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

* rename to context length

* fix spacing

appendix-D/01_main-chapter-code/previous_chapters.py

@@ -61,7 +61,7 @@ def create_dataloader_v1(txt, batch_size=4, max_length=256,
 #####################################
 
 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 n_heads"
 
@@ -74,7 +74,7 @@ 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
@@ -164,7 +164,7 @@ 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["ctx_len"],
             num_heads=cfg["n_heads"],
             dropout=cfg["drop_rate"],
             qkv_bias=cfg["qkv_bias"])