add kernel for flatten_op, test=develop (#19472)

* add kernel for flatten_op, test=develop

* add kernel for flatten_op, test=develop

* fix the license and remove redundant code, test=develop

paddle/fluid/operators/flatten_op.cc

-/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+/* Copyright (c) 2019 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.
@@ -12,6 +12,10 @@ 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/flatten_op.h"
+#include <memory>
+#include <string>
+#include <unordered_map>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 
@@ -20,18 +24,21 @@ namespace operators {
 
 using Tensor = framework::Tensor;
 
-class FlattenOpInferShape : public framework::InferShapeBase {
+class FlattenOp : public framework::OperatorWithKernel {
  public:
-  void operator()(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input (X) of Flatten op should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output (Output) of Flatten op should not be null.");
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
+                      "Input (X) of Flatten op should not be null.");
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
+                      "Output (Output) of Flatten op should not be null.");
     const auto &axis = ctx->Attrs().Get<int>("axis");
     const auto &in_dims = ctx->GetInputDim("X");
-    PADDLE_ENFORCE(axis >= 0, "The axis should be greater than or equal to 0.");
-    PADDLE_ENFORCE(
-        axis <= in_dims.size(),
+    PADDLE_ENFORCE_GE(axis, 0,
+                      "The axis should be greater than or equal to 0.");
+    PADDLE_ENFORCE_LE(
+        axis, in_dims.size(),
         "The axis should be less than or equal to input tensor's rank.");
 
     const auto &out_dims = GetOutputShape(axis, in_dims);
@@ -58,28 +65,12 @@ class FlattenOpInferShape : public framework::InferShapeBase {
     out_shape[1] = inner;
     return out_shape;
   }
-};
 
-class FlattenOp : public framework::OperatorBase {
- public:
-  using OperatorBase::OperatorBase;
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &place) const override {
-    auto &axis = Attr<int>("axis");
-    auto in_dims =
-        scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
-    const auto &out_dims = FlattenOpInferShape::GetOutputShape(axis, in_dims);
-
-    framework::AttributeMap attrs;
-    attrs["shape"] = out_dims;
-    attrs["inplace"] = false;
-    // Invoke Reshape Op
-    auto reshape_op = framework::OpRegistry::CreateOp(
-        "reshape", {{"X", {Input("X")}}, {"Shape", {}}},
-        {{"Out", {Output("Out")}}}, attrs);
-    reshape_op->Run(scope, place);
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
+                                   ctx.device_context());
   }
 };
 
@@ -126,34 +117,21 @@ Case 2:
   }
 };
 
-class FlattenGradInferShape : public framework::InferShapeBase {
+class FlattenGradOp : public framework::OperatorWithKernel {
  public:
-  void operator()(framework::InferShapeContext *context) const override {
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *context) const override {
     context->SetOutputDim(framework::GradVarName("X"),
                           context->GetInputDim("X"));
     context->ShareLoD("X", framework::GradVarName("X"));
   }
-};
 
-class FlattenGradOp : public framework::OperatorBase {
- public:
-  using OperatorBase::OperatorBase;
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &place) const override {
-    auto dx_name = Output(framework::GradVarName("X"));
-    auto dout_name = Input(framework::GradVarName("Out"));
-    auto in_dims =
-        scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
-    framework::AttributeMap attrs;
-    attrs["shape"] = framework::vectorize2int(in_dims);
-    attrs["inplace"] = false;
-
-    auto reshape_op = framework::OpRegistry::CreateOp(
-        "reshape", {{"X", {dout_name}}, {"Shape", {}}}, {{"Out", {dx_name}}},
-        attrs);
-    reshape_op->Run(scope, place);
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
+                                   ctx.device_context());
   }
 };
 
@@ -162,13 +140,33 @@ class FlattenGradOp : public framework::OperatorBase {
 // flatten_grad, in this way, the framework can reuse the memory of X
 // immediately the flatten2_op is finished.
 // Considering compatibility issues, we could not fix flatten2_op
-class Flatten2OpInferShape : public FlattenOpInferShape {
+class Flatten2Op : public framework::OperatorWithKernel {
  public:
-  void operator()(framework::InferShapeContext *ctx) const override {
-    FlattenOpInferShape::operator()(ctx);
-    PADDLE_ENFORCE(ctx->HasOutput("XShape"),
-                   "Output (XShape) of Flatten op should not be null.");
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
+                      "Input (X) of Flatten op should not be null.");
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
+                      "Output (Output) of Flatten op should not be null.");
+    const auto &axis = ctx->Attrs().Get<int>("axis");
     const auto &in_dims = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_GE(axis, 0,
+                      "The axis should be greater than or equal to 0.");
+    PADDLE_ENFORCE_LE(
+        axis, in_dims.size(),
+        "The axis should be less than or equal to input tensor's rank.");
+
+    const auto &out_dims = FlattenOp::GetOutputShape(axis, in_dims);
+    ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
+    if (in_dims[0] == out_dims[0]) {
+      // Only pass LoD when the first dimension of output and Input(X)
+      // are the same.
+      ctx->ShareLoD("X", "Out");
+    }
+
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("XShape"), true,
+                      "Output (XShape) of Flatten op should not be null.");
     std::vector<int64_t> xshape_dims(in_dims.size() + 1);
     xshape_dims[0] = 0;
     for (int i = 0; i < in_dims.size(); ++i) {
@@ -179,29 +177,6 @@ class Flatten2OpInferShape : public FlattenOpInferShape {
   }
 };
 
-class Flatten2Op : public framework::OperatorBase {
- public:
-  using OperatorBase::OperatorBase;
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &place) const override {
-    auto &axis = Attr<int>("axis");
-    auto in_dims =
-        scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
-    const auto &out_dims = FlattenOpInferShape::GetOutputShape(axis, in_dims);
-
-    framework::AttributeMap attrs;
-    attrs["shape"] = out_dims;
-    attrs["inplace"] = false;
-    // Invoke Reshape Op
-    auto reshape_op = framework::OpRegistry::CreateOp(
-        "reshape2", {{"X", {Input("X")}}, {"Shape", {}}},
-        {{"Out", {Output("Out")}}, {"XShape", {Output("XShape")}}}, attrs);
-    reshape_op->Run(scope, place);
-  }
-};
-
 class Flatten2OpMaker : public FlattenOpMaker {
  public:
   void Make() override {
@@ -228,43 +203,27 @@ class Flatten2GradOpMaker : public framework::SingleGradOpDescMaker {
   }
 };
 
-class Flatten2GradInferShape : public framework::InferShapeBase {
+class Flatten2GradOp : public framework::OperatorWithKernel {
  public:
-  void operator()(framework::InferShapeContext *context) const override {
-    PADDLE_ENFORCE(context->HasInput("XShape"),
-                   "Input(XShape) shouldn't be null.");
-    PADDLE_ENFORCE(context->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@GRAD) shouldn't be null.");
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *context) const override {
+    PADDLE_ENFORCE_EQ(context->HasInput("XShape"), true,
+                      "Input(XShape) shouldn't be null.");
+    PADDLE_ENFORCE_EQ(context->HasInput(framework::GradVarName("Out")), true,
+                      "Input(Out@GRAD) shouldn't be null.");
     auto xshape_dims = context->GetInputDim("XShape");
     auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
     context->SetOutputDim(framework::GradVarName("X"), x_dims);
     context->ShareLoD("XShape", framework::GradVarName("X"));
   }
-};
 
-class Flatten2GradOp : public framework::OperatorBase {
- public:
-  using OperatorBase::OperatorBase;
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &place) const override {
-    auto dx_name = Output(framework::GradVarName("X"));
-    auto dout_name = Input(framework::GradVarName("Out"));
-    auto xshape_name = Input("XShape");
-    auto xshape_dims =
-        scope.FindVar(xshape_name)->Get<framework::LoDTensor>().dims();
-    auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
-
-    framework::AttributeMap attrs;
-    attrs["shape"] = framework::vectorize2int(x_dims);
-    attrs["inplace"] = false;
-
-    auto reshape_grad_op = framework::OpRegistry::CreateOp(
-        "reshape2_grad",
-        {{"Out@GRAD", {dout_name}}, {"Shape", {}}, {"XShape", {xshape_name}}},
-        {{"X@GRAD", {dx_name}}}, attrs);
-    reshape_grad_op->Run(scope, place);
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))->type(),
+        ctx.device_context());
   }
 };
 
@@ -276,18 +235,41 @@ DECLARE_INPLACE_OP_INFERER(FlattenGradInplaceinToOut,
 }  // namespace operators
 }  // namespace paddle
 
-USE_OP(reshape);
-
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(flatten, ops::FlattenOp, ops::FlattenOpMaker,
-                  ops::FlattenOpInferShape,
                   paddle::framework::DefaultGradOpDescMaker<true>,
                   ops::FlattenOpInplaceInToOut);
-REGISTER_OPERATOR(flatten_grad, ops::FlattenGradOp, ops::FlattenGradInferShape,
+REGISTER_OPERATOR(flatten_grad, ops::FlattenGradOp,
                   ops::FlattenGradInplaceinToOut);
 
 REGISTER_OPERATOR(flatten2, ops::Flatten2Op, ops::Flatten2OpMaker,
-                  ops::Flatten2OpInferShape, ops::Flatten2GradOpMaker,
-                  ops::FlattenOpInplaceInToOut);
+                  ops::Flatten2GradOpMaker, ops::FlattenOpInplaceInToOut);
 REGISTER_OPERATOR(flatten2_grad, ops::Flatten2GradOp,
-                  ops::Flatten2GradInferShape, ops::FlattenGradInplaceinToOut);
+                  ops::FlattenGradInplaceinToOut);
+
+REGISTER_OP_CPU_KERNEL(
+    flatten, ops::FlattenKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::FlattenKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::FlattenKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::FlattenKernel<paddle::platform::CPUDeviceContext, int8_t>,
+    ops::FlattenKernel<paddle::platform::CPUDeviceContext, int64_t>);
+REGISTER_OP_CPU_KERNEL(
+    flatten_grad,
+    ops::FlattenGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::FlattenGradKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::FlattenGradKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::FlattenGradKernel<paddle::platform::CPUDeviceContext, int8_t>,
+    ops::FlattenGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
+REGISTER_OP_CPU_KERNEL(
+    flatten2, ops::Flatten2Kernel<paddle::platform::CPUDeviceContext, float>,
+    ops::Flatten2Kernel<paddle::platform::CPUDeviceContext, double>,
+    ops::Flatten2Kernel<paddle::platform::CPUDeviceContext, int>,
+    ops::Flatten2Kernel<paddle::platform::CPUDeviceContext, int8_t>,
+    ops::Flatten2Kernel<paddle::platform::CPUDeviceContext, int64_t>);
+REGISTER_OP_CPU_KERNEL(
+    flatten2_grad,
+    ops::Flatten2GradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::Flatten2GradKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::Flatten2GradKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::Flatten2GradKernel<paddle::platform::CPUDeviceContext, int8_t>,
+    ops::Flatten2GradKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/flatten_op.cu.cc

+/* Copyright (c) 2019 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/flatten_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_CUDA_KERNEL(
+    flatten, ops::FlattenKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::FlattenKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::FlattenKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::FlattenKernel<paddle::platform::CUDADeviceContext, int8_t>,
+    ops::FlattenKernel<paddle::platform::CUDADeviceContext, int64_t>);
+REGISTER_OP_CUDA_KERNEL(
+    flatten_grad,
+    ops::FlattenGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::FlattenGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::FlattenGradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::FlattenGradKernel<paddle::platform::CUDADeviceContext, int8_t>,
+    ops::FlattenGradKernel<paddle::platform::CUDADeviceContext, int64_t>);
+REGISTER_OP_CUDA_KERNEL(
+    flatten2, ops::Flatten2Kernel<paddle::platform::CUDADeviceContext, float>,
+    ops::Flatten2Kernel<paddle::platform::CUDADeviceContext, double>,
+    ops::Flatten2Kernel<paddle::platform::CUDADeviceContext, int>,
+    ops::Flatten2Kernel<paddle::platform::CUDADeviceContext, int8_t>,
+    ops::Flatten2Kernel<paddle::platform::CUDADeviceContext, int64_t>);
+REGISTER_OP_CUDA_KERNEL(
+    flatten2_grad,
+    ops::Flatten2GradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::Flatten2GradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::Flatten2GradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::Flatten2GradKernel<paddle::platform::CUDADeviceContext, int8_t>,
+    ops::Flatten2GradKernel<paddle::platform::CUDADeviceContext, int64_t>);

paddle/fluid/operators/flatten_op.h

+/* Copyright (c) 2019 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 <vector>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/blas.h"
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/operators/math/pooling.h"
+#include "paddle/fluid/platform/device_context.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class FlattenKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &context) const override {
+    auto *in = context.Input<framework::LoDTensor>("X");
+    auto *out = context.Output<framework::LoDTensor>("Out");
+
+    auto &axes = context.Attr<int>("axis");
+    auto x_dims = in->dims();
+    auto out_dims = framework::make_ddim(GetOutputShape(axes, x_dims));
+
+    out->mutable_data(context.GetPlace(), in->type());
+    framework::TensorCopy(
+        *in, context.GetPlace(),
+        context.template device_context<platform::DeviceContext>(), out);
+    out->Resize(out_dims);
+  }
+
+  static std::vector<int32_t> GetOutputShape(const int axis,
+                                             const framework::DDim &in_dims) {
+    int64_t outer = 1, inner = 1;
+    for (int i = 0; i < in_dims.size(); ++i) {
+      if (i < axis) {
+        outer *= in_dims[i];
+      } else {
+        inner *= in_dims[i];
+      }
+    }
+    std::vector<int32_t> out_shape(2);
+    out_shape[0] = outer;
+    out_shape[1] = inner;
+    return out_shape;
+  }
+};
+
+template <typename DeviceContext, typename T>
+class FlattenGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto *d_x = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
+    auto *d_out =
+        ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
+    auto in_dims = ctx.Input<framework::LoDTensor>("X")->dims();
+
+    d_x->mutable_data(ctx.GetPlace(), d_out->type());
+    framework::TensorCopySync(*d_out, ctx.GetPlace(), d_x);
+    d_x->Resize(in_dims);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class Flatten2Kernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &context) const override {
+    auto &axes = context.Attr<int>("axis");
+
+    auto *in = context.Input<framework::LoDTensor>("X");
+    auto x_dims = in->dims();
+
+    auto *out = context.Output<framework::LoDTensor>("Out");
+
+    auto out_dims = framework::make_ddim(
+        FlattenKernel<DeviceContext, T>::GetOutputShape(axes, x_dims));
+
+    out->mutable_data(context.GetPlace(), in->type());
+    framework::TensorCopy(
+        *in, context.GetPlace(),
+        context.template device_context<platform::DeviceContext>(), out);
+    out->Resize(out_dims);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class Flatten2GradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto *d_x = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
+    auto *d_out =
+        ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
+
+    auto xshape_dims = ctx.Input<framework::LoDTensor>("XShape")->dims();
+    auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
+
+    d_x->mutable_data(ctx.GetPlace(), d_out->type());
+    framework::TensorCopySync(*d_out, ctx.GetPlace(), d_x);
+    d_x->Resize(x_dims);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/tests/unittests/test_flatten2_op.py

+#   Copyright (c) 2019 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
+
+
+class TestFlattenOp(OpTest):
+    def setUp(self):
+        self.op_type = "flatten2"
+        self.init_test_case()
+        self.inputs = {"X": np.random.random(self.in_shape).astype("float32")}
+        self.init_attrs()
+        self.outputs = {
+            "Out": self.inputs["X"].reshape(self.new_shape),
+            "XShape": np.random.random(self.in_shape).astype("float32")
+        }
+
+    def test_check_output(self):
+        self.check_output(no_check_set=["XShape"])
+
+    def test_check_grad(self):
+        self.check_grad(["X"], "Out")
+
+    def init_test_case(self):
+        self.in_shape = (3, 2, 2, 5)
+        self.axis = 1
+        self.new_shape = (3, 20)
+
+    def init_attrs(self):
+        self.attrs = {"axis": self.axis}
+
+
+class TestFlattenOp(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 2, 3)
+        self.axis = 0
+        self.new_shape = (1, 36)
+
+
+class TestFlattenOpWithDefaultAxis(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 2, 3)
+        self.new_shape = (3, 12)
+
+    def init_attrs(self):
+        self.attrs = {}
+
+
+class TestFlattenOpSixDims(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = (3, 2, 3, 2, 4, 4)
+        self.axis = 4
+        self.new_shape = (36, 16)
+
+
+if __name__ == "__main__":
+    unittest.main()

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

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#   Copyright (c) 2019 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.
@@ -22,17 +22,14 @@ from op_test import OpTest
 
 class TestFlattenOp(OpTest):
     def setUp(self):
-        self.op_type = "flatten2"
+        self.op_type = "flatten"
         self.init_test_case()
         self.inputs = {"X": np.random.random(self.in_shape).astype("float32")}
         self.init_attrs()
-        self.outputs = {
-            "Out": self.inputs["X"].reshape(self.new_shape),
-            "XShape": np.random.random(self.in_shape).astype("float32")
-        }
+        self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
 
     def test_check_output(self):
-        self.check_output(no_check_set=["XShape"])
+        self.check_output()
 
     def test_check_grad(self):
         self.check_grad(["X"], "Out")