[Cherry-Pick 2.0][setitem] Support Tensor setitem in static mode (#29708) (#30104)

1. Type of index: int, slice(step must be 1).

2. Type of value:
 (1) int32, int64, float32, bool;
 (2) numpy.array(int32, int64, float32, bool);<Note: float64 is not supported>
 (3) paddle.Tensor(int32, int64, float32, float64, bool);

paddle/fluid/operators/set_value_op.cc

+//   Copyright (c) 2020 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/set_value_op.h"
+
+#include <string>
+
+namespace paddle {
+namespace operators {
+
+class SetValue : public framework::OperatorWithKernel {
+ public:
+  SetValue(const std::string &type, const framework::VariableNameMap &inputs,
+           const framework::VariableNameMap &outputs,
+           const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("Input"), "Input", "Input", "SetValue");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "SetValue");
+    auto in_dims = ctx->GetInputDim("Input");
+    PADDLE_ENFORCE_LT(
+        in_dims.size(), 7,
+        platform::errors::InvalidArgument(
+            "The rank of input should be less than 7, but received %d.",
+            in_dims.size()));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::proto::VarType::Type(ctx.Attr<int>("dtype")),
+        ctx.GetPlace());
+  }
+};
+
+class SetValueMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("Input", "(Tensor) Input tensor of set_value operator.");
+    AddInput("ValueTensor", "(Tensor) Value tensor of set_value operator.")
+        .AsDispensable();
+    AddOutput("Out",
+              "(Tensor) Output tensor of set_value operator. The output is the "
+              "same Tensor as input");
+
+    AddAttr<int>("dtype", "data type of input.")
+        .InEnum(
+            {framework::proto::VarType::BOOL, framework::proto::VarType::INT32,
+             framework::proto::VarType::INT64, framework::proto::VarType::FP32,
+             framework::proto::VarType::FP64})
+        .SetDefault(framework::proto::VarType::FP32);
+    AddAttr<std::vector<int64_t>>(
+        "axes", "(list<int64_t>) Axes that `starts` and `ends` apply to.");
+    AddAttr<std::vector<int64_t>>(
+        "starts",
+        "(list<int64_t>) Starting indices of corresponding axis in `axes`");
+    AddAttr<std::vector<int64_t>>(
+        "ends",
+        "(list<int64_t>) Ending indices of corresponding axis in `axes`.");
+
+    AddAttr<std::vector<int>>("bool_values", "store the bool values")
+        .SetDefault({});
+    AddAttr<std::vector<float>>("fp32_values", "store the float32 values")
+        .SetDefault({});
+    AddAttr<std::vector<int>>("int32_values", "store the int32 values")
+        .SetDefault({});
+    AddAttr<std::vector<int64_t>>("int64_values", "store the int64 values")
+        .SetDefault({});
+
+    AddAttr<std::vector<int64_t>>("shape", "(vector<int64_t>) Shape of values.")
+        .SetDefault({});
+    AddComment(R"DOC(SetValue operator.
+Assignment to a Tensor in static mode.
+)DOC");
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(
+    set_value, ops::SetValue, ops::SetValueMaker,
+    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
+    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
+
+REGISTER_OP_CPU_KERNEL(
+    set_value, ops::SetValueKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::SetValueKernel<paddle::platform::CPUDeviceContext, int64_t>,
+    ops::SetValueKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::SetValueKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::SetValueKernel<paddle::platform::CPUDeviceContext, bool>);

paddle/fluid/operators/set_value_op.cu

+//   Copyright (c) 2020 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/set_value_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_CUDA_KERNEL(
+    set_value, ops::SetValueKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::SetValueKernel<paddle::platform::CUDADeviceContext, int64_t>,
+    ops::SetValueKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::SetValueKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::SetValueKernel<paddle::platform::CUDADeviceContext, bool>);

paddle/fluid/operators/set_value_op.h

+//   Copyright (c) 2020 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 <algorithm>
+#include <string>
+#include <vector>
+
+#include <utility>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/operators/assign_value_op.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+inline std::string GetValueName(framework::proto::VarType::Type data_type) {
+  std::string value_name;
+  switch (data_type) {
+    case framework::proto::VarType::INT32:
+      value_name = "int32_values";
+      break;
+    case framework::proto::VarType::INT64:
+      value_name = "int64_values";
+      break;
+    case framework::proto::VarType::FP32:
+      value_name = "fp32_values";
+      break;
+    case framework::proto::VarType::BOOL:
+      value_name = "bool_values";
+      break;
+    default:
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "Unsupported data type(code %d) for SetValue operator, only "
+          "supports bool, int32, float32 and int64.",
+          data_type));
+  }
+  return value_name;
+}
+
+inline framework::DDim GetSliceDims(const framework::DDim in_dims,
+                                    const std::vector<int64_t> axes,
+                                    const std::vector<int64_t> starts,
+                                    const std::vector<int64_t> ends) {
+  framework::DDim slice_dims(in_dims);
+
+  for (size_t i = 0; i < axes.size(); ++i) {
+    int64_t axis = axes[i];
+    int64_t dim_value = in_dims[axis];
+
+    int64_t start = starts[i] < 0 ? (starts[i] + dim_value) : starts[i];
+    int64_t end = ends[i] < 0 ? (ends[i] + dim_value) : ends[i];
+    start = std::max(start, static_cast<int64_t>(0));
+    end = std::min(end, dim_value);
+
+    PADDLE_ENFORCE_GT(end, start, platform::errors::InvalidArgument(
+                                      "end should greater than start, but "
+                                      "received end = %d, start = %d",
+                                      end, start));
+    slice_dims[axis] = end - start;
+  }
+  return slice_dims;
+}
+
+template <typename DeviceContext, typename T>
+class SetValueKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    const int rank = ctx.Output<framework::LoDTensor>("Out")->dims().size();
+
+    // TODO(liym27): A more elegent code to do this. C++ has to make template
+    //  integer as constant, but we had better have alternative writing in the
+    //  future.
+    switch (rank) {
+      case 1:
+        SetValueCompute<1>(ctx);
+        break;
+      case 2:
+        SetValueCompute<2>(ctx);
+        break;
+      case 3:
+        SetValueCompute<3>(ctx);
+        break;
+      case 4:
+        SetValueCompute<4>(ctx);
+        break;
+      case 5:
+        SetValueCompute<5>(ctx);
+        break;
+      case 6:
+        SetValueCompute<6>(ctx);
+        break;
+    }
+  }
+
+ private:
+  template <size_t D>
+  void SetValueCompute(const framework::ExecutionContext& ctx) const {
+    auto* in = ctx.Input<framework::LoDTensor>("Input");
+    auto* out = ctx.Output<framework::LoDTensor>("Out");
+
+    auto dtype =
+        static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype"));
+    auto axes = ctx.Attr<std::vector<int64_t>>("axes");
+    auto starts = ctx.Attr<std::vector<int64_t>>("starts");
+    auto ends = ctx.Attr<std::vector<int64_t>>("ends");
+    auto shape = ctx.Attr<std::vector<int64_t>>("shape");
+    auto* value_tensor = ctx.Input<framework::LoDTensor>("ValueTensor");
+
+    auto in_dims = in->dims();
+    auto value_dims = framework::make_ddim(shape);
+    auto slice_dims = GetSliceDims(in_dims, axes, starts, ends);
+
+    auto place = ctx.GetPlace();
+    auto& eigen_place =
+        *ctx.template device_context<DeviceContext>().eigen_device();
+
+    // Here copy data from input to avoid data loss at PE and Graph level.
+    // TODO(liym27): Speed up in the future version.
+    // - Q: Why don't call ShareDataWith to speed up?
+    // - A: Because it's not supported to ShareDataWith on OP's input and output
+    // https://github.com/PaddlePaddle/Paddle/wiki/ShareDataWith-and-ShareBufferWith-are-prohibited-in-OP
+    // - Q: Why don't delete Input, after all, the input and output are the same
+    // Tensor at program level?
+    // - A: If deleting Input, the graph will be complex, such as there will
+    // be two ops points to the output in graph: op1 -> output <- set_value.
+    // In this case, we have to find a way to handle the running order of
+    // set_value is what we want.
+    TensorCopy(*in, place, out);
+
+    Tensor slice_t(dtype), pad_t(dtype);
+    slice_t.mutable_data<T>(slice_dims, place);
+    pad_t.mutable_data<T>(in_dims, place);
+
+    auto pad_e = framework::EigenTensor<T, D>::From(pad_t, in_dims);
+    auto out_e = framework::EigenTensor<T, D>::From(*out);
+    auto slice_e = framework::EigenTensor<T, D>::From(slice_t, slice_dims);
+
+    // Step 1: Set the value of out at `_index` to zero
+    // - Step 1.1 Get a slice tensor from out
+    Eigen::array<int64_t, D> offsets, extents;
+    Eigen::array<std::pair<int64_t, int64_t>, D> paddings;
+
+    for (size_t i = 0; i < D; ++i) {
+      offsets[i] = 0;
+      extents[i] = slice_dims[i];
+    }
+    int64_t start;
+    for (size_t i = 0; i < axes.size(); ++i) {
+      start = starts[i] < 0 ? (starts[i] + in_dims[axes[i]]) : starts[i];
+      start = std::max(start, static_cast<int64_t>(0));
+      offsets[axes[i]] = start;
+    }
+    for (size_t i = 0; i < paddings.size(); ++i) {
+      paddings[i].first = offsets[i];
+      paddings[i].second = (in_dims[i] - slice_dims[i]) - offsets[i];
+    }
+
+    slice_e.device(eigen_place) = out_e.slice(offsets, extents);
+
+    // - Step 1.2 Get paded tensor by padding 0 to slice tensor
+    pad_e.device(eigen_place) = slice_e.pad(paddings, T(0));
+
+    // - Step 1.3 Set 0 at `_index` of out tensor
+    out_e.device(eigen_place) = out_e - pad_e;
+
+    // Step 2: Set a tensor with the same shape as out tensor. And its data at
+    // '_index' is the same as value_tensor, and data out of '_index' to zero
+
+    // - Step 2.1 Set the data of slice tensor to 0
+    slice_e.device(eigen_place) = slice_e.constant(T(0));
+
+    // - Step 2.2 Set slice tensor with value
+    if (value_tensor != nullptr) {
+      // ElementwiseComputeEx can do broadcasting
+      ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
+          ctx, &slice_t, value_tensor, -1, SubFunctor<T>(), &slice_t);
+    } else {
+      Tensor value_t(dtype);
+      value_t.mutable_data<T>(value_dims, place);
+      auto value_name = GetValueName(dtype);
+      CopyVecotorToTensor<T>(value_name.c_str(), &value_t, ctx);
+      value_t.Resize(value_dims);
+      ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
+          ctx, &slice_t, &value_t, -1, SubFunctor<T>(), &slice_t);
+    }
+
+    // - Step 2.3 Pad slice tensor with 0
+    pad_e.device(eigen_place) = slice_e.pad(paddings, T(0));
+
+    // Step 3: Set out tensor with value_tensor
+    out_e.device(eigen_place) = out_e - pad_e;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/framework.py

@@ -1815,6 +1815,88 @@ class Variable(object):
     def __getitem__(self, item):
         return _getitem_impl_(self, item)
 
+    def __setitem__(self, item, value):
+        inputs = {'Input': self}
+
+        # 1. Parse item
+        if not isinstance(item, tuple):
+            item = [item]
+
+        axes = []
+        starts = []
+        ends = []
+        max_integer = sys.maxsize
+        for dim, slice_item in enumerate(item):
+            if isinstance(slice_item, slice):
+                start = slice_item.start
+                end = slice_item.stop
+                step = slice_item.step
+
+                if start is None and end is None and step is None:
+                    continue
+
+                start = 0 if start is None else start
+                step = 1 if step is None else step
+
+                # TODO: support cases when step != 1
+                if step != 1:
+                    raise ValueError(
+                        "When assign a value to a paddle.Tensor, only support step is 1, "
+                        "but received step is {}.".format(step))
+                end = max_integer if end is None else end
+            else:
+                start = slice_item
+                end = slice_item + 1 if slice_item != -1 else max_integer
+            axes.append(dim)
+            starts.append(start)
+            ends.append(end)
+
+        attrs = {'axes': axes, 'starts': starts, 'ends': ends}
+
+        # 2. Parse value
+        dtype = self.dtype
+        attrs['dtype'] = dtype
+
+        #  2.1 value is an integer of float
+        if isinstance(value, (int, float)):
+            value = np.array([value])
+
+        #  2.2 value is a np.ndarray
+        if isinstance(value, np.ndarray):
+            shape = list(value.shape)
+            if dtype == core.VarDesc.VarType.BOOL:
+                value_name = "bool_values"
+                values = [bool(v) for v in value.flat]
+            elif dtype == core.VarDesc.VarType.FP32:
+                value_name = "fp32_values"
+                values = [float(v) for v in value.flat]
+            elif dtype == core.VarDesc.VarType.INT32:
+                value_name = "int32_values"
+                values = [int(v) for v in value.flat]
+            elif dtype == core.VarDesc.VarType.INT64:
+                value_name = "int64_values"
+                values = [int(v) for v in value.flat]
+            else:
+                from .data_feeder import convert_dtype
+                raise TypeError(
+                    "When assign a numpy.ndarray, integer or float to a paddle.Tensor, "
+                    "the data type of the paddle.Tensor must be bool, float32, int32 or int64, but "
+                    "received %s." % convert_dtype(dtype))
+            attrs[value_name] = values
+            attrs["shape"] = shape
+
+        elif isinstance(value, Variable):
+            inputs["ValueTensor"] = value
+        else:
+            raise TypeError(
+                "Only support to assign an integer, float, numpy.ndarray or "
+                "paddle.Tensor to a paddle.Tensor, but received {}".format(
+                    type(value)))
+
+        self.block.append_op(
+            type="set_value", inputs=inputs, outputs={'Out': self}, attrs=attrs)
+        return self
+
 
 def get_all_op_protos():
     """

python/paddle/fluid/tests/unittests/dygraph_to_static/test_slice.py

@@ -84,14 +84,25 @@ def test_slice_in_for_loop(x, iter_num=3):
     return out
 
 
+@paddle.jit.to_static
+def test_set_value(x):
+    x = paddle.to_tensor(x)
+    x[0] = paddle.full(shape=[1], fill_value=2, dtype="float32")
+    x[1:2, 0:1] = 10
+    return x
+
+
 class TestSliceWithoutControlFlow(unittest.TestCase):
     def setUp(self):
-        self.input = np.random.random((3)).astype('int32')
+        self.init_input()
         self.place = paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda(
         ) else paddle.CPUPlace()
         self.init_dygraph_func()
         paddle.disable_static()
 
+    def init_input(self):
+        self.input = np.random.random((3)).astype('int32')
+
     def init_dygraph_func(self):
         self.dygraph_func = test_slice_without_control_flow
 
@@ -125,10 +136,18 @@ class TestSliceInWhileLoop(TestSliceWithoutControlFlow):
         self.dygraph_func = test_slice_in_while_loop
 
 
-class TestSliceInForLoop(TestSliceInWhileLoop):
+class TestSliceInForLoop(TestSliceWithoutControlFlow):
     def init_dygraph_func(self):
         self.dygraph_func = test_slice_in_for_loop
 
 
+class TestSetValue(TestSliceWithoutControlFlow):
+    def init_input(self):
+        self.input = np.full([3, 4, 5], 5).astype('float32')
+
+    def init_dygraph_func(self):
+        self.dygraph_func = test_set_value
+
+
 if __name__ == '__main__':
     unittest.main()

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

+#   Copyright (c) 2020 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.
+
+# Test set_value op in static mode
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+
+import paddle
+
+
+class TestSetValueBase(unittest.TestCase):
+    def setUp(self):
+        paddle.enable_static()
+        self.set_dtype()
+        self.set_value()
+        self.shape = [2, 3, 4]
+        self.data = np.ones(self.shape).astype(self.dtype)
+        self.program = paddle.static.Program()
+
+    def set_value(self):
+        self.value = 6
+
+    def set_dtype(self):
+        self.dtype = "float32"
+
+    def _call_setitem(self, x):
+        x[0, 0] = self.value
+
+    def _get_answer(self):
+        self.data[0, 0] = self.value
+
+
+class TestSetValueApi(TestSetValueBase):
+    def test_api(self):
+        with paddle.static.program_guard(self.program):
+            x = paddle.ones(shape=self.shape, dtype=self.dtype)
+            self._call_setitem(x)
+
+        exe = paddle.static.Executor(paddle.CPUPlace())
+        out = exe.run(self.program, fetch_list=[x])
+
+        self._get_answer()
+        self.assertTrue(
+            (self.data == out).all(),
+            msg="\nExpected res = \n{}, \n\nbut received : \n{}".format(
+                self.data, out))
+
+
+# 1. Test different type of item: int, python slice
+class TestSetValueItemInt(TestSetValueApi):
+    def _call_setitem(self, x):
+        x[0] = self.value
+
+    def _get_answer(self):
+        self.data[0] = self.value
+
+
+class TestSetValueItemSlice(TestSetValueApi):
+    def _call_setitem(self, x):
+        x[0:2] = self.value
+
+    def _get_answer(self):
+        self.data[0:2] = self.value
+
+
+class TestSetValueItemSlice2(TestSetValueApi):
+    def _call_setitem(self, x):
+        x[0:-1] = self.value
+
+    def _get_answer(self):
+        self.data[0:-1] = self.value
+
+
+class TestSetValueItemSlice3(TestSetValueApi):
+    def _call_setitem(self, x):
+        x[0:-1, 0:2] = self.value
+
+    def _get_answer(self):
+        self.data[0:-1, 0:2] = self.value
+
+
+class TestSetValueItemSlice4(TestSetValueApi):
+    def _call_setitem(self, x):
+        x[0:, 1:2, :] = self.value
+
+    def _get_answer(self):
+        self.data[0:, 1:2, :] = self.value
+
+
+# 2. Test different type of value: int, float, numpy.ndarray, Tensor
+# 2.1 value is int32, int64, float32, bool
+
+
+def create_test_value_int32(parent):
+    class TestValueInt(parent):
+        def set_value(self):
+            self.value = 7
+
+        def set_dtype(self):
+            self.dtype = "int32"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ValueInt32")
+    TestValueInt.__name__ = cls_name
+    globals()[cls_name] = TestValueInt
+
+
+create_test_value_int32(TestSetValueItemInt)
+create_test_value_int32(TestSetValueItemSlice)
+create_test_value_int32(TestSetValueItemSlice2)
+create_test_value_int32(TestSetValueItemSlice3)
+create_test_value_int32(TestSetValueItemSlice4)
+
+
+def create_test_value_int64(parent):
+    class TestValueInt(parent):
+        def set_value(self):
+            self.value = 7
+
+        def set_dtype(self):
+            self.dtype = "int64"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ValueInt64")
+    TestValueInt.__name__ = cls_name
+    globals()[cls_name] = TestValueInt
+
+
+create_test_value_int64(TestSetValueItemInt)
+create_test_value_int64(TestSetValueItemSlice)
+create_test_value_int64(TestSetValueItemSlice2)
+create_test_value_int64(TestSetValueItemSlice3)
+create_test_value_int64(TestSetValueItemSlice4)
+
+
+def create_test_value_fp32(parent):
+    class TestValueInt(parent):
+        def set_value(self):
+            self.value = 3.3
+
+        def set_dtype(self):
+            self.dtype = "float32"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ValueFp32")
+    TestValueInt.__name__ = cls_name
+    globals()[cls_name] = TestValueInt
+
+
+create_test_value_fp32(TestSetValueItemInt)
+create_test_value_fp32(TestSetValueItemSlice)
+create_test_value_fp32(TestSetValueItemSlice2)
+create_test_value_fp32(TestSetValueItemSlice3)
+create_test_value_fp32(TestSetValueItemSlice4)
+
+
+def create_test_value_bool(parent):
+    class TestValueInt(parent):
+        def set_value(self):
+            self.value = 0
+
+        def set_dtype(self):
+            self.dtype = "bool"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ValueBool")
+    TestValueInt.__name__ = cls_name
+    globals()[cls_name] = TestValueInt
+
+
+create_test_value_bool(TestSetValueItemInt)
+create_test_value_bool(TestSetValueItemSlice)
+create_test_value_bool(TestSetValueItemSlice2)
+create_test_value_bool(TestSetValueItemSlice3)
+create_test_value_bool(TestSetValueItemSlice4)
+
+
+# 2.2 value is numpy.array (int32, int64, float32, bool)
+def create_test_value_numpy_int32(parent):
+    class TestValueInt(parent):
+        def set_value(self):
+            self.value = np.array([5])
+
+        def set_dtype(self):
+            self.dtype = "int32"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ValueNumpyInt32")
+    TestValueInt.__name__ = cls_name
+    globals()[cls_name] = TestValueInt
+
+
+create_test_value_numpy_int32(TestSetValueItemInt)
+create_test_value_numpy_int32(TestSetValueItemSlice)
+create_test_value_numpy_int32(TestSetValueItemSlice2)
+create_test_value_numpy_int32(TestSetValueItemSlice3)
+create_test_value_numpy_int32(TestSetValueItemSlice4)
+
+
+def create_test_value_numpy_int64(parent):
+    class TestValueInt(parent):
+        def set_value(self):
+            self.value = np.array([1])
+
+        def set_dtype(self):
+            self.dtype = "int64"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ValueNumpyInt64")
+    TestValueInt.__name__ = cls_name
+    globals()[cls_name] = TestValueInt
+
+
+create_test_value_numpy_int64(TestSetValueItemInt)
+create_test_value_numpy_int64(TestSetValueItemSlice)
+create_test_value_numpy_int64(TestSetValueItemSlice2)
+create_test_value_numpy_int64(TestSetValueItemSlice3)
+create_test_value_numpy_int64(TestSetValueItemSlice4)
+
+
+def create_test_value_numpy_fp32(parent):
+    class TestValueInt(parent):
+        def set_value(self):
+            self.value = np.array([1])
+
+        def set_dtype(self):
+            self.dtype = "float32"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ValueNumpyFp32")
+    TestValueInt.__name__ = cls_name
+    globals()[cls_name] = TestValueInt
+
+
+create_test_value_numpy_fp32(TestSetValueItemInt)
+create_test_value_numpy_fp32(TestSetValueItemSlice)
+create_test_value_numpy_fp32(TestSetValueItemSlice2)
+create_test_value_numpy_fp32(TestSetValueItemSlice3)
+create_test_value_numpy_fp32(TestSetValueItemSlice4)
+
+
+def create_test_value_numpy_bool(parent):
+    class TestValueInt(parent):
+        def set_value(self):
+            self.value = np.array([0])
+
+        def set_dtype(self):
+            self.dtype = "bool"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ValueNumpyBool")
+    TestValueInt.__name__ = cls_name
+    globals()[cls_name] = TestValueInt
+
+
+create_test_value_numpy_bool(TestSetValueItemInt)
+create_test_value_numpy_bool(TestSetValueItemSlice)
+create_test_value_numpy_bool(TestSetValueItemSlice2)
+create_test_value_numpy_bool(TestSetValueItemSlice3)
+create_test_value_numpy_bool(TestSetValueItemSlice4)
+
+
+# 2.3 value is a Paddle Tensor (int32, int64, float32, float64, bool)
+def create_test_value_tensor_int32(parent):
+    class TestValueInt(parent):
+        def set_dtype(self):
+            self.dtype = "int32"
+
+        def _call_setitem(self, x):
+            value = paddle.full(shape=[1], fill_value=3, dtype=self.dtype)
+            x[0, 1] = value
+
+        def _get_answer(self):
+            self.data[0, 1] = 3
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ValueTensorInt32")
+    TestValueInt.__name__ = cls_name
+    globals()[cls_name] = TestValueInt
+
+
+create_test_value_tensor_int32(TestSetValueItemInt)
+create_test_value_tensor_int32(TestSetValueItemSlice)
+create_test_value_tensor_int32(TestSetValueItemSlice2)
+create_test_value_tensor_int32(TestSetValueItemSlice3)
+create_test_value_tensor_int32(TestSetValueItemSlice4)
+
+
+def create_test_value_tensor_int64(parent):
+    class TestValueInt(parent):
+        def set_dtype(self):
+            self.dtype = "int64"
+
+        def _call_setitem(self, x):
+            value = paddle.full(shape=[1], fill_value=3, dtype=self.dtype)
+            x[0, 1] = value
+
+        def _get_answer(self):
+            self.data[0, 1] = 3
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ValueTensorInt64")
+    TestValueInt.__name__ = cls_name
+    globals()[cls_name] = TestValueInt
+
+
+create_test_value_tensor_int64(TestSetValueItemInt)
+create_test_value_tensor_int64(TestSetValueItemSlice)
+create_test_value_tensor_int64(TestSetValueItemSlice2)
+create_test_value_tensor_int64(TestSetValueItemSlice3)
+create_test_value_tensor_int64(TestSetValueItemSlice4)
+
+
+def create_test_value_tensor_fp32(parent):
+    class TestValueInt(parent):
+        def set_dtype(self):
+            self.dtype = "float32"
+
+        def _call_setitem(self, x):
+            value = paddle.full(shape=[1], fill_value=3, dtype=self.dtype)
+            x[0, 1] = value
+
+        def _get_answer(self):
+            self.data[0, 1] = 3
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ValueTensorFp32")
+    TestValueInt.__name__ = cls_name
+    globals()[cls_name] = TestValueInt
+
+
+create_test_value_tensor_fp32(TestSetValueItemInt)
+create_test_value_tensor_fp32(TestSetValueItemSlice)
+create_test_value_tensor_fp32(TestSetValueItemSlice2)
+create_test_value_tensor_fp32(TestSetValueItemSlice3)
+create_test_value_tensor_fp32(TestSetValueItemSlice4)
+
+
+def create_test_value_tensor_fp64(parent):
+    class TestValueInt(parent):
+        def set_dtype(self):
+            self.dtype = "float64"
+
+        def _call_setitem(self, x):
+            value = paddle.full(shape=[1], fill_value=3, dtype=self.dtype)
+            x[0, 1] = value
+
+        def _get_answer(self):
+            self.data[0, 1] = 3
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ValueTensorFp64")
+    TestValueInt.__name__ = cls_name
+    globals()[cls_name] = TestValueInt
+
+
+create_test_value_tensor_fp64(TestSetValueItemInt)
+create_test_value_tensor_fp64(TestSetValueItemSlice)
+create_test_value_tensor_fp64(TestSetValueItemSlice2)
+create_test_value_tensor_fp64(TestSetValueItemSlice3)
+create_test_value_tensor_fp64(TestSetValueItemSlice4)
+
+
+def create_test_value_tensor_bool(parent):
+    class TestValueInt(parent):
+        def set_dtype(self):
+            self.dtype = "bool"
+
+        def _call_setitem(self, x):
+            value = paddle.full(shape=[1], fill_value=False, dtype=self.dtype)
+            x[0, 1] = value
+
+        def _get_answer(self):
+            self.data[0, 1] = False
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ValueTensorBool")
+    TestValueInt.__name__ = cls_name
+    globals()[cls_name] = TestValueInt
+
+
+create_test_value_tensor_bool(TestSetValueItemInt)
+create_test_value_tensor_bool(TestSetValueItemSlice)
+create_test_value_tensor_bool(TestSetValueItemSlice2)
+create_test_value_tensor_bool(TestSetValueItemSlice3)
+create_test_value_tensor_bool(TestSetValueItemSlice4)
+
+
+# 3. Test different shape of value
+class TestSetValueValueShape1(TestSetValueApi):
+    def set_value(self):
+        self.value = np.array([3, 4, 5, 6])  # shape is (4,)
+
+    def _call_setitem(self, x):
+        x[0] = self.value
+
+    def _get_answer(self):
+        self.data[0] = self.value
+
+
+class TestSetValueValueShape2(TestSetValueApi):
+    def set_value(self):
+        self.value = np.array([[3, 4, 5, 6]])  # shape is (1,4)
+
+    def _call_setitem(self, x):
+        x[0:1] = self.value
+
+    def _get_answer(self):
+        self.data[0:1] = self.value
+
+
+class TestSetValueValueShape3(TestSetValueApi):
+    def set_value(self):
+        self.value = np.array(
+            [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]])  # shape is (3,4)
+
+    def _call_setitem(self, x):
+        x[0] = self.value
+
+    def _get_answer(self):
+        self.data[0] = self.value
+
+
+class TestSetValueValueShape4(TestSetValueApi):
+    def set_value(self):
+        self.value = np.array(
+            [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]]).astype(
+                self.dtype)  # shape is (3,4)
+
+    def _call_setitem(self, x):
+        x[0] = paddle.assign(self.value)  # x is Paddle.Tensor
+
+    def _get_answer(self):
+        self.data[0] = self.value
+
+
+# 4. Test error
+class TestError(TestSetValueBase):
+    def _value_type_error(self):
+        with self.assertRaisesRegexp(
+                TypeError,
+                "Only support to assign an integer, float, numpy.ndarray or paddle.Tensor"
+        ):
+            x = paddle.ones(shape=self.shape, dtype=self.dtype)
+            value = [1]
+            x[0] = value
+
+    def _dtype_error(self):
+        with self.assertRaisesRegexp(
+                TypeError,
+                "When assign a numpy.ndarray, integer or float to a paddle.Tensor, "
+        ):
+            y = paddle.ones(shape=self.shape, dtype="float64")
+            y[0] = 1
+
+    def _step_error(self):
+        with self.assertRaisesRegexp(ValueError, "only support step is 1"):
+            x = paddle.ones(shape=self.shape, dtype=self.dtype)
+            x[0:1:2] = self.value
+
+    def _broadcast_mismatch(self):
+        program = paddle.static.Program()
+        with paddle.static.program_guard(program):
+            x = paddle.ones(shape=self.shape, dtype=self.dtype)
+            value = np.array([3, 4, 5, 6, 7])
+            x[0] = value
+        exe = paddle.static.Executor(paddle.CPUPlace())
+        with self.assertRaisesRegexp(ValueError,
+                                     "Broadcast dimension mismatch."):
+            exe.run(program)
+
+    def test_error(self):
+        with paddle.static.program_guard(self.program):
+            self._value_type_error()
+            self._dtype_error()
+            self._step_error()
+        self._broadcast_mismatch()
+
+
+if __name__ == '__main__':
+    unittest.main()