bigramlm.py

from Block import *

class BigramLanguageModel(nn.Module):
    def __init__(self, vocab_size):
        super().__init__()
        # Each token reads off the logits for next token from a lookup table 
        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
        self.position_embedding_table = nn.Embedding(block_size, n_embd)

        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd) # final layer norm
        self.lm_head = nn.Linear(n_embd, vocab_size)

    def forward(self, idx, targets=None):
        B, T = idx.shape

        # idx and targets are both (B,T) tensor of integers
        tok_emb = self.token_embedding_table(idx) # (B, T, C)
        pos_emb = self.position_embedding_table(torch.arange(T, device=device))
        x = tok_emb + pos_emb
        x = self.blocks(x)
        x = self.ln_f(x)
        logits = self.lm_head(x) # (B, T, vocab_size)

        if targets is None:
            loss = None 
        else:
            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[:, -block_size:]

            # get the predictions
            logits, loss = self(idx_cond)

            # focus only on the last time step
            logits = logits[:, -1, :]

            # apply softmax to get probabilites
            probs = F.softmax(logits, dim=-1)

            # sample from the distribution
            idx_next = torch.multinomial(probs, num_samples=1)

            # append sampled index to the running sequence
            idx = torch.cat((idx, idx_next), dim=1)
        return idx