[cherry-pick][Dy2Stat]supplet several interface of static Variable to...

 [cherry-pick][Dy2Stat]supplet several interface of static Variable to consistent with dygraph Tensor (#33330) #34401 

As the title

[cherry-pick][Dy2Stat]supplet several interface of static Variable to consistent with dygraph Tensor (#33330)

paddle/fluid/operators/share_data_op.cc

+/* 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. */
+
+#include "paddle/fluid/operators/share_data_op.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+class ShareDataOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "ShareData");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "ShareData");
+    auto in_type = ctx->GetInputsVarType("X")[0];
+    auto out_type = ctx->GetOutputsVarType("Out")[0];
+
+    PADDLE_ENFORCE_EQ(
+        in_type == framework::proto::VarType::LOD_TENSOR ||
+            in_type == framework::proto::VarType::SELECTED_ROWS,
+        true, platform::errors::InvalidArgument(
+                  "Type of Variable[X] must be LoDTensor or SelectedRows!"));
+    PADDLE_ENFORCE_EQ(
+        in_type, out_type,
+        platform::errors::InvalidArgument(
+            "The type of input (X) and output (Out) are inconsistent."));
+
+    ctx->ShareDim("X", "Out");
+  }
+};
+
+class ShareDataOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor), The input tensor of share_data op");
+    AddOutput("Out", "(Tensor), The output tensor of share_data op");
+    AddComment(R"DOC(
+ShareData Operator.
+
+Return a tensor $Out$ that shares data with the input tensor $X$ and without tensor copy.
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(
+    share_data, ops::ShareDataOp, ops::ShareDataOpMaker,
+    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
+    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
+REGISTER_OP_CPU_KERNEL(share_data, ops::ShareDataKernel<bool>,
+                       ops::ShareDataKernel<int>, ops::ShareDataKernel<int8_t>,
+                       ops::ShareDataKernel<uint8_t>,
+                       ops::ShareDataKernel<paddle::platform::float16>,
+                       ops::ShareDataKernel<int64_t>,
+                       ops::ShareDataKernel<float>,
+                       ops::ShareDataKernel<double>)

paddle/fluid/operators/share_data_op.cu

+/* 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. */
+
+#include "paddle/fluid/operators/share_data_op.h"
+
+REGISTER_OP_CUDA_KERNEL(
+    share_data, paddle::operators::ShareDataKernel<bool>,
+    paddle::operators::ShareDataKernel<int>,
+    paddle::operators::ShareDataKernel<int8_t>,
+    paddle::operators::ShareDataKernel<uint8_t>,
+    paddle::operators::ShareDataKernel<paddle::platform::float16>,
+    paddle::operators::ShareDataKernel<int64_t>,
+    paddle::operators::ShareDataKernel<float>,
+    paddle::operators::ShareDataKernel<double>);

paddle/fluid/operators/share_data_op.h

+/* 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. */
+
+#pragma once
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class ShareDataKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto *in_var = ctx.InputVar("X");
+    auto *out_var = ctx.OutputVar("Out");
+    if (in_var->IsType<framework::LoDTensor>()) {
+      const auto &origin_tensor = in_var->Get<framework::LoDTensor>();
+      auto *detach_tensor = out_var->GetMutable<framework::LoDTensor>();
+      detach_tensor->ShareDataWith(origin_tensor);
+    } else {
+      const auto &origin_selected_rows = in_var->Get<framework::SelectedRows>();
+      auto *detach_selected_rows =
+          out_var->GetMutable<framework::SelectedRows>();
+      detach_selected_rows->mutable_value()->ShareDataWith(
+          origin_selected_rows.value());
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/framework.py

@@ -942,35 +942,43 @@ class Variable(object):
         self._stop_gradient = stop_gradient
         self.is_data = is_data
 
-    @fake_interface_only
     def detach(self):
         """
-        **Notes**:
-            **This API is ONLY available in Dygraph mode**
-
         Returns a new Variable, detached from the current graph.
+        It will share data with origin Variable and without tensor copy.
+        In addition, the detached Variable doesn't provide gradient propagation.
 
         Returns:
              ( :ref:`api_guide_Variable_en` | dtype is same as current Variable): The detached Variable.
 
-
         Examples:
             .. code-block:: python
 
-                import paddle.fluid as fluid
-                from paddle.fluid.dygraph.base import to_variable
-                from paddle.fluid.dygraph import Linear
-                import numpy as np
+                import paddle
 
-                data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
-                with fluid.dygraph.guard():
-                    linear = Linear(32, 64)
-                    data = to_variable(data)
-                    x = linear(data)
-                    y = x.detach()
+                paddle.enable_static()
+
+                # create a static Variable
+                x = paddle.static.data(name='x', shape=[3, 2, 1])
 
+                # create a detached Variable
+                y = x.detach()
         """
-        pass
+
+        assert self.type == core.VarDesc.VarType.SELECTED_ROWS or \
+            self.type == core.VarDesc.VarType.LOD_TENSOR, \
+            "only support a variable with SELECTED_ROWS or LOD_TENSOR to be detached"
+
+        output = self.block.create_var(
+            name=unique_name.generate_with_ignorable_key("detach_" + self.name),
+            dtype=self.dtype,
+            type=self.type,
+            persistable=self.persistable,
+            stop_gradient=True)
+
+        self.block.append_op(
+            type='share_data', inputs={'X': [self]}, outputs={'Out': [output]})
+        return output
 
     @fake_interface_only
     def numpy(self):
@@ -1805,6 +1813,35 @@ class Variable(object):
 
         t.set(value, place)
 
+    def size(self):
+        """
+        Returns the number of elements for current Variable, which is a int64 Variable with shape [1]
+
+        Returns:
+            Variable: the number of elements for current Variable
+
+        Examples:
+            .. code-block:: python
+
+                import paddle
+
+                paddle.enable_static()
+
+                # create a static Variable
+                x = paddle.static.data(name='x', shape=[3, 2, 1])
+
+                # get the number of elements of the Variable
+                y = x.size()
+        """
+
+        output = self.block.create_var(
+            name=unique_name.generate_with_ignorable_key(self.name + "_size"),
+            dtype=core.VarDesc.VarType.INT64)
+
+        self.block.append_op(
+            type='size', inputs={'Input': [self]}, outputs={'Out': [output]})
+        return output
+
 
 def get_all_op_protos():
     """

python/paddle/fluid/layers/math_op_patch.py

@@ -47,6 +47,7 @@ EXPRESSION_MAP = {
     "__rpow__": "A **= B",
     "__floordiv__": "A //B",
     "__mod__": "A % B",
+    "__matmul__": "A @ B",
     "__eq__": "A == B",
     "__ne__": "A != B",
     "__lt__": "A < B",
@@ -197,6 +198,28 @@ def monkey_patch_variable():
     def _neg_(var):
         return _scalar_op_(var, -1.0, 0.0)
 
+    @property
+    def _ndim_(self):
+        """
+        Returns the dimension of current Variable
+
+        Returns:
+            the dimension
+
+        Examples:
+            .. code-block:: python
+
+                import paddle
+
+                paddle.enable_static()
+
+                # create a static Variable
+                x = paddle.static.data(name='x', shape=[3, 2, 1])
+                # print the dimension of the Variable
+                print(x.ndim)
+        """
+        return len(self.shape)
+
     def _scalar_add_(var, value):
         return _scalar_op_(var, 1.0, value)
 
@@ -233,9 +256,9 @@ def monkey_patch_variable():
                 other_var = float(other_var)
                 # division is a special case
                 # NOTE(chenweihang): because we cast tensor to float32 instead float64,
-                # the division result can only guarantee the numerical accuracy of 6 digits 
-                # after the decimal point. The result of numpy calculation is of float64 type, 
-                # so the calculation result here and the calculation result of numpy are 
+                # the division result can only guarantee the numerical accuracy of 6 digits
+                # after the decimal point. The result of numpy calculation is of float64 type,
+                # so the calculation result here and the calculation result of numpy are
                 # different after 6 decimal point. If necessary, we can also use float64 here.
                 # torch's behavior here is consistent with ours
                 if op_type == 'elementwise_div' and self.dtype in _supported_int_dtype_:
@@ -323,6 +346,9 @@ def monkey_patch_variable():
         #   b=-a
         ('__neg__', _neg_),
         ('astype', astype),
+        ('dim', lambda x: len(x.shape)),
+        ('ndimension', lambda x: len(x.shape)),
+        ('ndim', _ndim_),
         ('__add__', _binary_creator_('__add__', 'elementwise_add', False,
                                      _scalar_add_)),
         #  a+b == b+a. Do not need to reverse explicitly
@@ -353,6 +379,8 @@ def monkey_patch_variable():
                                           'elementwise_floordiv', False, None)),
         ('__mod__', _binary_creator_('__mod__', 'elementwise_mod', False,
                                      None)),
+        ('__matmul__', _binary_creator_('__matmul__', "matmul_v2", False,
+                                        None)),
         #  for logical compare
         ('__eq__', _binary_creator_('__eq__', 'equal', False, None)),
         ('__ne__', _binary_creator_('__ne__', 'not_equal', False, None)),

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

@@ -149,12 +149,6 @@ class Test_Detach(unittest.TestCase):
         array_detach_multi = self.detach_multi()
         assert np.array_equal(array_no_detach_single, array_detach_multi)
 
-    def test_detach_exception(self):
-        x = fluid.layers.data(name="a", shape=[3, 4], dtype='float32')
-        y = fluid.layers.fc(input=x, size=10, bias_attr=True)
-        with self.assertRaises(AssertionError):
-            y_detach = y.detach()
-
 
 class TestInplace(unittest.TestCase):
     def test_forward_version(self):

python/paddle/fluid/tests/unittests/test_share_data_op.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.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+from paddle.fluid import core
+from paddle.fluid.op import Operator
+
+
+class TestShareDataOp(OpTest):
+    def setUp(self):
+        self.op_type = "share_data"
+        input = np.random.rand(2, 3, 5).astype("float32")
+        self.inputs = {'X': input}
+        self.outputs = {'Out': input}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestShareDataOpOnDifferentPlaces(unittest.TestCase):
+    def get_places(self):
+        places = [core.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(core.CUDAPlace(0))
+        return places
+
+    def check_with_tensor(self, place):
+        scope = core.Scope()
+        np_array = np.random.rand(2, 3, 5).astype("float32")
+
+        # initialize input and output variable
+        x = scope.var('X').get_tensor()
+        x.set(np_array, place)
+        out = scope.var("Out").get_tensor()
+
+        op = Operator("share_data", X="X", Out="Out")
+        op.run(scope, place)
+        self.assertTrue(np.allclose(np_array, out))
+
+    def check_with_selected_rows(self, place):
+        scope = core.Scope()
+        x_rows = [0, 1, 5, 4, 19]
+        x_height = 20
+        row_numel = 2
+        np_array = np.ones((len(x_rows), row_numel)).astype("float32")
+
+        # initialize input variable
+        x = scope.var('X').get_selected_rows()
+        x.set_rows(x_rows)
+        x.set_height(x_height)
+        x_tensor = x.get_tensor()
+        x_tensor.set(np_array, place)
+
+        # initialize the Out variable
+        out = scope.var("Out").get_selected_rows()
+        out_tensor = out.get_tensor()
+
+        op = Operator("share_data", X="X", Out="Out")
+        op.run(scope, place)
+
+        out_height = out.height()
+        out_rows = out.rows()
+        self.assertTrue(np.allclose(np_array, out_tensor))
+        self.assertEqual(x_height, out_height)
+        self.assertEqual(x_rows, out_rows)
+
+    def test_check_output(self):
+        for place in self.get_places():
+            self.check_with_selected_rows(place)
+            self.check_with_tensor(place)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -305,7 +305,6 @@ class TestVariable(unittest.TestCase):
         b = default_main_program().current_block()
         var = b.create_var(dtype="float64", lod_level=0)
         with fluid.dygraph.guard():
-            self.assertRaises(AssertionError, var.detach)
             self.assertRaises(AssertionError, var.numpy)
             self.assertRaises(AssertionError, var.backward)
             self.assertRaises(AssertionError, var.gradient)
@@ -345,6 +344,60 @@ class TestVariable(unittest.TestCase):
 
         self.assertRaises(Exception, _test)
 
+    def test_size(self):
+        prog = paddle.static.Program()
+        with paddle.static.program_guard(prog):
+            x = paddle.assign(np.random.rand(2, 3, 4).astype("float32"))
+            exe = paddle.static.Executor(fluid.CPUPlace())
+            exe.run(paddle.static.default_startup_program())
+
+            output = exe.run(prog, fetch_list=[x.size()])
+            self.assertEqual(output[0], [24])
+
+    def test_detach(self):
+        b = default_main_program().current_block()
+        x = b.create_var(shape=[2, 3, 5], dtype="float64", lod_level=0)
+        detach_x = x.detach()
+        self.assertEqual(x.persistable, detach_x.persistable)
+        self.assertEqual(x.shape, detach_x.shape)
+        self.assertEqual(x.dtype, detach_x.dtype)
+        self.assertEqual(x.type, detach_x.type)
+        self.assertTrue(detach_x.stop_gradient)
+
+        xx = b.create_var(name='xx', type=core.VarDesc.VarType.STEP_SCOPES)
+        self.assertRaises(AssertionError, xx.detach)
+
+        startup = paddle.static.Program()
+        main = paddle.static.Program()
+        scope = fluid.core.Scope()
+        with paddle.static.scope_guard(scope):
+            with paddle.static.program_guard(main, startup):
+                x = paddle.static.data(
+                    name='x', shape=[3, 2, 1], dtype='float32')
+                x.persistable = True
+                feed_data = np.ones(shape=[3, 2, 1], dtype=np.float32)
+                detach_x = x.detach()
+                exe = paddle.static.Executor(paddle.CPUPlace())
+                exe.run(startup)
+                result = exe.run(main,
+                                 feed={'x': feed_data},
+                                 fetch_list=[x, detach_x])
+                self.assertTrue((result[1] == feed_data).all())
+                self.assertTrue((result[0] == result[1]).all())
+
+                modified_value = np.zeros(shape=[3, 2, 1], dtype=np.float32)
+                detach_x.set_value(modified_value, scope)
+                result = exe.run(main, fetch_list=[x, detach_x])
+                self.assertTrue((result[1] == modified_value).all())
+                self.assertTrue((result[0] == result[1]).all())
+
+                modified_value = np.random.uniform(
+                    -1, 1, size=[3, 2, 1]).astype('float32')
+                x.set_value(modified_value, scope)
+                result = exe.run(main, fetch_list=[x, detach_x])
+                self.assertTrue((result[1] == modified_value).all())
+                self.assertTrue((result[0] == result[1]).all())
+
 
 class TestVariableSlice(unittest.TestCase):
     def _test_item_none(self, place):

tools/static_mode_white_list.py

@@ -465,6 +465,7 @@ STATIC_MODE_TESTING_LIST = [
     'test_sign_op',
     'test_similarity_focus_op',
     'test_size_op',
+    'test_share_data_op',
     'test_smooth_l1_loss',
     'test_smooth_l1_loss_op',
     'test_softmax_with_cross_entropy_op',