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working, a little

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wassname
2025-02-16 15:36:43 +08:00
parent c7984ffc0c
commit 66bc0be046
3 changed files with 1076 additions and 127 deletions
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nbs/TQA_regr.ipynb
+584 -121
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@@ -13,7 +13,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -23,7 +23,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -45,7 +45,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -69,7 +69,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -81,7 +81,7 @@
"})"
]
},
"execution_count": 8,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -117,14 +117,14 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<torch.utils.data.dataloader.DataLoader object at 0x7898946bb140>\n"
"<torch.utils.data.dataloader.DataLoader object at 0x7ae6336a3fb0>\n"
]
}
],
@@ -139,7 +139,7 @@
" return_tensors=\"pt\",\n",
" padding_side=\"left\", \n",
" max_length=max_length, \n",
" truncation=True, \n",
" # truncate=True, \n",
" )\n",
"ds = DataLoader(ds2, batch_size=6, collate_fn=collate_fn)\n",
"print(ds)\n"
@@ -154,20 +154,20 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2025-02-16 13:14:41.807\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mactivation_store.collect\u001b[0m:\u001b[36mactivation_store\u001b[0m:\u001b[36m84\u001b[0m - \u001b[1mcreating dataset /media/wassname/SGIronWolf/projects5/elk/cache_transformer_acts/outputs/.ds/ds__b7d3fdaac13055f3.parquet\u001b[0m\n"
"\u001b[32m2025-02-16 14:31:44.835\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mactivation_store.collect\u001b[0m:\u001b[36mactivation_store\u001b[0m:\u001b[36m84\u001b[0m - \u001b[1mcreating dataset /media/wassname/SGIronWolf/projects5/elk/cache_transformer_acts/outputs/.ds/ds__c3618481558aa795.parquet\u001b[0m\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "543e82eed22e4417b98d62d8528765fe",
"model_id": "75a4e881fcdd44fab86a44841a565227",
"version_major": 2,
"version_minor": 0
},
@@ -182,16 +182,18 @@
"name": "stderr",
"output_type": "stream",
"text": [
"You're using a Qwen2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
"You're using a Qwen2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
"/media/wassname/SGIronWolf/projects5/elk/cache_transformer_acts/.venv/lib/python3.12/site-packages/transformers/tokenization_utils_base.py:2699: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/plain": [
"PosixPath('/media/wassname/SGIronWolf/projects5/elk/cache_transformer_acts/outputs/.ds/ds__b7d3fdaac13055f3.parquet')"
"PosixPath('/media/wassname/SGIronWolf/projects5/elk/cache_transformer_acts/outputs/.ds/ds__c3618481558aa795.parquet')"
]
},
"execution_count": 10,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -203,13 +205,13 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d32aee9cd5354060b76f6369a9ee1c46",
"model_id": "4690faab1ed24cb69edb35a6d6f213f9",
"version_major": 2,
"version_minor": 0
},
@@ -229,7 +231,7 @@
"})"
]
},
"execution_count": 11,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -242,7 +244,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -320,7 +322,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -362,16 +364,16 @@
" diffs_inv2, \"(l b t) h -> l b t h\", l=hs.shape[0] - 1, b=hs.shape[1], t=1\n",
" ).to(w_out.dtype)\n",
" # TODO just return this?\n",
" eps = 1.0e-1\n",
" eps = 1.e-2\n",
" supressed_mask = (diffs_inv < -eps).to(hs.dtype)\n",
" # supressed_mask = repeat(supressed_mask, 'l b 1 h -> l b t h', t=hs.shape[2])\n",
" supressed_act = hs[1:] * supressed_mask\n",
" return supressed_act"
" return supressed_act, supressed_mask"
]
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -380,7 +382,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -388,9 +390,9 @@
"output_type": "stream",
"text": [
"before ['0', '0 ', '0\\n', 'false', 'False ']\n",
"after ['0', '0', '0', 'False', 'false']\n",
"before ['1', '\\n1', '1\\n', 'true', 'True']\n",
"after ['1', 'true', '\\n', 'True', '1']\n"
"after ['0', 'False', 'false', '0', '0']\n",
"before ['1', '1 ', '1\\n', 'true', 'True ']\n",
"after ['1', 'True', '1', '1', 'true']\n"
]
}
],
@@ -413,13 +415,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a9f7d40368044f5d958a7fe137dc2d98",
"model_id": "b97ade5f24e2404c87e2f21cb3b1cb76",
"version_major": 2,
"version_minor": 0
},
@@ -434,12 +436,12 @@
"data": {
"text/plain": [
"Dataset({\n",
" features: ['logits', 'hidden_states', 'attention_mask', 'label', 'llm_ans', 'llm_log_prob_true', 'hs_sup'],\n",
" features: ['logits', 'hidden_states', 'attention_mask', 'label', 'llm_ans', 'llm_log_prob_true', 'hs_sup', 'supressed_mask'],\n",
" num_rows: 316\n",
"})"
]
},
"execution_count": 24,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -464,14 +466,18 @@
" hs = o['hidden_states'][None]\n",
" hs = rearrange(hs, \"b l t h -> l b t h\")\n",
" layer_half = hs.shape[0] // 2\n",
" hs_s = get_supressed_activations(hs, Wo.to(hs.dtype), Wo_inv.to(hs.dtype))\n",
" hs_s, supressed_mask = get_supressed_activations(hs, Wo.to(hs.dtype), Wo_inv.to(hs.dtype))\n",
" hs_s = rearrange(hs_s, \"l b t h -> b l t h\").squeeze(0)\n",
" # we will only take the last half of layers, and the last token\n",
" hs_s = hs_s[layer_half:-2, -1:]\n",
" hs_s = hs_s[layer_half:-2, :]\n",
" o['hs_sup'] = hs_s.half()\n",
"\n",
" supressed_mask = rearrange(supressed_mask, \"l b t h -> b l t h\").squeeze(0)\n",
" supressed_mask = supressed_mask[layer_half:-2, :]\n",
" o['supressed_mask'] = supressed_mask\n",
"\n",
" # should I just get the last token for the hs, and only the later layers\n",
" o['hidden_states'] = o['hidden_states'][layer_half:-2, -1]\n",
" o['hidden_states'] = o['hidden_states'][layer_half:-2, ]\n",
" return o\n",
"\n",
"ds_a2 = ds_a.map(proc, writer_batch_size=1, num_proc=None)\n",
@@ -492,12 +498,12 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# https://github.com/EleutherAI/ccs/blob/8a4bf687712cc03ef72973c8235944566d59053b/ccs/training/supervised.py#L9\n",
"from dataclasses import dataclass, field\n",
"\n",
"\n",
"import torch\n",
"from torch import Tensor\n",
@@ -586,44 +592,44 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from einops import rearrange, repeat\n",
"# import torch\n",
"# from einops import rearrange, repeat\n",
"\n",
"\n",
"def to_one_hot(labels: Tensor, n_classes: int) -> Tensor:\n",
" \"\"\"\n",
" Convert a tensor of class labels to a one-hot representation.\n",
"# def to_one_hot(labels: Tensor, n_classes: int) -> Tensor:\n",
"# \"\"\"\n",
"# Convert a tensor of class labels to a one-hot representation.\n",
"\n",
" Args:\n",
" labels (Tensor): A tensor of class labels of shape (N,).\n",
" n_classes (int): The total number of unique classes.\n",
"# Args:\n",
"# labels (Tensor): A tensor of class labels of shape (N,).\n",
"# n_classes (int): The total number of unique classes.\n",
"\n",
" Returns:\n",
" Tensor: A one-hot representation tensor of shape (N, n_classes).\n",
" \"\"\"\n",
" one_hot_labels = labels.new_zeros(*labels.shape, n_classes)\n",
" return one_hot_labels.scatter_(-1, labels.unsqueeze(-1).long(), 1)\n"
"# Returns:\n",
"# Tensor: A one-hot representation tensor of shape (N, n_classes).\n",
"# \"\"\"\n",
"# one_hot_labels = labels.new_zeros(*labels.shape, n_classes)\n",
"# return one_hot_labels.scatter_(-1, labels.unsqueeze(-1).long(), 1)\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dataset({\n",
" features: ['logits', 'hidden_states', 'attention_mask', 'label', 'llm_ans', 'llm_log_prob_true', 'hs_sup'],\n",
" features: ['logits', 'hidden_states', 'attention_mask', 'label', 'llm_ans', 'llm_log_prob_true', 'hs_sup', 'supressed_mask'],\n",
" num_rows: 316\n",
"})"
]
},
"execution_count": 27,
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -634,20 +640,9 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(0.5512)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"# first try llm\n",
"\n",
@@ -708,14 +703,14 @@
},
{
"cell_type": "code",
"execution_count": 37,
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LLM score: 0.55 roc auc, n=116\n"
"LLM score: 0.54 roc auc, n=116\n"
]
}
],
@@ -735,54 +730,13 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# x = ds_a2['hidden_states']\n",
"# [xx.shape for xx in x]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor(1.6489)"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_a2['hs_sup'].std()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"score for probe(llm_ans): 0.624 roc auc, n=116\n",
"score for probe(hs_sup): 0.741 roc auc, n=116\n",
"score for probe(hidden_states): 0.729 roc auc, n=116\n"
]
}
],
"source": [
"\n",
"\n",
"def train_linear_prob_on_dataset(data_name: str, device: str = \"cuda\"):\n",
" X = ds_a2[data_name].view(len(ds_a2), -1).to(device)\n",
"def train_linear_prob_on_dataset(X, name=\"\", device: str = \"cuda\", ):\n",
" print(X.shape)\n",
" X = X.view(len(X), -1).to(device)\n",
"\n",
" # norm X\n",
" X = (X - X.mean()) / X.std()\n",
@@ -796,20 +750,529 @@
" y_pred = lr_model.forward(X_test)\n",
"\n",
" score = roc_auc(y_test, y_pred)\n",
" print(f'score for probe({data_name}): {score:.3f} roc auc, n={len(X_test)}')\n",
" return lr_model\n",
"\n",
"train_linear_prob_on_dataset('llm_ans')\n",
"train_linear_prob_on_dataset('hs_sup')\n",
"train_linear_prob_on_dataset('hidden_states');"
" print(f'score for probe({name}): {score:.3f} roc auc, n={len(X_test)}')\n",
" return score.cpu().item()"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([316, 896])\n",
"score for probe(hs_sup mean mean): 0.675 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup mean max): 0.666 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup mean sum): 0.673 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup mean last): 0.779 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup mean first): 0.660 roc auc, n=116\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup max mean): 0.730 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup max max): 0.607 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup max sum): 0.724 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup max last): 0.749 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup max first): 0.521 roc auc, n=116\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup sum mean): 0.674 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup sum max): 0.667 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup sum sum): 0.674 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup sum last): 0.779 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup sum first): 0.660 roc auc, n=116\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup last mean): 0.684 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup last max): 0.660 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup last sum): 0.687 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup last last): 0.753 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup last first): 0.685 roc auc, n=116\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup first mean): 0.706 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup first max): 0.676 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup first sum): 0.703 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup first last): 0.687 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hs_sup first first): 0.659 roc auc, n=116\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"stack expects each tensor to be equal size, but got [10, 56, 896] at entry 0 and [10, 54, 896] at entry 6\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states mean mean): 0.683 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states mean max): 0.474 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states mean sum): 0.689 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states mean last): 0.745 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states mean first): 0.481 roc auc, n=116\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states max mean): 0.670 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states max max): 0.518 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states max sum): 0.669 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states max last): 0.718 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states max first): 0.498 roc auc, n=116\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states sum mean): 0.683 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states sum max): 0.474 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states sum sum): 0.687 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states sum last): 0.744 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states sum first): 0.481 roc auc, n=116\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states last mean): 0.726 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states last max): 0.549 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states last sum): 0.719 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states last last): 0.736 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states last first): 0.705 roc auc, n=116\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states first mean): 0.648 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states first max): 0.455 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states first sum): 0.645 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states first last): 0.692 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(hidden_states first first): 0.458 roc auc, n=116\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"stack expects each tensor to be equal size, but got [56, 896] at entry 0 and [54, 896] at entry 6\n",
"stack expects each tensor to be equal size, but got [11, 56, 896] at entry 0 and [11, 54, 896] at entry 6\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask mean mean): 0.738 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask mean max): 0.738 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask mean sum): 0.738 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask mean last): 0.738 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask mean first): 0.738 roc auc, n=116\n",
"torch.Size([316, 1, 896])\n",
"score for probe(supressed_mask mean none): 0.738 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask max mean): 0.514 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask max max): 0.514 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask max sum): 0.514 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask max last): 0.514 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask max first): 0.514 roc auc, n=116\n",
"torch.Size([316, 1, 896])\n",
"score for probe(supressed_mask max none): 0.514 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask sum mean): 0.738 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask sum max): 0.738 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask sum sum): 0.738 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask sum last): 0.738 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask sum first): 0.738 roc auc, n=116\n",
"torch.Size([316, 1, 896])\n",
"score for probe(supressed_mask sum none): 0.738 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask last mean): 0.699 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask last max): 0.699 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask last sum): 0.699 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask last last): 0.699 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask last first): 0.699 roc auc, n=116\n",
"torch.Size([316, 1, 896])\n",
"score for probe(supressed_mask last none): 0.699 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask first mean): 0.722 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask first max): 0.722 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask first sum): 0.722 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask first last): 0.722 roc auc, n=116\n",
"torch.Size([316, 896])\n",
"score for probe(supressed_mask first first): 0.722 roc auc, n=116\n",
"torch.Size([316, 1, 896])\n",
"score for probe(supressed_mask first none): 0.722 roc auc, n=116\n",
"torch.Size([316, 1, 896])\n",
"score for probe(supressed_mask none mean): 0.738 roc auc, n=116\n",
"torch.Size([316, 1, 896])\n",
"score for probe(supressed_mask none max): 0.514 roc auc, n=116\n",
"torch.Size([316, 1, 896])\n",
"score for probe(supressed_mask none sum): 0.738 roc auc, n=116\n",
"torch.Size([316, 1, 896])\n",
"score for probe(supressed_mask none last): 0.699 roc auc, n=116\n",
"torch.Size([316, 1, 896])\n",
"score for probe(supressed_mask none first): 0.722 roc auc, n=116\n",
"torch.Size([316, 10, 1, 896])\n",
"score for probe(supressed_mask none none): 0.755 roc auc, n=116\n"
]
}
],
"source": [
"reductions = {\n",
" 'mean': lambda x: x.mean(0),\n",
" 'max': lambda x: x.max(0)[0],\n",
" 'sum': lambda x: x.sum(0),\n",
" 'last': lambda x: x[-1],\n",
" 'first': lambda x: x[0],\n",
" 'none': lambda x: x,\n",
"}\n",
"results = []\n",
"data_names = ['hs_sup', 'hidden_states', 'supressed_mask']\n",
"for dn in data_names:\n",
" \n",
" for r1 in reductions:\n",
" r1f = reductions[r1]\n",
" for r2 in reductions:\n",
" r2f = reductions[r2]\n",
" try:\n",
" X = torch.stack([r2f(r1f(x)) for x in ds_a2[dn]])\n",
" name = f'{dn} {r1} {r2}'\n",
" score = train_linear_prob_on_dataset(X, name)\n",
" results.append((name, score))\n",
" except Exception as e:\n",
" print(e)\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>hs_sup none last</td>\n",
" <td>0.769643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>hs_sup last none</td>\n",
" <td>0.769643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>hidden_states none mean</td>\n",
" <td>0.755059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>hidden_states last none</td>\n",
" <td>0.755059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>hidden_states sum none</td>\n",
" <td>0.755059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>hidden_states first mean</td>\n",
" <td>0.447917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>hidden_states max first</td>\n",
" <td>0.434524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>hidden_states max mean</td>\n",
" <td>0.430060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>hidden_states max max</td>\n",
" <td>0.427679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>hidden_states max sum</td>\n",
" <td>0.426488</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>106 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" name score\n",
"33 hs_sup none last 0.769643\n",
"23 hs_sup last none 0.769643\n",
"100 hidden_states none mean 0.755059\n",
"93 hidden_states last none 0.755059\n",
"87 hidden_states sum none 0.755059\n",
".. ... ...\n",
"59 hidden_states first mean 0.447917\n",
"45 hidden_states max first 0.434524\n",
"41 hidden_states max mean 0.430060\n",
"42 hidden_states max max 0.427679\n",
"43 hidden_states max sum 0.426488\n",
"\n",
"[106 rows x 2 columns]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"# note hs_sup seems to get more important as we lower the thresh\n",
"df = pd.DataFrame(results, columns=['name', 'score']).sort_values('score', ascending=False)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>hs_sup none last</td>\n",
" <td>0.769643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>hs_sup last none</td>\n",
" <td>0.769643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>hidden_states none mean</td>\n",
" <td>0.755059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>hidden_states last none</td>\n",
" <td>0.755059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>hidden_states sum none</td>\n",
" <td>0.755059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>hidden_states none none</td>\n",
" <td>0.755059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>hidden_states none last</td>\n",
" <td>0.755059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>hidden_states none sum</td>\n",
" <td>0.755059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>hidden_states first none</td>\n",
" <td>0.754762</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>hidden_states none max</td>\n",
" <td>0.754762</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>hidden_states mean none</td>\n",
" <td>0.754464</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>hidden_states none first</td>\n",
" <td>0.754464</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>hidden_states max none</td>\n",
" <td>0.754167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>hs_sup max none</td>\n",
" <td>0.753274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>hs_sup none max</td>\n",
" <td>0.752679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>hidden_states last max</td>\n",
" <td>0.747024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>hidden_states last none</td>\n",
" <td>0.739881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>hidden_states none last</td>\n",
" <td>0.739583</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>hidden_states last sum</td>\n",
" <td>0.730357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>hidden_states last mean</td>\n",
" <td>0.729762</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name score\n",
"33 hs_sup none last 0.769643\n",
"23 hs_sup last none 0.769643\n",
"100 hidden_states none mean 0.755059\n",
"93 hidden_states last none 0.755059\n",
"87 hidden_states sum none 0.755059\n",
"105 hidden_states none none 0.755059\n",
"103 hidden_states none last 0.755059\n",
"102 hidden_states none sum 0.755059\n",
"99 hidden_states first none 0.754762\n",
"101 hidden_states none max 0.754762\n",
"75 hidden_states mean none 0.754464\n",
"104 hidden_states none first 0.754464\n",
"81 hidden_states max none 0.754167\n",
"11 hs_sup max none 0.753274\n",
"31 hs_sup none max 0.752679\n",
"54 hidden_states last max 0.747024\n",
"58 hidden_states last none 0.739881\n",
"68 hidden_states none last 0.739583\n",
"55 hidden_states last sum 0.730357\n",
"53 hidden_states last mean 0.729762"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(20)"
]
}
],
"metadata": {
pyproject.toml
+1
View File
@@ -35,4 +35,5 @@ dev = [
"ipykernel>=6.29.5",
"ipywidgets>=8.1.5",
"accelerate>=1.3.0",
"pandas>=2.2.3",
]
uv.lock
Generated
+491 -6
View File
@@ -1,5 +1,10 @@
version = 1
requires-python = ">=3.12"
requires-python = ">=3.10"
resolution-markers = [
"python_full_version < '3.11'",
"python_full_version == '3.11.*'",
"python_full_version >= '3.12'",
]
[[package]]
name = "accelerate"
@@ -24,7 +29,6 @@ name = "activation-store"
version = "0.1.0"
source = { editable = "." }
dependencies = [
{ name = "accelerate" },
{ name = "baukit" },
{ name = "datasets" },
{ name = "einops" },
@@ -37,14 +41,15 @@ dependencies = [
[package.dev-dependencies]
dev = [
{ name = "accelerate" },
{ name = "ipykernel" },
{ name = "ipywidgets" },
{ name = "pandas" },
]
[package.metadata]
requires-dist = [
{ name = "accelerate", specifier = ">=1.3.0" },
{ name = "baukit", git = "https://github.com/davidbau/baukit.git" },
{ name = "baukit", git = "https://github.com/davidbau/baukit.git?rev=9d51abd51ebf29769aecc38c4cbef459b731a36e" },
{ name = "datasets", specifier = ">=3.3.0" },
{ name = "einops", specifier = ">=0.8.1" },
{ name = "jaxtyping", specifier = ">=0.2.38" },
@@ -56,8 +61,10 @@ requires-dist = [
[package.metadata.requires-dev]
dev = [
{ name = "accelerate", specifier = ">=1.3.0" },
{ name = "ipykernel", specifier = ">=6.29.5" },
{ name = "ipywidgets", specifier = ">=8.1.5" },
{ name = "pandas", specifier = ">=2.2.3" },
]
[[package]]
@@ -76,6 +83,7 @@ source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "aiohappyeyeballs" },
{ name = "aiosignal" },
{ name = "async-timeout", marker = "python_full_version < '3.11'" },
{ name = "attrs" },
{ name = "frozenlist" },
{ name = "multidict" },
@@ -84,6 +92,38 @@ dependencies = [
]
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