mirror of
https://github.com/rasbt/LLMs-from-scratch.git
synced 2025-06-26 23:50:03 +00:00
278 lines
9.4 KiB
Python
278 lines
9.4 KiB
Python
# This file collects all the relevant code that we covered thus far
|
|
# throughout Chapters 2-4.
|
|
# This file can be run as a standalone script.
|
|
|
|
import tiktoken
|
|
import torch
|
|
import torch.nn as nn
|
|
from torch.utils.data import Dataset, DataLoader
|
|
|
|
#####################################
|
|
# Chapter 2
|
|
#####################################
|
|
|
|
|
|
class GPTDatasetV1(Dataset):
|
|
def __init__(self, txt, tokenizer, max_length, stride):
|
|
self.input_ids = []
|
|
self.target_ids = []
|
|
|
|
# Tokenize the entire text
|
|
token_ids = tokenizer.encode(txt, allowed_special={"<|endoftext|>"})
|
|
|
|
# Use a sliding window to chunk the book into overlapping sequences of max_length
|
|
for i in range(0, len(token_ids) - max_length, stride):
|
|
input_chunk = token_ids[i:i + max_length]
|
|
target_chunk = token_ids[i + 1: i + max_length + 1]
|
|
self.input_ids.append(torch.tensor(input_chunk))
|
|
self.target_ids.append(torch.tensor(target_chunk))
|
|
|
|
def __len__(self):
|
|
return len(self.input_ids)
|
|
|
|
def __getitem__(self, idx):
|
|
return self.input_ids[idx], self.target_ids[idx]
|
|
|
|
|
|
def create_dataloader_v1(txt, batch_size=4, max_length=256,
|
|
stride=128, shuffle=True, drop_last=True, num_workers=0):
|
|
# Initialize the tokenizer
|
|
tokenizer = tiktoken.get_encoding("gpt2")
|
|
|
|
# Create dataset
|
|
dataset = GPTDatasetV1(txt, tokenizer, max_length, stride)
|
|
|
|
# Create dataloader
|
|
dataloader = DataLoader(
|
|
dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=num_workers)
|
|
|
|
return dataloader
|
|
|
|
|
|
#####################################
|
|
# 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 num_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.contiguous().view(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
|
|
|
|
|
|
def generate_text_simple(model, idx, max_new_tokens, context_size):
|
|
# idx is (B, T) array of indices in the current context
|
|
for _ in range(max_new_tokens):
|
|
|
|
# Crop current context if it exceeds the supported context size
|
|
# E.g., if LLM supports only 5 tokens, and the context size is 10
|
|
# then only the last 5 tokens are used as context
|
|
idx_cond = idx[:, -context_size:]
|
|
|
|
# Get the predictions
|
|
with torch.no_grad():
|
|
logits = model(idx_cond)
|
|
|
|
# Focus only on the last time step
|
|
# (batch, n_token, vocab_size) becomes (batch, vocab_size)
|
|
logits = logits[:, -1, :]
|
|
|
|
# Get the idx of the vocab entry with the highest logits value
|
|
idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch, 1)
|
|
|
|
# Append sampled index to the running sequence
|
|
idx = torch.cat((idx, idx_next), dim=1) # (batch, n_tokens+1)
|
|
|
|
return idx
|
|
|
|
|
|
def main():
|
|
GPT_CONFIG_124M = {
|
|
"vocab_size": 50257, # Vocabulary size
|
|
"context_length": 1024, # Context length
|
|
"emb_dim": 768, # Embedding dimension
|
|
"n_heads": 12, # Number of attention heads
|
|
"n_layers": 12, # Number of layers
|
|
"drop_rate": 0.1, # Dropout rate
|
|
"qkv_bias": False # Query-Key-Value bias
|
|
}
|
|
|
|
torch.manual_seed(123)
|
|
model = GPTModel(GPT_CONFIG_124M)
|
|
model.eval() # disable dropout
|
|
|
|
start_context = "Hello, I am"
|
|
|
|
tokenizer = tiktoken.get_encoding("gpt2")
|
|
encoded = tokenizer.encode(start_context)
|
|
encoded_tensor = torch.tensor(encoded).unsqueeze(0)
|
|
|
|
print(f"\n{50*'='}\n{22*' '}IN\n{50*'='}")
|
|
print("\nInput text:", start_context)
|
|
print("Encoded input text:", encoded)
|
|
print("encoded_tensor.shape:", encoded_tensor.shape)
|
|
|
|
out = generate_text_simple(
|
|
model=model,
|
|
idx=encoded_tensor,
|
|
max_new_tokens=10,
|
|
context_size=GPT_CONFIG_124M["context_length"]
|
|
)
|
|
decoded_text = tokenizer.decode(out.squeeze(0).tolist())
|
|
|
|
print(f"\n\n{50*'='}\n{22*' '}OUT\n{50*'='}")
|
|
print("\nOutput:", out)
|
|
print("Output length:", len(out[0]))
|
|
print("Output text:", decoded_text)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|