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| """ Architecture of the TransformerDecoder """ | |
| import torch | |
| import torch.nn as nn | |
| from torch.nn import functional as F | |
| class TransformerDecoder(nn.Module): | |
| """ GPT-style decoder-only language model """ | |
| def __init__(self, vocab_size, hyperparam_cfg, device): | |
| super(TransformerDecoder, self).__init__() | |
| self.device = device | |
| # model hyperparameters | |
| embedding_dim = hyperparam_cfg.embedding_dim | |
| num_layers = hyperparam_cfg.num_layers | |
| self.context_len = hyperparam_cfg.context_len | |
| # lookup table of tokens is used so that each token reads the logits for the next token | |
| self.token_embedding_table = nn.Embedding(vocab_size, embedding_dim) | |
| # pos embedding table adds information about the position of each token in the context | |
| self.pos_embedding_table = nn.Embedding(self.context_len, embedding_dim) | |
| # stack multiple transformer blocks to increase model capacity | |
| self.tfblocks = nn.Sequential(*[TFBlock(hyperparam_cfg) for _ in range(num_layers)]) | |
| # final normalization and linear layer to produce logits for each token in the vocabulary | |
| self.ln_f = nn.LayerNorm(embedding_dim) | |
| self.lm_head = nn.Linear(embedding_dim, vocab_size) | |
| # better weight initialization for | |
| 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): | |
| """ | |
| The forward pass of the model returns the logits of shape (B,T,C) | |
| # where: B=batch_size T=context_len C=vocab_size | |
| """ | |
| # idx is a (B,T) tensor of integers which are indices in the current context | |
| B, T = idx.shape | |
| token_embd = self.token_embedding_table(idx) # (batch_size, context_len, embedding_dim) | |
| positions = torch.arange(T).to(self.device) # tensor([0, 1, 2, ..., T-1]) | |
| pos_embd = self.pos_embedding_table(positions) # (context_len, embedding_dim) | |
| x = token_embd + pos_embd # (batch_size, context_len, embedding_dim) | |
| x = self.tfblocks(x) # (batch_size, context_len, embedding_dim) | |
| x = self.ln_f(x) # (batch_size, context_len, embedding_dim) | |
| logits = self.lm_head(x) # (batch_size, context_len, vocab_size) | |
| return logits | |
| def generate(self, idx, max_new_tokens): | |
| """ Generate new tokens from the model """ | |
| for _ in range(max_new_tokens): | |
| # crop idx to the last context_len tokens | |
| idx_context = idx[:, -self.context_len:] | |
| # get the predictions | |
| logits = self(idx_context) # (B,T,C) | |
| # focus only on the last time step | |
| logits = logits[:, -1, :] # (B, C) | |
| # apply softmax to get probabilities | |
| probs = F.softmax(logits, dim=-1) # (B, C) | |
| # sample from the distribution to get the next token index | |
| 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 | |
| class TFBlock(nn.Module): | |
| """ Single transformer block: communication (attention) followed by computation (dense) """ | |
| def __init__(self, hyperparam_cfg): | |
| super(TFBlock, self).__init__() | |
| # model hyperparameters | |
| embedding_dim = hyperparam_cfg.embedding_dim | |
| num_heads = hyperparam_cfg.num_heads | |
| context_len = hyperparam_cfg.context_len | |
| dropout = hyperparam_cfg.dropout | |
| # size of MultiHeadAttention matches the embedding dimension (num_heads * head_size = embedding_dim) | |
| self.sa_heads = MultiHeadAttention(num_heads=num_heads, | |
| head_size=embedding_dim // num_heads, | |
| embedding_dim=embedding_dim, | |
| context_len=context_len, | |
| dropout=dropout) | |
| self.feed_forward = FeedForward(embedding_dim, dropout) | |
| self.ln1 = nn.LayerNorm(embedding_dim) | |
| self.ln2 = nn.LayerNorm(embedding_dim) | |
| def forward(self, x): | |
| # both attention and feed-forward layers have residual connections | |
| x = x + self.sa_heads(self.ln1(x)) | |
| x = x + self.feed_forward(self.ln2(x)) | |
| return x | |
| class MultiHeadAttention(nn.Module): | |
| """ Multiple heads of self-attention in parallel """ | |
| def __init__(self, num_heads, head_size, embedding_dim, context_len, dropout): | |
| super(MultiHeadAttention, self).__init__() | |
| self.heads = nn.ModuleList([AttentionHead(embedding_dim, head_size, context_len, dropout) for _ in range(num_heads)]) | |
| # projection is needed due to residual connection to bring all heads back to embedding_dim | |
| self.projection = nn.Linear(num_heads * head_size, embedding_dim) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, x): | |
| x = torch.cat([h(x) for h in self.heads], dim=-1) # (batch, context_len, num_heads * head_size) | |
| out = self.dropout(self.projection(x)) # (batch, context_len, embedding_dim) | |
| return out | |
| class AttentionHead(nn.Module): | |
| """ One head of self-attention """ | |
| def __init__(self, embedding_dim, head_size, context_len, dropout): | |
| super(AttentionHead, self).__init__() | |
| self.queries = nn.Linear(embedding_dim, head_size, bias=False) | |
| self.keys = nn.Linear(embedding_dim, head_size, bias=False) | |
| self.values = nn.Linear(embedding_dim, head_size, bias=False) | |
| self.dropout = nn.Dropout(dropout) | |
| # lower triangular matrix is used to mask out future tokens in the attention mechanism | |
| self.register_buffer("mask", torch.tril(torch.ones(context_len, context_len))) | |
| def forward(self, x): | |
| B, T, C = x.shape # (batch_size, context_len, embedding_dim) | |
| q = self.queries(x) # (batch, context_len, head_size) | |
| k = self.keys(x) # (batch, context_len, head_size) | |
| v = self.values(x) # (batch, context_len, head_size) | |
| # compute attention matrix (key and query dot product) | |
| weights = q @ k.transpose(-2, -1) # (B,T,C) @ (B,C,T) -> (B,T,T) | |
| # scale by sqrt(head_size) to prevent large dot products (stabilizes gradients) | |
| weights = weights * C**-0.5 | |
| # mask replaces 0 with -inf and keeps 1 as is (ones are on and below diagonal; zeros above diagonal) | |
| weights = weights.masked_fill(self.mask[:T, :T] == 0, float('-inf')) | |
| # softmax along the last dimension to get probabilities per row | |
| weights = F.softmax(weights, dim=-1) | |
| weights = self.dropout(weights) | |
| output = weights @ v # matrix multiplication (T,T) @ (B,T,C) -> (B,T,C) = (batch, context_len, head_size) | |
| return output | |
| class FeedForward(nn.Module): | |
| """ Single feed-forward layer followed by a non-linearity """ | |
| def __init__(self, embedding_dim, dropout): | |
| super(FeedForward, self).__init__() | |
| # embedding_dim is multiplied by 4 to reflect the original transformer paper | |
| self.net = nn.Sequential( | |
| nn.Linear(embedding_dim, embedding_dim * 4), | |
| nn.ReLU(), | |
| nn.Linear(embedding_dim * 4, embedding_dim), | |
| nn.Dropout(dropout) | |
| ) | |
| def forward(self, x): | |
| return self.net(x) | |