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353 lines
15 KiB
353 lines
15 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/unsqueeze_op.h"
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#include <memory>
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#include <string>
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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 UnsqueezeOp : 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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platform::errors::InvalidArgument(
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"Input(X) of "
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"Unsqueeze operator should not be null."));
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PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
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platform::errors::InvalidArgument(
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"Output(Out) of "
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"Unsqueeze operator should not be null."));
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const auto &axes = ctx->Attrs().Get<std::vector<int>>("axes");
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const auto &x_dims = ctx->GetInputDim("X");
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// Validity Check: input tensor dims (<6).
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PADDLE_ENFORCE_LE(x_dims.size(), 6,
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platform::errors::InvalidArgument(
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"Invalid "
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"dimensions, the rank of Input(X) "
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"should be in the range of [1, 6] (Eigen limit)"));
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if (!axes.empty()) {
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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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} else if (ctx->HasInputs("AxesTensorList")) {
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auto AxesTensorList = ctx->Inputs("AxesTensorList");
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int output_size = x_dims.size() + static_cast<int>(AxesTensorList.size());
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PADDLE_ENFORCE_LE(output_size, 6,
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platform::errors::InvalidArgument(
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"The output tensor's rank should be less than 6."));
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std::vector<int> vec_out_dims(output_size, -1);
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ctx->SetOutputDim("Out", framework::make_ddim(vec_out_dims));
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} else if (ctx->HasInput("AxesTensor")) {
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auto axes_dims = ctx->GetInputDim("AxesTensor");
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PADDLE_ENFORCE_EQ(axes_dims.size(), 1,
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platform::errors::InvalidArgument(
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"Input(AxesTensor)'s dimension of "
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"Op(unsqueeze) must be 1. "
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"But received AxesTensor's shape = [%s], "
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"AxesTensor's dimension = %d.",
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axes_dims, axes_dims.size()));
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PADDLE_ENFORCE_GE(
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axes_dims[0], 0,
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platform::errors::InvalidArgument(
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"Input(AxesTensor)'s shape must be known. But received "
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"AxesTensor's shape = [%s]",
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axes_dims));
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int output_size = x_dims.size() + static_cast<int>(axes_dims[0]);
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PADDLE_ENFORCE_LE(output_size, 6,
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platform::errors::InvalidArgument(
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"The output tensor's rank should be less than 6."));
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std::vector<int> vec_out_dims(output_size, -1);
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ctx->SetOutputDim("Out", framework::make_ddim(vec_out_dims));
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}
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}
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static framework::DDim GetOutputShape(const std::vector<int> unsqz_dims,
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const framework::DDim &in_dims) {
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int output_size = in_dims.size() + static_cast<int>(unsqz_dims.size());
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int cur_output_size = in_dims.size();
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std::vector<int64_t> output_shape(output_size, 0);
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// Validity Check: rank range.
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PADDLE_ENFORCE_LE(output_size, 6,
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platform::errors::InvalidArgument(
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"The output tensor's rank should be less than 6."));
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for (int axis : unsqz_dims) {
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int cur = axis < 0 ? axis + cur_output_size + 1 : axis;
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// Vaildity Check: the axis bound
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PADDLE_ENFORCE_GE(cur, 0, platform::errors::InvalidArgument(
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"The insert dimension value should "
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"not be less than 0"));
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PADDLE_ENFORCE_LE(cur, cur_output_size,
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platform::errors::InvalidArgument(
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"The insert dimension value shoud not be larger "
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"than the dimension size of input tensor"));
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// Move old axis, and insert new axis
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for (int i = cur_output_size; i >= cur; --i) {
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if (output_shape[i] == 1) {
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// Move axis
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output_shape[i + 1] = 1;
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output_shape[i] = 0;
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}
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}
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output_shape[cur] = 1;
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// Add the output size.
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cur_output_size++;
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}
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// Make output shape
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for (int in_idx = 0, out_idx = 0; out_idx < output_size; ++out_idx) {
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if (output_shape[out_idx] == 0) {
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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(ctx.Input<framework::LoDTensor>("X")->type(),
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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 framework::Tensor &tensor,
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const framework::OpKernelType &expected_kernel_type) const override {
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if (var_name == "AxesTensor" || var_name == "AxesTensorList") {
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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 UnsqueezeOpMaker : 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 unsqueeze operator.");
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AddInput("AxesTensor",
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"(Tensor<int32>, optional). The dimensions to be inserted. "
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"If it exists, it will replace Attr(axes).")
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.AsDispensable();
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AddInput(
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"AxesTensorList",
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"(vector<Tensor<int32>>, optional). The dimensions to be inserted. "
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"If it exists, it will replace Attr(axes)."
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"The shape of the element in vector must be [1].")
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.AsDuplicable()
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.AsDispensable();
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AddOutput("Out", "(Tensor). The output tensor of unsqueeze 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 be inserted")
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.SetDefault({})
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.AddCustomChecker([](const std::vector<int> &axes) {
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// Validity Check: axes dims (<6).
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PADDLE_ENFORCE_LT(static_cast<int>(axes.size()), 6,
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platform::errors::InvalidArgument(
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"Invalid "
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"dimensions, dynamic dimensions should be "
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"within [1, 6] dimensions (Eigen limit)."));
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// Validity Check: the range of unsqueeze axis.
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for (int axis : axes) {
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PADDLE_ENFORCE_LT(axis, 6,
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platform::errors::InvalidArgument(
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"Invalid "
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"dimensions, input axis should be"
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"within [1, 6] dimensions (Eigen limit)."));
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}
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});
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AddComment(R"DOC(
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Unsqueeze Operator.
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Insert single-dimensional entries to the shape of a tensor.
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Takes one required argument axes, a list of dimensions that will be inserted.
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Dimension indices in axes are as seen in the output tensor.
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For example:
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Given a tensor such that tensor with shape [3, 4, 5],
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then Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1]
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)DOC");
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}
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};
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class UnsqueezeGradOp : 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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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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ctx->ShareLoD("X", framework::GradVarName("X"));
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}
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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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template <typename T>
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class UnsqueezeGradOpMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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void Apply(GradOpPtr<T> grad_op) const override {
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grad_op->SetType("unsqueeze_grad");
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grad_op->SetInput("X", this->Input("X"));
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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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}
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};
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// FIXME(zcd): unsqueeze2 adds an intermediate output(XShape) based on
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// unsqueeze, the XShape is used to carry the shape and lod of X which
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// will be used in unsqueeze_grad, in this way, the framework can reuse
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// the memory of X immediately the unsqueeze2_op is finished.
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// Considering compatibility issues, we could not fix unsqueeze2_op
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class Unsqueeze2Op : public UnsqueezeOp {
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public:
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using UnsqueezeOp::UnsqueezeOp;
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void InferShape(framework::InferShapeContext *ctx) const override {
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UnsqueezeOp::InferShape(ctx);
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const auto &x_dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE_EQ(
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ctx->HasOutput("XShape"), true,
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platform::errors::InvalidArgument("Output(XShape) of Unsqueeze "
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"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 Unsqueeze2OpMaker : public UnsqueezeOpMaker {
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public:
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void Make() override {
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UnsqueezeOpMaker::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 UnsqueezeGradOp.")
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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 Unsqueeze2GradOpMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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void Apply(GradOpPtr<T> grad_op) const override {
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grad_op->SetType("unsqueeze2_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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}
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};
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class Unsqueeze2GradOp : 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(
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context->HasInput("XShape"), true,
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platform::errors::InvalidArgument("Input(XShape) shouldn't be null."));
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PADDLE_ENFORCE_EQ(context->HasInput(framework::GradVarName("Out")), true,
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platform::errors::InvalidArgument(
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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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DECLARE_INPLACE_OP_INFERER(UnsqueezeInplaceInferer, {"X", "Out"});
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DECLARE_INPLACE_OP_INFERER(UnsqueezeGradInplaceInferer,
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{framework::GradVarName("Out"),
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framework::GradVarName("X")});
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DECLARE_NO_NEED_BUFFER_VARS_INFERER(UnsqueezeGradOpNoNeedBufferVarInferer, "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(unsqueeze, ops::UnsqueezeOp, ops::UnsqueezeOpMaker,
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ops::UnsqueezeGradOpMaker<paddle::framework::OpDesc>,
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ops::UnsqueezeGradOpMaker<paddle::imperative::OpBase>);
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REGISTER_OPERATOR(unsqueeze_grad, ops::UnsqueezeGradOp,
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ops::UnsqueezeGradOpNoNeedBufferVarInferer);
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REGISTER_OPERATOR(unsqueeze2, ops::Unsqueeze2Op, ops::Unsqueeze2OpMaker,
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ops::Unsqueeze2GradOpMaker<paddle::framework::OpDesc>,
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ops::Unsqueeze2GradOpMaker<paddle::imperative::OpBase>,
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ops::UnsqueezeInplaceInferer);
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REGISTER_OPERATOR(unsqueeze2_grad, ops::Unsqueeze2GradOp,
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ops::UnsqueezeGradInplaceInferer);
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REGISTER_OP_CPU_KERNEL(
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unsqueeze, ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, float>,
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ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, double>,
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ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, int>,
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ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, int8_t>,
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ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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unsqueeze_grad,
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ops::UnsqueezeGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::UnsqueezeGradKernel<paddle::platform::CPUDeviceContext, double>,
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ops::UnsqueezeGradKernel<paddle::platform::CPUDeviceContext, int>,
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ops::UnsqueezeGradKernel<paddle::platform::CPUDeviceContext, int8_t>,
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ops::UnsqueezeGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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unsqueeze2, ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, float>,
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ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, double>,
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ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, int>,
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ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, int8_t>,
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ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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unsqueeze2_grad,
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ops::Unsqueeze2GradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::Unsqueeze2GradKernel<paddle::platform::CPUDeviceContext, double>,
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ops::Unsqueeze2GradKernel<paddle::platform::CPUDeviceContext, int>,
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ops::Unsqueeze2GradKernel<paddle::platform::CPUDeviceContext, int8_t>,
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ops::Unsqueeze2GradKernel<paddle::platform::CPUDeviceContext, int64_t>);
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