Add 12 inplace APIs including auto generated (#32573)

* add relu6_ hardsigmoid_ leaky_relu_ Inplace APIs

* add softmax_with_cross_entropy_ Inplace API

* add clip_ scale_ add_ subtract_ Inplace APIs

* add wlist

* fix parameter of scale api

* add add_n_ Inplace API and remove log_ Inplace API

* fix elementwise_add_ and elementwise_sub_ broadcast problem

* elementwise inplace api give error message before run the op

* use broadcast_shape in elementwise inplace op

* add 8 inplace apis that is auto generated

* add unittest for all inplace apis

* add decorator for inplace apis in static mode

* fix windows blas fail of exp inplace api, change array_equal to allclose

* add flatten inplace api

* add flatten unittest

* fix flatten unittest

* add decorator

* fix grad.numpy in test_pylayer_op

* unsupport softmax_with_cross_entropy_

* add test_inplace_softmax_with_cross_entropy to static_mode_white_list

* delete __all__ in inplace_utils

* delete activation inplace function and add Tensor.inplace_func

* change paddle.inplace_ to Tensor.inplace_

* fix little problem

* add paddle in inplace_utils

paddle/fluid/imperative/basic_engine.cc

@@ -408,7 +408,8 @@ void BasicEngine::Execute() {
             VLOG(10) << "create temporary var of " << var->Name()
                      << " for sum gradient within this graph!";
           } else if (!inplace_grad_name_map.empty() &&
-                     inplace_grad_name_map.count(pair.first)) {
+                     inplace_grad_name_map.count(pair.first) &&
+                     bwd_ins.count(inplace_grad_name_map.at(pair.first))) {
             // When calculate Inplace grad op, create a new output var.
             // If a tmp var has been created, there is no need to create it
             // again.

paddle/fluid/operators/flatten_op.h

@@ -120,23 +120,9 @@ template <typename DeviceContext, typename T>
 class FlattenContiguousRangeKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &context) const override {
-    auto &start_axis = context.Attr<int>("start_axis");
-    auto &stop_axis = context.Attr<int>("stop_axis");
-
     auto *in = context.Input<framework::LoDTensor>("X");
-    auto x_dims = in->dims();
-    int in_dims_size = x_dims.size();
-    int real_start_axis = start_axis, real_stop_axis = stop_axis;
-    if (start_axis < 0) {
-      real_start_axis = start_axis + in_dims_size;
-    }
-    if (stop_axis < 0) {
-      real_stop_axis = stop_axis + in_dims_size;
-    }
     auto *out = context.Output<framework::LoDTensor>("Out");
-
-    auto out_dims = framework::make_ddim(
-        GetOutputShape(real_start_axis, real_stop_axis, x_dims));
+    auto out_dims = out->dims();
 
     out->mutable_data(context.GetPlace(), in->type());
     framework::TensorCopy(
@@ -144,27 +130,6 @@ class FlattenContiguousRangeKernel : public framework::OpKernel<T> {
         context.template device_context<platform::DeviceContext>(), out);
     out->Resize(out_dims);
   }
-  static std::vector<int32_t> GetOutputShape(const int start_axis,
-                                             const int stop_axis,
-                                             const framework::DDim &in_dims) {
-    int64_t outer = 1;
-    std::vector<int32_t> out_shape;
-    int in_dims_size = in_dims.size();
-    out_shape.reserve(in_dims_size - stop_axis + start_axis);
-
-    for (int i = 0; i < start_axis; ++i) {
-      out_shape.push_back(in_dims[i]);
-    }
-    for (int i = start_axis; i <= stop_axis; i++) {
-      outer *= in_dims[i];
-    }
-    out_shape.push_back(outer);
-    for (int i = stop_axis + 1; i < in_dims_size; i++) {
-      out_shape.push_back(in_dims[i]);
-    }
-
-    return out_shape;
-  }
 };
 
 template <typename DeviceContext, typename T>

python/paddle/fluid/dygraph/__init__.py

@@ -58,6 +58,8 @@ from .amp import *
 
 from .math_op_patch import monkey_patch_math_varbase
 
+from .inplace_utils import inplace_apis_in_dygraph_only
+
 __all__ = []
 __all__ += layers.__all__
 __all__ += base.__all__

python/paddle/fluid/dygraph/inplace_utils.py

+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ..wrapped_decorator import wrap_decorator
+from ..framework import in_dygraph_mode
+import warnings
+import paddle
+
+
+# NOTE(pangyoki): The Inplace APIs with underline(`_`) is only valid for the method of calling `core.ops`
+# in dygraph mode. If static mode is used, the inplace mechanism will not be used, and the static method
+# of the original API will be called.
+def _inplace_apis_in_dygraph_only_(func):
+    def __impl__(*args, **kwargs):
+        if not in_dygraph_mode():
+            origin_api_name = func.__name__[:-1]
+            warnings.warn(
+                "In static mode, {}() is the same as {}() and does not perform inplace operation.".
+                format(func.__name__, origin_api_name))
+            origin_func = "{}.{}".format(func.__module__, origin_api_name)
+            return eval(origin_func)(*args, **kwargs)
+        return func(*args, **kwargs)
+
+    return __impl__
+
+
+inplace_apis_in_dygraph_only = wrap_decorator(_inplace_apis_in_dygraph_only_)

python/paddle/fluid/layers/layer_function_generator.py

@@ -25,7 +25,8 @@ from ..layer_helper import LayerHelper
 from ..data_feeder import check_variable_and_dtype
 
 __all__ = [
-    'generate_layer_fn', 'generate_activation_fn', 'autodoc', 'templatedoc'
+    'generate_layer_fn', 'generate_activation_fn', 'generate_inplace_fn',
+    'autodoc', 'templatedoc'
 ]
 
 
@@ -283,6 +284,35 @@ def generate_activation_fn(op_type):
     return func
 
 
+def generate_inplace_fn(inplace_op_type):
+    """Register the Python layer for an Inplace Operator without Attribute.
+
+    Args:
+       inplace_op_type: The name of the inplace operator to be created.
+
+    This function takes in the inplace operator type (exp_ , ceil_ etc) and
+    creates the operator functionality.
+    """
+    origin_op_type = inplace_op_type[:-1]
+
+    def func(x, name=None):
+        if in_dygraph_mode():
+            op = getattr(core.ops, inplace_op_type)
+            return op(x)
+        warnings.warn(
+            "In static mode, {}() is the same as {}() and does not perform inplace operation.".
+            format(inplace_op_type, origin_op_type))
+        return generate_activation_fn(origin_op_type)(x, name)
+
+    func.__name__ = inplace_op_type
+    func.__doc__ = """
+Inplace version of ``{0}`` API, the output Tensor will be inplaced with input ``x``.
+Please refer to :ref:`api_fluid_layers_{1}`.
+""".format(origin_op_type, origin_op_type)
+
+    return func
+
+
 def autodoc(comment=""):
     def __impl__(func):
         func.__doc__ = _generate_doc_string_(OpProtoHolder.instance(

python/paddle/fluid/layers/ops.py

@@ -14,7 +14,7 @@
 
 from __future__ import print_function
 import os
-from .layer_function_generator import generate_layer_fn, generate_activation_fn, add_sample_code
+from .layer_function_generator import generate_layer_fn, generate_activation_fn, generate_inplace_fn, add_sample_code
 from .. import core
 from ..framework import convert_np_dtype_to_dtype_, Variable
 from ..data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
@@ -55,6 +55,16 @@ __unary_func__ = [
     'square',
 ]
 
+__inplace_unary_func__ = [
+    'exp_',
+    'sqrt_',
+    'rsqrt_',
+    'ceil_',
+    'floor_',
+    'round_',
+    'reciprocal_',
+]
+
 __all__ = []
 
 for _OP in set(__all__):
@@ -69,6 +79,7 @@ globals()['_elementwise_div'] = generate_layer_fn('elementwise_div')
 
 __all__ += __activations_noattr__
 __all__ += __unary_func__
+__all__ += __inplace_unary_func__
 
 for _OP in set(__activations_noattr__):
     _new_OP = _OP
@@ -87,6 +98,14 @@ for _OP in set(__unary_func__):
     func = deprecated(since="2.0.0", update_to="paddle.%s" % (_new_OP))(func)
     globals()[_OP] = func
 
+for _OP in set(__inplace_unary_func__):
+    _new_OP = _OP
+    if _OP in __deprecated_func_name__:
+        _new_OP = __deprecated_func_name__[_OP]
+    func = generate_inplace_fn(_OP)
+    func = deprecated(since="2.0.0", update_to="paddle.%s" % (_new_OP))(func)
+    globals()[_OP] = func
+
 add_sample_code(globals()["sigmoid"], r"""
 Examples:
     .. code-block:: python

python/paddle/fluid/tests/unittests/test_clip_op.py

@@ -124,6 +124,9 @@ class TestClipOpError(unittest.TestCase):
 
 
 class TestClipAPI(unittest.TestCase):
+    def _executed_api(self, x, min=None, max=None):
+        return paddle.clip(x, min, max)
+
     def test_clip(self):
         paddle.enable_static()
         data_shape = [1, 9, 9, 4]
@@ -136,18 +139,20 @@ class TestClipAPI(unittest.TestCase):
         ) else fluid.CPUPlace()
         exe = fluid.Executor(place)
 
-        out_1 = paddle.clip(images, min=min, max=max)
-        out_2 = paddle.clip(images, min=0.2, max=0.9)
-        out_3 = paddle.clip(images, min=0.3)
-        out_4 = paddle.clip(images, max=0.7)
-        out_5 = paddle.clip(images, min=min)
-        out_6 = paddle.clip(images, max=max)
-        out_7 = paddle.clip(images, max=-1.)
-        out_8 = paddle.clip(images)
-        out_9 = paddle.clip(paddle.cast(images, 'float64'), min=0.2, max=0.9)
-
-        out_10 = paddle.clip(paddle.cast(images * 10, 'int32'), min=2, max=8)
-        out_11 = paddle.clip(paddle.cast(images * 10, 'int64'), min=2, max=8)
+        out_1 = self._executed_api(images, min=min, max=max)
+        out_2 = self._executed_api(images, min=0.2, max=0.9)
+        out_3 = self._executed_api(images, min=0.3)
+        out_4 = self._executed_api(images, max=0.7)
+        out_5 = self._executed_api(images, min=min)
+        out_6 = self._executed_api(images, max=max)
+        out_7 = self._executed_api(images, max=-1.)
+        out_8 = self._executed_api(images)
+        out_9 = self._executed_api(
+            paddle.cast(images, 'float64'), min=0.2, max=0.9)
+        out_10 = self._executed_api(
+            paddle.cast(images * 10, 'int32'), min=2, max=8)
+        out_11 = self._executed_api(
+            paddle.cast(images * 10, 'int64'), min=2, max=8)
 
         res1, res2, res3, res4, res5, res6, res7, res8, res9, res10, res11 = exe.run(
             fluid.default_main_program(),
@@ -188,12 +193,16 @@ class TestClipAPI(unittest.TestCase):
         v_min = paddle.to_tensor(np.array([0.2], dtype=np.float32))
         v_max = paddle.to_tensor(np.array([0.8], dtype=np.float32))
 
-        out_1 = paddle.clip(images, min=0.2, max=0.8)
-        out_2 = paddle.clip(images, min=0.2, max=0.9)
-        out_3 = paddle.clip(images, min=v_min, max=v_max)
+        out_1 = self._executed_api(images, min=0.2, max=0.8)
+        images = paddle.to_tensor(data, dtype='float32')
+        out_2 = self._executed_api(images, min=0.2, max=0.9)
+        images = paddle.to_tensor(data, dtype='float32')
+        out_3 = self._executed_api(images, min=v_min, max=v_max)
 
-        out_4 = paddle.clip(paddle.cast(images * 10, 'int32'), min=2, max=8)
-        out_5 = paddle.clip(paddle.cast(images * 10, 'int64'), min=2, max=8)
+        out_4 = self._executed_api(
+            paddle.cast(images * 10, 'int32'), min=2, max=8)
+        out_5 = self._executed_api(
+            paddle.cast(images * 10, 'int64'), min=2, max=8)
 
         self.assertTrue(np.allclose(out_1.numpy(), data.clip(0.2, 0.8)))
         self.assertTrue(np.allclose(out_2.numpy(), data.clip(0.2, 0.9)))
@@ -212,5 +221,10 @@ class TestClipAPI(unittest.TestCase):
         paddle.disable_static()
 
 
+class TestInplaceClipAPI(TestClipAPI):
+    def _executed_api(self, x, min=None, max=None):
+        return x.clip_(min, max)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_elementwise_add_op.py

@@ -408,13 +408,16 @@ class TestElementwiseAddOpError(unittest.TestCase):
             self.assertRaises(TypeError, fluid.layers.elementwise_add, x2, y2)
 
 
-class TestAddOp(unittest.TestCase):
+class TestAddApi(unittest.TestCase):
+    def _executed_api(self, x, y, name=None):
+        return paddle.add(x, y, name)
+
     def test_name(self):
         with fluid.program_guard(fluid.Program()):
             x = fluid.data(name="x", shape=[2, 3], dtype="float32")
             y = fluid.data(name='y', shape=[2, 3], dtype='float32')
 
-            y_1 = paddle.add(x, y, name='add_res')
+            y_1 = self._executed_api(x, y, name='add_res')
             self.assertEqual(('add_res' in y_1.name), True)
 
     def test_declarative(self):
@@ -428,7 +431,7 @@ class TestAddOp(unittest.TestCase):
 
             x = fluid.data(name="x", shape=[3], dtype='float32')
             y = fluid.data(name="y", shape=[3], dtype='float32')
-            z = paddle.add(x, y)
+            z = self._executed_api(x, y)
 
             place = fluid.CPUPlace()
             exe = fluid.Executor(place)
@@ -442,12 +445,75 @@ class TestAddOp(unittest.TestCase):
             np_y = np.array([1, 5, 2]).astype('float64')
             x = fluid.dygraph.to_variable(np_x)
             y = fluid.dygraph.to_variable(np_y)
-            z = paddle.add(x, y)
+            z = self._executed_api(x, y)
             np_z = z.numpy()
             z_expected = np.array([3., 8., 6.])
             self.assertEqual((np_z == z_expected).all(), True)
 
 
+class TestAddInplaceApi(TestAddApi):
+    def _executed_api(self, x, y, name=None):
+        return x.add_(y, name)
+
+
+class TestAddInplaceBroadcastSuccess(unittest.TestCase):
+    def init_data(self):
+        self.x_numpy = np.random.rand(2, 3, 4).astype('float')
+        self.y_numpy = np.random.rand(3, 4).astype('float')
+
+    def test_broadcast_success(self):
+        paddle.disable_static()
+        self.init_data()
+        x = paddle.to_tensor(self.x_numpy)
+        y = paddle.to_tensor(self.y_numpy)
+        inplace_result = x.add_(y)
+        numpy_result = self.x_numpy + self.y_numpy
+        self.assertEqual((inplace_result.numpy() == numpy_result).all(), True)
+        paddle.enable_static()
+
+
+class TestAddInplaceBroadcastSuccess2(TestAddInplaceBroadcastSuccess):
+    def init_data(self):
+        self.x_numpy = np.random.rand(1, 2, 3, 1).astype('float')
+        self.y_numpy = np.random.rand(3, 1).astype('float')
+
+
+class TestAddInplaceBroadcastSuccess3(TestAddInplaceBroadcastSuccess):
+    def init_data(self):
+        self.x_numpy = np.random.rand(2, 3, 1, 5).astype('float')
+        self.y_numpy = np.random.rand(1, 3, 1, 5).astype('float')
+
+
+class TestAddInplaceBroadcastError(unittest.TestCase):
+    def init_data(self):
+        self.x_numpy = np.random.rand(3, 4).astype('float')
+        self.y_numpy = np.random.rand(2, 3, 4).astype('float')
+
+    def test_broadcast_errors(self):
+        paddle.disable_static()
+        self.init_data()
+        x = paddle.to_tensor(self.x_numpy)
+        y = paddle.to_tensor(self.y_numpy)
+
+        def broadcast_shape_error():
+            x.add_(y)
+
+        self.assertRaises(ValueError, broadcast_shape_error)
+        paddle.enable_static()
+
+
+class TestAddInplaceBroadcastError2(TestAddInplaceBroadcastError):
+    def init_data(self):
+        self.x_numpy = np.random.rand(2, 1, 4).astype('float')
+        self.y_numpy = np.random.rand(2, 3, 4).astype('float')
+
+
+class TestAddInplaceBroadcastError3(TestAddInplaceBroadcastError):
+    def init_data(self):
+        self.x_numpy = np.random.rand(5, 2, 1, 4).astype('float')
+        self.y_numpy = np.random.rand(2, 3, 4).astype('float')
+
+
 class TestComplexElementwiseAddOp(OpTest):
     def setUp(self):
         self.op_type = "elementwise_add"

python/paddle/fluid/tests/unittests/test_elementwise_sub_op.py

@@ -16,6 +16,7 @@ from __future__ import print_function
 import unittest
 import numpy as np
 import paddle
+import paddle.fluid as fluid
 from op_test import OpTest, skip_check_grad_ci
 
 
@@ -237,6 +238,111 @@ class TestRealComplexElementwiseSubOp(TestComplexElementwiseSubOp):
         self.grad_y = -self.grad_out
 
 
+class TestSubtractApi(unittest.TestCase):
+    def _executed_api(self, x, y, name=None):
+        return paddle.subtract(x, y, name)
+
+    def test_name(self):
+        with fluid.program_guard(fluid.Program()):
+            x = fluid.data(name="x", shape=[2, 3], dtype="float32")
+            y = fluid.data(name='y', shape=[2, 3], dtype='float32')
+
+            y_1 = self._executed_api(x, y, name='subtract_res')
+            self.assertEqual(('subtract_res' in y_1.name), True)
+
+    def test_declarative(self):
+        with fluid.program_guard(fluid.Program()):
+
+            def gen_data():
+                return {
+                    "x": np.array([2, 3, 4]).astype('float32'),
+                    "y": np.array([1, 5, 2]).astype('float32')
+                }
+
+            x = fluid.data(name="x", shape=[3], dtype='float32')
+            y = fluid.data(name="y", shape=[3], dtype='float32')
+            z = self._executed_api(x, y)
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            z_value = exe.run(feed=gen_data(), fetch_list=[z.name])
+            z_expected = np.array([1., -2., 2.])
+            self.assertEqual((z_value == z_expected).all(), True)
+
+    def test_dygraph(self):
+        with fluid.dygraph.guard():
+            np_x = np.array([2, 3, 4]).astype('float64')
+            np_y = np.array([1, 5, 2]).astype('float64')
+            x = fluid.dygraph.to_variable(np_x)
+            y = fluid.dygraph.to_variable(np_y)
+            z = self._executed_api(x, y)
+            np_z = z.numpy()
+            z_expected = np.array([1., -2., 2.])
+            self.assertEqual((np_z == z_expected).all(), True)
+
+
+class TestSubtractInplaceApi(TestSubtractApi):
+    def _executed_api(self, x, y, name=None):
+        return x.subtract_(y, name)
+
+
+class TestSubtractInplaceBroadcastSuccess(unittest.TestCase):
+    def init_data(self):
+        self.x_numpy = np.random.rand(2, 3, 4).astype('float')
+        self.y_numpy = np.random.rand(3, 4).astype('float')
+
+    def test_broadcast_success(self):
+        paddle.disable_static()
+        self.init_data()
+        x = paddle.to_tensor(self.x_numpy)
+        y = paddle.to_tensor(self.y_numpy)
+        inplace_result = x.subtract_(y)
+        numpy_result = self.x_numpy - self.y_numpy
+        self.assertEqual((inplace_result.numpy() == numpy_result).all(), True)
+        paddle.enable_static()
+
+
+class TestSubtractInplaceBroadcastSuccess2(TestSubtractInplaceBroadcastSuccess):
+    def init_data(self):
+        self.x_numpy = np.random.rand(1, 2, 3, 1).astype('float')
+        self.y_numpy = np.random.rand(3, 1).astype('float')
+
+
+class TestSubtractInplaceBroadcastSuccess3(TestSubtractInplaceBroadcastSuccess):
+    def init_data(self):
+        self.x_numpy = np.random.rand(2, 3, 1, 5).astype('float')
+        self.y_numpy = np.random.rand(1, 3, 1, 5).astype('float')
+
+
+class TestSubtractInplaceBroadcastError(unittest.TestCase):
+    def init_data(self):
+        self.x_numpy = np.random.rand(3, 4).astype('float')
+        self.y_numpy = np.random.rand(2, 3, 4).astype('float')
+
+    def test_broadcast_errors(self):
+        paddle.disable_static()
+        self.init_data()
+        x = paddle.to_tensor(self.x_numpy)
+        y = paddle.to_tensor(self.y_numpy)
+
+        def broadcast_shape_error():
+            x.subtract_(y)
+
+        self.assertRaises(ValueError, broadcast_shape_error)
+        paddle.enable_static()
+
+
+class TestSubtractInplaceBroadcastError2(TestSubtractInplaceBroadcastError):
+    def init_data(self):
+        self.x_numpy = np.random.rand(2, 1, 4).astype('float')
+        self.y_numpy = np.random.rand(2, 3, 4).astype('float')
+
+
+class TestSubtractInplaceBroadcastError3(TestSubtractInplaceBroadcastError):
+    def init_data(self):
+        self.x_numpy = np.random.rand(5, 2, 1, 4).astype('float')
+        self.y_numpy = np.random.rand(2, 3, 4).astype('float')
+
+
 if __name__ == '__main__':
     paddle.enable_static()
     unittest.main()

python/paddle/fluid/tests/unittests/test_flatten_contiguous_range_op.py

@@ -182,6 +182,30 @@ class TestFlatten2OpError(unittest.TestCase):
         self.assertRaises(ValueError, test_InputError)
 
 
+class TestStaticFlattenPythonAPI(unittest.TestCase):
+    def execute_api(self, x, start_axis=0, stop_axis=-1):
+        return paddle.flatten(x, start_axis, stop_axis)
+
+    def test_static_api(self):
+        paddle.enable_static()
+        np_x = np.random.rand(2, 3, 4, 4).astype('float32')
+
+        main_prog = paddle.static.Program()
+        with paddle.static.program_guard(main_prog, paddle.static.Program()):
+            x = paddle.static.data(
+                name="x", shape=[2, 3, 4, 4], dtype='float32')
+            out = self.execute_api(x, start_axis=-2, stop_axis=-1)
+
+        exe = paddle.static.Executor(place=paddle.CPUPlace())
+        fetch_out = exe.run(main_prog, feed={"x": np_x}, fetch_list=[out])
+        self.assertTrue((2, 3, 16) == fetch_out[0].shape)
+
+
+class TestStaticInplaceFlattenPythonAPI(TestStaticFlattenPythonAPI):
+    def execute_api(self, x, start_axis=0, stop_axis=-1):
+        return x.flatten_(start_axis, stop_axis)
+
+
 class TestFlattenPython(unittest.TestCase):
     def test_python_api(self):
         image_shape = (2, 3, 4, 4)
@@ -204,5 +228,23 @@ class TestFlattenPython(unittest.TestCase):
         self.assertTrue((2, 3, 16) == res_shape)
 
 
+class TestDygraphInplaceFlattenPython(unittest.TestCase):
+    def test_python_api(self):
+        image_shape = (2, 3, 4, 4)
+        x = np.arange(image_shape[0] * image_shape[1] * image_shape[2] *
+                      image_shape[3]).reshape(image_shape) / 100.
+        x = x.astype('float32')
+
+        def test_Negative():
+            paddle.disable_static()
+            img = paddle.to_tensor(x)
+            out = img.flatten_(start_axis=-2, stop_axis=-1)
+            return out.numpy().shape
+
+        res_shape = test_Negative()
+        self.assertTrue((2, 3, 16) == res_shape)
+        paddle.enable_static()
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_inplace.py

@@ -98,11 +98,15 @@ class TestInplace(unittest.TestCase):
 class TestDygraphInplace(unittest.TestCase):
     def setUp(self):
         self.init_data()
+        self.set_np_compare_func()
 
     def init_data(self):
-        self.input_var_numpy = np.random.rand(2, 3, 1)
+        self.input_var_numpy = np.random.uniform(-5, 5, [10, 20, 1])
         self.dtype = "float32"
 
+    def set_np_compare_func(self):
+        self.np_compare = np.array_equal
+
     def non_inplace_api_processing(self, var):
         return paddle.squeeze(var)
 
@@ -190,7 +194,7 @@ class TestDygraphInplace(unittest.TestCase):
             loss.backward()
             grad_var_a = var_a.grad.numpy()
 
-        self.assertTrue(np.array_equal(grad_var_a_inplace, grad_var_a))
+        self.assertTrue(self.np_compare(grad_var_a_inplace, grad_var_a))
 
     def test_backward_success_2(self):
         # Although var_b is modified inplace after using it, it does not used in gradient computation.
@@ -244,6 +248,14 @@ class TestDygraphInplaceReshape(TestDygraphInplace):
         return paddle.reshape_(var, [-1])
 
 
+class TestDygraphInplaceFlatten(TestDygraphInplace):
+    def non_inplace_api_processing(self, var):
+        return var.flatten()
+
+    def inplace_api_processing(self, var):
+        return var.flatten_()
+
+
 class TestDygraphInplaceScatter(TestDygraphInplace):
     def init_data(self):
         self.input_var_numpy = np.array([[1, 1], [2, 2], [3, 3]])
@@ -296,5 +308,106 @@ class TestDygraphInplaceTanh(TestDygraphInplace):
         return paddle.tanh_(var)
 
 
+class TestDygraphInplaceCeil(TestDygraphInplace):
+    def non_inplace_api_processing(self, var):
+        return var.ceil()
+
+    def inplace_api_processing(self, var):
+        return var.ceil_()
+
+
+class TestDygraphInplaceFloor(TestDygraphInplace):
+    def non_inplace_api_processing(self, var):
+        return var.floor()
+
+    def inplace_api_processing(self, var):
+        return var.floor_()
+
+
+class TestDygraphInplaceExp(TestDygraphInplace):
+    def set_np_compare_func(self):
+        self.np_compare = np.allclose
+
+    def non_inplace_api_processing(self, var):
+        return var.exp()
+
+    def inplace_api_processing(self, var):
+        return var.exp_()
+
+
+class TestDygraphInplaceReciprocal(TestDygraphInplace):
+    def non_inplace_api_processing(self, var):
+        return var.reciprocal()
+
+    def inplace_api_processing(self, var):
+        return var.reciprocal_()
+
+
+class TestDygraphInplaceRound(TestDygraphInplace):
+    def non_inplace_api_processing(self, var):
+        return var.round()
+
+    def inplace_api_processing(self, var):
+        return var.round_()
+
+
+class TestDygraphInplaceSqrt(TestDygraphInplace):
+    def init_data(self):
+        self.input_var_numpy = np.random.uniform(0, 5, [10, 20, 1])
+        self.dtype = "float32"
+
+    def non_inplace_api_processing(self, var):
+        return var.sqrt()
+
+    def inplace_api_processing(self, var):
+        return var.sqrt_()
+
+
+class TestDygraphInplaceRsqrt(TestDygraphInplaceSqrt):
+    def non_inplace_api_processing(self, var):
+        return var.rsqrt()
+
+    def inplace_api_processing(self, var):
+        return var.rsqrt_()
+
+
+class TestDygraphInplaceClip(TestDygraphInplace):
+    def non_inplace_api_processing(self, var):
+        return var.clip(0.6, 1.5)
+
+    def inplace_api_processing(self, var):
+        return var.clip_(0.6, 1.5)
+
+
+class TestDygraphInplaceScale(TestDygraphInplace):
+    def non_inplace_api_processing(self, var):
+        return var.scale(scale=2.0, bias=3.0)
+
+    def inplace_api_processing(self, var):
+        return var.scale_(scale=2.0, bias=3.0)
+
+
+class TestDygraphInplaceAdd(TestDygraphInplace):
+    def init_data(self):
+        self.input_var_numpy = np.random.rand(2, 3, 4)
+        self.dtype = "float32"
+        input_var_numpy_2 = np.random.rand(2, 3, 4).astype(self.dtype)
+        self.input_var_2 = paddle.to_tensor(input_var_numpy_2)
+
+    def non_inplace_api_processing(self, var):
+        return var.add(self.input_var_2)
+
+    def inplace_api_processing(self, var):
+        return var.add_(self.input_var_2)
+
+
+class TestDygraphInplaceSubtract(TestDygraphInplaceAdd):
+    def non_inplace_api_processing(self, var):
+        return var.subtract(self.input_var_2)
+
+    def inplace_api_processing(self, var):
+        return var.subtract_(self.input_var_2)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_inplace_auto_generated_apis.py

+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+
+from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+from paddle.static import Program, program_guard
+
+
+# In static mode, inplace strategy will not be used in Inplace APIs.
+class TestStaticAutoGeneratedAPI(unittest.TestCase):
+    def setUp(self):
+        paddle.enable_static()
+        self.init_data()
+        self.set_np_compare_func()
+
+    def init_data(self):
+        self.dtype = 'float32'
+        self.shape = [10, 20]
+        self.np_x = np.random.uniform(-5, 5, self.shape).astype(self.dtype)
+
+    def set_np_compare_func(self):
+        self.np_compare = np.array_equal
+
+    def executed_paddle_api(self, x):
+        return x.ceil()
+
+    def executed_numpy_api(self, x):
+        return np.ceil(x)
+
+    def test_api(self):
+        main_prog = Program()
+        with program_guard(main_prog, Program()):
+            x = paddle.static.data(name="x", shape=self.shape, dtype=self.dtype)
+            out = self.executed_paddle_api(x)
+
+        exe = paddle.static.Executor(place=paddle.CPUPlace())
+        fetch_x, fetch_out = exe.run(main_prog,
+                                     feed={"x": self.np_x},
+                                     fetch_list=[x, out])
+
+        self.assertTrue(np.array_equal(fetch_x, self.np_x))
+        self.assertTrue(
+            self.np_compare(fetch_out, self.executed_numpy_api(self.np_x)))
+
+
+class TestStaticInplaceAutoGeneratedAPI(TestStaticAutoGeneratedAPI):
+    def executed_paddle_api(self, x):
+        return x.ceil_()
+
+
+class TestStaticFloorAPI(TestStaticAutoGeneratedAPI):
+    def executed_paddle_api(self, x):
+        return x.floor()
+
+    def executed_numpy_api(self, x):
+        return np.floor(x)
+
+
+class TestStaticInplaceFloorAPI(TestStaticFloorAPI):
+    def executed_paddle_api(self, x):
+        return x.floor_()
+
+
+class TestStaticExpAPI(TestStaticAutoGeneratedAPI):
+    def set_np_compare_func(self):
+        self.np_compare = np.allclose
+
+    def executed_paddle_api(self, x):
+        return x.exp()
+
+    def executed_numpy_api(self, x):
+        return np.exp(x)
+
+
+class TestStaticInplaceExpAPI(TestStaticExpAPI):
+    def executed_paddle_api(self, x):
+        return x.exp_()
+
+
+class TestStaticReciprocalAPI(TestStaticAutoGeneratedAPI):
+    def executed_paddle_api(self, x):
+        return x.reciprocal()
+
+    def executed_numpy_api(self, x):
+        return np.reciprocal(x)
+
+
+class TestStaticInplaceReciprocalAPI(TestStaticReciprocalAPI):
+    def executed_paddle_api(self, x):
+        return x.reciprocal_()
+
+
+class TestStaticRoundAPI(TestStaticAutoGeneratedAPI):
+    def executed_paddle_api(self, x):
+        return x.round()
+
+    def executed_numpy_api(self, x):
+        return np.round(x)
+
+
+class TestStaticInplaceRoundAPI(TestStaticRoundAPI):
+    def executed_paddle_api(self, x):
+        return x.round_()
+
+
+class TestStaticSqrtAPI(TestStaticAutoGeneratedAPI):
+    def init_data(self):
+        self.dtype = 'float32'
+        self.shape = [10, 20]
+        self.np_x = np.random.uniform(0, 5, self.shape).astype(self.dtype)
+
+    def set_np_compare_func(self):
+        self.np_compare = np.allclose
+
+    def executed_paddle_api(self, x):
+        return x.sqrt()
+
+    def executed_numpy_api(self, x):
+        return np.sqrt(x)
+
+
+class TestStaticInplaceSqrtAPI(TestStaticSqrtAPI):
+    def executed_paddle_api(self, x):
+        return x.sqrt_()
+
+
+class TestStaticRsqrtAPI(TestStaticSqrtAPI):
+    def executed_paddle_api(self, x):
+        return x.rsqrt()
+
+    def executed_numpy_api(self, x):
+        return 1 / np.sqrt(x)
+
+
+class TestStaticInplaceRsqrtAPI(TestStaticRsqrtAPI):
+    def executed_paddle_api(self, x):
+        return x.rsqrt_()
+
+
+# In dygraph mode, inplace strategy will be used in Inplace APIs.
+class TestDygraphAutoGeneratedAPI(unittest.TestCase):
+    def setUp(self):
+        paddle.disable_static()
+        self.init_data()
+        self.set_np_compare_func()
+
+    def init_data(self):
+        self.dtype = 'float32'
+        self.shape = [10, 20]
+        self.np_x = np.random.uniform(-5, 5, self.shape).astype(self.dtype)
+
+    def set_np_compare_func(self):
+        self.np_compare = np.array_equal
+
+    def executed_paddle_api(self, x):
+        return x.ceil()
+
+    def executed_numpy_api(self, x):
+        return np.ceil(x)
+
+    def test_api(self):
+        x = paddle.to_tensor(self.np_x, dtype=self.dtype)
+        out = self.executed_paddle_api(x)
+
+        self.assertTrue(
+            self.np_compare(out.numpy(), self.executed_numpy_api(self.np_x)))
+
+
+class TestDygraphInplaceAutoGeneratedAPI(TestDygraphAutoGeneratedAPI):
+    def executed_paddle_api(self, x):
+        return x.ceil_()
+
+
+class TestDygraphFloorAPI(TestDygraphAutoGeneratedAPI):
+    def executed_paddle_api(self, x):
+        return x.floor()
+
+    def executed_numpy_api(self, x):
+        return np.floor(x)
+
+
+class TestDygraphInplaceFloorAPI(TestDygraphFloorAPI):
+    def executed_paddle_api(self, x):
+        return x.floor_()
+
+
+class TestDygraphExpAPI(TestDygraphAutoGeneratedAPI):
+    def executed_paddle_api(self, x):
+        return x.exp()
+
+    def executed_numpy_api(self, x):
+        return np.exp(x)
+
+    def set_np_compare_func(self):
+        self.np_compare = np.allclose
+
+
+class TestDygraphInplaceExpAPI(TestDygraphExpAPI):
+    def executed_paddle_api(self, x):
+        return x.exp_()
+
+
+class TestDygraphReciprocalAPI(TestDygraphAutoGeneratedAPI):
+    def executed_paddle_api(self, x):
+        return x.reciprocal()
+
+    def executed_numpy_api(self, x):
+        return np.reciprocal(x)
+
+
+class TestDygraphInplaceReciprocalAPI(TestDygraphReciprocalAPI):
+    def executed_paddle_api(self, x):
+        return x.reciprocal_()
+
+
+class TestDygraphRoundAPI(TestDygraphAutoGeneratedAPI):
+    def executed_paddle_api(self, x):
+        return x.round()
+
+    def executed_numpy_api(self, x):
+        return np.round(x)
+
+
+class TestDygraphInplaceRoundAPI(TestDygraphRoundAPI):
+    def executed_paddle_api(self, x):
+        return x.round_()
+
+
+class TestDygraphSqrtAPI(TestDygraphAutoGeneratedAPI):
+    def init_data(self):
+        self.dtype = 'float32'
+        self.shape = [10, 20]
+        self.np_x = np.random.uniform(0, 100, self.shape).astype(self.dtype)
+
+    def set_np_compare_func(self):
+        self.np_compare = np.allclose
+
+    def executed_paddle_api(self, x):
+        return x.sqrt()
+
+    def executed_numpy_api(self, x):
+        return np.sqrt(x)
+
+
+class TestDygraphInplaceSqrtAPI(TestDygraphSqrtAPI):
+    def executed_paddle_api(self, x):
+        return x.sqrt_()
+
+
+class TestDygraphRsqrtAPI(TestDygraphSqrtAPI):
+    def executed_paddle_api(self, x):
+        return x.rsqrt()
+
+    def executed_numpy_api(self, x):
+        return 1. / np.sqrt(x)
+
+
+class TestDygraphInplaceRsqrtAPI(TestDygraphRsqrtAPI):
+    def executed_paddle_api(self, x):
+        return x.rsqrt_()
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/test_scale_op.py

@@ -17,9 +17,11 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.op import Operator
+from paddle.static import Program, program_guard
 
 
 class TestScaleOp(OpTest):
@@ -168,5 +170,45 @@ class TestScaleFp16OpSelectedRows(TestScaleOpSelectedRows):
             self.check_with_place(place, 'in', 'in')
 
 
+class TestScaleApiStatic(unittest.TestCase):
+    def _executed_api(self, x, scale=1.0, bias=0.0):
+        return paddle.scale(x, scale, bias)
+
+    def test_api(self):
+        paddle.enable_static()
+        input = np.random.random([2, 25]).astype("float32")
+        main_prog = Program()
+        with program_guard(main_prog, Program()):
+            x = paddle.static.data(name="x", shape=[2, 25], dtype="float32")
+            out = self._executed_api(x, scale=2.0, bias=3.0)
+
+        exe = paddle.static.Executor(place=paddle.CPUPlace())
+        out = exe.run(main_prog, feed={"x": input}, fetch_list=[out])
+        self.assertEqual(np.array_equal(out[0], input * 2.0 + 3.0), True)
+
+
+class TestScaleInplaceApiStatic(TestScaleApiStatic):
+    def _executed_api(self, x, scale=1.0, bias=0.0):
+        return x.scale_(scale, bias)
+
+
+class TestScaleApiDygraph(unittest.TestCase):
+    def _executed_api(self, x, scale=1.0, bias=0.0):
+        return paddle.scale(x, scale, bias)
+
+    def test_api(self):
+        paddle.disable_static()
+        input = np.random.random([2, 25]).astype("float32")
+        x = paddle.to_tensor(input)
+        out = self._executed_api(x, scale=2.0, bias=3.0)
+        self.assertEqual(np.array_equal(out.numpy(), input * 2.0 + 3.0), True)
+        paddle.enable_static()
+
+
+class TestScaleInplaceApiDygraph(TestScaleApiDygraph):
+    def _executed_api(self, x, scale=1.0, bias=0.0):
+        return x.scale_(scale, bias)
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/nn/functional/activation.py

@@ -16,7 +16,7 @@ from ...fluid.layers import sigmoid  # noqa: F401
 from ...tensor.math import tanh  # noqa: F401
 from ...tensor.math import tanh_  # noqa: F401
 
-from ...tensor.manipulation import _print_warning_in_static_mode
+from ...fluid.dygraph.inplace_utils import inplace_apis_in_dygraph_only
 from ...tensor.manipulation import chunk
 from ...tensor.math import multiply
 
@@ -73,17 +73,13 @@ def elu(x, alpha=1.0, name=None):
     return out
 
 
+@inplace_apis_in_dygraph_only
 def elu_(x, alpha=1.0, name=None):
     r"""
     Inplace version of ``elu`` API, the output Tensor will be inplaced with input ``x``.
     Please refer to :ref:`api_nn_cn_elu`.
     """
-
-    if in_dygraph_mode():
-        return core.ops.elu_(x, 'alpha', alpha)
-
-    _print_warning_in_static_mode("elu")
-    return elu(x, alpha, name)
+    return core.ops.elu_(x, 'alpha', alpha)
 
 
 def gelu(x, approximate=False, name=None):
@@ -501,17 +497,13 @@ def relu(x, name=None):
     return out
 
 
+@inplace_apis_in_dygraph_only
 def relu_(x, name=None):
     """
     Inplace version of ``relu`` API, the output Tensor will be inplaced with input ``x``.
     Please refer to :ref:`api_nn_cn_relu`.
     """
-
-    if in_dygraph_mode():
-        return core.ops.relu_(x)
-
-    _print_warning_in_static_mode("relu")
-    return relu(x, name)
+    return core.ops.relu_(x)
 
 
 def log_sigmoid(x, name=None):
@@ -912,21 +904,16 @@ def softmax(x, axis=-1, dtype=None, name=None):
     return outs_softmax
 
 
+@inplace_apis_in_dygraph_only
 def softmax_(x, axis=-1, dtype=None, name=None):
     r"""
     Inplace version of ``softmax`` API, the output Tensor will be inplaced with input ``x``.
     Please refer to :ref:`api_nn_cn_softmax`.
     """
-
     if (dtype is not None) and (not isinstance(dtype, core.VarDesc.VarType)):
         dtype = convert_np_dtype_to_dtype_(dtype)
     use_cudnn = True
-
-    if in_dygraph_mode():
-        return core.ops.softmax_(x, 'axis', axis, 'use_cudnn', use_cudnn)
-
-    _print_warning_in_static_mode("softmax")
-    return softmax(x, axis, dtype, name)
+    return core.ops.softmax_(x, 'axis', axis, 'use_cudnn', use_cudnn)
 
 
 def softplus(x, beta=1, threshold=20, name=None):

python/paddle/tensor/__init__.py

@@ -65,6 +65,7 @@ from .manipulation import broadcast_to  # noqa: F401
 from .manipulation import expand_as  # noqa: F401
 from .manipulation import tile  # noqa: F401
 from .manipulation import flatten  # noqa: F401
+from .manipulation import flatten_  # noqa: F401
 from .manipulation import gather  # noqa: F401
 from .manipulation import gather_nd  # noqa: F401
 from .manipulation import reshape  # noqa: F401
@@ -95,24 +96,32 @@ from .math import acos  # noqa: F401
 from .math import asin  # noqa: F401
 from .math import atan  # noqa: F401
 from .math import ceil  # noqa: F401
+from .math import ceil_  # noqa: F401
 from .math import cos  # noqa: F401
 from .math import tan  # noqa: F401
 from .math import cosh  # noqa: F401
 from .math import cumsum  # noqa: F401
 from .math import exp  # noqa: F401
+from .math import exp_  # noqa: F401
 from .math import floor  # noqa: F401
+from .math import floor_  # noqa: F401
 from .math import increment  # noqa: F401
 from .math import log  # noqa: F401
 from .math import multiplex  # noqa: F401
 from .math import pow  # noqa: F401
 from .math import reciprocal  # noqa: F401
+from .math import reciprocal_  # noqa: F401
 from .math import round  # noqa: F401
+from .math import round_  # noqa: F401
 from .math import rsqrt  # noqa: F401
+from .math import rsqrt_  # noqa: F401
 from .math import scale  # noqa: F401
+from .math import scale_  # noqa: F401
 from .math import sign  # noqa: F401
 from .math import sin  # noqa: F401
 from .math import sinh  # noqa: F401
 from .math import sqrt  # noqa: F401
+from .math import sqrt_  # noqa: F401
 from .math import square  # noqa: F401
 from .math import stanh  # noqa: F401
 from .math import sum  # noqa: F401
@@ -131,7 +140,9 @@ from .math import mod  # noqa: F401
 from .math import floor_mod  # noqa: F401
 from .math import multiply  # noqa: F401
 from .math import add  # noqa: F401
+from .math import add_  # noqa: F401
 from .math import subtract  # noqa: F401
+from .math import subtract_  # noqa: F401
 from .math import atan  # noqa: F401
 from .math import logsumexp  # noqa: F401
 from .math import inverse  # noqa: F401
@@ -141,6 +152,7 @@ from .math import log1p  # noqa: F401
 from .math import erf  # noqa: F401
 from .math import addmm  # noqa: F401
 from .math import clip  # noqa: F401
+from .math import clip_  # noqa: F401
 from .math import trace  # noqa: F401
 from .math import kron  # noqa: F401
 from .math import isfinite  # noqa: F401
@@ -202,11 +214,14 @@ tensor_method_func  = [ #noqa
            'asin',
            'atan',
            'ceil',
+           'ceil_',
            'cos',
            'cosh',
            'cumsum',
            'exp',
+           'exp_',
            'floor',
+           'floor_',
            'increment',
            'log',
            'log2',
@@ -217,13 +232,18 @@ tensor_method_func  = [ #noqa
            'pow',
            'prod',
            'reciprocal',
+           'reciprocal_',
            'round',
+           'round_',
            'rsqrt',
+           'rsqrt_',
            'scale',
+           'scale_',
            'sign',
            'sin',
            'sinh',
            'sqrt',
+           'sqrt_',
            'square',
            'stanh',
            'sum',
@@ -242,7 +262,9 @@ tensor_method_func  = [ #noqa
            'floor_mod',
            'multiply',
            'add',
+           'add_',
            'subtract',
+           'subtract_',
            'atan',
            'logsumexp',
            'inverse',
@@ -250,6 +272,7 @@ tensor_method_func  = [ #noqa
            'erf',
            'addmm',
            'clip',
+           'clip_',
            'trace',
            'kron',
            'isfinite',
@@ -277,6 +300,7 @@ tensor_method_func  = [ #noqa
            'broadcast_to',
            'expand_as',
            'flatten',
+           'flatten_',
            'gather',
            'gather_nd',
            'reshape',

python/paddle/tensor/manipulation.py

@@ -31,18 +31,12 @@ from ..fluid.layers import unstack  # noqa: F401
 from ..fluid.layers import scatter_nd  # noqa: F401
 from ..fluid.layers import shard_index  # noqa: F401
 from ..fluid import layers
+from ..fluid.dygraph.inplace_utils import inplace_apis_in_dygraph_only
 import paddle
-import warnings
 
 __all__ = []
 
 
-def _print_warning_in_static_mode(api_name):
-    warnings.warn(
-        "In static mode, {}_() is the same as {}() and does not perform inplace operation.".
-        format(api_name, api_name))
-
-
 @dygraph_only
 def tolist(x):
     """
@@ -289,6 +283,36 @@ def flatten(x, start_axis=0, stop_axis=-1, name=None):
     return out
 
 
+@inplace_apis_in_dygraph_only
+def flatten_(x, start_axis=0, stop_axis=-1, name=None):
+    """
+    Inplace version of ``flatten`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_tensor_flatten`.
+    """
+    if not (isinstance(x, Variable)):
+        raise ValueError("The input x should be a Tensor")
+
+    x_dim = len(x.shape)
+    if not (isinstance(start_axis, int)) or (
+            start_axis > x_dim - 1) or start_axis < -x_dim:
+        raise ValueError(
+            "The start_axis should be a int, and in range [-rank(x), rank(x))")
+    if not (isinstance(stop_axis, int)) or (
+            stop_axis > x_dim - 1) or stop_axis < -x_dim:
+        raise ValueError(
+            "The stop_axis should be a int, and in range [-rank(x), rank(x))")
+    if start_axis < 0:
+        start_axis = start_axis + x_dim
+    if stop_axis < 0:
+        stop_axis = stop_axis + x_dim
+    if start_axis > stop_axis:
+        raise ValueError("The stop_axis should be larger than stat_axis")
+
+    dy_out, _ = core.ops.flatten_contiguous_range_(x, 'start_axis', start_axis,
+                                                   'stop_axis', stop_axis)
+    return dy_out
+
+
 def roll(x, shifts, axis=None, name=None):
     """
     Roll the `x` tensor along the given axis(axes). With specific 'shifts', Elements that 
@@ -582,6 +606,7 @@ def squeeze(x, axis=None, name=None):
     return layers.squeeze(x, axis, name)
 
 
+@inplace_apis_in_dygraph_only
 def squeeze_(x, axis=None, name=None):
     """
     Inplace version of ``squeeze`` API, the output Tensor will be inplaced with input ``x``.
@@ -594,12 +619,8 @@ def squeeze_(x, axis=None, name=None):
     elif isinstance(axis, tuple):
         axis = list(axis)
 
-    if in_dygraph_mode():
-        out, _ = core.ops.squeeze2_(x, 'axes', axis)
-        return out
-
-    _print_warning_in_static_mode("squeeze")
-    return squeeze(x, axis, name)
+    out, _ = core.ops.squeeze2_(x, 'axes', axis)
+    return out
 
 
 def unique(x,
@@ -775,26 +796,23 @@ def unsqueeze(x, axis, name=None):
     return layers.unsqueeze(x, axis, name)
 
 
+@inplace_apis_in_dygraph_only
 def unsqueeze_(x, axis, name=None):
     """
     Inplace version of ``unsqueeze`` API, the output Tensor will be inplaced with input ``x``.
     Please refer to :ref:`api_paddle_tensor_unsqueeze`.
     """
-    if in_dygraph_mode():
-        if isinstance(axis, int):
-            axis = [axis]
-        elif isinstance(axis, Variable):
-            axis = axis.numpy().tolist()
-        elif isinstance(axis, (list, tuple)):
-            axis = [
-                item.numpy().item(0) if isinstance(item, Variable) else item
-                for item in axis
-            ]
-        out, _ = core.ops.unsqueeze2_(x, 'axes', axis)
-        return out
-
-    _print_warning_in_static_mode("unsqueeze")
-    return unsqueeze(x, axis, name)
+    if isinstance(axis, int):
+        axis = [axis]
+    elif isinstance(axis, Variable):
+        axis = axis.numpy().tolist()
+    elif isinstance(axis, (list, tuple)):
+        axis = [
+            item.numpy().item(0) if isinstance(item, Variable) else item
+            for item in axis
+        ]
+    out, _ = core.ops.unsqueeze2_(x, 'axes', axis)
+    return out
 
 
 def gather(x, index, axis=None, name=None):
@@ -1023,16 +1041,13 @@ def scatter(x, index, updates, overwrite=True, name=None):
     return out
 
 
+@inplace_apis_in_dygraph_only
 def scatter_(x, index, updates, overwrite=True, name=None):
     """
     Inplace version of ``scatter`` API, the output Tensor will be inplaced with input ``x``.
     Please refer to :ref:`api_paddle_tensor_scatter`.
     """
-    if in_dygraph_mode():
-        return core.ops.scatter_(x, index, updates, 'overwrite', overwrite)
-
-    _print_warning_in_static_mode("scatter")
-    return scatter(x, index, updates, overwrite, name)
+    return core.ops.scatter_(x, index, updates, 'overwrite', overwrite)
 
 
 def scatter_nd_add(x, index, updates, name=None):
@@ -1555,26 +1570,23 @@ def reshape(x, shape, name=None):
     return paddle.fluid.layers.reshape(x=x, shape=shape, name=name)
 
 
+@inplace_apis_in_dygraph_only
 def reshape_(x, shape, name=None):
     """
     Inplace version of ``reshape`` API, the output Tensor will be inplaced with input ``x``.
     Please refer to :ref:`api_paddle_tensor_reshape`.
     """
-    if in_dygraph_mode():
-        if isinstance(shape, (list, tuple)):
-            shape = [
-                item.numpy().item(0) if isinstance(item, Variable) else item
-                for item in shape
-            ]
-            out, _ = core.ops.reshape2_(x, None, 'shape', shape)
-            return out
-        elif isinstance(shape, Variable):
-            shape.stop_gradient = True
-            out, _ = core.ops.reshape2_(x, shape)
-            return out
-
-    _print_warning_in_static_mode("reshape")
-    return reshape(x, shape, name)
+    if isinstance(shape, (list, tuple)):
+        shape = [
+            item.numpy().item(0) if isinstance(item, Variable) else item
+            for item in shape
+        ]
+        out, _ = core.ops.reshape2_(x, None, 'shape', shape)
+        return out
+    elif isinstance(shape, Variable):
+        shape.stop_gradient = True
+        out, _ = core.ops.reshape2_(x, shape)
+        return out
 
 
 def gather_nd(x, index, name=None):

python/paddle/tensor/math.py

@@ -30,7 +30,7 @@ from ..fluid.framework import core, _varbase_creator, in_dygraph_mode, Variable,
 from ..fluid.layer_helper import LayerHelper
 from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
 from ..fluid.layers.layer_function_generator import _generate_doc_string_, generate_activation_fn, generate_layer_fn
-from .manipulation import _print_warning_in_static_mode
+from ..fluid.dygraph.inplace_utils import inplace_apis_in_dygraph_only
 
 # TODO: define math functions
 # yapf: disable
@@ -38,22 +38,29 @@ from ..fluid.layers import abs    # noqa: F401
 from ..fluid.layers import acos    # noqa: F401
 from ..fluid.layers import asin    # noqa: F401
 from ..fluid.layers import ceil    # noqa: F401
+from ..fluid.layers import ceil_    # noqa: F401
 from ..fluid.layers import cos    # noqa: F401
 from ..fluid.layers import tan    # noqa: F401
 from ..fluid.layers import sinh    # noqa: F401
 from ..fluid.layers import cosh    # noqa: F401
 from ..fluid.layers import exp    # noqa: F401
+from ..fluid.layers import exp_    # noqa: F401
 from ..fluid.layers import floor    # noqa: F401
+from ..fluid.layers import floor_    # noqa: F401
 from ..fluid.layers import log    # noqa: F401
 from ..fluid.layers import reciprocal    # noqa: F401
+from ..fluid.layers import reciprocal_    # noqa: F401
 from ..fluid.layers import round    # noqa: F401
+from ..fluid.layers import round_    # noqa: F401
 from ..fluid.layers import rsqrt    # noqa: F401
+from ..fluid.layers import rsqrt_    # noqa: F401
 from ..fluid.layers import scale    # noqa: F401
 from ..fluid.layers import square    # noqa: F401
 from ..fluid.layers import stanh    # noqa: F401
 from ..fluid.layers import atan    # noqa: F401
 from ..fluid.layers import erf    # noqa: F401
 from ..fluid.layers import sqrt    # noqa: F401
+from ..fluid.layers import sqrt_    # noqa: F401
 from ..fluid.layers import sin    # noqa: F401
 
 from ..fluid.layers import multiplex    # noqa: F401
@@ -74,6 +81,19 @@ _supported_float_dtype_ = [
     VarDesc.VarType.FP64,
 ]
 
+
+@inplace_apis_in_dygraph_only
+def scale_(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
+    """
+    Inplace version of ``scale`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_tensor_scale`.
+    """
+    _scale = scale.numpy().item(0) if isinstance(scale, Variable) else scale
+    return core.ops.scale_(x, 'scale',
+                            float(_scale), 'bias',
+                            float(bias), 'bias_after_scale', bias_after_scale)
+
+
 def pow(x, y, name=None):
     """
     Compute the power of tensor elements. The equation is:
@@ -221,6 +241,24 @@ def add(x, y, name=None):
     return _elementwise_op(LayerHelper(op_type, **locals()))
 
 
+@inplace_apis_in_dygraph_only
+def add_(x, y, name=None):
+    """
+    Inplace version of ``add`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_tensor_add`.
+    """
+    op_type = 'elementwise_add_'
+    axis = -1
+
+    out_shape = broadcast_shape(x.shape, y.shape)
+    if out_shape != x.shape:
+        raise ValueError("The shape of broadcast output {} is different from that of inplace tensor {} in the Inplace operation.".format(out_shape, x.shape))
+
+    out = _elementwise_op_in_dygraph(
+        x, y, axis=axis, op_name=op_type)
+    return out
+
+
 def subtract(x, y, name=None):
     """
     Substract two tensors element-wise. The equation is:
@@ -282,6 +320,24 @@ def subtract(x, y, name=None):
     return _elementwise_op(LayerHelper(op_type, **locals()))
 
 
+@inplace_apis_in_dygraph_only
+def subtract_(x, y, name=None):
+    """
+    Inplace version of ``subtract`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_tensor_subtract`.
+    """
+    axis = -1
+    act = None
+
+    out_shape = broadcast_shape(x.shape, y.shape)
+    if out_shape != x.shape:
+        raise ValueError("The shape of broadcast output {} is different from that of inplace tensor {} in the Inplace operation.".format(out_shape, x.shape))
+
+    out = _elementwise_op_in_dygraph(
+        x, y, axis=axis, act=act, op_name='elementwise_sub_')
+    return out
+
+
 def divide(x, y, name=None):
     """
     Divide two tensors element-wise. The equation is:
@@ -1489,6 +1545,24 @@ def clip(x, min=None, max=None, name=None):
     return output
 
 
+@inplace_apis_in_dygraph_only
+def clip_(x, min=None, max=None, name=None):
+    """
+    Inplace version of ``clip`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_tensor_clip`.
+    """
+    fmin = float(np.finfo(np.float32).min)
+    fmax = float(np.finfo(np.float32).max)
+    if isinstance(min, Variable):
+        min = min.numpy().item(0)
+    if isinstance(max, Variable):
+        max = max.numpy().item(0)
+    min = fmin if min is None else min
+    max = fmax if max is None else max
+    return core.ops.clip_(x, "min", min, "max", max)
+
+
+
 def trace(x, offset=0, axis1=0, axis2=1, name=None):
     """
     **trace**
@@ -1908,16 +1982,14 @@ def tanh(x, name=None):
     helper.append_op(type='tanh', inputs={'X': x}, outputs={'Out': out})
     return out
 
+@inplace_apis_in_dygraph_only
 def tanh_(x, name=None):
     r"""
     Inplace version of ``tanh`` API, the output Tensor will be inplaced with input ``x``.
     Please refer to :ref:`api_tensor_tanh`.
     """
-    if in_dygraph_mode():
-        return core.ops.tanh_(x)
+    return core.ops.tanh_(x)
 
-    _print_warning_in_static_mode("tanh")
-    return tanh(x, name)
 
 def increment(x, value=1.0, name=None):
     """

tools/wlist.json

@@ -34,6 +34,10 @@
             "name":"reshape_",
             "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
         },
+        {
+            "name":"flatten_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
         {
             "name":"scatter_",
             "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
@@ -53,6 +57,50 @@
         {
             "name":"tanh_",
             "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"ceil_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"floor_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"exp_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"reciprocal_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"round_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"sqrt_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"rsqrt_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"clip_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"scale_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"subtract_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"add_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
         }
     ],
     "wlist_temp_api":[