Refactor the test code for attention kernels (vllm-project#13)

tests/kernels/attention.py

@@ -1,5 +1,5 @@
 import random
-from typing import Optional
+from typing import List, Optional
 
 from flash_attn.flash_attention import FlashAttention
 import torch
@@ -64,6 +64,39 @@ def ref_single_query_cached_kv_attention(
         output[i].copy_(out, non_blocking=True)
 
 
+def ref_multi_query_kv_attention(
+    cu_seq_lens: List[int],
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    head_size = query.shape[-1]
+    scale = 1.0 / (head_size ** 0.5)
+
+    num_seqs = len(cu_seq_lens) - 1
+    ref_outputs = []
+    for i in range(num_seqs):
+        start_idx = cu_seq_lens[i]
+        end_idx = cu_seq_lens[i + 1]
+        seq_len = end_idx - start_idx
+
+        # Create attention mask
+        attn_mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=1) * -1e5
+        attn_mask = attn_mask.to(dtype=dtype, device='cuda')
+
+        ref_output = ref_masked_attention(
+            query[start_idx:end_idx],
+            key[start_idx:end_idx],
+            value[start_idx:end_idx],
+            scale,
+            attn_mask=attn_mask,
+        )
+        ref_outputs.append(ref_output)
+    ref_output = torch.cat(ref_outputs, dim=0)
+    return ref_output
+
+
 def test_single_query_cached_kv_attention(
     num_tokens: int,
     num_heads: int,
@@ -156,30 +189,29 @@ def test_multi_query_kv_attention(
         causal=True,
     )[0]
 
-    ref_outputs = []
-    for i, seq_len in enumerate(seq_lens):
-        attn_mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=1) * -1e5
-        attn_mask = attn_mask.to(dtype=dtype, device='cuda')
-        start_idx = cu_seq_lens[i]
-        end_idx = cu_seq_lens[i + 1]
-        ref_output = ref_masked_attention(
-            query[start_idx:end_idx],
-            key[start_idx:end_idx],
-            value[start_idx:end_idx],
-            scale,
-            attn_mask=attn_mask,
-        )
-        ref_outputs.append(ref_output)
-    ref_output = torch.cat(ref_outputs, dim=0)
-
+    cu_seq_lens = cu_seq_lens.cpu().tolist()
+    ref_output = ref_multi_query_kv_attention(
+        cu_seq_lens,
+        query,
+        key,
+        value,
+        dtype,
+    )
     assert torch.allclose(output, ref_output, atol=1e-3, rtol=1e-5)
 
 
 @torch.inference_mode()
-def test_attention() -> None:
+def test_attention(seed: int) -> None:
+    # NOTE(woosuk): Even when the seed is fixed, there is a chance that
+    # the test fails due to the precision issue. Re-run the test if it fails.
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
     for dtype in [torch.half, torch.float]:
         for block_size in [8, 16]:
             for head_size in [32, 64, 80, 96, 128, 160, 192, 256]:
+                print(f'Testing single_query_cached_kv_attention with '
+                      f'dtype={dtype}, block_size={block_size}, '
+                      f'head_size={head_size}')
                 test_single_query_cached_kv_attention(
                     num_tokens=37,
                     num_heads=3,
@@ -193,6 +225,8 @@ def test_attention() -> None:
     for dtype in [torch.half]:
         # NOTE(woosuk): FlashAttention does not support head_size > 128.
         for head_size in [64, 80, 96, 128]:
+            print(f'Testing multi_query_kv_attention with dtype={dtype}, '
+                  f'head_size={head_size}')
             test_multi_query_kv_attention(
                 num_seqs=11,
                 num_heads=3,
@@ -202,4 +236,4 @@ def test_attention() -> None:
 
 
 if __name__ == '__main__':
-    test_attention()
+    test_attention(seed=0)