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543 lines
20 KiB
Python
543 lines
20 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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#
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# This file collects all the relevant code that we covered thus far
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# throughout Chapters 2-6.
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# This file can be run as a standalone script.
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import os
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from pathlib import Path
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import urllib
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import zipfile
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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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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#####################################
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# Chapter 2
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#####################################
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class GPTDatasetV1(Dataset):
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def __init__(self, txt, tokenizer, max_length, stride):
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self.tokenizer = tokenizer
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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)
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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):
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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)
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return dataloader
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#####################################
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# Chapter 3
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#####################################
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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 n_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.reshape(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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#####################################
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# Chapter 4
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#####################################
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class LayerNorm(nn.Module):
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def __init__(self, emb_dim):
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super().__init__()
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self.eps = 1e-5
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self.scale = nn.Parameter(torch.ones(emb_dim))
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self.shift = nn.Parameter(torch.zeros(emb_dim))
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def forward(self, x):
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mean = x.mean(dim=-1, keepdim=True)
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var = x.var(dim=-1, keepdim=True, unbiased=False)
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norm_x = (x - mean) / torch.sqrt(var + self.eps)
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return self.scale * norm_x + self.shift
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class GELU(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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return 0.5 * x * (1 + torch.tanh(
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torch.sqrt(torch.tensor(2.0 / torch.pi)) *
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(x + 0.044715 * torch.pow(x, 3))
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))
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class FeedForward(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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self.layers = nn.Sequential(
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nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
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GELU(),
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nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
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)
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def forward(self, x):
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return self.layers(x)
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class TransformerBlock(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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self.att = MultiHeadAttention(
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d_in=cfg["emb_dim"],
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d_out=cfg["emb_dim"],
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context_length=cfg["context_length"],
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num_heads=cfg["n_heads"],
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dropout=cfg["drop_rate"],
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qkv_bias=cfg["qkv_bias"])
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self.ff = FeedForward(cfg)
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self.norm1 = LayerNorm(cfg["emb_dim"])
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self.norm2 = LayerNorm(cfg["emb_dim"])
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self.drop_resid = nn.Dropout(cfg["drop_rate"])
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def forward(self, x):
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# Shortcut connection for attention block
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shortcut = x
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x = self.norm1(x)
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x = self.att(x) # Shape [batch_size, num_tokens, emb_size]
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x = self.drop_resid(x)
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x = x + shortcut # Add the original input back
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# Shortcut connection for feed-forward block
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shortcut = x
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x = self.norm2(x)
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x = self.ff(x)
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x = self.drop_resid(x)
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x = x + shortcut # Add the original input back
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return x
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class GPTModel(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
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self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
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self.drop_emb = nn.Dropout(cfg["drop_rate"])
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self.trf_blocks = nn.Sequential(
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*[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
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self.final_norm = LayerNorm(cfg["emb_dim"])
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self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
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def forward(self, in_idx):
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batch_size, seq_len = in_idx.shape
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tok_embeds = self.tok_emb(in_idx)
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pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
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x = tok_embeds + pos_embeds # Shape [batch_size, num_tokens, emb_size]
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x = self.drop_emb(x)
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x = self.trf_blocks(x)
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x = self.final_norm(x)
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logits = self.out_head(x)
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return logits
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def generate_text_simple(model, idx, max_new_tokens, context_size):
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# idx is (B, T) array of indices in the current context
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for _ in range(max_new_tokens):
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# Crop current context if it exceeds the supported context size
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# E.g., if LLM supports only 5 tokens, and the context size is 10
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# then only the last 5 tokens are used as context
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idx_cond = idx[:, -context_size:]
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# Get the predictions
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with torch.no_grad():
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logits = model(idx_cond)
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# Focus only on the last time step
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# (batch, n_token, vocab_size) becomes (batch, vocab_size)
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logits = logits[:, -1, :]
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# Get the idx of the vocab entry with the highest logits value
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idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch, 1)
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# Append sampled index to the running sequence
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idx = torch.cat((idx, idx_next), dim=1) # (batch, n_tokens+1)
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return idx
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#####################################
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# Chapter 5
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#####################################
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def assign(left, right):
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if left.shape != right.shape:
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raise ValueError(f"Shape mismatch. Left: {left.shape}, Right: {right.shape}")
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return torch.nn.Parameter(torch.tensor(right))
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def load_weights_into_gpt(gpt, params):
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gpt.pos_emb.weight = assign(gpt.pos_emb.weight, params['wpe'])
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gpt.tok_emb.weight = assign(gpt.tok_emb.weight, params['wte'])
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for b in range(len(params["blocks"])):
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q_w, k_w, v_w = np.split(
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(params["blocks"][b]["attn"]["c_attn"])["w"], 3, axis=-1)
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gpt.trf_blocks[b].att.W_query.weight = assign(
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gpt.trf_blocks[b].att.W_query.weight, q_w.T)
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gpt.trf_blocks[b].att.W_key.weight = assign(
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gpt.trf_blocks[b].att.W_key.weight, k_w.T)
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gpt.trf_blocks[b].att.W_value.weight = assign(
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gpt.trf_blocks[b].att.W_value.weight, v_w.T)
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q_b, k_b, v_b = np.split(
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(params["blocks"][b]["attn"]["c_attn"])["b"], 3, axis=-1)
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gpt.trf_blocks[b].att.W_query.bias = assign(
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gpt.trf_blocks[b].att.W_query.bias, q_b)
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gpt.trf_blocks[b].att.W_key.bias = assign(
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gpt.trf_blocks[b].att.W_key.bias, k_b)
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gpt.trf_blocks[b].att.W_value.bias = assign(
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gpt.trf_blocks[b].att.W_value.bias, v_b)
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gpt.trf_blocks[b].att.out_proj.weight = assign(
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gpt.trf_blocks[b].att.out_proj.weight,
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params["blocks"][b]["attn"]["c_proj"]["w"].T)
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gpt.trf_blocks[b].att.out_proj.bias = assign(
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gpt.trf_blocks[b].att.out_proj.bias,
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params["blocks"][b]["attn"]["c_proj"]["b"])
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gpt.trf_blocks[b].ff.layers[0].weight = assign(
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gpt.trf_blocks[b].ff.layers[0].weight,
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params["blocks"][b]["mlp"]["c_fc"]["w"].T)
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gpt.trf_blocks[b].ff.layers[0].bias = assign(
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gpt.trf_blocks[b].ff.layers[0].bias,
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params["blocks"][b]["mlp"]["c_fc"]["b"])
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gpt.trf_blocks[b].ff.layers[2].weight = assign(
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gpt.trf_blocks[b].ff.layers[2].weight,
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params["blocks"][b]["mlp"]["c_proj"]["w"].T)
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gpt.trf_blocks[b].ff.layers[2].bias = assign(
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gpt.trf_blocks[b].ff.layers[2].bias,
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params["blocks"][b]["mlp"]["c_proj"]["b"])
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gpt.trf_blocks[b].norm1.scale = assign(
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gpt.trf_blocks[b].norm1.scale,
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params["blocks"][b]["ln_1"]["g"])
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gpt.trf_blocks[b].norm1.shift = assign(
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gpt.trf_blocks[b].norm1.shift,
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params["blocks"][b]["ln_1"]["b"])
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gpt.trf_blocks[b].norm2.scale = assign(
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gpt.trf_blocks[b].norm2.scale,
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params["blocks"][b]["ln_2"]["g"])
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gpt.trf_blocks[b].norm2.shift = assign(
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gpt.trf_blocks[b].norm2.shift,
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params["blocks"][b]["ln_2"]["b"])
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gpt.final_norm.scale = assign(gpt.final_norm.scale, params["g"])
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gpt.final_norm.shift = assign(gpt.final_norm.shift, params["b"])
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gpt.out_head.weight = assign(gpt.out_head.weight, params["wte"])
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def text_to_token_ids(text, tokenizer):
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encoded = tokenizer.encode(text, allowed_special={'<|endoftext|>'})
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encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension
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return encoded_tensor
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def token_ids_to_text(token_ids, tokenizer):
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flat = token_ids.squeeze(0) # remove batch dimension
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return tokenizer.decode(flat.tolist())
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def calc_loss_loader(data_loader, model, device, num_batches=None):
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total_loss = 0.
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if len(data_loader) == 0:
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return float("nan")
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elif num_batches is None:
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num_batches = len(data_loader)
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else:
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# Reduce the number of batches to match the total number of batches in the data loader
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# if num_batches exceeds the number of batches in the data loader
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num_batches = min(num_batches, len(data_loader))
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for i, (input_batch, target_batch) in enumerate(data_loader):
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if i < num_batches:
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loss = calc_loss_batch(input_batch, target_batch, model, device)
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total_loss += loss.item()
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else:
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break
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return total_loss / num_batches
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def evaluate_model(model, train_loader, val_loader, device, eval_iter):
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model.eval()
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with torch.no_grad():
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train_loss = calc_loss_loader(train_loader, model, device, num_batches=eval_iter)
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val_loss = calc_loss_loader(val_loader, model, device, num_batches=eval_iter)
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model.train()
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return train_loss, val_loss
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#####################################
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# Chapter 6
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#####################################
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def download_and_unzip_spam_data(url, zip_path, extracted_path, data_file_path):
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if data_file_path.exists():
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print(f"{data_file_path} already exists. Skipping download and extraction.")
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return
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# Downloading the file
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with urllib.request.urlopen(url) as response:
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with open(zip_path, "wb") as out_file:
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out_file.write(response.read())
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# Unzipping the file
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with zipfile.ZipFile(zip_path, "r") as zip_ref:
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zip_ref.extractall(extracted_path)
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# Add .tsv file extension
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original_file_path = Path(extracted_path) / "SMSSpamCollection"
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os.rename(original_file_path, data_file_path)
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print(f"File downloaded and saved as {data_file_path}")
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def create_balanced_dataset(df):
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# Count the instances of "spam"
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num_spam = df[df["Label"] == "spam"].shape[0]
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# Randomly sample "ham' instances to match the number of 'spam' instances
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ham_subset = df[df["Label"] == "ham"].sample(num_spam, random_state=123)
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# Combine ham "subset" with "spam"
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balanced_df = pd.concat([ham_subset, df[df["Label"] == "spam"]])
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return balanced_df
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def random_split(df, train_frac, validation_frac):
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# Shuffle the entire DataFrame
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df = df.sample(frac=1, random_state=123).reset_index(drop=True)
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# Calculate split indices
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train_end = int(len(df) * train_frac)
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validation_end = train_end + int(len(df) * validation_frac)
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# Split the DataFrame
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train_df = df[:train_end]
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validation_df = df[train_end:validation_end]
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test_df = df[validation_end:]
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return train_df, validation_df, test_df
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class SpamDataset(Dataset):
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def __init__(self, csv_file, tokenizer, max_length=None, pad_token_id=50256):
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self.data = pd.read_csv(csv_file)
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# Pre-tokenize texts
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self.encoded_texts = [
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tokenizer.encode(text) for text in self.data["Text"]
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]
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if max_length is None:
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self.max_length = self._longest_encoded_length()
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else:
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self.max_length = max_length
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# Truncate sequences if they are longer than max_length
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self.encoded_texts = [
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encoded_text[:self.max_length]
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for encoded_text in self.encoded_texts
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]
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# Pad sequences to the longest sequence
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self.encoded_texts = [
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encoded_text + [pad_token_id] * (self.max_length - len(encoded_text))
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for encoded_text in self.encoded_texts
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]
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def __getitem__(self, index):
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encoded = self.encoded_texts[index]
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label = self.data.iloc[index]["Label"]
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return torch.tensor(encoded, dtype=torch.long), torch.tensor(label, dtype=torch.long)
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def __len__(self):
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return len(self.data)
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def _longest_encoded_length(self):
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max_length = 0
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for encoded_text in self.encoded_texts:
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encoded_length = len(encoded_text)
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if encoded_length > max_length:
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max_length = encoded_length
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return max_length
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@torch.no_grad() # Disable gradient tracking for efficiency
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def calc_accuracy_loader(data_loader, model, device, num_batches=None):
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model.eval()
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correct_predictions, num_examples = 0, 0
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if num_batches is None:
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num_batches = len(data_loader)
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else:
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num_batches = min(num_batches, len(data_loader))
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for i, (input_batch, target_batch) in enumerate(data_loader):
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if i < num_batches:
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input_batch, target_batch = input_batch.to(device), target_batch.to(device)
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logits = model(input_batch)[:, -1, :] # Logits of last output token
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predicted_labels = torch.argmax(logits, dim=-1)
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|
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num_examples += predicted_labels.shape[0]
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correct_predictions += (predicted_labels == target_batch).sum().item()
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else:
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break
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return correct_predictions / num_examples
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|
|
|
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def calc_loss_batch(input_batch, target_batch, model, device):
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input_batch, target_batch = input_batch.to(device), target_batch.to(device)
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logits = model(input_batch)[:, -1, :] # Logits of last output token
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loss = torch.nn.functional.cross_entropy(logits, target_batch)
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return loss
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|
|
|
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# Overall the same as `train_model_simple` in chapter 5
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|
def train_classifier_simple(model, train_loader, val_loader, optimizer, device, num_epochs,
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|
eval_freq, eval_iter, tokenizer):
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|
# Initialize lists to track losses and tokens seen
|
|
train_losses, val_losses, train_accs, val_accs = [], [], [], []
|
|
examples_seen, global_step = 0, -1
|
|
|
|
# Main training loop
|
|
for epoch in range(num_epochs):
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|
model.train() # Set model to training mode
|
|
|
|
for input_batch, target_batch in train_loader:
|
|
optimizer.zero_grad() # Reset loss gradients from previous epoch
|
|
loss = calc_loss_batch(input_batch, target_batch, model, device)
|
|
loss.backward() # Calculate loss gradients
|
|
optimizer.step() # Update model weights using loss gradients
|
|
examples_seen += input_batch.shape[0] # New: track examples instead of tokens
|
|
global_step += 1
|
|
|
|
# Optional evaluation step
|
|
if global_step % eval_freq == 0:
|
|
train_loss, val_loss = evaluate_model(
|
|
model, train_loader, val_loader, device, eval_iter)
|
|
train_losses.append(train_loss)
|
|
val_losses.append(val_loss)
|
|
print(f"Ep {epoch+1} (Step {global_step:06d}): "
|
|
f"Train loss {train_loss:.3f}, Val loss {val_loss:.3f}")
|
|
|
|
# Calculate accuracy after each epoch
|
|
train_accuracy = calc_accuracy_loader(train_loader, model, device, num_batches=eval_iter)
|
|
val_accuracy = calc_accuracy_loader(val_loader, model, device, num_batches=eval_iter)
|
|
print(f"Training accuracy: {train_accuracy*100:.2f}% | ", end="")
|
|
print(f"Validation accuracy: {val_accuracy*100:.2f}%")
|
|
train_accs.append(train_accuracy)
|
|
val_accs.append(val_accuracy)
|
|
|
|
return train_losses, val_losses, train_accs, val_accs, examples_seen
|
|
|
|
|
|
def plot_values(epochs_seen, examples_seen, train_values, val_values, label="loss"):
|
|
fig, ax1 = plt.subplots(figsize=(5, 3))
|
|
|
|
# Plot training and validation loss against epochs
|
|
ax1.plot(epochs_seen, train_values, label=f"Training {label}")
|
|
ax1.plot(epochs_seen, val_values, linestyle="-.", label=f"Validation {label}")
|
|
ax1.set_xlabel("Epochs")
|
|
ax1.set_ylabel(label.capitalize())
|
|
ax1.legend()
|
|
|
|
# Create a second x-axis for tokens seen
|
|
ax2 = ax1.twiny() # Create a second x-axis that shares the same y-axis
|
|
ax2.plot(examples_seen, train_values, alpha=0) # Invisible plot for aligning ticks
|
|
ax2.set_xlabel("Examples seen")
|
|
|
|
fig.tight_layout() # Adjust layout to make room
|
|
plt.savefig(f"{label}-plot.pdf")
|
|
plt.show()
|