baseline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device:  cuda\n"
     ]
    }
   ],
   "source": [
    "import transformer_lens\n",
    "import sae_lens\n",
    "from transformer_lens import HookedTransformer\n",
    "from sae_lens import SAE\n",
    "import torch\n",
    "from utils.ioi_dataset import IOIDataset\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(\"Using device: \", device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded pretrained model gpt2-small into HookedTransformer\n"
     ]
    }
   ],
   "source": [
    "model = HookedTransformer.from_pretrained('gpt2-small', device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sae, _, _ = SAE.from_pretrained(release='gpt2-small-resid-post-v5-32k', sae_id=\"blocks.6.hook_resid_post\", device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/utils/ioi_dataset.py:634: UserWarning: S2 index has been computed as the same for S and S2\n",
      "  warnings.warn(\"S2 index has been computed as the same for S and S2\")\n"
     ]
    }
   ],
   "source": [
    "ds_base= IOIDataset('mixed', N=200, tokenizer=model.tokenizer)\n",
    "abc_dataset_base = (  # TODO seeded\n",
    "    ds_base.gen_flipped_prompts((\"IO\", \"RAND\"))\n",
    "    .gen_flipped_prompts((\"S\", \"RAND\"))\n",
    "    .gen_flipped_prompts((\"S1\", \"RAND\"))\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper functions \n",
    "from jaxtyping import Bool, Float\n",
    "from torch import Tensor\n",
    "import gc\n",
    "\n",
    "def logits_to_ave_logit_diff_2(\n",
    "    logits: Float[Tensor, \"batch seq d_vocab\"],\n",
    "    ioi_dataset: IOIDataset ,\n",
    "    per_prompt=False\n",
    ") -> Float[Tensor, \"*batch\"]:\n",
    "    '''\n",
    "    Returns logit difference between the correct and incorrect answer.\n",
    "\n",
    "    If per_prompt=True, return the array of differences rather than the average.\n",
    "    '''\n",
    "\n",
    "    # Only the final logits are relevant for the answer\n",
    "    # Get the logits corresponding to the indirect object / subject tokens respectively\n",
    "    io_logits: Float[Tensor, \"batch\"] = logits[range(logits.size(0)), ioi_dataset.word_idx[\"end\"], ioi_dataset.io_tokenIDs]\n",
    "    s_logits: Float[Tensor, \"batch\"] = logits[range(logits.size(0)), ioi_dataset.word_idx[\"end\"], ioi_dataset.s_tokenIDs]\n",
    "    # Find logit difference\n",
    "    answer_logit_diff = io_logits - s_logits\n",
    "    return answer_logit_diff if per_prompt else answer_logit_diff.mean()\n",
    "\n",
    "def cleanup_cuda():\n",
    "   torch.cuda.empty_cache()\n",
    "   gc.collect()\n",
    "\n",
    "def run_with_saes_cache(tokens, model, sae):\n",
    "    model.reset_hooks()\n",
    "    sae_outs = {}\n",
    "    hook_point = sae.cfg.hook_name\n",
    "\n",
    "    # Define the filtered hook function (optimized)\n",
    "    def filtered_hook(act, hook, sae=sae):\n",
    "        enc_sae = sae.encode(act) \n",
    "        sae_outs[hook.name] = enc_sae.detach().cpu() \n",
    "        modified_act = sae.decode(enc_sae) \n",
    "        return modified_act\n",
    "\n",
    "    model.add_hook(hook_point, filtered_hook, dir='fwd')\n",
    "    # Run the model with the tokens (no gradients needed)\n",
    "    with torch.no_grad():\n",
    "        logits = model(tokens)\n",
    "\n",
    "    # Reset the hooks after computation to free memory\n",
    "    model.reset_hooks()\n",
    "    return logits, sae_outs \n",
    "\n",
    "def run_with_patched_saes(tokens, model, sae, cache, feature_idx):\n",
    "    model.reset_hooks()\n",
    "    hook_point = sae.cfg.hook_name\n",
    "\n",
    "    # Define the filtered hook function (optimized)\n",
    "    def filtered_hook(act, hook, sae=sae):\n",
    "        enc_sae = sae.encode(act) \n",
    "        enc_sae[:, :, feature_idx] = cache[hook.name][:, :, feature_idx]\n",
    "        modified_act = sae.decode(enc_sae) \n",
    "        return modified_act\n",
    "\n",
    "    model.add_hook(hook_point, filtered_hook, dir='fwd')\n",
    "    # Run the model with the tokens (no gradients needed)\n",
    "    with torch.no_grad():\n",
    "        logits = model(tokens)\n",
    "\n",
    "    # Reset the hooks after computation to free memory\n",
    "    model.reset_hooks()\n",
    "    return logits\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(3.4980, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "# clean cache \n",
    "cleanup_cuda()\n",
    "logits, sae_outs = run_with_saes_cache(ds_base.toks, model, sae)\n",
    "clean_logit_diff = logits_to_ave_logit_diff_2(logits, ds_base)\n",
    "print(clean_logit_diff)\n",
    "del logits\n",
    "cleanup_cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0873, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "# corr cache \n",
    "cleanup_cuda()\n",
    "logits, sae_outs_corr = run_with_saes_cache(abc_dataset_base.toks, model, sae)\n",
    "corr_logit_diff = logits_to_ave_logit_diff_2(logits, ds_base)\n",
    "print(corr_logit_diff)\n",
    "del logits\n",
    "cleanup_cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Denoising Progress:  20%|█▉        | 6444/32768 [11:39<47:37,  9.21it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/baseline.ipynb Cell 8\u001b[0m line \u001b[0;36m1\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bgpu018.unity.rc.umass.edu/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/baseline.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=8'>9</a>\u001b[0m filtered_ids \u001b[39m=\u001b[39m [model\u001b[39m.\u001b[39mtokenizer\u001b[39m.\u001b[39mbos_token_id] \n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bgpu018.unity.rc.umass.edu/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/baseline.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=9'>10</a>\u001b[0m logits \u001b[39m=\u001b[39m run_with_patched_saes(abc_dataset_base\u001b[39m.\u001b[39mtoks, model, sae, sae_outs, latent)\n\u001b[0;32m---> <a href='vscode-notebook-cell://ssh-remote%2Bgpu018.unity.rc.umass.edu/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/baseline.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=10'>11</a>\u001b[0m patched_err_metric \u001b[39m=\u001b[39m logits_to_ave_logit_diff_2(logits, ds_base)\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bgpu018.unity.rc.umass.edu/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/baseline.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=11'>12</a>\u001b[0m normalized_metric \u001b[39m=\u001b[39m (patched_err_metric \u001b[39m-\u001b[39m corr_logit_diff) \u001b[39m/\u001b[39m (clean_logit_diff \u001b[39m-\u001b[39m corr_logit_diff)\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bgpu018.unity.rc.umass.edu/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/baseline.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=12'>13</a>\u001b[0m \u001b[39mif\u001b[39;00m normalized_metric\u001b[39m>\u001b[39m\u001b[39m0.1\u001b[39m:\n",
      "\u001b[1;32m/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/baseline.ipynb Cell 8\u001b[0m line \u001b[0;36m2\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bgpu018.unity.rc.umass.edu/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/baseline.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=16'>17</a>\u001b[0m \u001b[39m# Only the final logits are relevant for the answer\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bgpu018.unity.rc.umass.edu/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/baseline.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=17'>18</a>\u001b[0m \u001b[39m# Get the logits corresponding to the indirect object / subject tokens respectively\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bgpu018.unity.rc.umass.edu/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/baseline.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=18'>19</a>\u001b[0m io_logits: Float[Tensor, \u001b[39m\"\u001b[39m\u001b[39mbatch\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m logits[\u001b[39mrange\u001b[39m(logits\u001b[39m.\u001b[39msize(\u001b[39m0\u001b[39m)), ioi_dataset\u001b[39m.\u001b[39mword_idx[\u001b[39m\"\u001b[39m\u001b[39mend\u001b[39m\u001b[39m\"\u001b[39m], ioi_dataset\u001b[39m.\u001b[39mio_tokenIDs]\n\u001b[0;32m---> <a href='vscode-notebook-cell://ssh-remote%2Bgpu018.unity.rc.umass.edu/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/baseline.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=19'>20</a>\u001b[0m s_logits: Float[Tensor, \u001b[39m\"\u001b[39m\u001b[39mbatch\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m logits[\u001b[39mrange\u001b[39m(logits\u001b[39m.\u001b[39;49msize(\u001b[39m0\u001b[39;49m)), ioi_dataset\u001b[39m.\u001b[39mword_idx[\u001b[39m\"\u001b[39m\u001b[39mend\u001b[39m\u001b[39m\"\u001b[39m], ioi_dataset\u001b[39m.\u001b[39ms_tokenIDs]\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bgpu018.unity.rc.umass.edu/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/baseline.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=20'>21</a>\u001b[0m \u001b[39m# Find logit difference\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2Bgpu018.unity.rc.umass.edu/work/pi_jensen_umass_edu/jnainani_umass_edu/OptSAE/baseline.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=21'>22</a>\u001b[0m answer_logit_diff \u001b[39m=\u001b[39m io_logits \u001b[39m-\u001b[39m s_logits\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# patching clean to corr - denoising\n",
    "cleanup_cuda()\n",
    "from tqdm import tqdm\n",
    "total_steps = sae.cfg.d_sae\n",
    "denoising_results = {}\n",
    "with tqdm(total=total_steps, desc=\"Denoising Progress\") as pbar:\n",
    "    for latent in range(sae.cfg.d_sae):\n",
    "        \n",
    "        filtered_ids = [model.tokenizer.bos_token_id] \n",
    "        logits = run_with_patched_saes(abc_dataset_base.toks, model, sae, sae_outs, latent)\n",
    "        patched_err_metric = logits_to_ave_logit_diff_2(logits, ds_base)\n",
    "        normalized_metric = (patched_err_metric - corr_logit_diff) / (clean_logit_diff - corr_logit_diff)\n",
    "        if normalized_metric>0.1:\n",
    "            print(f\"Latent: {latent}\")\n",
    "            print(f\"Error Metric: {normalized_metric}\")\n",
    "        denoising_results[latent] = normalized_metric.detach().cpu().item()\n",
    "        pbar.update(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Index: 3527, Max Value: 0.004557418636977673\n"
     ]
    }
   ],
   "source": [
    "# find the max index and value of denoising results\n",
    "max_idx = max(denoising_results, key=denoising_results.get)\n",
    "max_val = denoising_results[max_idx]\n",
    "print(f\"Max Index: {max_idx}, Max Value: {max_val}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([200, 21, 32768])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sae_outs['blocks.6.hook_resid_post'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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