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321 lines
12 KiB
321 lines
12 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/squeeze_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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class SqueezeOp : 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 Squeeze operator should not be null.");
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PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
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"Output(Out) of Squeeze operator should not be null.");
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const auto &x_dims = ctx->GetInputDim("X");
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// Check input tensor dims (<6) Eigen limit.
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PADDLE_ENFORCE_LE(x_dims.size(), 6,
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"ShapeError: the dimensions of Input(X) "
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"should be in the range of [1, 6] (Eigen limit)."
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"But received X's dimensions = %d, X's shape=[%s].",
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x_dims.size(), x_dims);
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const auto &axes = ctx->Attrs().Get<std::vector<int>>("axes");
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for (int a : axes) {
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PADDLE_ENFORCE_LT(
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a, x_dims.size(),
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"ShapeError: The squeeze axis should be less than input "
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"tensor's dimensions. But received axis = %d, input "
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"tensor's dimensions = %d, input tensor's shape = [%s].",
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a, x_dims.size(), x_dims);
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}
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auto out_dims = GetOutputShape(axes, x_dims);
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ctx->SetOutputDim("Out", out_dims);
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if (x_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 framework::DDim GetOutputShape(const std::vector<int> squeeze_dims,
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const framework::DDim &in_dims) {
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size_t num_squeeze_dims = squeeze_dims.size();
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int cnt_squeezed_dims = 0;
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bool should_squeeze[9] = {false};
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// Determines number of dimensions of output tensor after squeeze.
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// Mark and count the dimensions need to be squeezed
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if (num_squeeze_dims == 0) {
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for (int idx = 0; idx < in_dims.size(); ++idx) {
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if (in_dims[idx] == 1) {
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should_squeeze[idx] = true;
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++cnt_squeezed_dims;
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}
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}
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} else {
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for (size_t idx = 0; idx < num_squeeze_dims; ++idx) {
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int current = squeeze_dims[idx] < 0 ? squeeze_dims[idx] + in_dims.size()
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: squeeze_dims[idx];
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PADDLE_ENFORCE_GE(current, 0,
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"Invalid axis, the axis should >= 0."
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"Current axis is:%d, input tensor's shape = [%s].",
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current, in_dims);
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if (!(should_squeeze[current])) {
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++cnt_squeezed_dims;
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}
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should_squeeze[current] = true;
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}
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}
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// Make output dimensions
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std::vector<int64_t> output_shape(in_dims.size() - cnt_squeezed_dims, 0);
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for (int in_idx = 0, out_idx = 0; in_idx < in_dims.size(); ++in_idx) {
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if (!should_squeeze[in_idx]) {
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output_shape[out_idx++] = in_dims[in_idx];
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}
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}
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return framework::make_ddim(output_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(
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OperatorWithKernel::IndicateVarDataType(ctx, "X"),
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ctx.device_context());
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}
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};
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class SqueezeGradOp : 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(
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OperatorWithKernel::IndicateVarDataType(ctx, "X"),
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ctx.device_context());
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}
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};
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class SqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "(Tensor). The input tensor of squeeze operator.");
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AddOutput("Out", "(Tensor). The output tensor of squeeze operator.");
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AddAttr<std::vector<int>>("axes",
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"(std::vector<int>). List of integers,"
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" indicating the dimensions to squeeze.")
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.SetDefault({});
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AddComment(R"DOC(
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Squeeze Operator.
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Remove single-dimensional entries from the shape of a tensor.
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Takes a parameter axes with a list of axes to squeeze.
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If axes is not provided, all the single dimensions will be removed from the shape.
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If an axis is selected with shape entry not equal to one, an error is raised.
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Examples:
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Case 1:
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Given
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X.shape = (1, 3, 1, 5)
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and
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axes = [0]
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we get:
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Out.shape = (3, 1, 5)
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Case 2:
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Given
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X.shape = (1, 3, 1, 5)
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and
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axes = []
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we get:
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Out.shape = (3, 5)
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)DOC");
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}
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};
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class Squeeze2Op : 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 Squeeze operator should not be null.");
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PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
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"Output(Out) of Squeeze operator should not be null.");
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const auto &x_dims = ctx->GetInputDim("X");
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// Check input tensor dims (<6) Eigen limit.
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PADDLE_ENFORCE_LE(x_dims.size(), 6,
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"ShapeError: the dimensions of Input(X) "
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"should be in the range of [1, 6] (Eigen limit)."
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"But received X's dimensions = %d, X's shape = [%s].",
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x_dims.size(), x_dims);
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const auto &axes = ctx->Attrs().Get<std::vector<int>>("axes");
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for (int a : axes) {
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PADDLE_ENFORCE_LT(
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a, x_dims.size(),
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"ShapeError: The squeeze axis should be less than input "
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"tensor's dimensions. But received axis = %d, input "
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"tensor's dimensions = %d, input tensor's shape = [%s].",
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a, x_dims.size(), x_dims);
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}
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auto out_dims = SqueezeOp::GetOutputShape(axes, x_dims);
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ctx->SetOutputDim("Out", out_dims);
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if (x_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 Squeeze operator should not be null.");
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std::vector<int64_t> xshape_dims(x_dims.size() + 1);
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xshape_dims[0] = 0;
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for (int i = 0; i < x_dims.size(); ++i) {
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xshape_dims[i + 1] = x_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 Squeeze2GradOp : 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(OperatorWithKernel::IndicateVarDataType(
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ctx, framework::GradVarName("Out")),
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ctx.device_context());
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}
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};
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// FIXME(zcd): squeeze2 adds an intermediate output(XShape) based on squeeze,
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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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// squeeze_grad, in this way, the framework can reuse the memory of X
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// immediately the squeeze2_op is finished.
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// Considering compatibility issues, we could not fix squeeze2_op
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class Squeeze2OpMaker : public SqueezeOpMaker {
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public:
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void Make() override {
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SqueezeOpMaker::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 SqueezeGradOp.")
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.AsIntermediate();
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}
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};
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template <typename T>
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class Squeeze2GradOpMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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std::unique_ptr<T> Apply() const override {
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auto *grad_op = new T();
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grad_op->SetType("squeeze2_grad");
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grad_op->SetInput("XShape", this->Output("XShape"));
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grad_op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
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grad_op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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grad_op->SetAttrMap(this->Attrs());
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return std::unique_ptr<T>(grad_op);
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}
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};
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DECLARE_INPLACE_OP_INFERER(SequeezeInplaceInferer, {"X", "Out"});
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DECLARE_INPLACE_OP_INFERER(SequeezeGradInplaceInferer,
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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(
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squeeze, ops::SqueezeOp, ops::SqueezeOpMaker,
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paddle::framework::DefaultGradOpMaker<paddle::framework::OpDesc, true>,
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paddle::framework::DefaultGradOpMaker<paddle::imperative::OpBase, true>);
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REGISTER_OPERATOR(squeeze_grad, ops::SqueezeGradOp);
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REGISTER_OPERATOR(squeeze2, ops::Squeeze2Op, ops::Squeeze2OpMaker,
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ops::Squeeze2GradOpMaker<paddle::framework::OpDesc>,
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ops::Squeeze2GradOpMaker<paddle::imperative::OpBase>,
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ops::SequeezeInplaceInferer);
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REGISTER_OPERATOR(squeeze2_grad, ops::Squeeze2GradOp,
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ops::SequeezeGradInplaceInferer);
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REGISTER_OP_CPU_KERNEL(
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squeeze, ops::SqueezeKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SqueezeKernel<paddle::platform::CPUDeviceContext, double>,
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ops::SqueezeKernel<paddle::platform::CPUDeviceContext, int>,
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ops::SqueezeKernel<paddle::platform::CPUDeviceContext, int8_t>,
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ops::SqueezeKernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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squeeze_grad,
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ops::SqueezeGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SqueezeGradKernel<paddle::platform::CPUDeviceContext, double>,
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ops::SqueezeGradKernel<paddle::platform::CPUDeviceContext, int>,
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ops::SqueezeGradKernel<paddle::platform::CPUDeviceContext, int8_t>,
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ops::SqueezeGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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squeeze2, ops::Squeeze2Kernel<paddle::platform::CPUDeviceContext, float>,
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ops::Squeeze2Kernel<paddle::platform::CPUDeviceContext, double>,
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ops::Squeeze2Kernel<paddle::platform::CPUDeviceContext, int>,
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ops::Squeeze2Kernel<paddle::platform::CPUDeviceContext, int8_t>,
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ops::Squeeze2Kernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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squeeze2_grad,
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ops::Squeeze2GradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::Squeeze2GradKernel<paddle::platform::CPUDeviceContext, double>,
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ops::Squeeze2GradKernel<paddle::platform::CPUDeviceContext, int>,
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ops::Squeeze2GradKernel<paddle::platform::CPUDeviceContext, int8_t>,
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ops::Squeeze2GradKernel<paddle::platform::CPUDeviceContext, int64_t>);
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