[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

transformer_engine/pytorch/module/base.py

@@ -654,7 +654,6 @@ def __setattr__(self, name: str, value: Any) -> None:
             # Default case
             super().__setattr__(name, value)
 
-
     def adjust_amax_history_length(self, length: int, fwd: Optional[bool] = None) -> None:
         """
         Delayed scaling only.
@@ -990,10 +989,7 @@ def init_fp8_metadata(self, num_gemms: int = 1) -> None:
 
         if fp8_parameters or fp8_enabled:
             _original_recipe = meta.get("recipe", None)
-            if (
-                self.fp8_initialized
-                and FP8GlobalStateManager.get_fp8_recipe() == _original_recipe
-            ):
+            if self.fp8_initialized and FP8GlobalStateManager.get_fp8_recipe() == _original_recipe:
                 # FP8 init has already been run and recipe is the same, don't do anything.
                 return
             meta["recipe"] = FP8GlobalStateManager.get_fp8_recipe()
@@ -1042,9 +1038,10 @@ def prepare_forward(
         allow_non_contiguous: bool = False,
         allow_different_data_and_param_types: bool = False,
     ) -> torch.Tensor:
-        """Checks and prepare for FWD execution.
-        """
-        self.fast_set_attr("allow_different_data_and_param_types", allow_different_data_and_param_types)
+        """Checks and prepare for FWD execution."""
+        self.fast_set_attr(
+            "allow_different_data_and_param_types", allow_different_data_and_param_types
+        )
         self.fast_set_attr("forwarded_at_least_once", True)
 
         # Activation recomputation is used and this is the second forward phase.
@@ -1098,9 +1095,9 @@ def prepare_forward_ctx(
         allow_non_contiguous: bool = False,
         allow_different_data_and_param_types: bool = False,
     ) -> Generator[torch.Tensor, None, None]:
-        yield self.prepare_forward(inp, num_gemms,
-                                   allow_non_contiguous,
-                                   allow_different_data_and_param_types)
+        yield self.prepare_forward(
+            inp, num_gemms, allow_non_contiguous, allow_different_data_and_param_types
+        )
         self.end_forward()
 
     def set_nccl_overlap_warning_if_tp(self) -> None:

transformer_engine/pytorch/module/linear.py

@@ -1397,8 +1397,7 @@ def forward(
             ).is_fp8_ubuf():
                 fp8_grad = True
 
-        inp = self.prepare_forward(inp,
-            allow_non_contiguous=isinstance(inp, QuantizedTensor))
+        inp = self.prepare_forward(inp, allow_non_contiguous=isinstance(inp, QuantizedTensor))
 
         weight_tensor, bias_tensor = self._get_weight_and_bias_tensors()