File size: 14,087 Bytes
4081493 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VktNs2NoNiDt"
},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"from torch.nn import functional as F\n",
"from tqdm import tqdm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3jOZxHu3NiDt",
"outputId": "8fe80f12-21b3-4b81-9388-5211f00f6848"
},
"outputs": [
{
"data": {
"text/plain": [
"<torch._C.Generator at 0x78c6be325b90>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.manual_seed(1337)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "T-pNtwn5NiDu"
},
"outputs": [],
"source": [
"# hyperparameters\n",
"batch_size = 8 # how many independent sequences will we process in parallel?\n",
"block_size = 128 # what is the maximum context length for predictions?\n",
"max_iters = 100\n",
"eval_interval = 10\n",
"learning_rate = 6.0 * 10**-4\n",
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"eval_iters = 200\n",
"n_embd = 768\n",
"n_head = 12\n",
"n_layer = 12\n",
"dropout = 0.25"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XYHnR6ETNiDu"
},
"outputs": [],
"source": [
"with open(\"\", \"r\", encoding=\"utf-8\") as f:\n",
" text = f.read()\n",
"\n",
"chars = sorted(list(set(text)))\n",
"vocab_size = len(chars)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gJZbe7PyNiDu"
},
"outputs": [],
"source": [
"# create a mapping from characters to integers\n",
"stoi = { ch:i for i,ch in enumerate(chars) }\n",
"itos = { i:ch for i,ch in enumerate(chars) }\n",
"encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers\n",
"decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OAN-qtdPNiDv"
},
"outputs": [],
"source": [
"# Train and test splits\n",
"data = torch.tensor(encode(text), dtype=torch.long)\n",
"n = int(0.9*len(data)) # first 90% will be train, rest val\n",
"train_data = data[:n]\n",
"val_data = data[n:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wZ70_NY-NiDv"
},
"outputs": [],
"source": [
"# data loading\n",
"def get_batch(split):\n",
" # generate a small batch of data of inputs x and targets y\n",
" data = train_data if split == 'train' else val_data\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\n",
"\n",
"@torch.no_grad()\n",
"def estimate_loss(model):\n",
" out = {}\n",
" model.eval()\n",
" for split in ['val']:\n",
" losses = torch.zeros(eval_iters)\n",
" for k in range(eval_iters):\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,
"metadata": {
"id": "KgnNQJUENiDv"
},
"outputs": [],
"source": [
"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",
" B,T,C = x.shape\n",
" k = self.key(x) # (B,T,C)\n",
" q = self.query(x) # (B,T,C)\n",
" # compute attention scores (\"affinities\")\n",
" wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, 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,C)\n",
" out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C)\n",
" return out\n",
"\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(n_embd, 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)\n",
" out = self.dropout(self.proj(out))\n",
" return out\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",
" x = x + self.sa(self.ln1(x))\n",
" x = x + self.ffwd(self.ln2(x))\n",
" return x\n",
"\n",
"# super simple bigram model\n",
"class BigramLanguageModel(nn.Module):\n",
"\n",
" def __init__(self):\n",
" super().__init__()\n",
" # each token directly reads off the logits for the next token from a lookup table\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",
" def forward(self, idx, targets=None):\n",
" B, T = idx.shape\n",
"\n",
" # idx and targets are both (B,T) tensor of integers\n",
" tok_emb = self.token_embedding_table(idx) # (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)\n",
" targets = targets.view(B*T)\n",
" loss = F.cross_entropy(logits, targets)\n",
"\n",
" return logits, loss\n",
"\n",
" def generate(self, idx, max_new_tokens):\n",
" # idx 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",
" idx_cond = idx[:, -block_size:]\n",
" # get the predictions\n",
" logits, loss = self(idx_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",
" idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)\n",
" # append sampled index to the running sequence\n",
" idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)\n",
" return idx"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2J-6tLksNiDv",
"outputId": "dc261c06-4699-45d6-883d-c94829e06e7c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"85.226561 M parameters\n"
]
}
],
"source": [
"model = BigramLanguageModel().to(device)\n",
"# print the number of parameters in the model\n",
"print(sum(p.numel() for p in model.parameters())/1e6, 'M parameters')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mJ_twWqrNiDw"
},
"outputs": [],
"source": [
"# create a pytorch optimizer\n",
"optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lyVGZiRPPKuy"
},
"outputs": [],
"source": [
"state = torch.load(\"\")\n",
"model.load_state_dict(state[\"model_state_dict\"])\n",
"optimizer.load_state_dict(state[\"optimizer_state_dict\"])\n",
"max_iters = state[\"epoch\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OQaLj9z3NiDw"
},
"outputs": [],
"source": [
"# train\n",
"iterator = tqdm(range(max_iters), desc=\"Training\", postfix={\"train_loss\": 0.0})\n",
"\n",
"for iter in iterator:\n",
"\n",
" # sample a batch of data\n",
" xb, yb = get_batch('train')\n",
"\n",
" # evaluate the loss\n",
" logits, loss = model(xb, yb)\n",
" val_loss = estimate_loss(model)[\"val\"]\n",
"\n",
" optimizer.zero_grad(set_to_none=True)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" # Update the postfix with current train loss\n",
" iterator.set_postfix({\"train_loss\": loss.item(), \"val_loss\": val_loss.item()}, refresh=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "byBjpL1f5gog"
},
"outputs": [],
"source": [
"torch.save({\n",
" \"epoch\": \"\",\n",
" \"model_state_dict\": model.state_dict(),\n",
" \"optimizer_state_dict\": optimizer.state_dict(),\n",
"}, \"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "SV7zpB87NiDw"
},
"outputs": [],
"source": [
"context = torch.zeros((1, 1), dtype=torch.long, device=device)\n",
"print(decode(model.generate(context, max_new_tokens=2000)[0].tolist()))"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |