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| import torch | |
| import torch.nn as nn | |
| from torch.nn import functional as F | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| # one head of self-attention using scaled-dot product attention | |
| class Head(nn.Module): | |
| def __init__(self, n_embed, head_size, context_size, dropout=0.1): | |
| super().__init__() | |
| self.key = nn.Linear(n_embed, head_size, bias=False) | |
| self.query = nn.Linear(n_embed, head_size, bias=False) | |
| self.value = nn.Linear(n_embed, head_size, bias=False) | |
| self.register_buffer('tril', torch.tril(torch.ones(context_size, context_size))) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| B,T,C = x.shape | |
| k = self.key(x) | |
| q = self.query(x) | |
| v = self.value(x) | |
| tril = torch.tril(torch.ones(T, T, device=device)) | |
| wei = q @ k.transpose(-2, -1) * (C**-0.5) | |
| wei = wei.masked_fill(tril == 0, float('-inf')) | |
| wei = F.softmax(wei, dim=-1) | |
| wei = self.dropout(wei) | |
| out = wei @ v | |
| return out | |
| class MultiHeadAttention(nn.Module): | |
| def __init__(self, n_embed, num_heads, context_size, head_size, dropout): | |
| super().__init__() | |
| self.heads = nn.ModuleList([ | |
| Head(n_embed, head_size, context_size) | |
| for _ in range(num_heads) | |
| ]) | |
| self.projection = nn.Linear(n_embed, n_embed) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| out = torch.cat([h(x) for h in self.heads], dim=-1) | |
| out = self.projection(out) | |
| return self.dropout(out) | |
| # simple feed forward layer | |
| 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(), | |
| # projection layer | |
| nn.Linear(4 * n_embeds, n_embeds), | |
| nn.Dropout(dropout) | |
| ) | |
| def forward(self, x): | |
| return self.net(x) | |
| # Transformer block | |
| class Block(nn.Module): | |
| def __init__(self, n_embeds, n_head, context_size, dropout): | |
| super().__init__() | |
| head_size = n_embeds // n_head | |
| self.sa = MultiHeadAttention(n_embeds, n_head, context_size, head_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(self.ln1(x)) | |
| x = x + self.ffwd(self.ln2(x)) | |
| return x | |
| # simple bigram model | |
| class DecoderTransformer(nn.Module): | |
| def __init__(self, vocab_size, n_embed, context_size, n_layer, n_head, dropout): | |
| super().__init__() | |
| self.token_embedding_table = nn.Embedding(vocab_size, n_embed) | |
| self.position_embedding_table = nn.Embedding(context_size, n_embed) | |
| self.blocks = nn.Sequential( | |
| *[Block( | |
| n_embeds=n_embed, | |
| n_head=n_head, | |
| context_size=context_size, | |
| dropout=dropout | |
| ) for _ in range(n_layer)] | |
| ) | |
| self.ln_f = nn.LayerNorm(n_embed) | |
| self.lm_head = nn.Linear(n_embed, vocab_size) | |
| def forward(self, idx, targets=None): | |
| B, T = idx.shape | |
| # idx and targets of size (B,T) | |
| token_embeds = self.token_embedding_table(idx) # yields (B, T, C) | |
| pos_embeds = self.position_embedding_table(torch.arange(T, device=device)) | |
| x = token_embeds + pos_embeds | |
| x = self.ln_f(self.blocks(x)) | |
| logits = self.lm_head(x) | |
| if targets is None: | |
| return logits, None | |
| # reshape elements | |
| B, T, C = logits.shape | |
| logits = logits.view(B*T,C) | |
| targets = targets.view(B*T) | |
| # compute loss (CE) | |
| loss = F.cross_entropy(logits, targets) | |
| return logits, loss | |
| def generate(self, idx, max_new_tokens, context_size): | |
| for _ in range(max_new_tokens): | |
| idx_cond = idx[:, -context_size:] | |
| logits, loss = self(idx_cond) | |
| logits = logits[:,-1,:] | |
| probs = F.softmax(logits, dim=-1) | |
| idx_next = torch.multinomial(probs, num_samples=1) | |
| idx = torch.cat([idx, idx_next], dim=1) | |
| return idx | |