convert to simple script

gpt_openwebtext.sync.ipynb

@@ -1,407 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "b84a347c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "from torch.nn import functional as F\n",
-    "import mmap\n",
-    "import random\n",
-    "import pickle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "058368c2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cuda\n"
-     ]
-    }
-   ],
-   "source": [
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(device)\n",
-    "block_size = 128\n",
-    "batch_size = 32\n",
-    "max_iters = 4000\n",
-    "learning_rate = 3e-4\n",
-    "eval_every = 500\n",
-    "n_embd = 384\n",
-    "n_head = 8\n",
-    "n_layer = 8\n",
-    "dropout = 0.2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "4ec3625c",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'openwebtext/vocab.txt'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m chars \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mopenwebtext/vocab.txt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m      3\u001b[0m     text \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m      4\u001b[0m     chars \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mset\u001b[39m(text)))\n",
-      "File \u001b[0;32m~/repos/main/llm-from-scratch/venv/lib/python3.10/site-packages/IPython/core/interactiveshell.py:310\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m    304\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    305\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    306\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    307\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    308\u001b[0m     )\n\u001b[0;32m--> 310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\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",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'openwebtext/vocab.txt'"
-     ]
-    }
-   ],
-   "source": [
-    "chars = \"\"\n",
-    "with open(\"./openwebtext/vocab.txt\", 'r', encoding='utf-8') as f:\n",
-    "    text = f.read()\n",
-    "    chars = sorted(list(set(text)))\n",
-    "        \n",
-    "vocab_size = len(chars)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "15e6af07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"Vocab size: {vocab_size}\")\n",
-    "print(f\"Text length: {len(text)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "425bf0b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "string_to_int = {ch: i for i, ch in enumerate(chars)}\n",
-    "int_to_string = {i: ch for i, ch in enumerate(chars)}\n",
-    "\n",
-    "encode = lambda s: [string_to_int[ch] for ch in s]\n",
-    "decode = lambda x: ''.join([int_to_string[i] for i in x])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1b141a3a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# memory map for using small snippets of text from a single file of any size\n",
-    "def get_random_chunk(split):\n",
-    "    filename = \"./openwebtext/train_split.txt\" if split == 'train' else \"./openwebtext/val_split.txt\"\n",
-    "    with open(filename, 'rb') as f:\n",
-    "        with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as mm:\n",
-    "            # Determine the file size and a random position to start reading\n",
-    "            file_size = len(mm)\n",
-    "            start_pos = random.randint(0, (file_size) - block_size*batch_size)\n",
-    "\n",
-    "            # Seek to the random position and read the block of text\n",
-    "            mm.seek(start_pos)\n",
-    "            block = mm.read(block_size*batch_size-1)\n",
-    "\n",
-    "            # Decode the block to a string, ignoring any invalid byte sequences\n",
-    "            decoded_block = block.decode('utf-8', errors='ignore').replace('\\r', '')\n",
-    "            \n",
-    "            # Train and test splits\n",
-    "            data = torch.tensor(encode(decoded_block), dtype=torch.long)\n",
-    "            \n",
-    "    return data\n",
-    "\n",
-    "\n",
-    "def get_batch(split):\n",
-    "    data = get_random_chunk(split)\n",
-    "    ix = torch.randint(len(data) - block_size, (batch_size,))\n",
-    "    x = torch.stack([data[i:i+block_size] for i in ix])\n",
-    "    y = torch.stack([data[i+1:i+block_size+1] for i in ix])\n",
-    "    x, y = x.to(device), y.to(device)\n",
-    "    return x, y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4b27acf7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@torch.no_grad()\n",
-    "def estimate_loss():\n",
-    "    out = {}\n",
-    "    model.eval()\n",
-    "    for split in ['train', 'val']:\n",
-    "        losses = torch.zeros(eval_every)\n",
-    "        for k in range(eval_every):\n",
-    "            X, Y = get_batch(split)\n",
-    "            logits, loss = model(X, Y)\n",
-    "            losses[k] = loss.item()\n",
-    "        out[split] = losses.mean()\n",
-    "    model.train()\n",
-    "    return out"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "517553b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "class Head(nn.Module):\n",
-    "    \"\"\" one head of self-attention \"\"\"\n",
-    "\n",
-    "    def __init__(self, head_size):\n",
-    "        super().__init__()\n",
-    "        self.key = nn.Linear(n_embd, head_size, bias=False)\n",
-    "        self.query = nn.Linear(n_embd, head_size, bias=False)\n",
-    "        self.value = nn.Linear(n_embd, head_size, bias=False)\n",
-    "        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))\n",
-    "\n",
-    "        self.dropout = nn.Dropout(dropout)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        # input of size (batch, time-step, channels)\n",
-    "        # output of size (batch, time-step, head size)\n",
-    "        B,T,C = x.shape\n",
-    "        k = self.key(x)   # (B,T,hs)\n",
-    "        q = self.query(x) # (B,T,hs)\n",
-    "        # compute attention scores (\"affinities\")\n",
-    "        wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, T) -> (B, T, T)\n",
-    "        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)\n",
-    "        wei = F.softmax(wei, dim=-1) # (B, T, T)\n",
-    "        wei = self.dropout(wei)\n",
-    "        # perform the weighted aggregation of the values\n",
-    "        v = self.value(x) # (B,T,hs)\n",
-    "        out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs)\n",
-    "        return out\n",
-    "\n",
-    "# [1, 0, 0]\n",
-    "# [1, 0.6, 0]\n",
-    "# [1, 0.6, 0.4]\n",
-    "class MultiHeadAttention(nn.Module):\n",
-    "    \"\"\" multiple heads of self-attention in parallel \"\"\"\n",
-    "\n",
-    "    def __init__(self, num_heads, head_size):\n",
-    "        super().__init__()\n",
-    "        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])\n",
-    "        self.proj = nn.Linear(head_size * num_heads, n_embd)\n",
-    "        self.dropout = nn.Dropout(dropout)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        out = torch.cat([h(x) for h in self.heads], dim=-1) # (B, T, F) -> (B, T, [h1, h1, h1, h1, h2, h2, h2, h2, h3, h3, h3, h3])\n",
-    "        out = self.dropout(self.proj(out))\n",
-    "        return out\n",
-    "    \n",
-    "\n",
-    "class FeedFoward(nn.Module):\n",
-    "    \"\"\" a simple linear layer followed by a non-linearity \"\"\"\n",
-    "\n",
-    "    def __init__(self, n_embd):\n",
-    "        super().__init__()\n",
-    "        self.net = nn.Sequential(\n",
-    "            nn.Linear(n_embd, 4 * n_embd),\n",
-    "            nn.ReLU(),\n",
-    "            nn.Linear(4 * n_embd, n_embd),\n",
-    "            nn.Dropout(dropout),\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        return self.net(x)\n",
-    "    \n",
-    "class Block(nn.Module):\n",
-    "    \"\"\" Transformer block: communication followed by computation \"\"\"\n",
-    "\n",
-    "    def __init__(self, n_embd, n_head):\n",
-    "        # n_embd: embedding dimension, n_head: the number of heads we'd like\n",
-    "        super().__init__()\n",
-    "        head_size = n_embd // n_head\n",
-    "        self.sa = MultiHeadAttention(n_head, head_size)\n",
-    "        self.ffwd = FeedFoward(n_embd)\n",
-    "        self.ln1 = nn.LayerNorm(n_embd)\n",
-    "        self.ln2 = nn.LayerNorm(n_embd)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        y = self.sa(x)\n",
-    "        x = self.ln1(x + y)\n",
-    "        y = self.ffwd(x)\n",
-    "        x = self.ln2(x + y)\n",
-    "        return x\n",
-    "    \n",
-    "class GPTLanguageModel(nn.Module):\n",
-    "    def __init__(self, vocab_size):\n",
-    "        super().__init__()\n",
-    "        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)\n",
-    "        self.position_embedding_table = nn.Embedding(block_size, n_embd)\n",
-    "        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])\n",
-    "        self.ln_f = nn.LayerNorm(n_embd) # final layer norm\n",
-    "        self.lm_head = nn.Linear(n_embd, vocab_size)\n",
-    "        \n",
-    "        \n",
-    "        self.apply(self._init_weights)\n",
-    "\n",
-    "    def _init_weights(self, module):\n",
-    "        if isinstance(module, nn.Linear):\n",
-    "            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)\n",
-    "            if module.bias is not None:\n",
-    "                torch.nn.init.zeros_(module.bias)\n",
-    "        elif isinstance(module, nn.Embedding):\n",
-    "            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)\n",
-    "\n",
-    "    def forward(self, index, targets=None):\n",
-    "        B, T = index.shape\n",
-    "        \n",
-    "        \n",
-    "        # idx and targets are both (B,T) tensor of integers\n",
-    "        tok_emb = self.token_embedding_table(index) # (B,T,C)\n",
-    "        pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)\n",
-    "        x = tok_emb + pos_emb # (B,T,C)\n",
-    "        x = self.blocks(x) # (B,T,C)\n",
-    "        x = self.ln_f(x) # (B,T,C)\n",
-    "        logits = self.lm_head(x) # (B,T,vocab_size)\n",
-    "        \n",
-    "        if targets is None:\n",
-    "            loss = None\n",
-    "        else:\n",
-    "            B, T, C = logits.shape\n",
-    "            logits = logits.view(B*T, C) # reshape to what torch.cross_entropy expects\n",
-    "            targets = targets.view(B*T)\n",
-    "            loss = F.cross_entropy(logits, targets) \n",
-    "        return logits, loss\n",
-    "    \n",
-    "    def generate(self, index, max_new_tokens):\n",
-    "        # index is (B, T) array of indices in the current context\n",
-    "        for _ in range(max_new_tokens):\n",
-    "            # crop idx to the last block_size tokens\n",
-    "            index_cond = index[:, -block_size:]\n",
-    "            # get the predictions\n",
-    "            logits, loss = self.forward(index_cond)\n",
-    "            # focus only on the last time step\n",
-    "            logits = logits[:, -1, :] # becomes (B, C)\n",
-    "            # apply softmax to get probabilities\n",
-    "            probs = F.softmax(logits, dim=-1) # (B, C)\n",
-    "            # sample from the distribution\n",
-    "            index_next = torch.multinomial(probs, num_samples=1) # (B, 1)\n",
-    "            # append sampled index to the running sequence\n",
-    "            index = torch.cat((index, index_next), dim=1) # (B, T+1)\n",
-    "        return index\n",
-    "\n",
-    "model = GPTLanguageModel(vocab_size).to(device)\n",
-    "\n",
-    "print('loading model parameters...')\n",
-    "with open('model-01.pkl', 'rb') as f:\n",
-    "    model = pickle.load(f)\n",
-    "print('loaded successfully!')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bb0f76ef",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create a PyTorch optimizer\n",
-    "optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n",
-    "\n",
-    "for iter in range(max_iters):\n",
-    "    if iter % eval_every == 0:\n",
-    "        losses = estimate_loss()\n",
-    "        print(f\"step: {iter}, train loss: {losses['train']:.3f}, val loss: {losses['val']:.3f}\")\n",
-    "\n",
-    "    # sample a batch of data\n",
-    "    xb, yb = get_batch('train')\n",
-    "\n",
-    "    # evaluate the loss\n",
-    "    logits, loss = model.forward(xb, yb)\n",
-    "    optimizer.zero_grad(set_to_none=True)\n",
-    "    loss.backward()\n",
-    "    optimizer.step()\n",
-    "print(loss.item())\n",
-    "\n",
-    "with open('model-01.pkl', 'wb') as f:\n",
-    "    pickle.dump(model, f)\n",
-    "print('model saved')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ccdc0134",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = 'Hello! Can you see me?'\n",
-    "context = torch.tensor(encode(prompt), dtype=torch.long, device=device)\n",
-    "generated_chars = decode(model.generate(context.unsqueeze(0), max_new_tokens=100)[0].tolist())\n",
-    "print(generated_chars)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
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-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
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-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
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-  "varInspector": {
-   "cols": {
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-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
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-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
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-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
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-   "window_display": false
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