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103 lines
4.1 KiB
Python
103 lines
4.1 KiB
Python
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
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# Source for "Build a Large Language Model From Scratch"
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# - https://www.manning.com/books/build-a-large-language-model-from-scratch
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# Code: https://github.com/rasbt/LLMs-from-scratch
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import tiktoken
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import torch
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import torch.nn as nn
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from torch.utils.data import Dataset, DataLoader
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class GPTDatasetV1(Dataset):
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def __init__(self, txt, tokenizer, max_length, stride):
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self.input_ids = []
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self.target_ids = []
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# Tokenize the entire text
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token_ids = tokenizer.encode(txt, allowed_special={"<|endoftext|>"})
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# Use a sliding window to chunk the book into overlapping sequences of max_length
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for i in range(0, len(token_ids) - max_length, stride):
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input_chunk = token_ids[i:i + max_length]
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target_chunk = token_ids[i + 1: i + max_length + 1]
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self.input_ids.append(torch.tensor(input_chunk))
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self.target_ids.append(torch.tensor(target_chunk))
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def __len__(self):
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return len(self.input_ids)
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def __getitem__(self, idx):
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return self.input_ids[idx], self.target_ids[idx]
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def create_dataloader_v1(txt, batch_size=4, max_length=256,
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stride=128, shuffle=True, drop_last=True, num_workers=0):
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# Initialize the tokenizer
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tokenizer = tiktoken.get_encoding("gpt2")
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# Create dataset
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dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)
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# Create dataloader
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dataloader = DataLoader(
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dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=num_workers)
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return dataloader
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class MultiHeadAttention(nn.Module):
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def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
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super().__init__()
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assert d_out % num_heads == 0, "d_out must be divisible by num_heads"
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self.d_out = d_out
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self.num_heads = num_heads
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self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
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self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
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self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
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self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
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self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
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self.dropout = nn.Dropout(dropout)
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self.register_buffer('mask', torch.triu(torch.ones(context_length, context_length), diagonal=1))
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def forward(self, x):
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b, num_tokens, d_in = x.shape
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keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
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queries = self.W_query(x)
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values = self.W_value(x)
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# We implicitly split the matrix by adding a `num_heads` dimension
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# Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
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keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
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values = values.view(b, num_tokens, self.num_heads, self.head_dim)
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queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
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# Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
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keys = keys.transpose(1, 2)
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queries = queries.transpose(1, 2)
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values = values.transpose(1, 2)
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# Compute scaled dot-product attention (aka self-attention) with a causal mask
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attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head
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# Original mask truncated to the number of tokens and converted to boolean
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mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
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# Use the mask to fill attention scores
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attn_scores.masked_fill_(mask_bool, -torch.inf)
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attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
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attn_weights = self.dropout(attn_weights)
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# Shape: (b, num_tokens, num_heads, head_dim)
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context_vec = (attn_weights @ values).transpose(1, 2)
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# Combine heads, where self.d_out = self.num_heads * self.head_dim
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context_vec = context_vec.contiguous().view(b, num_tokens, self.d_out)
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context_vec = self.out_proj(context_vec) # optional projection
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return context_vec
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