mohammadali.sadraei
/
SuperposPrompt_Thesis

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a50443d6-fe09-4905-b913-1be5f88c8c03",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from sklearn.model_selection import train_test_split\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from transformers import T5Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e677034-dc27-4939-8ea2-71fcbb2da57d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "np_rng = np.random.default_rng(seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d139e0a-b8e3-427b-a537-44bc0f14ba46",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.09141512, -0.31199523],\n",
       "       [ 0.22513536,  0.28216941],\n",
       "       [-0.58531056, -0.39065385],\n",
       "       [ 0.03835212, -0.09487278],\n",
       "       [-0.00504035, -0.25591318],\n",
       "       [ 0.26381939,  0.23333758],\n",
       "       [ 0.01980921,  0.33817236],\n",
       "       [ 0.1402528 , -0.25778774],\n",
       "       [ 0.11062524, -0.28766478],\n",
       "       [ 0.26353509, -0.01497777],\n",
       "       [-0.05545871, -0.20427886],\n",
       "       [ 0.3667624 , -0.04635884],\n",
       "       [-0.12849835, -0.10564007],\n",
       "       [ 0.15969276,  0.10963322],\n",
       "       [ 0.12381978,  0.1292463 ],\n",
       "       [ 0.64249428, -0.1219245 ],\n",
       "       [-0.15367282, -0.24413182],\n",
       "       [ 0.18479383,  0.33869169],\n",
       "       [-0.03418424, -0.25204694],\n",
       "       [-0.24734436,  0.19517784],\n",
       "       [ 0.22297625,  0.16294628],\n",
       "       [-0.19965291,  0.0696484 ],\n",
       "       [ 0.03500574,  0.06560658],\n",
       "       [ 0.26142863,  0.06707866],\n",
       "       [ 0.20367407,  0.02027372],\n",
       "       [ 0.08673582,  0.18938647],\n",
       "       [-0.43714675, -0.09590136],\n",
       "       [-0.1411118 , -0.19166335],\n",
       "       [-0.08254268,  0.44848239],\n",
       "       [-0.25974933,  0.29048351],\n",
       "       [-0.50486093, -0.10046551],\n",
       "       [ 0.04882592,  0.1758667 ]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_rng.normal(loc=0, scale=0.3, size=(32, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "544207bc-37fc-4376-9c63-bff44c72b32f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# BOTTLENECK_SIZE = 128\n",
    "TRAIN_BATCH_SIZE = 8192\n",
    "VALID_BATCH_SIZE = 8192\n",
    "RANDOM_SEED = 42\n",
    "\n",
    "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "37d2d256-a348-402b-999d-1a4edce360c5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_valid_test_split(total_range, random_seed=RANDOM_SEED):\n",
    "    train, testvalid = train_test_split(total_range, random_state=RANDOM_SEED, test_size=0.2)\n",
    "    test, valid = train_test_split(testvalid, random_state=RANDOM_SEED, test_size=0.5)\n",
    "    return train, valid, test\n",
    "\n",
    "def custom_dataloader(words_ids, batch_size, emb_dim, random_seed=RANDOM_SEED):\n",
    "    np_rng = np.random.default_rng(seed=random_seed)\n",
    "    while True:\n",
    "        word_ids = np_rng.choice(words_ids, size=(batch_size, 2))\n",
    "        additive_noise = np_rng.normal(loc=0, scale=0.1, size=(batch_size, emb_dim))\n",
    "        alpha = np_rng.uniform(size=(batch_size, 1))\n",
    "        yield torch.from_numpy(word_ids), torch.Tensor(additive_noise), torch.Tensor(alpha)\n",
    "        \n",
    "class FakeEpoch:\n",
    "    def __init__(self, dataloader, each_epoch_size):\n",
    "        self.dataloader_iter = iter(dataloader)\n",
    "        self.each_epoch_size = each_epoch_size\n",
    "        \n",
    "    def __len__(self):\n",
    "        return self.each_epoch_size\n",
    "        \n",
    "    def __iter__(self):\n",
    "        for _ in range(self.each_epoch_size):\n",
    "            yield next(self.dataloader_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "644ae479-3f9a-426a-bd0b-4ec7694bc675",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "def ez_freeze(module):\n",
    "    for param in module.parameters():\n",
    "        param.requires_grad = False\n",
    "        \n",
    "def ez_mlp(linear_dims, last_layer_bias=False):\n",
    "    layers = []\n",
    "    pairs_count = len(linear_dims) - 1\n",
    "    for idx in range(pairs_count):\n",
    "        in_dim, out_dim = linear_dims[idx], linear_dims[idx + 1]\n",
    "        if idx == pairs_count - 1:\n",
    "            layers.append(nn.Linear(in_dim, out_dim, bias=last_layer_bias))\n",
    "        else:\n",
    "            layers.append(nn.Linear(in_dim, out_dim, bias=True))\n",
    "            layers.append(nn.ReLU())\n",
    "    return nn.Sequential(*layers)\n",
    "\n",
    "def auto_encoder_model(linear_dims):\n",
    "    return nn.Sequential(\n",
    "        ez_mlp(linear_dims, last_layer_bias=False),\n",
    "        nn.LayerNorm(linear_dims[-1]),\n",
    "        ez_mlp(list(reversed(linear_dims)), last_layer_bias=True)\n",
    "    )\n",
    "\n",
    "class AutoEncoderModel(nn.Module):\n",
    "    def __init__(self, pretrained_name, bottleneck_sizes):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.bottleneck_size = bottleneck_sizes\n",
    "        \n",
    "        model = T5Model.from_pretrained(pretrained_name)\n",
    "        self.emb_layer = model.get_encoder().get_input_embeddings()\n",
    "        ez_freeze(self.emb_layer)\n",
    "        \n",
    "        self.auto_encoder = auto_encoder_model([\n",
    "            self.embedding_dim,\n",
    "            *bottleneck_sizes\n",
    "        ])\n",
    "        \n",
    "        self.loss_fn = nn.MSELoss()\n",
    "        \n",
    "    def forward(self, word_ids, additive_noise, alpha):\n",
    "        # word_ids.shape = (batch_size, 2)\n",
    "        # additive_noise.shape = (batch_size, embedding_dim)\n",
    "        # alpha.shape = (batch_size, 1)\n",
    "        \n",
    "        word_embs = self.emb_layer(word_ids)\n",
    "        # word_embs.shape = (batch_size, 2, embedding_dim)\n",
    "        \n",
    "        word_combs = word_embs[:, 0] * alpha + word_embs[:, 1] * (1 - alpha)\n",
    "        # word_combs.shape = (batch_size, embedding_dim)\n",
    "                        \n",
    "        y_hat = self.auto_encoder(word_combs + additive_noise)\n",
    "        loss = self.loss_fn(word_combs, y_hat)\n",
    "        return loss, y_hat\n",
    "        \n",
    "    @property\n",
    "    def embedding_dim(self):\n",
    "        return self.emb_layer.embedding_dim\n",
    "    \n",
    "    @property\n",
    "    def num_embeddings(self):\n",
    "        return self.emb_layer.num_embeddings   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aba28049-20bf-4ae6-9445-2f7c294686d8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = AutoEncoderModel('google/t5-large-lm-adapt', bottleneck_sizes=[768, 768, 512, 512, 256, 256, 128, 128])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cac6bc39-ba12-4052-bd5f-8834f57cfa15",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(96.9082)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(model.emb_layer.weight**2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "afe2efbf-e703-4c43-8f7b-a87d303ea89e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_ds, valid_ds, test_ds = train_valid_test_split(range(model.num_embeddings))\n",
    "train_loader = custom_dataloader(words_ids=train_ds, batch_size=TRAIN_BATCH_SIZE, emb_dim=model.embedding_dim)\n",
    "valid_loader = custom_dataloader(words_ids=valid_ds, batch_size=VALID_BATCH_SIZE, emb_dim=model.embedding_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c24ccc1c-4cbe-4373-871e-9090dceb69a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = FakeEpoch(train_loader, 1000)\n",
    "valid_loader = FakeEpoch(valid_loader, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71936e43-d718-45ef-8115-7fc63999ebd9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def _prefix_dict_keys(prefix, input_dict):\n",
    "    return {f'{prefix}_{key}': val for key, val in input_dict.items()}\n",
    "\n",
    "def train_loop(model, loader, optimizer, use_tqdm=False):\n",
    "    model.train()\n",
    "\n",
    "    batch_losses = []\n",
    "    \n",
    "    if use_tqdm:\n",
    "        loader = tqdm(loader, position=2, desc=\"Train Loop\", leave=False)\n",
    "        \n",
    "    for row in loader:\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        out = model(*(item.to(DEVICE) for item in row))\n",
    "        loss = out[0]\n",
    " \n",
    "        batch_loss_value = loss.item()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    " \n",
    "        batch_losses.append(batch_loss_value)\n",
    "    \n",
    "    loss_value = np.mean(batch_losses)\n",
    "    return _prefix_dict_keys('train', {\n",
    "        'loss': loss_value\n",
    "    })\n",
    "\n",
    "def valid_loop(model, loader, use_tqdm=False):\n",
    "    model.eval()\n",
    "\n",
    "    batch_losses = []\n",
    "    \n",
    "    all_true = []\n",
    "    all_pred = []\n",
    "    \n",
    "    if use_tqdm:\n",
    "        loader = tqdm(loader, position=2, desc=\"Valid Loop\", leave=False)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for row in loader:\n",
    "            out = model(*(item.to(DEVICE) for item in row))\n",
    "            loss = out[0]\n",
    "                        \n",
    "            batch_loss_value = loss.item()\n",
    "\n",
    "            batch_losses.append(batch_loss_value)\n",
    "\n",
    "    loss_value = np.mean(batch_losses)\n",
    "    \n",
    "    return_value = {\n",
    "        'loss': loss_value,\n",
    "    }\n",
    "    \n",
    "    return _prefix_dict_keys('valid', return_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "082b5384-827f-48b3-aa8e-40483668bbc0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 8\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1000\u001b[39m):\n\u001b[1;32m      5\u001b[0m     epoch_results \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m      7\u001b[0m     epoch_results\u001b[38;5;241m.\u001b[39mupdate(\n\u001b[0;32m----> 8\u001b[0m         \u001b[43mtrain_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m            \u001b[49m\u001b[43mloader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[43m            \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     12\u001b[0m \u001b[43m            \u001b[49m\u001b[43muse_tqdm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[1;32m     13\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     14\u001b[0m     )\n\u001b[1;32m     16\u001b[0m     epoch_results\u001b[38;5;241m.\u001b[39mupdate(\n\u001b[1;32m     17\u001b[0m         valid_loop(\n\u001b[1;32m     18\u001b[0m             model\u001b[38;5;241m=\u001b[39mmodel,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     21\u001b[0m         )\n\u001b[1;32m     22\u001b[0m     )\n\u001b[1;32m     23\u001b[0m     \u001b[38;5;28mprint\u001b[39m(epoch_results)\n",
      "Cell \u001b[0;32mIn[8], line 12\u001b[0m, in \u001b[0;36mtrain_loop\u001b[0;34m(model, loader, optimizer, use_tqdm)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_tqdm:\n\u001b[1;32m     10\u001b[0m     loader \u001b[38;5;241m=\u001b[39m tqdm(loader, position\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrain Loop\u001b[39m\u001b[38;5;124m\"\u001b[39m, leave\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m loader:\n\u001b[1;32m     13\u001b[0m     optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m     15\u001b[0m     out \u001b[38;5;241m=\u001b[39m model(\u001b[38;5;241m*\u001b[39m(item\u001b[38;5;241m.\u001b[39mto(DEVICE) \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m row))\n",
      "Cell \u001b[0;32mIn[3], line 24\u001b[0m, in \u001b[0;36mFakeEpoch.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__iter__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m     23\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meach_epoch_size):\n\u001b[0;32m---> 24\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataloader_iter\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[3], line 10\u001b[0m, in \u001b[0;36mcustom_dataloader\u001b[0;34m(words_ids, batch_size, emb_dim, random_seed)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m      9\u001b[0m     word_ids \u001b[38;5;241m=\u001b[39m np_rng\u001b[38;5;241m.\u001b[39mchoice(words_ids, size\u001b[38;5;241m=\u001b[39m(batch_size, \u001b[38;5;241m2\u001b[39m))\n\u001b[0;32m---> 10\u001b[0m     additive_noise \u001b[38;5;241m=\u001b[39m \u001b[43mnp_rng\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43memb_dim\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     alpha \u001b[38;5;241m=\u001b[39m np_rng\u001b[38;5;241m.\u001b[39muniform(size\u001b[38;5;241m=\u001b[39m(batch_size, \u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m     12\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mfrom_numpy(word_ids), torch\u001b[38;5;241m.\u001b[39mTensor(additive_noise), torch\u001b[38;5;241m.\u001b[39mTensor(alpha)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.to(DEVICE)\n",
    "optimizer = torch.optim.AdamW(model.parameters(), lr=0.001)\n",
    "\n",
    "for epoch in range(1000):\n",
    "    epoch_results = {}\n",
    "\n",
    "    epoch_results.update(\n",
    "        train_loop(\n",
    "            model=model,\n",
    "            loader=train_loader,\n",
    "            optimizer=optimizer,\n",
    "            use_tqdm=False\n",
    "        )\n",
    "    )\n",
    "\n",
    "    epoch_results.update(\n",
    "        valid_loop(\n",
    "            model=model,\n",
    "            loader=valid_loader,\n",
    "            use_tqdm=False\n",
    "        )\n",
    "    )\n",
    "    print(epoch_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53425637-6146-41d2-b59e-4617ae1f8521",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:deep]",
   "language": "python",
   "name": "conda-env-deep-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}