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| import torch, torch.nn as nn, torch.nn.functional as F | |
| batch_size = 64 | |
| max_len = 256 | |
| d_model = 384 | |
| n_layer = 6 # 6 blocks in the decoder | |
| n_head = 6 | |
| d_q = int(d_model / n_head) | |
| dropout = 0.2 | |
| vocab_size = 65 | |
| from block import Block | |
| class Model(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.token_embedding_table = nn.Embedding(vocab_size, d_model) # Embedding matrix size: (65, 384) | |
| self.positional_embedding_table = nn.Embedding(max_len, d_model) # Position matrix size: (256, 384) | |
| self.blocks = nn.Sequential(*[Block(d_model, n_head) for _ in range(n_layer)]) | |
| self.ln = nn.LayerNorm(d_model) | |
| self.unembedding_matrix_calc = nn.Linear(d_model, vocab_size) | |
| def forward(self, idx, targets=None): | |
| B, S = idx.shape | |
| tok_emb = self.token_embedding_table(idx) # Size of embedding: (B, S, 384) | |
| pos_emb = self.positional_embedding_table(torch.arange(S, device=idx.device)) # Shape: (S, 384) | |
| x = tok_emb + pos_emb | |
| x = self.blocks(x) # Pass through all 6 blocks each of all 6 heads | |
| x = self.ln(x) | |
| logits = self.unembedding_matrix_calc(x) # --> (B, S, 384) * (384, 65) --> (B, S, 65) | |
| if targets is None: | |
| loss = None | |
| else: | |
| B, S, V = logits.shape | |
| logits = logits.view(-1, V) # (B, S, V) --> (B*S, V) | |
| targets = targets.view(-1) # --> (B, S) --> (B*S) | |
| loss = F.cross_entropy(logits, targets) # Handles softmax interally as well (better because it does log addition which reduces errors instead of log multi) | |
| return logits, loss | |
| def generate(self, idx, max_new_tokens): | |
| for _ in range(max_new_tokens): | |
| idx_cond = idx[:, -max_len:] | |
| 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 | |
| if __name__ == "__main__": | |
| model = Model() | |
| idx = torch.zeros((batch_size, max_len), dtype=torch.long) | |
| logits, loss = model(idx, idx) | |
| print("Input shape:", idx.shape) | |
| print("Output logits shape:", logits.shape) | |
| print("Calculated loss:", loss.item()) |