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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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class Head(nn.Module): |
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def __init__(self, n_embeds, head_size, block_size, dropout) -> None: |
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super().__init__() |
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self.key = nn.Linear(n_embeds, head_size, bias=False) |
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self.query = nn.Linear(n_embeds, head_size, bias=False) |
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self.value = nn.Linear(n_embeds, 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, block_size, dropout): |
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super().__init__() |
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self.heads = nn.ModuleList( |
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[Head(n_embeds, head_size, block_size, dropout) for _ in range(n_heads)] |
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) |
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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, dropout): |
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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 Decoder(nn.Module): |
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def __init__(self, n_embeds, n_heads, block_size, dropout): |
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super().__init__() |
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head_size = n_embeds // n_heads |
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self.sa_heads = MultiHeadAttention( |
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n_heads, n_embeds, head_size, block_size, dropout |
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) |
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self.ffwd = FeedForward(n_embeds, dropout) |
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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 GPTModel(nn.Module): |
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def __init__( |
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self, vocab_size, n_embeds, block_size, n_heads, n_layers, dropout, device |
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): |
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super().__init__() |
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self.device = device |
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self.block_size = block_size |
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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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*[Decoder(n_embeds, n_heads, block_size, dropout) 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=self.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[:, -self.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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