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| import torch | |
| import torch.nn as nn | |
| from torch.nn import functional as F | |
| import gpt_config as config | |
| from block import Block | |
| class GPTLanguageModel(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| # each token directly reads off the logits for the next token from a lookup table | |
| self.token_embedding_table = nn.Embedding(config.vocab_size, config.n_embd) | |
| self.position_embedding_table = nn.Embedding(config.block_size, config.n_embd) | |
| self.blocks = nn.Sequential(*[Block(config.n_embd, n_head=config.n_head) for _ in range(config.n_layer)]) | |
| self.ln_f = nn.LayerNorm(config.n_embd) # final layer norm | |
| self.lm_head = nn.Linear(config.n_embd, config.vocab_size) | |
| # better init, not covered in the original GPT video, but important, will cover in followup video | |
| self.apply(self._init_weights) | |
| def _init_weights(self, module): | |
| if isinstance(module, nn.Linear): | |
| torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) | |
| if module.bias is not None: | |
| torch.nn.init.zeros_(module.bias) | |
| elif isinstance(module, nn.Embedding): | |
| torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) | |
| 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=config.device)) # (T,C) | |
| x = tok_emb + pos_emb # (B,T,C) | |
| x = self.blocks(x) # (B,T,C) | |
| x = self.ln_f(x) # (B,T,C) | |
| 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): | |
| # idx is (B, T) array of indices in the current context | |
| for _ in range(max_new_tokens): | |
| # crop idx to the last block_size tokens | |
| idx_cond = idx[:, -config.block_size:] | |
| # get the predictions | |
| logits, loss = self(idx_cond) | |
| # focus only on the last time step | |
| logits = logits[:, -1, :] # becomes (B, C) | |
| # apply softmax to get probabilities | |
| probs = F.softmax(logits, dim=-1) # (B, C) | |
| # sample from the distribution | |
| idx_next = torch.multinomial(probs, num_samples=1) # (B, 1) | |
| # append sampled index to the running sequence | |
| idx = torch.cat((idx, idx_next), dim=1) # (B, T+1) | |
| return idx |