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

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

tests/pytorch/test_fp8_model_init.py

@@ -29,25 +29,31 @@ def test_default(self) -> None:
             model = te.Linear(768, 768)
 
         assert isinstance(model.weight, Float8Tensor), "Weight should be Float8Tensor"
-        assert not hasattr(model.weight, "._high_precision_init_val"), \
-            "_high_precision_init_val should not exist"
-        assert not hasattr(model.weight, "get_high_precision_init_val"), \
-            "get_high_precision_init_val() should not exist"
-        assert not hasattr(model.weight, "clear_high_precision_init_val"), \
-            "clear_high_precision_init_val() should not exist"
+        assert not hasattr(
+            model.weight, "._high_precision_init_val"
+        ), "_high_precision_init_val should not exist"
+        assert not hasattr(
+            model.weight, "get_high_precision_init_val"
+        ), "get_high_precision_init_val() should not exist"
+        assert not hasattr(
+            model.weight, "clear_high_precision_init_val"
+        ), "clear_high_precision_init_val() should not exist"
 
     def test_preserve_high_precision_init_val(self) -> None:
         """Test fp8_model_init with preserve_high_precision_init_val=True"""
         with fp8_model_init(preserve_high_precision_init_val=True):
             model = te.Linear(768, 768)
 
         assert isinstance(model.weight, Float8Tensor), "Weight should be Float8Tensor"
-        assert hasattr(model.weight, "_high_precision_init_val"), \
-            "_high_precision_init_val not found"
-        assert hasattr(model.weight, "get_high_precision_init_val"), \
-            "get_high_precision_init_val() not found"
-        assert hasattr(model.weight, "clear_high_precision_init_val"), \
-            "clear_high_precision_init_val() not found"
+        assert hasattr(
+            model.weight, "_high_precision_init_val"
+        ), "_high_precision_init_val not found"
+        assert hasattr(
+            model.weight, "get_high_precision_init_val"
+        ), "get_high_precision_init_val() not found"
+        assert hasattr(
+            model.weight, "clear_high_precision_init_val"
+        ), "clear_high_precision_init_val() not found"
 
         high_precision = model.weight.get_high_precision_init_val()
         assert high_precision.device.type == "cpu", "high_precision_init_val is not on the CPU"
@@ -58,11 +64,14 @@ def test_preserve_high_precision_init_val(self) -> None:
             fp8_meta_index=model.weight._fp8_meta_index,
             amax=torch.empty(1, device="cuda"),  # Dummy amax to avoid overwriting history.
         )
-        assert torch.all(new_fp8._data == model.weight._data), \
-            "high_precision_init_val and model.weight are not equal"
+        assert torch.all(
+            new_fp8._data == model.weight._data
+        ), "high_precision_init_val and model.weight are not equal"
 
         model.weight.clear_high_precision_init_val()
-        assert model.weight.get_high_precision_init_val() is None, \
-            "clear_high_precision_init_val() not work"
-        assert not hasattr(model.weight, "._high_precision_init_val"), \
-            "clear_high_precision_init_val() not work"
+        assert (
+            model.weight.get_high_precision_init_val() is None
+        ), "clear_high_precision_init_val() not work"
+        assert not hasattr(
+            model.weight, "._high_precision_init_val"
+        ), "clear_high_precision_init_val() not work"

transformer_engine/pytorch/fp8.py

@@ -485,9 +485,9 @@ def restore_fp8_meta_tensors(fp8_meta: Dict[str, Any]) -> None:
 
 @contextmanager
 def fp8_model_init(
-        enabled: bool = True,
-        preserve_high_precision_init_val: bool = False,
-    ) -> None:
+    enabled: bool = True,
+    preserve_high_precision_init_val: bool = False,
+) -> None:
     """
     Context manager for FP8 initialization of parameters.
 

transformer_engine/pytorch/module/base.py

@@ -883,6 +883,7 @@ def reset_parameters(self, defer_init: Optional[bool] = False) -> None:
             #       a parameter so we always re-apply it just for extra safety.
             param = torch.nn.Parameter(param)
             if high_precision_init_val is not None:
+
                 def get(self):
                     if hasattr(self, "_high_precision_init_val"):
                         return self._high_precision_init_val