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import torch |
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from torch import nn |
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import torch.nn.functional as F |
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batch_size = 64 |
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block_size = 256 |
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max_iters = 5000 |
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eval_interval = 500 |
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learning_rate = 3e-4 |
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device = "cuda:1" if torch.cuda.is_available() else "cpu" |
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eval_iters = 200 |
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n_embeds = 384 |
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n_heads = 6 |
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n_layers = 6 |
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dropout = 0.2 |
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torch.manual_seed(1123) |
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with open("input.txt") as f: |
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text = f.read() |
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chars = sorted(list(set(text))) |
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vocab_size = len(chars) |
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stoi = {ch: i for i, ch in enumerate(chars)} |
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itos = {i: ch for i, ch in enumerate(chars)} |
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def encode(s): |
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return [stoi[c] for c in s] |
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def decode(l): |
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return "".join([itos[i] for i in l]) |
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data = torch.tensor(encode(text), dtype=torch.long) |
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n = int(0.9 * len(data)) |
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train_data = data[:n] |
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val_data = data[n:] |
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def get_batch(split): |
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data = train_data if split == "train" else val_data |
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ix = torch.randint(len(data) - block_size, (batch_size,)) |
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x = torch.stack([data[i : i + block_size] for i in ix]) |
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y = torch.stack([data[i + 1 : i + block_size + 1] for i in ix]) |
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return x, y |
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@torch.no_grad() |
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def estimate_loss(model: nn.Module): |
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out = {} |
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model.eval() |
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for split in ["train", "val"]: |
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losses = torch.zeros(eval_iters) |
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for k in range(eval_iters): |
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X, Y = get_batch(split) |
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X, Y = X.to(device), Y.to(device) |
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logits, loss = model(X, Y) |
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losses[k] = loss.item() |
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out[split] = losses.mean() |
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model.train() |
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return out |
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class Head(nn.Module): |
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def __init__(self, n_embed, head_size) -> None: |
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super().__init__() |
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self.key = nn.Linear(n_embed, head_size, bias=False) |
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self.query = nn.Linear(n_embed, head_size, bias=False) |
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self.value = nn.Linear(n_embed, head_size, bias=False) |
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self.dropout = nn.Dropout(dropout) |
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self.register_buffer("tril", torch.tril(torch.ones(block_size, block_size))) |
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def forward(self, x): |
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B, T, C = x.shape |
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k = self.key(x) |
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q = self.query(x) |
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wei = q @ k.transpose(-2, -1) * (C**-0.5) |
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wei = wei.masked_fill(self.tril[:T, :T] == 0, float("-inf")) |
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wei = F.softmax(wei, dim=-1) |
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wei = self.dropout(wei) |
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v = self.value(x) |
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out = wei @ v |
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return out |
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class MultiHeadAttention(nn.Module): |
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def __init__(self, n_heads, n_embeds, head_size): |
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super().__init__() |
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self.heads = nn.ModuleList([Head(n_embeds, head_size) for _ in range(n_heads)]) |
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self.proj = nn.Linear(n_embeds, n_embeds) |
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self.dropout = nn.Dropout(dropout) |
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def forward(self, x): |
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x = torch.cat([h(x) for h in self.heads], dim=-1) |
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x = self.proj(x) |
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x = self.dropout(x) |
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return x |
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class FeedForward(nn.Module): |
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def __init__(self, n_embeds): |
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super().__init__() |
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self.net = nn.Sequential( |
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nn.Linear(n_embeds, 4 * n_embeds), |
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nn.ReLU(), |
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nn.Linear(4 * n_embeds, n_embeds), |
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nn.Dropout(dropout), |
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) |
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def forward(self, x): |
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return self.net(x) |
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class Block(nn.Module): |
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def __init__(self, n_embeds, n_heads): |
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super().__init__() |
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head_size = n_embeds // n_heads |
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self.sa_heads = MultiHeadAttention(n_heads, n_embeds, head_size) |
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self.ffwd = FeedForward(n_embeds) |
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self.ln1 = nn.LayerNorm(n_embeds) |
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self.ln2 = nn.LayerNorm(n_embeds) |
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def forward(self, x): |
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x = x + self.sa_heads(self.ln1(x)) |
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x = x + self.ffwd(self.ln2(x)) |
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return x |
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class BigramLanguageModel(nn.Module): |
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def __init__(self, vocab_size, n_embeds, block_size): |
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super().__init__() |
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self.token_embedding_table = nn.Embedding(vocab_size, n_embeds) |
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self.position_embedding_table = nn.Embedding(block_size, n_embeds) |
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self.blocks = nn.Sequential( |
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*[Block(n_embeds, n_heads) for _ in range(n_layers)] |
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) |
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self.lnf = nn.LayerNorm(n_embeds) |
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self.lm_head = nn.Linear(n_embeds, vocab_size) |
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def forward(self, idx, targets=None): |
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B, T = idx.shape |
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tok_embeds = self.token_embedding_table(idx) |
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pos_embeds = self.position_embedding_table( |
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torch.arange(T, device=device) |
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) |
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x = tok_embeds + pos_embeds |
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x = self.blocks(x) |
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x = self.lnf(x) |
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logits = self.lm_head(x) |
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loss = None |
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if targets is not None: |
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B, T, C = logits.shape |
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logits = logits.view(B * T, C) |
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targets = targets.view(B * T) |
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loss = F.cross_entropy(logits, targets) |
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return logits, loss |
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def generate(self, idx, max_new_tokens): |
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for _ in range(max_new_tokens): |
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idx_cond = idx[:, -block_size:] |
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logits, loss = self(idx_cond) |
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logits = logits[:, -1, :] |
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probs = F.softmax(logits, dim=-1) |
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idx_next = torch.multinomial(probs, num_samples=1) |
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idx = torch.cat((idx, idx_next), dim=1) |
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return idx |
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model = BigramLanguageModel(vocab_size, n_embeds, block_size) |
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model = model.to(device) |
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optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3) |
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for iter in range(max_iters): |
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if iter % eval_interval == 0: |
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losses = estimate_loss(model) |
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print( |
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f"Step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}" |
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) |
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xb, yb = get_batch("train") |
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xb, yb = xb.to(device), yb.to(device) |
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logits, loss = model(xb, yb) |
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optimizer.zero_grad(set_to_none=True) |
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loss.backward() |
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optimizer.step() |
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context = torch.zeros((1, 1), dtype=torch.long, device=device) |
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results = decode(model.generate(context, max_new_tokens=100)[0].tolist()) |
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print(results) |
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