llama3_tf_hf_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d99bcc4264645cebfdce8e48699baa0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b70c6a933cc7432dba1cfad042f1021f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded pretrained model meta-llama/Meta-Llama-3-8B-Instruct into HookedTransformer\n"
     ]
    }
   ],
   "source": [
    "from nnsight import NNsight\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import torch\n",
    "from transformer_lens import HookedTransformer\n",
    "from transformers import AutoTokenizer, LlamaForCausalLM\n",
    "\n",
    "MODEL_PATH = 'meta-llama/Meta-Llama-3-8B-Instruct'\n",
    "\n",
    "# Load huggingface model and tokenizer using LlamaForCausalLM and shard it on 8 gpus\n",
    "\n",
    "hf_model = LlamaForCausalLM.from_pretrained(\n",
    "        MODEL_PATH\n",
    ").to('cuda:0')\n",
    "hf_tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)\n",
    "\n",
    "# Load HookedTransformer model \n",
    "hooked_model = HookedTransformer.from_pretrained_no_processing(\n",
    "    MODEL_PATH,\n",
    "    device=\"cuda\",\n",
    "    dtype=torch.float32,\n",
    "    default_padding_side='left',\n",
    "    n_devices=1,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF Greedy: tensor([4815], device='cuda:0') Logit: tensor(13.3366, device='cuda:0')\n",
      "TL Greedy: tensor([4815], device='cuda:0') Logit: tensor(13.6355, device='cuda:0')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    a_large_chunk_of_text = \"Generate is not a good way to check a model is running properly. Can you run the following and share the results?\"\n",
    "    tokens = hf_tokenizer(a_large_chunk_of_text, return_tensors=\"pt\").input_ids.to('cuda:0')\n",
    "    logits_hf = hf_model(tokens).logits\n",
    "    logits_tl = hooked_model(tokens, return_type=\"logits\")\n",
    "\n",
    "    logits_diff = (logits_hf - logits_tl)\n",
    "    logits_diff_last = logits_diff[:, -1, :]\n",
    "    print(\"TF Greedy:\", logits_hf[:, -1, :].argmax(dim=-1), \"Logit:\", logits_hf[:, -1, :].max())\n",
    "    print(\"TL Greedy:\", logits_tl[:, -1, :].argmax(dim=-1), \"Logit:\", logits_tl[:, -1, :].max())\n",
    "    # histogram of the difference between the logits from the hooked model and the huggingface model\n",
    "    sns.histplot(logits_diff_last.flatten().cpu().numpy(), bins=100)\n",
    "    # set title\n",
    "    plt.title(\"Difference between logits (last) from the hooked model and the huggingface model\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Difference between outputs from the hooked model and the huggingface model in layer 0')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dZ/Pmzct0fgCKYIBy9MEHHxhJZuPGjUWO/+yzz4wkM2bMGKvshRdeMBceupIK/eXPt+Df6tWrjTHGZGdnm5deeslEREQYV1dX4+/vbxISEsyRI0cclh8WFmY6d+5s5s2bZ5o0aWLc3NzMM888Y4wx5tChQ2bAgAGmevXqxsXFxYSHh5vRo0eb3Nxca/o9e/YYSWbixIlm8uTJJjw83Hh5eZnmzZub1NTUQuu7bt0606VLF+Pr62vc3NxM7dq1zZNPPukQ8/PPP5v77rvPVKtWzbi6upp69eqZqVOnlmp7SzKPP/64mTZtmrnxxhuNq6urqV+/vvn0008LxZa0fvnrVvCvb9++Ji0tzUgyn332mTW/TZs2GUmmQYMGDsvp2rWrufnmmx3K5syZY5o3b248PT2Nl5eXiYmJMVu2bClUx40bN5quXbsaHx8f4+bmZpo0aWLmzp3rEJN/LKxatco8+uijxs/Pz/j6+pq77rrL/Pe//y1xm23cuNHce++9JiwszLi7u5uwsDDTu3dvs3fv3kLLKOo4bNWqVZHj8l3qsfj111+bqKgo4+7ubiIiIsz7779fqnoYY0zfvn1NWFiYw3zPnDljnn32WRMeHm5cXFxMSEiIGTRokMnIyLjk5V/MsWPHzGOPPWZCQkKMi4uLqVWrlvm///s/c/bsWSsm/5jKr++FJJkXXnjBGPPnNaC48zu/rvPnzzeRkZHGzc3N1KpVy7z55psO88zfXnv27HEoX716tcP8StqHAFAU2jiOaOP8oSK1cfr27Wu8vLzMzp07TUxMjPH09DRBQUFm3LhxxhhjUlNTTcuWLY2np6e58cYbzYcffugwfcH75YXzTEtLM23btjWenp7G39/fPP744+b06dMl1skYY95//33TqFEj4+bmZnx8fEz37t3Njh07HGJatWplWrVqVeQ65bd1LrYfjfnzfNuyZYu56667TJUqVYy3t7e5//77C50vF7ZDLhQWFmbNr6R2GIC/Lu75jrjn/6Ei3vN//PFH06FDB1O5cmXTvHlzY0zpj6OVK1eaVq1aGV9fX+Pu7m5CQ0NNjx49HO7vRd0vU1NTTYsWLYybm5sJDg42zz77rHn33XcL/b94ae61xhhz5MgR89hjj5n69esbLy8vU61aNdOmTRvz3XffFZq2uHleKP8YGD9+vHn55ZdNaGiocXNzM02bNjUrVqwoFP/999+btm3bmsqVKxsPDw8THR1tFi9e7BBz+vRpM2LECBMeHm61Z5o2bWo++eQTY8wf+6Oo467gs4mCaCPBGGN4GoVyVVKj59SpU8bJycm0a9fOKivY6ElNTTWdOnUyHh4eJjU11aSmppr09HSTmppqoqKiTO3ata3yzMxMk5eXZzp27Gi8vLzMmDFjzPLly817771nqlevbho0aGB+//13a95hYWEmODjY1K5d28ycOdOsXr3abNiwwRw6dMiEhoaasLAwM336dLNixQrz0ksvGTc3N5OQkGBNn3+BDA8PNx07djQLFy40CxcuNJGRkcbHx8ecOHHCik1OTjYuLi6mUaNG5sMPPzSrVq0yM2fONL1797Zitm/fbux2u4mMjDQff/yxSUlJMSNGjDCVKlUyo0ePLnF7SzKhoaGmQYMG5tNPPzVfffWV6dixo5FkPv/8cyuuNOt39uxZk5ycbCSZfv36Wdt49+7dxhhjgoODzYABA6x5vvrqq8bDw8NIshobubm5xtvb2zz99NNW3NixY43NZjMPP/ywWbx4sZk/f76Jjo42Xl5eZvv27VbcqlWrjKurq7njjjvM3LlzTXJysklISCh0Q8g/xmrXrm0GDx5sli1bZt577z3j4+Nj2rRpU+I2+/zzz83zzz9vFixYYL799lszZ84c06pVK1OtWjVz9OhRY8wfjYlXXnnFSDJvvfWWtS2OHDlitm/fblq2bGmCgoKs8vwG76UeizVq1DANGjQwH3/8sVm2bJm55557jCTz7bffllgPYwonc86fP29iY2ONs7OzGTVqlElJSTGTJk0yXl5eJioqyiHRUprlF+fMmTOmUaNGxsvLy0yaNMmkpKSYUaNGGWdnZ9OpUycrrrTJnP3795vBgwcbSWb+/PkO53d+XatXr25q1qxpZs6caZYuXWruv/9+639A8pU2mXOxfQgAxaGNQxunordx+vbtaz0Ae/PNN83y5cvNQw89ZCSZkSNHmrp165r333/fLFu2zHTp0sVIMps2bbKmLy6Z4+rqamrWrGnGjh1rUlJSzOjRo42zs7Pp0qVLiXXKb8fcd999ZsmSJebjjz82tWvXNna73fz8889WXGkeVJS0H/PPt7CwMPPUU0+ZZcuWmddee81qB+Xk5FjzLc2DipLaYQD+urjnc8+/Hu75+cmscePGmZUrV5ply5aV+jjas2ePcXd3Nx06dDALFy4033zzjUlKSjLx8fEOL4IWvF9u377deHp6Wvvqyy+/NLGxsaZmzZqXncz56aefzGOPPWbmzJljvvnmG7N48WLTr18/U6lSJYc2ycXmeaH84zs0NNTcfvvtZt68eebzzz83t956q3FxcTFr1661Yr/55hvj4uJimjZtaubOnWsWLlxoYmJijM1mM3PmzLHiBg4caDw9Pc1rr71mVq9ebRYvXmxeffVVM2XKFGOMMbt37zY9e/Y0khyeMVz4DKYg2kjIRzIH5aqkRo8xxgQGBpr69etbwwUbPcb8+ZZBQa1atTI33XSTQ9mnn35qJJl58+Y5lG/cuNFIMm+//bZVFhYWZpycnMyuXbscYgcOHGgqV65sfvvtN4fySZMmGUnWzTn/phAZGWnOnTtnxW3YsMFIcnhzpE6dOqZOnTrmzJkzxW6L2NhYU6NGDeuhdb4nnnjCuLu7m+PHjxc7rTF/XGQ9PDxMenq6VXbu3DlTr149c8MNN1zy+h09erTYC/cDDzxgateubQ23b9/e9O/f3/j4+JiPPvrIGGPMv/71LyPJpKSkGGOM2bdvn3F2djaDBw92mNfJkydNUFCQ6dWrl1VWr149ExUV5fDGkDHGdOnSxQQHB5u8vDxjzJ/H2KBBgxziJkyYYCSZQ4cOXXSbFXTu3Dlz6tQp4+Xl5fClx+eff17ogUa+zp07F/oixphLPxbd3d0d9smZM2eMr6+vGThwYKnqUTCZk3/znjBhgkPc3LlzjSTz7rvvXvLyizJt2rRCbzQZY8z48eMd9n9pkznGGDNx4sRi31wJCwszNpvNbN261aG8Q4cOxtvb23pzqLTJHGOK34cAUBzaOLRxKnobJ/+t0AuPl9zcXFOtWjXrbcx8x44dM05OTmb48OFWWXHJHEmFvoYdO3askWTWrFlTbH0yMjKMh4eHw4sexvyx7dzc3EyfPn2sstI8qDDm4vsx/3wbNmyYQ3lSUpKRZGbPnm2VlfYB08XaYQD+urjnc8+/Xu75M2fOdCgv7XH0xRdfGEmF/h+7oILb8d577y12X11uMqegc+fOmdzcXNOuXTtz1113XbQ+Rck/vkNCQhyO26ysLOPr62vat29vlTVv3twEBASYkydPOiy/YcOGpkaNGub8+fPGGGMaNmxounfvftHlPv7444WuAcWhjYQLVRJQwRljynR+ixcvVtWqVdW1a1edO3fO+mvSpImCgoL0zTffOMQ3atRIdevWLTSPNm3aKCQkxGEecXFxkqRvv/3WIb5z585ycnJymKck/fbbb5Kkn3/+Wb/++qv69esnd3f3Iut99uxZrVy5UnfddZc8PT0dltupUyedPXtW69atK3H927VrZ/XTK0lOTk669957tXv3bh04cOCy1q+45fznP//Rnj17dPbsWa1Zs0YdO3ZUmzZttHz5cknSihUr5Obmpttvv12StGzZMp07d04PPvigw3Ld3d3VqlUra9/s3r1bP/30k9VncMFtcejQIe3atcuhPt26dXMYLrgPinPq1Ck988wzuuGGG+Ts7CxnZ2dVrlxZp0+f1s6dO0vcDhdzqcdikyZNVLNmTWvY3d1ddevWLXEdirNq1SpJUkJCgkP5PffcIy8vL61cubJMlr9q1Sp5eXmpZ8+eDuX5yy24nLJw0003qXHjxg5lffr0UVZWlrZs2VLmywOAy0Eb5w+0ccqnjSNJNptNnTp1soadnZ11ww03KDg4WFFRUVa5r6+vAgICSt3mKPi7Dn369JEkrV69uthpUlNTdebMmULtktDQULVt2/aqtBekwnXt1auXnJ2dL1pXALhU3PP/wD2//O750h+/43Sh0h5HTZo0kaurqwYMGKCPPvpI//nPf0q1vNWrVxe7r67EtGnTdPPNN8vd3V3Ozs5ycXHRypUrr+gZTY8ePRyO2ypVqqhr16767rvvlJeXp9OnT2v9+vXq2bOnKleu7LA+8fHxOnDggLW/brvtNn399dd69tln9c033+jMmTOXv7KijQRHzuVdAeBiTp8+rWPHjikyMrLM5nn48GGdOHFCrq6uRY7/3//+5zAcHBxc5DwWLVokFxeXUs3Dz8/PYdjNzU2SrAv60aNHJanQDyBe6NixYzp37pymTJmiKVOmlGq5RQkKCiq27NixY6pRo8Ylr19R2rdvL+mPhk2tWrWUm5urtm3b6vDhw3rppZescS1btrR+ePHw4cOSpFtvvbXIeVaqVMkhLjExUYmJiaWqY0n7oDh9+vTRypUrNWrUKN16663y9va2Hnxc6Q35Uo/Fgusg/bEel1uPY8eOydnZWdWqVXMot9lsCgoK0rFjx8pk+ceOHVNQUJBsNptDeUBAgJydnQstpyyUdJwDQHmjjfMn2jjl08aRJE9Pz0IP3FxdXeXr61so1tXVVWfPni1xns7OzoXqVJp7cP64oo7LkJAQ66FZWSt43OTXn/YCgLLCPf9P3PPL957v7e3tUFba46hOnTpasWKFJkyYoMcff1ynT59W7dq1NWTIED355JPFLjP/WUBBRZWV1muvvaYRI0bo0Ucf1UsvvSR/f385OTlp1KhRV5TMKa6eOTk5OnX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",
      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "hf_model_nnsight = NNsight(hf_model)\n",
    "\n",
    "with hf_model_nnsight.trace(tokens):\n",
    "    hf_attn_out = hf_model_nnsight.model.layers[0].self_attn.output.save()\n",
    "    hf_mlp_out = hf_model_nnsight.model.layers[0].mlp.output.save()\n",
    "    hf_resid_post = hf_model_nnsight.model.layers[0].output.save()\n",
    "_, cache = hooked_model.run_with_cache(tokens)\n",
    "\n",
    "layer0_attn_out_diff = hf_attn_out[0] - cache[\"blocks.0.hook_attn_out\"]\n",
    "layer0_mlp_out_diff = hf_mlp_out[0] - cache[\"blocks.0.hook_mlp_out\"]\n",
    "layer0_resid_post_diff = hf_resid_post[0] - cache[\"blocks.0.hook_resid_post\"]\n",
    "\n",
    "# histogram of the difference between the attention output from the hooked model and the huggingface model\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 5))\n",
    "sns.histplot(layer0_attn_out_diff.detach().flatten().cpu().numpy(), bins=100, ax=ax1)\n",
    "ax1.set_title(\"Difference between attention output\")\n",
    "# histogram of the difference between the mlp output from the hooked model and the huggingface model\n",
    "sns.histplot(layer0_mlp_out_diff.detach().flatten().cpu().numpy(), bins=100, ax=ax2)\n",
    "ax2.set_title(\"Difference between mlp output\")\n",
    "# histogram of the difference between the residual post output from the hooked model and the huggingface model\n",
    "sns.histplot(layer0_resid_post_diff.detach().flatten().cpu().numpy(), bins=100, ax=ax3)\n",
    "ax3.set_title(\"Difference between residual post output\")\n",
    "plt.suptitle(\"Difference between outputs from the hooked model and the huggingface model in layer 0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(True, device='cuda:0'),\n",
       " tensor(True, device='cuda:0'),\n",
       " tensor(True, device='cuda:0'))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(hooked_model.blocks[0].mlp._parameters[\"W_gate\"] == hf_model.model.layers[0].mlp.gate_proj.weight.T).all(), (hooked_model.blocks[0].mlp._parameters[\"W_in\"] == hf_model.model.layers[0].mlp.up_proj.weight.T).all(), (hooked_model.blocks[0].mlp._parameters[\"W_out\"] == hf_model.model.layers[0].mlp.down_proj.weight.T).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(119), tensor(False))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_in = torch.ones(1, 1, 4096).to('cuda:0')\n",
    "hf_out = hf_model.model.layers[0].mlp(test_in).detach().cpu()\n",
    "hooked_out = hooked_model.blocks[0].mlp(test_in).detach().cpu()\n",
    "isclose = torch.isclose(hf_out,hooked_out)\n",
    "(~isclose).sum(), isclose.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(9), torch.Size([1, 1, 14336]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fancy_einsum import einsum\n",
    "hf_w_gate_out = hf_model.model.layers[0].mlp.gate_proj(test_in).detach().cpu()\n",
    "# https://github.com/TransformerLensOrg/TransformerLens/blob/318236402ddcc9cabace3f2fca40c71f0c2e9e57/transformer_lens/components/gated_mlp.py#L102C13-L107C18\n",
    "hooked_w_gate_out = einsum(\n",
    "                    \"batch pos d_model, d_model d_mlp -> batch pos d_mlp\",\n",
    "                    test_in,\n",
    "                    hooked_model.blocks[0].mlp.W_gate,\n",
    "                ).detach().cpu()\n",
    "is_close = torch.isclose(hf_w_gate_out, hooked_w_gate_out)\n",
    "(~is_close).sum(), is_close.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(9), torch.Size([1, 1, 14336]))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from opt_einsum import contract\n",
    "hf_w_gate_out = hf_model.model.layers[0].mlp.gate_proj(test_in).detach().cpu()\n",
    "# https://github.com/TransformerLensOrg/TransformerLens/blob/318236402ddcc9cabace3f2fca40c71f0c2e9e57/transformer_lens/components/gated_mlp.py#L102C13-L107C18\n",
    "hooked_w_gate_out = contract(\"bpk, kd -> bpd\",\n",
    "                    test_in,\n",
    "                    hooked_model.blocks[0].mlp.W_gate,\n",
    "                ).detach().cpu()\n",
    "is_close = torch.isclose(hf_w_gate_out, hooked_w_gate_out)\n",
    "(~is_close).sum(), is_close.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(9), torch.Size([1, 1, 14336]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hooked_w_gate_out = (test_in @ hooked_model.blocks[0].mlp.W_gate).detach().cpu()\n",
    "is_close = torch.isclose(hf_w_gate_out, hooked_w_gate_out)\n",
    "(~is_close).sum(), is_close.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(9), torch.Size([1, 1, 14336]))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin = torch.nn.Linear(4096, 14336, bias=False)\n",
    "lin.weight = torch.nn.Parameter(hooked_model.blocks[0].mlp.W_gate.T)\n",
    "hooked_w_gate_out = lin(test_in).detach().cpu()\n",
    "is_close = torch.isclose(hf_w_gate_out, hooked_w_gate_out)\n",
    "(~is_close).sum(), is_close.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(9), tensor(4819))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hooked_w_gate_out = torch.nn.functional.linear(test_in, hooked_model.blocks[0].mlp.W_gate.T).detach().cpu()\n",
    "hf_w_gate_out = torch.nn.functional.linear(test_in, hf_model.model.layers[0].mlp.gate_proj.weight).detach().cpu()\n",
    "is_close = torch.isclose(hf_w_gate_out, hooked_w_gate_out)\n",
    "(~is_close).sum(), (hf_w_gate_out != hooked_w_gate_out).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(0), torch.Size([1, 1, 14336]))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hooked_model.blocks[0].mlp.W_gate.data = hf_model.model.layers[0].mlp.gate_proj.weight.T\n",
    "hf_w_gate_out = hf_model.model.layers[0].mlp.gate_proj(test_in).detach().cpu()\n",
    "# https://github.com/TransformerLensOrg/TransformerLens/blob/318236402ddcc9cabace3f2fca40c71f0c2e9e57/transformer_lens/components/gated_mlp.py#L102C13-L107C18\n",
    "hooked_w_gate_out = einsum(\n",
    "                    \"batch pos d_model, d_model d_mlp -> batch pos d_mlp\",\n",
    "                    test_in,\n",
    "                    hooked_model.blocks[0].mlp.W_gate,\n",
    "                ).detach().cpu()\n",
    "is_close = torch.isclose(hf_w_gate_out, hooked_w_gate_out)\n",
    "(~is_close).sum(), is_close.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(True, device='cuda:0')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(hooked_model.blocks[0].mlp.W_gate == hf_model.model.layers[0].mlp.gate_proj.weight.T).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[True, True, True,  ..., True, True, True],\n",
       "        [True, True, True,  ..., True, True, True],\n",
       "        [True, True, True,  ..., True, True, True],\n",
       "        ...,\n",
       "        [True, True, True,  ..., True, True, True],\n",
       "        [True, True, True,  ..., True, True, True],\n",
       "        [True, True, True,  ..., True, True, True]], device='cuda:0')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.isclose(hooked_model.blocks[0].mlp.W_gate, hf_model.model.layers[0].mlp.gate_proj.weight.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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