Optimize the python overhead of reshape and layer_norm. (#48635)

python/paddle/nn/functional/norm.py

@@ -382,16 +382,11 @@ def layer_norm(
         )
 
     if in_dygraph_mode():
-        (
-            pre_act,
-            _,
-            _,
-        ) = _C_ops.layer_norm(x, weight, bias, epsilon, begin_norm_axis)
-
-        return dygraph_utils._append_activation_in_dygraph(pre_act, act=None)
+        out, _, _ = _C_ops.layer_norm(x, weight, bias, epsilon, begin_norm_axis)
+        return out
 
     if _in_legacy_dygraph():
-        pre_act, _, _ = _legacy_C_ops.layer_norm(
+        out, _, _ = _legacy_C_ops.layer_norm(
             x,
             weight,
             bias,
@@ -400,7 +395,7 @@ def layer_norm(
             'begin_norm_axis',
             begin_norm_axis,
         )
-        return dygraph_utils._append_activation_in_dygraph(pre_act, act=None)
+        return out
 
     check_variable_and_dtype(
         x, 'input', ['float16', 'float32', 'float64'], 'LayerNorm'

python/paddle/tensor/manipulation.py

@@ -14,7 +14,6 @@
 
 # TODO: define functions to manipulate a tensor
 
-import warnings
 from collections import Counter
 
 import numpy as np
@@ -22,7 +21,7 @@ import numpy as np
 import paddle
 from paddle import _C_ops, _legacy_C_ops
 
-from ..common_ops_import import _varbase_creator, dygraph_utils, fill_constant
+from ..common_ops_import import _varbase_creator, fill_constant
 from ..fluid.data_feeder import (
     check_dtype,
     check_type,
@@ -3564,16 +3563,9 @@ def reshape(x, shape, name=None):
 
     """
     actual_shape = None
-    act = None
-    inplace = False
 
     if in_dygraph_mode():
         tmp_tensor_type = core.eager.Tensor
-        # TODO(zhiqiu): enable inplace in dygraph mode.
-        if inplace:
-            warnings.warn(
-                "Inplace on reshape is not allowed and will be discarded in dygraph mode currently."
-            )
         if isinstance(shape, (list, tuple)):
             shape = [
                 item.numpy().item(0)
@@ -3581,8 +3573,11 @@ def reshape(x, shape, name=None):
                 else item
                 for item in shape
             ]
+            if shape == x.shape:
+                out = x
+            else:
                 out = _C_ops.reshape(x, shape)
-        elif isinstance(shape, tmp_tensor_type):
+        elif isinstance(shape, core.eager.Tensor):
             shape.stop_gradient = True
             out = _C_ops.reshape(x, shape)
         else:
@@ -3591,14 +3586,10 @@ def reshape(x, shape, name=None):
                 " got '{}.'".format(type(shape))
             )
 
-        return dygraph_utils._append_activation_in_dygraph(out, act)
+        return out
     else:
         if _in_legacy_dygraph():
             tmp_tensor_type = Variable
-            if inplace:
-                warnings.warn(
-                    "Inplace on reshape is not allowed and will be discarded in dygraph mode currently."
-                )
             if isinstance(shape, (list, tuple)):
                 shape = [
                     item.numpy().item(0) if isinstance(item, Variable) else item
@@ -3614,7 +3605,7 @@ def reshape(x, shape, name=None):
                     " got '{}.'".format(type(shape))
                 )
 
-            return dygraph_utils._append_activation_in_dygraph(out, act)
+            return out
 
     check_variable_and_dtype(
         x,
@@ -3690,11 +3681,7 @@ def reshape(x, shape, name=None):
             actual_shape.stop_gradient = True
             inputs["Shape"] = actual_shape
 
-    out = (
-        x
-        if inplace
-        else helper.create_variable_for_type_inference(dtype=x.dtype)
-    )
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     x_shape = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
         type="reshape2",
@@ -3703,7 +3690,7 @@ def reshape(x, shape, name=None):
         outputs={"Out": out, "XShape": x_shape},
     )
 
-    return helper.append_activation(out)
+    return out
 
 
 @inplace_apis_in_dygraph_only
@@ -3721,6 +3708,9 @@ def reshape_(x, shape, name=None):
                 else item
                 for item in shape
             ]
+            if shape == x.shape:
+                out = x
+            else:
                 out = _C_ops.reshape_(x, shape)
         elif isinstance(shape, tmp_tensor_type):
             shape.stop_gradient = True