diff --git a/vllm/model_executor/layers/quantization/fp8_rocm.py b/vllm/model_executor/layers/quantization/fp8_rocm.py
index 5401df72fb67a..ddd3b304280e7 100644
--- a/vllm/model_executor/layers/quantization/fp8_rocm.py
+++ b/vllm/model_executor/layers/quantization/fp8_rocm.py
@@ -219,12 +219,12 @@ def apply_fp8_16(
         algo = self._config._tuned.get((m, n, k))
         if algo is None:
             _save_shape(m, n, k)
-            res, _ = torch._scaled_mm(x8, 
-                                weight.t(),
-                                out_dtype=x.dtype,
-                                scale_a=asf,
-                                scale_b=wsf,
-                                bias=bias)
+            res, _ = torch._scaled_mm(x8,
+                                      weight.t(),
+                                      out_dtype=x.dtype,
+                                      scale_a=asf,
+                                      scale_b=wsf,
+                                      bias=bias)
         else:
             res = vllm_ops.fp8_gemm_16(x8, weight.t(), asf, wsf, int(algo))
         return res
@@ -291,17 +291,16 @@ def _per_tensor_dequantize(tensor: torch.Tensor,
     dq_weight = fake_qweight * inv_scale
     return dq_weight
 
+
 def _save_shape(m, n, k):
     if os.getenv("TUNE_FP8") == "1":
         try:
             df = pd.read_csv("/tmp/fp8_shapes.csv")
-        except (IOError, pd.errors.EmptyDataError,
-                pd.errors.ParserError):
+        except (IOError, pd.errors.EmptyDataError, pd.errors.ParserError):
             df = pd.DataFrame(columns=["M", "N", "K"])
-        df = pd.concat(
-            [df, pd.DataFrame({
-                "M": [m],
-                "N": [n],
-                "K": [k]
-            })]).drop_duplicates()
+        df = pd.concat([df, pd.DataFrame({
+            "M": [m],
+            "N": [n],
+            "K": [k]
+        })]).drop_duplicates()
         df.to_csv("/tmp/fp8_shapes.csv", index=False)