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- # Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
- # Source for "Build a Large Language Model From Scratch"
- # - https://www.manning.com/books/build-a-large-language-model-from-scratch
- # Code: https://github.com/rasbt/LLMs-from-scratch
- #
- # This file collects all the relevant code that we covered thus far
- # throughout Chapters 2-5.
- import torch
- import torch.nn as nn
- #####################################
- # Chapter 3
- #####################################
- class MultiHeadAttention(nn.Module):
- def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
- super().__init__()
- assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
- self.d_out = d_out
- self.num_heads = num_heads
- self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
- self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
- self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
- self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
- self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
- self.dropout = nn.Dropout(dropout)
- self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))
- def forward(self, x):
- b, num_tokens, d_in = x.shape
- keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
- queries = self.W_query(x)
- values = self.W_value(x)
- # We implicitly split the matrix by adding a `num_heads` dimension
- # Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
- keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
- values = values.view(b, num_tokens, self.num_heads, self.head_dim)
- queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
- # Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
- keys = keys.transpose(1, 2)
- queries = queries.transpose(1, 2)
- values = values.transpose(1, 2)
- # Compute scaled dot-product attention (aka self-attention) with a causal mask
- attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head
- # Original mask truncated to the number of tokens and converted to boolean
- mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
- # Use the mask to fill attention scores
- attn_scores.masked_fill_(mask_bool, -torch.inf)
- attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
- attn_weights = self.dropout(attn_weights)
- # Shape: (b, num_tokens, num_heads, head_dim)
- context_vec = (attn_weights @ values).transpose(1, 2)
- # Combine heads, where self.d_out = self.num_heads * self.head_dim
- context_vec = context_vec.reshape(b, num_tokens, self.d_out)
- context_vec = self.out_proj(context_vec) # optional projection
- return context_vec
- #####################################
- # Chapter 4
- #####################################
- class LayerNorm(nn.Module):
- def __init__(self, emb_dim):
- super().__init__()
- self.eps = 1e-5
- self.scale = nn.Parameter(torch.ones(emb_dim))
- self.shift = nn.Parameter(torch.zeros(emb_dim))
- def forward(self, x):
- mean = x.mean(dim=-1, keepdim=True)
- var = x.var(dim=-1, keepdim=True, unbiased=False)
- norm_x = (x - mean) / torch.sqrt(var + self.eps)
- return self.scale * norm_x + self.shift
- class GELU(nn.Module):
- def __init__(self):
- super().__init__()
- def forward(self, x):
- return 0.5 * x * (1 + torch.tanh(
- torch.sqrt(torch.tensor(2.0 / torch.pi)) *
- (x + 0.044715 * torch.pow(x, 3))
- ))
- class FeedForward(nn.Module):
- def __init__(self, cfg):
- super().__init__()
- self.layers = nn.Sequential(
- nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
- GELU(),
- nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
- )
- def forward(self, x):
- return self.layers(x)
- class TransformerBlock(nn.Module):
- def __init__(self, cfg):
- super().__init__()
- self.att = MultiHeadAttention(
- d_in=cfg["emb_dim"],
- d_out=cfg["emb_dim"],
- context_length=cfg["context_length"],
- num_heads=cfg["n_heads"],
- dropout=cfg["drop_rate"],
- qkv_bias=cfg["qkv_bias"])
- self.ff = FeedForward(cfg)
- self.norm1 = LayerNorm(cfg["emb_dim"])
- self.norm2 = LayerNorm(cfg["emb_dim"])
- self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
- def forward(self, x):
- # Shortcut connection for attention block
- shortcut = x
- x = self.norm1(x)
- x = self.att(x) # Shape [batch_size, num_tokens, emb_size]
- x = self.drop_shortcut(x)
- x = x + shortcut # Add the original input back
- # Shortcut connection for feed-forward block
- shortcut = x
- x = self.norm2(x)
- x = self.ff(x)
- x = self.drop_shortcut(x)
- x = x + shortcut # Add the original input back
- return x
- class GPTModel(nn.Module):
- def __init__(self, cfg):
- super().__init__()
- self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
- self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
- self.drop_emb = nn.Dropout(cfg["drop_rate"])
- self.trf_blocks = nn.Sequential(
- *[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
- self.final_norm = LayerNorm(cfg["emb_dim"])
- self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
- def forward(self, in_idx):
- batch_size, seq_len = in_idx.shape
- tok_embeds = self.tok_emb(in_idx)
- pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
- x = tok_embeds + pos_embeds # Shape [batch_size, num_tokens, emb_size]
- x = self.drop_emb(x)
- x = self.trf_blocks(x)
- x = self.final_norm(x)
- logits = self.out_head(x)
- return logits
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