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| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| class Head(nn.Module): | |
| def __init__(self, n_embeds, head_size, block_size, dropout) -> None: | |
| super().__init__() | |
| self.key = nn.Linear(n_embeds, head_size, bias=False) | |
| self.query = nn.Linear(n_embeds, head_size, bias=False) | |
| self.value = nn.Linear(n_embeds, head_size, bias=False) | |
| self.dropout = nn.Dropout(dropout) | |
| self.register_buffer("tril", torch.tril(torch.ones(block_size, block_size))) | |
| def forward(self, x): | |
| B, T, C = x.shape | |
| k = self.key(x) | |
| q = self.query(x) | |
| wei = q @ k.transpose(-2, -1) * (C**-0.5) # (B,T,16) @ (B,16,T) --> (B,T,T) | |
| wei = wei.masked_fill(self.tril[:T, :T] == 0, float("-inf")) | |
| wei = F.softmax(wei, dim=-1) | |
| wei = self.dropout(wei) | |
| v = self.value(x) | |
| out = wei @ v | |
| return out | |
| class MultiHeadAttention(nn.Module): | |
| def __init__(self, n_heads, n_embeds, head_size, block_size, dropout): | |
| super().__init__() | |
| self.heads = nn.ModuleList( | |
| [Head(n_embeds, head_size, block_size, dropout) for _ in range(n_heads)] | |
| ) | |
| self.proj = nn.Linear(n_embeds, n_embeds) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| x = torch.cat([h(x) for h in self.heads], dim=-1) | |
| x = self.proj(x) | |
| x = self.dropout(x) | |
| return x | |
| class FeedForward(nn.Module): | |
| def __init__(self, n_embeds, dropout): | |
| super().__init__() | |
| self.net = nn.Sequential( | |
| nn.Linear(n_embeds, 4 * n_embeds), | |
| nn.ReLU(), | |
| nn.Linear(4 * n_embeds, n_embeds), | |
| nn.Dropout(dropout), | |
| ) | |
| def forward(self, x): | |
| return self.net(x) | |
| class Decoder(nn.Module): | |
| def __init__(self, n_embeds, n_heads, block_size, dropout): | |
| super().__init__() | |
| head_size = n_embeds // n_heads | |
| self.sa_heads = MultiHeadAttention( | |
| n_heads, n_embeds, head_size, block_size, dropout | |
| ) | |
| self.ffwd = FeedForward(n_embeds, dropout) | |
| self.ln1 = nn.LayerNorm(n_embeds) | |
| self.ln2 = nn.LayerNorm(n_embeds) | |
| def forward(self, x): | |
| x = x + self.sa_heads(self.ln1(x)) | |
| x = x + self.ffwd(self.ln2(x)) | |
| return x | |
| class GPTModel(nn.Module): | |
| def __init__( | |
| self, vocab_size, n_embeds, block_size, n_heads, n_layers, dropout, device | |
| ): | |
| super().__init__() | |
| self.device = device | |
| self.block_size = block_size | |
| self.token_embedding_table = nn.Embedding(vocab_size, n_embeds) | |
| self.position_embedding_table = nn.Embedding(block_size, n_embeds) | |
| self.blocks = nn.Sequential( | |
| *[Decoder(n_embeds, n_heads, block_size, dropout) for _ in range(n_layers)] | |
| ) | |
| self.lnf = nn.LayerNorm(n_embeds) | |
| self.lm_head = nn.Linear(n_embeds, vocab_size) | |
| def forward(self, idx, targets=None): | |
| B, T = idx.shape | |
| tok_embeds = self.token_embedding_table(idx) # BxTxNemb | |
| pos_embeds = self.position_embedding_table( | |
| torch.arange(T, device=self.device) | |
| ) # TXNemb | |
| x = tok_embeds + pos_embeds # BxTxNemb | |
| x = self.blocks(x) | |
| x = self.lnf(x) | |
| logits = self.lm_head(x) # BxTxVocabSize | |
| loss = None | |
| if targets is not None: | |
| B, T, C = logits.shape | |
| logits = logits.view(B * T, C) | |
| targets = targets.view(B * T) | |
| loss = F.cross_entropy(logits, targets) | |
| return logits, loss | |
| def generate(self, idx, max_new_tokens): | |
| for _ in range(max_new_tokens): | |
| idx_cond = idx[:, -self.block_size :] | |
| logits, loss = self(idx_cond) # BxTxC | |
| logits = logits[:, -1, :] # BxC | |
| probs = F.softmax(logits, dim=-1) # BxC | |
| idx_next = torch.multinomial(probs, num_samples=1) # Bx1 | |
| idx = torch.cat((idx, idx_next), dim=1) # BxT+1 | |
| return idx | |