Fix formatting issues

Signed-off-by: Pavani Majety <[email protected]>

vllm/attention/backends/flashinfer.py

@@ -22,34 +22,21 @@
 
 import vllm.envs as envs
 from vllm import _custom_ops as ops
-from vllm.attention.backends.abstract import (
-    AttentionBackend,
-    AttentionImpl,
-    AttentionMetadata,
-    AttentionMetadataBuilder,
-    AttentionState,
-    AttentionType,
-)
-from vllm.attention.backends.utils import (
-    PAD_SLOT_ID,
-    compute_slot_mapping,
-    compute_slot_mapping_start_idx,
-    is_block_tables_empty,
-)
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionMetadata,
+                                              AttentionMetadataBuilder,
+                                              AttentionState, AttentionType)
+from vllm.attention.backends.utils import (PAD_SLOT_ID, compute_slot_mapping,
+                                           compute_slot_mapping_start_idx,
+                                           is_block_tables_empty)
 from vllm.attention.ops.paged_attn import PagedAttention
 from vllm.forward_context import get_forward_context
-from vllm.utils import (
-    async_tensor_h2d,
-    direct_register_custom_op,
-    get_kv_cache_torch_dtype,
-    make_tensor_with_pad,
-)
+from vllm.utils import (async_tensor_h2d, direct_register_custom_op,
+                        get_kv_cache_torch_dtype, make_tensor_with_pad)
 
 if TYPE_CHECKING:
-    from vllm.worker.model_runner import (
-        ModelInputForGPUBuilder,
-        ModelInputForGPUWithSamplingMetadata,
-    )
+    from vllm.worker.model_runner import (ModelInputForGPUBuilder,
+                                          ModelInputForGPUWithSamplingMetadata)
 
 
 class FlashInferBackend(AttentionBackend):
@@ -878,7 +865,10 @@ def unified_flash_infer(
 
     assert query.shape[0] == num_prefill_tokens
     assert decode_query.shape[0] == num_decode_tokens
-    assert window_size.le
+
+    window_left = -1
+    if window_size is not None:
+        window_left = window_size[0]
 
     prefill_output: Optional[torch.Tensor] = None
     decode_output: Optional[torch.Tensor] = None
@@ -911,7 +901,7 @@ def unified_flash_infer(
                 causal=True,
                 k_scale=k_scale,
                 v_scale=v_scale,
-                window_left=window_size[0])
+                window_left=window_left)
     if decode_meta := attn_metadata.decode_metadata:
         assert attn_metadata.decode_metadata is not None
         assert attn_metadata.decode_metadata.decode_wrapper is not None
@@ -922,7 +912,7 @@ def unified_flash_infer(
             logits_soft_cap=logits_soft_cap,
             k_scale=k_scale,
             v_scale=v_scale,
-            window_left=window_size[0])
+            window_left=window_left)
 
     if prefill_output is None and decode_output is not None:
         # Decode only batch.