[Misc] Add sliding window to flashinfer test (#21282)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

tests/kernels/attention/test_flashinfer.py

@@ -77,6 +77,7 @@ def ref_paged_attn(
 @pytest.mark.parametrize("block_size", BLOCK_SIZES)
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("soft_cap", [None, 30.0, 50.0])
+@pytest.mark.parametrize("sliding_window", [None, 64])
 @torch.inference_mode
 def test_flashinfer_decode_with_paged_kv(
     kv_lens: list[int],
@@ -85,6 +86,7 @@ def test_flashinfer_decode_with_paged_kv(
     dtype: torch.dtype,
     block_size: int,
     soft_cap: Optional[float],
+    sliding_window: Optional[int],
 ) -> None:
     torch.set_default_device("cuda")
     current_platform.seed_everything(0)
@@ -136,17 +138,20 @@ def test_flashinfer_decode_with_paged_kv(
                 use_tensor_cores=(
                     (num_query_heads//num_kv_heads) > 4)
                 )
-    wrapper.plan(kv_indptr,
-                 kv_indices,
-                 kv_last_page_lens,
-                 num_query_heads,
-                 num_kv_heads,
-                 head_size,
-                 block_size,
-                 "NONE",
-                 q_data_type=dtype,
-                 kv_data_type=dtype,
-                 logits_soft_cap=soft_cap)
+    wrapper.plan(
+        kv_indptr,
+        kv_indices,
+        kv_last_page_lens,
+        num_query_heads,
+        num_kv_heads,
+        head_size,
+        block_size,
+        "NONE",
+        window_left=sliding_window - 1 if sliding_window is not None else -1,
+        q_data_type=dtype,
+        kv_data_type=dtype,
+        logits_soft_cap=soft_cap,
+    )
 
     output = wrapper.run(query, key_value_cache)
 
@@ -157,7 +162,8 @@ def test_flashinfer_decode_with_paged_kv(
                                 kv_lens=kv_lens,
                                 block_tables=block_tables,
                                 scale=scale,
-                                soft_cap=soft_cap)
+                                soft_cap=soft_cap,
+                                sliding_window=sliding_window)
     torch.testing.assert_close(output, ref_output, atol=1e-2, rtol=1e-2), \
         f"{torch.max(torch.abs(output - ref_output))}"
 
@@ -168,12 +174,17 @@ def test_flashinfer_decode_with_paged_kv(
 @pytest.mark.parametrize("block_size", BLOCK_SIZES)
 @pytest.mark.parametrize("dtype", DTYPES)
 @pytest.mark.parametrize("soft_cap", [None, 30.0, 50.0])
+@pytest.mark.parametrize("sliding_window", [None, 64])
 @torch.inference_mode
-def test_flashinfer_prefill_with_paged_kv(seq_lens: list[tuple[int, int]],
-                                          num_heads: tuple[int, int],
-                                          head_size: int, dtype: torch.dtype,
-                                          block_size: int,
-                                          soft_cap: Optional[float]) -> None:
+def test_flashinfer_prefill_with_paged_kv(
+    seq_lens: list[tuple[int, int]],
+    num_heads: tuple[int, int],
+    head_size: int,
+    dtype: torch.dtype,
+    block_size: int,
+    soft_cap: Optional[float],
+    sliding_window: Optional[int],
+) -> None:
     torch.set_default_device("cuda")
     current_platform.seed_everything(0)
     num_seqs = len(seq_lens)
@@ -242,6 +253,7 @@ def test_flashinfer_prefill_with_paged_kv(seq_lens: list[tuple[int, int]],
         num_kv_heads,
         head_size,
         block_size,
+        window_left=sliding_window - 1 if sliding_window is not None else -1,
         q_data_type=dtype,
         kv_data_type=dtype,
         logits_soft_cap=soft_cap,
@@ -259,7 +271,8 @@ def test_flashinfer_prefill_with_paged_kv(seq_lens: list[tuple[int, int]],
                                 kv_lens=kv_lens,
                                 block_tables=block_tables,
                                 scale=scale,
-                                soft_cap=soft_cap)
+                                soft_cap=soft_cap,
+                                sliding_window=sliding_window)
     torch.testing.assert_close(output, ref_output, atol=5e-2, rtol=1e-2), \
         f"{torch.max(torch.abs(output - ref_output))}"