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Rename variable to context_length to make it easier on readers (#106)

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* rename to context length

* fix spacing
This commit is contained in:
Sebastian Raschka
2024-04-04 07:27:41 -05:00
committed by GitHub
parent a940373a14
commit 2de60d1bfb
25 changed files with 242 additions and 242 deletions
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24
ch04/01_main-chapter-code/gpt.py
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@@ -54,7 +54,7 @@ def create_dataloader_v1(txt, batch_size=4, max_length=256,
# Chapter 3
#####################################
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, block_size, dropout, num_heads, qkv_bias=False):
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"
@@ -67,7 +67,7 @@ class MultiHeadAttention(nn.Module):
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(block_size, block_size), diagonal=1))
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
@@ -156,7 +156,7 @@ class TransformerBlock(nn.Module):
self.att = MultiHeadAttention(
d_in=cfg["emb_dim"],
d_out=cfg["emb_dim"],
block_size=cfg["ctx_len"],
context_length=cfg["context_length"],
num_heads=cfg["n_heads"],
dropout=cfg["drop_rate"],
qkv_bias=cfg["qkv_bias"])
@@ -187,7 +187,7 @@ 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["ctx_len"], 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(
@@ -236,13 +236,13 @@ def generate_text_simple(model, idx, max_new_tokens, context_size):
def main():
GPT_CONFIG_124M = {
"vocab_size": 50257, # Vocabulary size
"ctx_len": 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
"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)
@@ -264,7 +264,7 @@ def main():
model=model,
idx=encoded_tensor,
max_new_tokens=10,
context_size=GPT_CONFIG_124M["ctx_len"]
context_size=GPT_CONFIG_124M["context_length"]
)
decoded_text = tokenizer.decode(out.squeeze(0).tolist())