[mypy] Enable type checking for test directory (#5017)

tests/kernels/test_attention.py

@@ -72,27 +72,27 @@ def ref_single_query_cached_kv_attention(
     block_size = value_cache.shape[3]
     num_seqs = query.shape[0]
 
-    block_tables = block_tables.cpu().tolist()
-    seq_lens = seq_lens.cpu().tolist()
+    block_tables_lst = block_tables.cpu().tolist()
+    seq_lens_lst = seq_lens.cpu().tolist()
     for i in range(num_seqs):
         q = query[i].unsqueeze(0)
-        block_table = block_tables[i]
-        seq_len = int(seq_lens[i])
+        block_table = block_tables_lst[i]
+        seq_len = int(seq_lens_lst[i])
 
-        keys = []
-        values = []
+        keys_lst: List[torch.Tensor] = []
+        values_lst: List[torch.Tensor] = []
         for j in range(seq_len):
             block_number = int(block_table[j // block_size])
             block_offset = j % block_size
 
             k = key_cache[block_number, :, :, block_offset, :]
             k = k.reshape(num_kv_heads, head_size)
-            keys.append(k)
+            keys_lst.append(k)
 
             v = value_cache[block_number, :, :, block_offset]
-            values.append(v)
-        keys = torch.stack(keys, dim=0)
-        values = torch.stack(values, dim=0)
+            values_lst.append(v)
+        keys = torch.stack(keys_lst, dim=0)
+        values = torch.stack(values_lst, dim=0)
         if num_queries_per_kv > 1:
             # Handle MQA and GQA
             keys = torch.repeat_interleave(keys, num_queries_per_kv, dim=1)
@@ -157,14 +157,15 @@ def test_paged_attention(
 
     # Create the block tables.
     max_num_blocks_per_seq = (max_seq_len + block_size - 1) // block_size
-    block_tables = []
+    block_tables_lst: List[List[int]] = []
     for _ in range(num_seqs):
         block_table = [
             random.randint(0, NUM_BLOCKS - 1)
             for _ in range(max_num_blocks_per_seq)
         ]
-        block_tables.append(block_table)
-    block_tables = torch.tensor(block_tables, dtype=torch.int)
+        block_tables_lst.append(block_table)
+
+    block_tables = torch.tensor(block_tables_lst, dtype=torch.int)
 
     # Create the KV caches.
     key_caches, value_caches = kv_cache_factory(NUM_BLOCKS, block_size, 1,
@@ -283,7 +284,7 @@ def ref_multi_query_kv_attention(
     dtype: torch.dtype,
 ) -> torch.Tensor:
     num_seqs = len(cu_seq_lens) - 1
-    ref_outputs = []
+    ref_outputs: List[torch.Tensor] = []
     for i in range(num_seqs):
         start_idx = cu_seq_lens[i]
         end_idx = cu_seq_lens[i + 1]
@@ -303,8 +304,8 @@ def ref_multi_query_kv_attention(
             attn_mask=attn_mask,
         )
         ref_outputs.append(ref_output)
-    ref_output = torch.cat(ref_outputs, dim=0)
-    return ref_output
+
+    return torch.cat(ref_outputs, dim=0)
 
 
 # TODO(woosuk): Add tests for USE_ALIBI=True.