device).to(inputs_embeds.dtype)
# # return noise
# def tcopy(x: torch.Tensor):
# return x.clone().detach().cpu()
# def counterfactual_loss(model, scores, token_y, token_n):
# """do a backwards pass where the loss is the distance to the opposite scores"""
# eps = 1e-4
# model.zero_grad()
# assert token_y.shape[1]<2, 'FIXME just use the first token for now'
# score_y = torch.index_select(scores, 1, token_y[:, 0])
# score_n = torch.index_select(scores, 1, token_n[:, 0])
# # this loss would be zero if the logits of the positive and negative tokens werre flipped
# loss = F.l1_loss(score_y, score_n) + F.l1_loss(score_n, score_y)
# return loss
# def stack_trace_returns(ret: TraceDict, names: List[str]) -> torch.Tensor:
# hs = [ret[h].output for h in names]
# hs = [h[0] if isinstance(h, tuple) else h for h in hs] # from a head it's a tuple
# return rearrange(hs, 'layers b s hs -> b layers s hs')[:, :, -1]
# # def stack_trace_grad_returns(ret: TraceDict, names: List[str]) -> torch.Tensor:
# # hs = [ret[h].output.grad.detach() for h in names]
# # return rearrange(hs, 'layers b s hs -> b layers s hs')[:, :, -1]
# # def select_weight_grads(weight_grads: Dict[str, torch.Tensor], pattern:str= ".+attn.c_proj.weight", mean_axis:int=1):
# # grads = [g.mean(mean_axis) for k,g in weight_grads.items() if re.match(pattern, k)]
# # assert len(grads), f"non of pattern='{pattern}' found in {weight_grads.keys()}"
# # return rearrange(grads, "lyrs b hs -> b lyrs hs")
# @dataclass
# class ExtractHiddenStates:
# model: PreTrainedModel
# tokenizer: PreTrainedTokenizer
# intervention_dicts: Optional[InterventionDict] = None
# layer_stride: int = 8
# layer_padding: int = 3
# def get_layer_names(self):
# # for WizardLM/WizardCoder-3B-V1.0
# # HEADS = [f"transformer.h.{i}.attn.c_proj" for i in range(self.model.config.num_hidden_layers)]
# # MLPS = [f"transformer.h.{i}.mlp" for i in range(self.model.config.num_hidden_layers)]
# # for "WizardLM/WizardCoder-Python-13B-V1.0"
# # HACK: depends on model layout
# layers_names_h = [f"model.layers.{i}.self_attn" for i in range(self.model.config.num_hidden_layers)]
# layers_names_mlp = [f"model.layers.{i}.mlp" for i in range(self.model.config.num_hidden_layers)]
# return self.get_layer_selection(layers_names_h) + self.get_layer_selection(layers_names_mlp)
# def get_batch_of_hidden_states(
# self,
# input_text: Optional[List[str]] = None,
# input_ids: torch.Tensor = None,
# attention_mask: Optional[torch.Tensor] = None,
# choice_ids: List[torch.Tensor] = None,
# truncation_length=999,
# debug=False,
# ):
# """
# Given a decoder model and a batch of texts, gets a pair of hidden states (in a given layer) on that input texts
# The idea is this: given two pairs of hidden states, where everything is the same except r dropout. Then tell me which one is more truthful?
# """
# assert (input_ids is not None) or (input_text is not None), "need to provide input_ids or input_text"
# assert self.tokenizer.truncation_side == 'left'
# if input_text:
# raise NotImplementedError("FIXME")
# t = self.tokenizer(
# input_text,
# return_tensors="pt",
# add_special_tokens=True,
# padding='max_length', max_length=truncation_length, truncation=True, return_attention_mask=True,
# )
# input_ids = t.input_ids.to(self.model.device)
# attention_mask = t.attention_mask.to(self.model.device)
# else:
# input_ids = input_ids.to(self.model.device)
# attention_mask = attention_mask.to(self.model.device)
# choice_ids = choice_ids.to(self.model.device)
# # forward pass
# last_token = -1
# layers_names = self.get_layer_names()
# self.model.eval()
# if self.intervention_dicts is not None:
# # extraction mode
# # 15 is a magic number from honest_llama
# intervention_fn1 = partial(intervention_meta_fn, interventions=self.intervention_dicts, alpha=-15)
# intervention_fn2 = partial(intervention_meta_fn, interventions=self.intervention_dicts, alpha=15)
# edit_outputs = [intervention_fn1, intervention_fn2]
# else:
# # calibration mode
# edit_outputs = [None]
# with torch.no_grad():
# multi_outs = defaultdict(list)
# for edit_output in edit_outputs:
# with TraceDict(self.model, layers_names, retain_grad=False, detach=True, edit_output=edit_output) as ret:
# model_inputs = self.model.prepare_inputs_for_generation(input_ids=input_ids, attention_mask=attention_mask, use_cache=False)
# outputs = self.model.forward(
# **model_inputs,
# return_dict=True,
# output_hidden_states=True,
# )
# outputs["scores"] = outputs.logits[:, last_token, :].float()
# # stack
# hidden_states = list(outputs.hidden_states)
# hidden_states = rearrange(hidden_states, 'lyrs b seq hs -> b lyrs seq hs')[:, :, last_token]
# ## from ret, we get the layer activation and the grads on them
# head_activation = tcopy(stack_trace_returns(ret, layers_names))
# # mlp_activation = tcopy(stack_trace_returns(ret, MLPS))
# # collect outputs
# multi_outs['scores'].append(outputs["scores"])
# # multi_outs['hidden_states'].append(hidden_states)
# multi_outs['head_activation'].append(head_activation)
# # multi_outs['mlp_activation'].append(mlp_activation)
# # stack
# multi_outs['scores'] = torch.stack(multi_outs['scores'], -1)
# # multi_outs['mlp_activation'] = torch.stack(multi_outs['mlp_activation'], -1)
# multi_outs['head_activation'] = torch.stack(multi_outs['head_activation'], -1)
# # combine
# out_common = dict(input_ids=input_ids, attention_mask=attention_mask, layers=layers_names,)
# if debug:
# out_common['input_truncated'] = self.tokenizer.batch_decode(input_ids)
# out_common['text_ans'] = self.tokenizer.batch_decode(outputs["scores"].softmax(-1).argmax(-1))
# out = {**multi_outs, **out_common}
# # detach
# out = {k: detachcpu(v) for k, v in out.items()}
# # I shouldn't have to do this but I get memory leaks
# outputs = hidden_states = hidden_states2 = loss = orig_state_dict = scores = token_y = token_n = input_ids = attention_mask = choice_ids = residual_stream = residual_stream2 = None
# clear_mem()
# return out
# def get_layer_selection(self, layer_names):
# """Sometimes we don't want to save all layers.
# We skip the first few (data leakage?). Stride the the middle (could be valuable), and include the last few (possibly high level concepts).
# See also https://www.lesswrong.com/posts/bWxNPMy5MhPnQTzKz/what-discovering-latent-knowledge-did-and-did-not-find-4
# """
# module_names = [k for k,v in self.model.named_modules()]
# layers_not_found = set(layer_names)-set(module_names)
# assert len(layers_not_found)==0, f"some layers not found in model: {layers_not_found}. we have {layer_names}"
# # for self.layer_padding, skip the first few
# num_layers = len(layer_names)-1
# strided_layers = torch.arange(
# self.layer_padding,
# num_layers-self.layer_padding,
# self.layer_stride,
# ).tolist()
# # for self.layer_padding ALWAYS include the last few. Why, this is based on the intuition that the last layers may be the most valuable
# last_few = torch.arange(num_layers-self.layer_padding, num_layers).tolist()
# layers_inds = sorted(set(list(strided_layers)+list(last_few)))
# return [layer_names[i] for i in layers_inds]
src/datasets/intervene.py
+3
View File
@@ -13,6 +13,7 @@ from src.config import root_folder
from src.prompts.prompt_loading import load_preproc_dataset
from transformers import AutoTokenizer, pipeline, Pipeline
from loguru import logger
from src.repe.rep_readers import project_onto_direction
Activations = NewType("Activations", Dict[str, torch.Tensor])
@@ -28,6 +29,8 @@ def intervene(output, activation):
), f"expected output to be (batch, seq, vocab), got {output.shape}"
# assert torch.isfinite(output).all(), 'model output nan'
output2 = output + activation.to(output.device)[None, :]
output2 = project_onto_direction(output, activation)
# assert torch.isfinite(output2).all(), 'intervention lead to nan'
return output2
src/datasets/scores.py
+4 -5
View File
@@ -53,11 +53,10 @@ def choice2id(tokenizer, c: str, whitespace_first=False) -> List[int]:
ids2 += tokenizer(f'{c}', add_special_tokens=False)["input_ids"]
ids = list(set(ids2))
print(ids2)
print(ids)
print([f'`{t}`' for t in tokenizer.batch_decode(ids, skip_special_tokens=True)])
print([c.strip().startswith(tokenizer.decode(i)) for i in ids])
# print(ids2)
# print(ids)
# print([f'`{t}`' for t in tokenizer.batch_decode(ids, skip_special_tokens=True)])
# print([c.strip().startswith(tokenizer.decode(i)) for i in ids])
# only include ones that decode to our original
ids = [i for i in ids if c.strip().startswith(tokenizer.decode(i).strip()) and len(tokenizer.decode(i).strip())]
src/repe/rep_readers.py
+5 -2
View File
@@ -3,13 +3,16 @@ from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import numpy as np
from itertools import islice
import torch
### Util Functions ###
def project_onto_direction(H, direction):
"""Project matrix H (n, d_1) onto direction vector (d_2,)"""
# TODO: should we require direction vectors to be unit vectors? then return H.dot(direction)
mag = np.linalg.norm(direction)
assert not np.isinf(mag)
direction = direction.to(H.device).squeeze(0)
mag = torch.linalg.norm(direction).to(H.device)
assert torch.isfinite(mag)
return (H[0] @ direction[:, None] # check that the dimensions match
return H.dot(direction) / mag
def recenter_mean(x, mean=None):