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| import torch | |
| import torch.nn as nn | |
| from torch.nn import functional as F | |
| class hyperparams: | |
| block_size = 128 | |
| batch_size = 32 | |
| max_iters = 12000 | |
| learning_rate = 3e-4 | |
| eval_every = 100 | |
| n_embd = 384 | |
| n_head = 8 | |
| n_layer = 8 | |
| dropout = 0.2 | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| block_size = hyperparams.block_size | |
| batch_size = hyperparams.batch_size | |
| max_iters = hyperparams.max_iters | |
| learning_rate = hyperparams.learning_rate | |
| eval_every = hyperparams.eval_every | |
| n_embd = hyperparams.n_embd | |
| n_head = hyperparams.n_head | |
| n_layer = hyperparams.n_layer | |
| dropout = hyperparams.dropout | |
| device = hyperparams.device | |
| class Head(nn.Module): | |
| """one head of self-attention""" | |
| def __init__(self, head_size): | |
| super().__init__() | |
| self.key = nn.Linear(n_embd, head_size, bias=False) | |
| self.query = nn.Linear(n_embd, head_size, bias=False) | |
| self.value = nn.Linear(n_embd, head_size, bias=False) | |
| self.register_buffer("tril", torch.tril(torch.ones(block_size, block_size))) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| # input of size (batch, time-step, channels) | |
| # output of size (batch, time-step, head size) | |
| B, T, C = x.shape | |
| k = self.key(x) # (B,T,hs) | |
| q = self.query(x) # (B,T,hs) | |
| # compute attention scores ("affinities") | |
| wei = ( | |
| q @ k.transpose(-2, -1) * k.shape[-1] ** -0.5 | |
| ) # (B, T, hs) @ (B, hs, T) -> (B, T, T) | |
| wei = wei.masked_fill(self.tril[:T, :T] == 0, float("-inf")) # (B, T, T) | |
| wei = F.softmax(wei, dim=-1) # (B, T, T) | |
| wei = self.dropout(wei) | |
| # perform the weighted aggregation of the values | |
| v = self.value(x) # (B,T,hs) | |
| out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs) | |
| return out | |
| class MultiHeadAttention(nn.Module): | |
| """multiple heads of self-attention in parallel""" | |
| def __init__(self, num_heads, head_size): | |
| super().__init__() | |
| self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)]) | |
| self.proj = nn.Linear(head_size * num_heads, n_embd) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| out = torch.cat( | |
| [h(x) for h in self.heads], dim=-1 | |
| ) # (B, T, F) -> (B, T, [h1, h1, h1, h1, h2, h2, h2, h2, h3, h3, h3, h3]) | |
| out = self.dropout(self.proj(out)) | |
| return out | |
| class FeedFoward(nn.Module): | |
| """a simple linear layer followed by a non-linearity""" | |
| def __init__(self, n_embd): | |
| super().__init__() | |
| self.net = nn.Sequential( | |
| nn.Linear(n_embd, 4 * n_embd), | |
| nn.ReLU(), | |
| nn.Linear(4 * n_embd, n_embd), | |
| nn.Dropout(dropout), | |
| ) | |
| def forward(self, x): | |
| return self.net(x) | |
| class Block(nn.Module): | |
| """Transformer block: communication followed by computation""" | |
| def __init__(self, n_embd, n_head): | |
| # n_embd: embedding dimension, n_head: the number of heads we'd like | |
| super().__init__() | |
| head_size = n_embd // n_head | |
| self.sa = MultiHeadAttention(n_head, head_size) | |
| self.ffwd = FeedFoward(n_embd) | |
| self.ln1 = nn.LayerNorm(n_embd) | |
| self.ln2 = nn.LayerNorm(n_embd) | |
| def forward(self, x): | |
| y = self.sa(x) | |
| x = self.ln1(x + y) | |
| y = self.ffwd(x) | |
| x = self.ln2(x + y) | |
| return x | |
| class GPTLanguageModel(nn.Module): | |
| def __init__(self, vocab_size): | |
| super().__init__() | |
| 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) | |
| 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, index, targets=None): | |
| B, T = index.shape | |
| # idx and targets are both (B,T) tensor of integers | |
| tok_emb = self.token_embedding_table(index) # (B,T,C) | |
| pos_emb = self.position_embedding_table(torch.arange(T, device=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 | |
| ) # reshape to what torch.cross_entropy expects | |
| targets = targets.view(B * T) | |
| loss = F.cross_entropy(logits, targets) | |
| return logits, loss | |
| def generate(self, index, max_new_tokens): | |
| # index is (B, T) array of indices in the current context | |
| for _ in range(max_new_tokens): | |
| # crop idx to the last block_size tokens | |
| index_cond = index[:, -block_size:] | |
| # get the predictions | |
| logits, loss = self.forward(index_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 | |
| index_next = torch.multinomial(probs, num_samples=1) # (B, 1) | |
| # append sampled index to the running sequence | |
| index = torch.cat((index, index_next), dim=1) # (B, T+1) | |
| return index | |