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276 lines
10 KiB
276 lines
10 KiB
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/flatten_op.h"
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#include <memory>
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#include <string>
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#include <unordered_map>
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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class FlattenOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
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"Input (X) of Flatten op should not be null.");
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PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
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"Output (Output) of Flatten op should not be null.");
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const auto &axis = ctx->Attrs().Get<int>("axis");
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const auto &in_dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE_GE(axis, 0,
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"The axis should be greater than or equal to 0.");
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PADDLE_ENFORCE_LE(
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axis, in_dims.size(),
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"The axis should be less than or equal to input tensor's rank.");
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const auto &out_dims = GetOutputShape(axis, in_dims);
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ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
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if (in_dims[0] == out_dims[0]) {
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// Only pass LoD when the first dimension of output and Input(X)
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// are the same.
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ctx->ShareLoD("X", "Out");
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}
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}
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static std::vector<int32_t> GetOutputShape(const int axis,
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const framework::DDim &in_dims) {
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int64_t outer = 1, inner = 1;
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for (int i = 0; i < in_dims.size(); ++i) {
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if (i < axis) {
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outer *= in_dims[i];
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} else {
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inner *= in_dims[i];
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}
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}
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std::vector<int32_t> out_shape(2);
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out_shape[0] = outer;
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out_shape[1] = inner;
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return out_shape;
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
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ctx.device_context());
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}
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};
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class FlattenOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "(Tensor) A tensor of rank >= axis.");
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AddOutput("Out",
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"A 2D tensor is reshaped input tensor. The input dimensions"
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"up to axis are flattened to the outer dimension of the output"
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"and the remaining input dimensions are flattened into the inner"
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"dimension of the output.");
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AddAttr<int>("axis",
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"(int)"
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"Indicate up to which input dimensions (exclusive) should be"
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"flattened to the outer dimension of the output. The value"
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"for axis must be in the range [0, R], where R is the rank of"
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"the input tensor. When axis = 0, the shape of the output"
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"tensor is (1, (d_0 X d_1 ... d_n), where the shape of the"
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"input tensor is (d_0, d_1, ... d_n).")
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.SetDefault(1);
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AddComment(R"DOC(
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Flatten Operator
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Flattens the input tensor into a 2D matrix.
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Examples:
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Case 1:
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Given
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X.shape = (3, 100, 100, 4)
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and
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axis = 2
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We get:
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Out.shape = (3 * 100, 4 * 100)
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Case 2:
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Given
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X.shape = (3, 100, 100, 4)
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and
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axis = 0
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We get:
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Out.shape = (1, 3 * 100 * 100 * 4)
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)DOC");
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}
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};
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class FlattenGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext *context) const override {
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context->SetOutputDim(framework::GradVarName("X"),
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context->GetInputDim("X"));
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context->ShareLoD("X", framework::GradVarName("X"));
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
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ctx.device_context());
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}
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};
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// FIXME(zcd): flatten2 adds an intermediate output(XShape) based on flatten,
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// the XShape is used to carry the shape and lod of X which will be used in
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// flatten_grad, in this way, the framework can reuse the memory of X
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// immediately the flatten2_op is finished.
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// Considering compatibility issues, we could not fix flatten2_op
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class Flatten2Op : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
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"Input (X) of Flatten op should not be null.");
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PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
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"Output (Output) of Flatten op should not be null.");
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const auto &axis = ctx->Attrs().Get<int>("axis");
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const auto &in_dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE_GE(axis, 0,
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"The axis should be greater than or equal to 0.");
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PADDLE_ENFORCE_LE(
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axis, in_dims.size(),
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"The axis should be less than or equal to input tensor's rank.");
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const auto &out_dims = FlattenOp::GetOutputShape(axis, in_dims);
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ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
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if (in_dims[0] == out_dims[0]) {
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// Only pass LoD when the first dimension of output and Input(X)
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// are the same.
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ctx->ShareLoD("X", "Out");
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}
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PADDLE_ENFORCE_EQ(ctx->HasOutput("XShape"), true,
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"Output (XShape) of Flatten op should not be null.");
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std::vector<int64_t> xshape_dims(in_dims.size() + 1);
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xshape_dims[0] = 0;
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for (int i = 0; i < in_dims.size(); ++i) {
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xshape_dims[i + 1] = in_dims[i];
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}
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ctx->SetOutputDim("XShape", framework::make_ddim(xshape_dims));
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ctx->ShareLoD("X", "XShape");
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}
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};
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class Flatten2OpMaker : public FlattenOpMaker {
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public:
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void Make() override {
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FlattenOpMaker::Make();
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AddOutput("XShape",
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"XShape is just used to store the shape and lod of X, which will "
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"be used in FlattenGradOp.")
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.AsIntermediate();
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}
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};
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class Flatten2GradOpMaker : public framework::SingleGradOpDescMaker {
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public:
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using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
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std::unique_ptr<framework::OpDesc> Apply() const override {
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auto *grad_op = new framework::OpDesc();
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grad_op->SetType("flatten2_grad");
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grad_op->SetInput("XShape", Output("XShape"));
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grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
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grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
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grad_op->SetAttrMap(Attrs());
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return std::unique_ptr<framework::OpDesc>(grad_op);
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}
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};
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class Flatten2GradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext *context) const override {
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PADDLE_ENFORCE_EQ(context->HasInput("XShape"), true,
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"Input(XShape) shouldn't be null.");
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PADDLE_ENFORCE_EQ(context->HasInput(framework::GradVarName("Out")), true,
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"Input(Out@GRAD) shouldn't be null.");
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auto xshape_dims = context->GetInputDim("XShape");
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auto x_dims = framework::slice_ddim(xshape_dims, 1, xshape_dims.size());
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context->SetOutputDim(framework::GradVarName("X"), x_dims);
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context->ShareLoD("XShape", framework::GradVarName("X"));
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return framework::OpKernelType(
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ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))->type(),
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ctx.device_context());
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}
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};
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DECLARE_INPLACE_OP_INFERER(FlattenOpInplaceInToOut, {"X", "Out"});
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DECLARE_INPLACE_OP_INFERER(FlattenGradInplaceinToOut,
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{framework::GradVarName("Out"),
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framework::GradVarName("X")});
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(flatten, ops::FlattenOp, ops::FlattenOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>,
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ops::FlattenOpInplaceInToOut);
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REGISTER_OPERATOR(flatten_grad, ops::FlattenGradOp,
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ops::FlattenGradInplaceinToOut);
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REGISTER_OPERATOR(flatten2, ops::Flatten2Op, ops::Flatten2OpMaker,
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ops::Flatten2GradOpMaker, ops::FlattenOpInplaceInToOut);
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REGISTER_OPERATOR(flatten2_grad, ops::Flatten2GradOp,
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ops::FlattenGradInplaceinToOut);
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REGISTER_OP_CPU_KERNEL(
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flatten, ops::FlattenKernel<paddle::platform::CPUDeviceContext, float>,
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ops::FlattenKernel<paddle::platform::CPUDeviceContext, double>,
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ops::FlattenKernel<paddle::platform::CPUDeviceContext, int>,
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ops::FlattenKernel<paddle::platform::CPUDeviceContext, int8_t>,
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ops::FlattenKernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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flatten_grad,
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ops::FlattenGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::FlattenGradKernel<paddle::platform::CPUDeviceContext, double>,
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ops::FlattenGradKernel<paddle::platform::CPUDeviceContext, int>,
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ops::FlattenGradKernel<paddle::platform::CPUDeviceContext, int8_t>,
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ops::FlattenGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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flatten2, ops::Flatten2Kernel<paddle::platform::CPUDeviceContext, float>,
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ops::Flatten2Kernel<paddle::platform::CPUDeviceContext, double>,
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ops::Flatten2Kernel<paddle::platform::CPUDeviceContext, int>,
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ops::Flatten2Kernel<paddle::platform::CPUDeviceContext, int8_t>,
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ops::Flatten2Kernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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flatten2_grad,
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ops::Flatten2GradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::Flatten2GradKernel<paddle::platform::CPUDeviceContext, double>,
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ops::Flatten2GradKernel<paddle::platform::CPUDeviceContext, int>,
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ops::Flatten2GradKernel<paddle::platform::CPUDeviceContext, int8_t>,
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ops::Flatten2GradKernel<paddle::platform::CPUDeviceContext, int64_t>);
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