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* Show epochs as integers on x-axis * Update ch07/01_main-chapter-code/previous_chapters.py * remove extra s * modify exercise plots * update chapter 7 plot * resave ch07 for better file diff
471 lines
18 KiB
Python
471 lines
18 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 matplotlib.pyplot as plt
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from matplotlib.ticker import MaxNLocator
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import numpy as np
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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, 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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#####################################
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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 generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None):
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# For-loop is the same as before: Get logits, and only focus on last time step
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for _ in range(max_new_tokens):
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idx_cond = idx[:, -context_size:]
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with torch.no_grad():
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logits = model(idx_cond)
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logits = logits[:, -1, :]
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# New: Filter logits with top_k sampling
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if top_k is not None:
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# Keep only top_k values
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top_logits, _ = torch.topk(logits, top_k)
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min_val = top_logits[:, -1]
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logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)
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# New: Apply temperature scaling
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if temperature > 0.0:
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logits = logits / temperature
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# Apply softmax to get probabilities
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probs = torch.softmax(logits, dim=-1) # (batch_size, context_len)
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# Sample from the distribution
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idx_next = torch.multinomial(probs, num_samples=1) # (batch_size, 1)
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# Otherwise same as before: get idx of the vocab entry with the highest logits value
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else:
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idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch_size, 1)
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if idx_next == eos_id: # Stop generating early if end-of-sequence token is encountered and eos_id is specified
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break
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# Same as before: append sampled index to the running sequence
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idx = torch.cat((idx, idx_next), dim=1) # (batch_size, num_tokens+1)
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return idx
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def train_model_simple(model, train_loader, val_loader, optimizer, device, num_epochs,
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eval_freq, eval_iter, start_context, tokenizer):
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# Initialize lists to track losses and tokens seen
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train_losses, val_losses, track_tokens_seen = [], [], []
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tokens_seen, global_step = 0, -1
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# Main training loop
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for epoch in range(num_epochs):
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model.train() # Set model to training mode
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for input_batch, target_batch in train_loader:
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optimizer.zero_grad() # Reset loss gradients from previous batch iteration
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loss = calc_loss_batch(input_batch, target_batch, model, device)
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loss.backward() # Calculate loss gradients
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optimizer.step() # Update model weights using loss gradients
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tokens_seen += input_batch.numel()
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global_step += 1
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# Optional evaluation step
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if global_step % eval_freq == 0:
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train_loss, val_loss = evaluate_model(
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model, train_loader, val_loader, device, eval_iter)
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train_losses.append(train_loss)
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val_losses.append(val_loss)
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track_tokens_seen.append(tokens_seen)
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print(f"Ep {epoch+1} (Step {global_step:06d}): "
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f"Train loss {train_loss:.3f}, Val loss {val_loss:.3f}")
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# Print a sample text after each epoch
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generate_and_print_sample(
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model, tokenizer, device, start_context
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)
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return train_losses, val_losses, track_tokens_seen
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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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def generate_and_print_sample(model, tokenizer, device, start_context):
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model.eval()
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context_size = model.pos_emb.weight.shape[0]
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encoded = text_to_token_ids(start_context, tokenizer).to(device)
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with torch.no_grad():
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token_ids = generate_text_simple(
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model=model, idx=encoded,
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max_new_tokens=50, context_size=context_size
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)
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decoded_text = token_ids_to_text(token_ids, tokenizer)
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print(decoded_text.replace("\n", " ")) # Compact print format
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model.train()
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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_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)
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loss = torch.nn.functional.cross_entropy(logits.flatten(0, 1), target_batch.flatten())
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return loss
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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:
|
|
# Reduce the number of batches to match the total number of batches in the data loader
|
|
# if num_batches exceeds the number of batches in the data loader
|
|
num_batches = min(num_batches, len(data_loader))
|
|
for i, (input_batch, target_batch) in enumerate(data_loader):
|
|
if i < num_batches:
|
|
loss = calc_loss_batch(input_batch, target_batch, model, device)
|
|
total_loss += loss.item()
|
|
else:
|
|
break
|
|
return total_loss / num_batches
|
|
|
|
|
|
def plot_losses(epochs_seen, tokens_seen, train_losses, val_losses):
|
|
fig, ax1 = plt.subplots(figsize=(5, 3))
|
|
|
|
# Plot training and validation loss against epochs
|
|
ax1.plot(epochs_seen, train_losses, label="Training loss")
|
|
ax1.plot(epochs_seen, val_losses, linestyle="-.", label="Validation loss")
|
|
ax1.set_xlabel("Epochs")
|
|
ax1.set_ylabel("Loss")
|
|
ax1.legend(loc="upper right")
|
|
ax1.xaxis.set_major_locator(MaxNLocator(integer=True)) # only show integer labels on x-axis
|
|
|
|
# Create a second x-axis for tokens seen
|
|
ax2 = ax1.twiny() # Create a second x-axis that shares the same y-axis
|
|
ax2.plot(tokens_seen, train_losses, alpha=0) # Invisible plot for aligning ticks
|
|
ax2.set_xlabel("Tokens seen")
|
|
|
|
fig.tight_layout() # Adjust layout to make room
|
|
plt.savefig("loss-plot.pdf")
|
|
plt.show()
|