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278 lines
10 KiB
278 lines
10 KiB
/* Copyright (c) 2016 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/expand_v2_op.h"
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#include <memory>
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#include <string>
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#include <vector>
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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class ExpandV2Op : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "ExpandV2");
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "ExpandV2");
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auto x_dims = ctx->GetInputDim("X");
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auto expand_shape = ctx->Attrs().Get<std::vector<int>>("shape");
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if (expand_shape.size() == 0) {
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expand_shape = std::vector<int>(x_dims.size(), -1);
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}
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PADDLE_ENFORCE_GE(
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expand_shape.size(), static_cast<size_t>(x_dims.size()),
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platform::errors::InvalidArgument(
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"The number of elements (%d) of 'shape' for "
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"expand_v2 op must be greater than or equal to the rank "
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"(%d) of the input.",
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expand_shape.size(), static_cast<size_t>(x_dims.size())));
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PADDLE_ENFORCE_LE(expand_shape.size(), MAX_RANK_SUPPORTED,
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platform::errors::InvalidArgument(
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"The number of elements (%d) of 'shape' for "
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"must not be greater than %d.",
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expand_shape.size(), MAX_RANK_SUPPORTED));
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PADDLE_ENFORCE_GE(expand_shape.size(), 1,
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platform::errors::InvalidArgument(
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"The number of elements (%d) of 'shape' for "
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"must be a positive integer.",
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expand_shape.size()));
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auto out_rank =
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std::max(static_cast<size_t>(x_dims.size()), expand_shape.size());
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std::vector<int64_t> out_shape(out_rank);
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auto x_dim_vec = framework::vectorize<int>(x_dims);
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auto diff = expand_shape.size() - x_dim_vec.size();
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x_dim_vec.insert(x_dim_vec.begin(), diff, -1);
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for (size_t i = 0; i < expand_shape.size(); ++i) {
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if (x_dims[i] == -1) {
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out_shape[i] = -1;
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} else if (expand_shape[i] == -1) {
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out_shape[i] = x_dims[i];
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} else {
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PADDLE_ENFORCE_GT(
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expand_shape[i], 0,
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platform::errors::InvalidArgument(
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"The %uth element of 'shape' for expand_v2 op must be "
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"greater than 0, but the value given is %d.",
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i, expand_shape[i]));
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out_shape[i] = expand_shape[i];
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}
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}
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ctx->SetOutputDim("Out", framework::make_ddim(out_shape));
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if (out_shape[0] == x_dims[0]) {
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ctx->ShareLoD("X", "Out");
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}
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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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framework::OpKernelType GetKernelTypeForVar(
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const std::string& var_name, const Tensor& tensor,
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const framework::OpKernelType& expected_kernel_type) const override {
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if (var_name == "expand_shapes_tensor" || var_name == "Shape") {
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return expected_kernel_type;
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}
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return framework::OpKernelType(expected_kernel_type.data_type_,
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tensor.place(), tensor.layout());
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}
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};
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class ExpandV2OpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(Tensor, default Tensor<float>). A tensor with rank in [1, 6]."
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"X is the input to be expanded.");
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AddInput("Shape",
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"(Tensor<int>), optional). If provided, expand according to "
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"this given Shape. It has a higher priority than "
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"expand_shapes_tensor and the shape attribute.")
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.AsDispensable();
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AddInput("expand_shapes_tensor",
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"(Tensor Tensor<int>), epxanded shape for X."
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"It has a higher priority than shape attribute, but a lower "
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"priority than the input Shape")
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.AsDuplicable()
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.AsDispensable();
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AddOutput("Out",
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"(Tensor, default Tensor<float>). A tensor with rank in [1, 6]."
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"The rank of Output(Out) have the same with Input(X). "
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"After expanding, size of each dimension of Output(Out) is equal "
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"to size of the corresponding dimension of Input(X) multiplying "
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"the corresponding value given by Attr(expand_times).");
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AddAttr<std::vector<int>>("shape", "The expanded shape for each dimension.")
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.SetDefault({});
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AddComment(R"DOC(
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Expand the input to the given shape. The rank of X
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should be in [1, 6] and size of 'shape' must be in [1, 6] also.
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Following is a using case:
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Input(X) is a 3-D tensor with shape [2, 3, 1]:
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[
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[[1], [2], [3]],
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[[4], [5], [6]]
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]
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Attr(shape): [2, 6, 2]
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Output(Out) is a 3-D tensor with shape [2, 6, 2]:
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[
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[[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
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[[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
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]
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)DOC");
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}
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};
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class ExpandV2GradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "ExpandV2Grad");
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OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
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framework::GradVarName("Out"), "ExpandV2Grad");
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auto x_dims = ctx->GetInputDim("X");
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std::vector<int> expand_shape = ctx->Attrs().Get<std::vector<int>>("shape");
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if (expand_shape.size() == 0) {
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expand_shape = std::vector<int>(x_dims.size(), -1);
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}
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auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
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auto x_dim_vec = framework::vectorize<int>(x_dims);
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auto diff = expand_shape.size() - x_dim_vec.size();
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x_dim_vec.insert(x_dim_vec.begin(), diff, -1);
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for (size_t i = 0; i < expand_shape.size(); ++i) {
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if (expand_shape[i] == -1 || x_dim_vec[i] == -1) {
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continue;
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} else {
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if (ctx->IsRuntime()) {
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PADDLE_ENFORCE_EQ(
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expand_shape[i], out_dims[i],
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platform::errors::InvalidArgument(
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"The size (%d) of the dimension %d of Input(Out@GRAD) should "
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"be equal to the crroresponding dimension size of shape(%d).",
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out_dims[i], i, expand_shape[i]));
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}
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}
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}
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auto x_grad_name = framework::GradVarName("X");
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if (ctx->HasOutput(x_grad_name)) {
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ctx->SetOutputDim(x_grad_name, x_dims);
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}
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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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framework::OpKernelType GetKernelTypeForVar(
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const std::string& var_name, const Tensor& tensor,
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const framework::OpKernelType& expected_kernel_type) const override {
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if (var_name == "expand_shapes_tensor" || var_name == "Shape") {
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return expected_kernel_type;
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}
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return framework::OpKernelType(expected_kernel_type.data_type_,
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tensor.place(), tensor.layout());
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}
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};
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template <typename T>
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class ExpandV2GradOpMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> op) const override {
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op->SetType("expand_v2_grad");
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op->SetInput("X", this->Input("X"));
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op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
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op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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op->SetInput("expand_shapes_tensor", this->Input("expand_shapes_tensor"));
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op->SetInput("Shape", this->Input("Shape"));
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op->SetAttrMap(this->Attrs());
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}
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};
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template <typename T>
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class ExpandV2DoubleGradOpMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> op) const override {
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op->SetType("expand_v2");
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op->SetInput("X", this->OutputGrad(framework::GradVarName("X")));
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op->SetOutput("Out", this->InputGrad(framework::GradVarName("Out")));
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if (this->HasInput("expand_shapes_tensor")) {
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op->SetInput("expand_shapes_tensor", this->Input("expand_shapes_tensor"));
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}
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if (this->HasInput("Shape")) {
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op->SetInput("Shape", this->Input("Shape"));
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}
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op->SetAttrMap(this->Attrs());
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}
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};
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DECLARE_NO_NEED_BUFFER_VARS_INFERER(ExpandV2GradNoNeedBufVarsInferer, "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(expand_v2, ops::ExpandV2Op, ops::ExpandV2OpMaker,
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ops::ExpandV2GradOpMaker<paddle::framework::OpDesc>,
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ops::ExpandV2GradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(expand_v2_grad, ops::ExpandV2GradOp,
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ops::ExpandV2DoubleGradOpMaker<paddle::framework::OpDesc>,
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ops::ExpandV2DoubleGradOpMaker<paddle::imperative::OpBase>,
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ops::ExpandV2GradNoNeedBufVarsInferer);
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REGISTER_OP_CPU_KERNEL(
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expand_v2, ops::ExpandV2Kernel<paddle::platform::CPUDeviceContext, float>,
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ops::ExpandV2Kernel<paddle::platform::CPUDeviceContext, double>,
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ops::ExpandV2Kernel<paddle::platform::CPUDeviceContext, int>,
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ops::ExpandV2Kernel<paddle::platform::CPUDeviceContext, int64_t>,
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ops::ExpandV2Kernel<paddle::platform::CPUDeviceContext, bool>);
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REGISTER_OP_CPU_KERNEL(
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expand_v2_grad,
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ops::ExpandV2GradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::ExpandV2GradKernel<paddle::platform::CPUDeviceContext, double>,
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ops::ExpandV2GradKernel<paddle::platform::CPUDeviceContext, int>,
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ops::ExpandV2GradKernel<paddle::platform::CPUDeviceContext, int64_t>);
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