Update pep8 (#78)

* simplify requirements file

* style

* apply linter

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

@@ -36,7 +36,7 @@ class GPTDatasetV1(Dataset):
         return self.input_ids[idx], self.target_ids[idx]
 
 
-def create_dataloader_v1(txt, batch_size=4, max_length=256, 
+def create_dataloader_v1(txt, batch_size=4, max_length=256,
                          stride=128, shuffle=True, drop_last=True):
     # Initialize the tokenizer
     tokenizer = tiktoken.get_encoding("gpt2")
@@ -80,7 +80,7 @@ class MultiHeadAttention(nn.Module):
 
         # 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) 
+        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)
 
@@ -102,7 +102,7 @@ class MultiHeadAttention(nn.Module):
         attn_weights = self.dropout(attn_weights)
 
         # Shape: (b, num_tokens, num_heads, head_dim)
-        context_vec = (attn_weights @ values).transpose(1, 2) 
+        context_vec = (attn_weights @ values).transpose(1, 2)
 
         # Combine heads, where self.d_out = self.num_heads * self.head_dim
         context_vec = context_vec.reshape(b, num_tokens, self.d_out)
@@ -135,7 +135,7 @@ class GELU(nn.Module):
 
     def forward(self, x):
         return 0.5 * x * (1 + torch.tanh(
-            torch.sqrt(torch.tensor(2.0 / torch.pi)) * 
+            torch.sqrt(torch.tensor(2.0 / torch.pi)) *
             (x + 0.044715 * torch.pow(x, 3))
         ))
 
@@ -161,7 +161,7 @@ class TransformerBlock(nn.Module):
             d_in=cfg["emb_dim"],
             d_out=cfg["emb_dim"],
             block_size=cfg["ctx_len"],
-            num_heads=cfg["n_heads"], 
+            num_heads=cfg["n_heads"],
             dropout=cfg["drop_rate"],
             qkv_bias=cfg["qkv_bias"])
         self.ff = FeedForward(cfg)
@@ -227,7 +227,7 @@ def generate_text_simple(model, idx, max_new_tokens, context_size):
 
         # Focus only on the last time step
         # (batch, n_token, vocab_size) becomes (batch, vocab_size)
-        logits = logits[:, -1, :]  
+        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)
@@ -315,4 +315,4 @@ def text_to_token_ids(text, tokenizer):
 
 def token_ids_to_text(token_ids, tokenizer):
     flat = token_ids.squeeze(0)  # remove batch dimension
-    return tokenizer.decode(flat.tolist())   
+    return tokenizer.decode(flat.tolist())