import math import torch.nn as nn class CausalMultiHeadAttention(nn.Module): def __init__(self, config: LLMConfig): super().__init__() assert config.hidden_size % config.num_attention_heads == 0 self.num_attention_heads = config.num_attention_heads self.head_dim = config.hidden_size // config.num_attention_heads # Key, Query, Value projections combined into one linear layer self.c_attn = nn.Linear(config.hidden_size, 3 * config.hidden_size) # Output projection self.c_proj = nn.Linear(config.hidden_size, config.hidden_size) # Causal mask register (prevents looking forward) self.register_buffer("bias", torch.tril(torch.ones(config.max_position_embeddings, config.max_position_embeddings)) .view(1, 1, config.max_position_embeddings, config.max_position_embeddings)) def forward(self, x): B, T, C = x.size() # Batch size, Sequence length, Embedding dim # Calculate Q, K, V q, k, v = self.c_attn(x).split(self.hidden_size, dim=2) # Reshape for multi-head processing: (B, num_heads, T, head_dim) q = q.view(B, T, self.num_attention_heads, self.head_dim).transpose(1, 2) k = k.view(B, T, self.num_attention_heads, self.head_dim).transpose(1, 2) v = v.view(B, T, self.num_attention_heads, self.head_dim).transpose(1, 2) # Scaled dot-product attention att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) # Apply causal mask att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf')) att = torch.softmax(att, dim=-1) y = att @ v # Re-assemble heads into single tensor y = y.transpose(1, 2).contiguous().view(B, T, C) return self.c_proj(y) Use code with caution. Feed-Forward Network Block
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Replicating the model across GPUs and splitting the batch. import math import torch
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import torch import torch.nn as nn import torch.optim as optim