test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0374368-eb08-4ceb-8c8e-c8f409aa7213",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from huggingface_hub import PyTorchModelHubMixin\n",
    "from transformers import AutoConfig, AutoModel\n",
    "from transformers.models.mixtral.modeling_mixtral import MixtralDecoderLayer\n",
    "from transformers.models.gpt_neo.modeling_gpt_neo import GPTNeoBlock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "94c38ffa-afbf-4056-a0f0-9015a3e18fc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resolved_archive_file='/Users/joel/.cache/huggingface/hub/models--EleutherAI--gpt-neo-125M/snapshots/21def0189f5705e2521767faed922f1f15e7d7db/model.safetensors'\n",
      "f=<builtins.safe_open object at 0x1677db7b0>\n"
     ]
    }
   ],
   "source": [
    "hf_model = AutoModel.from_pretrained(\"EleutherAI/gpt-neo-125M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "defef29c-671a-43af-861a-c64eda75ec8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyModel(nn.Module, PyTorchModelHubMixin):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        self.layer = GPTNeoBlock(config, 0)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.layer(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f313e455-503d-4c36-b736-d3154b136f50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f11cacd4b4f148c99a126baa2ffa5174",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/28.3M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/joelb/my-awesome-model/commit/7a904445fda79f1cf2337fbfde36b4d42e42479d', commit_message='Push model using huggingface_hub.', commit_description='', oid='7a904445fda79f1cf2337fbfde36b4d42e42479d', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = AutoConfig.from_pretrained(\"EleutherAI/gpt-neo-125M\")\n",
    "model = MyModel(config)\n",
    "model.push_to_hub(\"joelb/my-awesome-model\", config=config.to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1de3df1f-7012-4f34-a77c-b47c7dd2c322",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'dict' object has no attribute 'hidden_size'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model2 \u001b[38;5;241m=\u001b[39m \u001b[43mMyModel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mjoelb/my-awesome-model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/code/github/TransformerLens/.venv/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py:119\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    117\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/code/github/TransformerLens/.venv/lib/python3.11/site-packages/huggingface_hub/hub_mixin.py:420\u001b[0m, in \u001b[0;36mModelHubMixin.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, force_download, resume_download, proxies, token, cache_dir, local_files_only, revision, **model_kwargs)\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_hub_mixin_inject_config:\n\u001b[1;32m    418\u001b[0m         model_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m config\n\u001b[0;32m--> 420\u001b[0m instance \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_from_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    421\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmodel_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    422\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    423\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    424\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    425\u001b[0m \u001b[43m    \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    426\u001b[0m \u001b[43m    \u001b[49m\u001b[43mresume_download\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    427\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    428\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    429\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    430\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    432\u001b[0m \u001b[38;5;66;03m# Implicitly set the config as instance attribute if not already set by the class\u001b[39;00m\n\u001b[1;32m    433\u001b[0m \u001b[38;5;66;03m# This way `config` will be available when calling `save_pretrained` or `push_to_hub`.\u001b[39;00m\n\u001b[1;32m    434\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;28mgetattr\u001b[39m(instance, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_hub_mixin_config\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;28;01mNone\u001b[39;00m, {})):\n",
      "File \u001b[0;32m~/code/github/TransformerLens/.venv/lib/python3.11/site-packages/huggingface_hub/hub_mixin.py:643\u001b[0m, in \u001b[0;36mPyTorchModelHubMixin._from_pretrained\u001b[0;34m(cls, model_id, revision, cache_dir, force_download, proxies, resume_download, local_files_only, token, map_location, strict, **model_kwargs)\u001b[0m\n\u001b[1;32m    626\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m    627\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_from_pretrained\u001b[39m(\n\u001b[1;32m    628\u001b[0m     \u001b[38;5;28mcls\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    640\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs,\n\u001b[1;32m    641\u001b[0m ):\n\u001b[1;32m    642\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Load Pytorch pretrained weights and return the loaded model.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 643\u001b[0m     model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    644\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(model_id):\n\u001b[1;32m    645\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLoading weights from local directory\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[0;32mIn[3], line 4\u001b[0m, in \u001b[0;36mMyModel.__init__\u001b[0;34m(self, config)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, config):\n\u001b[1;32m      3\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m()\n\u001b[0;32m----> 4\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer \u001b[38;5;241m=\u001b[39m \u001b[43mGPTNeoBlock\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/code/github/TransformerLens/.venv/lib/python3.11/site-packages/transformers/models/gpt_neo/modeling_gpt_neo.py:552\u001b[0m, in \u001b[0;36mGPTNeoBlock.__init__\u001b[0;34m(self, config, layer_id)\u001b[0m\n\u001b[1;32m    550\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, config, layer_id):\n\u001b[1;32m    551\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m()\n\u001b[0;32m--> 552\u001b[0m     hidden_size \u001b[38;5;241m=\u001b[39m \u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhidden_size\u001b[49m\n\u001b[1;32m    553\u001b[0m     inner_dim \u001b[38;5;241m=\u001b[39m config\u001b[38;5;241m.\u001b[39mintermediate_size \u001b[38;5;28;01mif\u001b[39;00m config\u001b[38;5;241m.\u001b[39mintermediate_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m4\u001b[39m \u001b[38;5;241m*\u001b[39m hidden_size\n\u001b[1;32m    554\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mln_1 \u001b[38;5;241m=\u001b[39m nn\u001b[38;5;241m.\u001b[39mLayerNorm(hidden_size, eps\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mlayer_norm_epsilon)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'dict' object has no attribute 'hidden_size'"
     ]
    }
   ],
   "source": [
    "model2 = MyModel.from_pretrained(\"joelb/my-awesome-model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bdcc644c-0bfa-49c3-8318-fcd490e2f910",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resolved_archive_file='/Users/joel/.cache/huggingface/hub/models--joelb--my-awesome-model/snapshots/7a904445fda79f1cf2337fbfde36b4d42e42479d/model.safetensors'\n",
      "f=<builtins.safe_open object at 0x168eb12f0>\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'get'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model2 \u001b[38;5;241m=\u001b[39m \u001b[43mAutoModel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mjoelb/my-awesome-model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/code/github/TransformerLens/.venv/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:563\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m    561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(config) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m    562\u001b[0m     model_class \u001b[38;5;241m=\u001b[39m _get_model_class(config, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping)\n\u001b[0;32m--> 563\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    564\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhub_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    565\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    566\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    567\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnrecognized configuration class \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for this kind of AutoModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    568\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel type should be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(c\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    569\u001b[0m )\n",
      "File \u001b[0;32m~/code/github/TransformerLens/.venv/lib/python3.11/site-packages/transformers/modeling_utils.py:3315\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   3312\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mf\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   3313\u001b[0m     metadata \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39mmetadata()\n\u001b[0;32m-> 3315\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m   3316\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m   3317\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m metadata\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'get'"
     ]
    }
   ],
   "source": [
    "model2 = AutoModel.from_pretrained(\"joelb/my-awesome-model\")"
   ]
  }
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