diff --git a/vllm/v1/attention/backends/flashinfer.py b/vllm/v1/attention/backends/flashinfer.py
index c56e721dff8cf..44f95c7686863 100755
--- a/vllm/v1/attention/backends/flashinfer.py
+++ b/vllm/v1/attention/backends/flashinfer.py
@@ -36,6 +36,7 @@ from vllm.v1.attention.backends.utils import (AttentionCGSupport,
                                               get_per_layer_parameters,
                                               infer_global_hyperparameters,
                                               split_decodes_and_prefills)
+# yapf: enable
 from vllm.v1.kv_cache_interface import AttentionSpec
 
 FLASHINFER_WORKSPACE_BUFFER_SIZE = 256 * 1024 * 1024
@@ -541,12 +542,22 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
         if cache_dtype.startswith("fp8") and enable_fusion:
             q_dtype = kv_cache_dtype
 
-        prefill_use_trtllm = use_trtllm_attention(
-            num_qo_heads, num_kv_heads, num_prefill_tokens, max_seq_len,
-            cache_dtype, q_dtype, is_prefill=True, has_sinks=has_sinks)
-        decode_use_trtllm = use_trtllm_attention(
-            num_qo_heads, num_kv_heads, num_decode_tokens, max_seq_len,
-            cache_dtype, q_dtype, is_prefill=False, has_sinks=has_sinks)
+        prefill_use_trtllm = use_trtllm_attention(num_qo_heads,
+                                                  num_kv_heads,
+                                                  num_prefill_tokens,
+                                                  max_seq_len,
+                                                  cache_dtype,
+                                                  q_dtype,
+                                                  is_prefill=True,
+                                                  has_sinks=has_sinks)
+        decode_use_trtllm = use_trtllm_attention(num_qo_heads,
+                                                 num_kv_heads,
+                                                 num_decode_tokens,
+                                                 max_seq_len,
+                                                 cache_dtype,
+                                                 q_dtype,
+                                                 is_prefill=False,
+                                                 has_sinks=has_sinks)
 
         attn_metadata = FlashInferMetadata(
             num_actual_tokens=num_actual_tokens,
@@ -654,19 +665,18 @@ class FlashInferImpl(AttentionImpl):
                 raise ValueError(
                     "Sinks must have the same number of heads as the number of "
                     f"heads in the layer. Expected {num_heads}, but got "
-                    f"{sinks.shape[0]}."
-                )
+                    f"{sinks.shape[0]}.")
             self.sinks = sinks
 
-        self.support_trtllm_attn = (supports_trtllm_attention() and
-                                    num_heads % num_kv_heads == 0)
+        self.support_trtllm_attn = (supports_trtllm_attention()
+                                    and num_heads % num_kv_heads == 0)
         self.bmm1_scale: Optional[float] = None
         self.bmm2_scale: Optional[float] = None
 
     def fused_output_quant_supported(self, dtype: torch.dtype, static: bool,
                                      group_shape: GroupShape):
-        supported_quant_type = (dtype == FP8_DTYPE and static and
-                                group_shape == GroupShape.PER_TENSOR)
+        supported_quant_type = (dtype == FP8_DTYPE and static
+                                and group_shape == GroupShape.PER_TENSOR)
         return (self.support_trtllm_attn
                 and self.kv_cache_dtype.startswith("fp8")
                 and supported_quant_type)
@@ -731,7 +741,8 @@ class FlashInferImpl(AttentionImpl):
             # Insert FP8 quant for query
             num_tokens, num_heads, head_size = query.shape
             query, _ = ops.scaled_fp8_quant(
-                query.reshape((num_tokens, num_heads * head_size)).contiguous(),
+                query.reshape(
+                    (num_tokens, num_heads * head_size)).contiguous(),
                 layer._q_scale)
             query = query.reshape((num_tokens, num_heads, head_size))