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351 lines
12 KiB
351 lines
12 KiB
/* Copyright (c) 2018 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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#pragma once
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#include <algorithm>
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#include <utility>
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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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inline std::vector<int> get_new_data_from_tensorlist(
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const std::vector<const Tensor*>& list_new_data_tensor) {
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// get tensor from
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std::vector<int> vec_new_data;
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for (size_t i = 0; i < list_new_data_tensor.size(); ++i) {
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auto tensor = list_new_data_tensor[i];
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PADDLE_ENFORCE_EQ(tensor->dims(), framework::make_ddim({1}),
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"shape of dim tensor should be [1]");
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if (platform::is_gpu_place(tensor->place())) {
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framework::Tensor temp;
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TensorCopySync(*tensor, platform::CPUPlace(), &temp);
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vec_new_data.push_back(static_cast<int32_t>(*temp.data<int32_t>()));
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} else {
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vec_new_data.push_back(static_cast<int32_t>(*tensor->data<int32_t>()));
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}
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}
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return vec_new_data;
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}
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inline std::vector<int> get_new_data_from_tensor(
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const Tensor* new_data_tensor) {
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std::vector<int> vec_new_data;
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auto* new_data = new_data_tensor->data<int>();
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framework::Tensor cpu_starts_tensor;
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if (platform::is_gpu_place(new_data_tensor->place())) {
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TensorCopySync(*new_data_tensor, platform::CPUPlace(), &cpu_starts_tensor);
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new_data = cpu_starts_tensor.data<int>();
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}
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vec_new_data =
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std::vector<int>(new_data, new_data + new_data_tensor->numel());
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return vec_new_data;
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}
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template <typename DeviceContext, typename T>
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class SliceKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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int rank = ctx.Input<framework::Tensor>("Input")->dims().size();
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switch (rank) {
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case 1:
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SliceCompute<1>(ctx);
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break;
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case 2:
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SliceCompute<2>(ctx);
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break;
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case 3:
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SliceCompute<3>(ctx);
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break;
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case 4:
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SliceCompute<4>(ctx);
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break;
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case 5:
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SliceCompute<5>(ctx);
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break;
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case 6:
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SliceCompute<6>(ctx);
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break;
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}
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}
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private:
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template <size_t D>
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void SliceCompute(const framework::ExecutionContext& context) const {
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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auto in = context.Input<framework::Tensor>("Input");
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auto out = context.Output<framework::Tensor>("Out");
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auto out_dims = out->dims();
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auto in_dims = in->dims();
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auto axes = context.Attr<std::vector<int>>("axes");
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auto starts = context.Attr<std::vector<int>>("starts");
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auto ends = context.Attr<std::vector<int>>("ends");
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auto decrease_axis = context.Attr<std::vector<int>>("decrease_axis");
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auto infer_flags = context.Attr<std::vector<int>>("infer_flags");
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auto list_new_ends_tensor =
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context.MultiInput<framework::Tensor>("EndsTensorList");
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auto list_new_starts_tensor =
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context.MultiInput<framework::Tensor>("StartsTensorList");
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bool need_infer = false;
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if (context.HasInput("StartsTensor") || context.HasInput("EndsTensor")) {
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need_infer = true;
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}
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if (list_new_starts_tensor.size() > 0 || list_new_ends_tensor.size() > 0) {
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need_infer = true;
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}
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if (need_infer) {
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if (context.HasInput("StartsTensor")) {
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auto* starts_tensor = context.Input<framework::Tensor>("StartsTensor");
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starts = get_new_data_from_tensor(starts_tensor);
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} else if (list_new_starts_tensor.size() > 0) {
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starts = get_new_data_from_tensorlist(list_new_starts_tensor);
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}
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PADDLE_ENFORCE_EQ(
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starts.size(), axes.size(),
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"The size of starts must be equal to the size of axes.");
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if (context.HasInput("EndsTensor")) {
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auto* ends_tensor = context.Input<framework::Tensor>("EndsTensor");
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ends = get_new_data_from_tensor(ends_tensor);
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} else if (list_new_ends_tensor.size() > 0) {
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ends = get_new_data_from_tensorlist(list_new_ends_tensor);
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}
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PADDLE_ENFORCE_EQ(ends.size(), axes.size(),
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"The size of ends must be equal to the size of axes.");
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out_dims = in_dims;
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int dim_value, start, end;
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for (size_t i = 0; i < axes.size(); ++i) {
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dim_value = out_dims[axes[i]];
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if (dim_value > 0) {
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// when end = start+1 and start == -1
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if (starts[i] == -1 && ends[i] == 0 && infer_flags[i] == -1) {
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auto ret =
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std::find(decrease_axis.begin(), decrease_axis.end(), axes[i]);
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if (ret != decrease_axis.end()) {
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ends[i] = 10000000;
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}
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}
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start = starts[i] < 0 ? (starts[i] + dim_value) : starts[i];
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end = ends[i] < 0 ? (ends[i] + dim_value) : ends[i];
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start = std::max(start, 0);
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end = std::max(end, 0);
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end = std::min(end, dim_value);
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PADDLE_ENFORCE_GT(end, start, "end should greater than start");
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out_dims[axes[i]] = end - start;
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}
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}
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out->Resize(out_dims);
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// generate new shape
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if (decrease_axis.size() > 0) {
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std::vector<int> new_out_shape;
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for (size_t i = 0; i < decrease_axis.size(); ++i) {
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PADDLE_ENFORCE_EQ(out_dims[decrease_axis[i]], 1,
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"decrease dim should be 1");
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out_dims[decrease_axis[i]] = 0;
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}
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for (int i = 0; i < out_dims.size(); ++i) {
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if (out_dims[i] != 0) {
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new_out_shape.push_back(out_dims[i]);
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}
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}
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if (new_out_shape.size() == 0) {
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new_out_shape.push_back(1);
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}
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out_dims = framework::make_ddim(new_out_shape);
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}
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}
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// resize out_dims
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if (decrease_axis.size() > 0) {
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if (decrease_axis.size() == (size_t)in_dims.size()) {
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std::vector<int> vec_origin_out_shape(decrease_axis.size(), 1);
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out->Resize(framework::make_ddim(vec_origin_out_shape));
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} else {
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std::vector<int> vec_origin_out_shape(
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out_dims.size() + decrease_axis.size(), -1);
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for (size_t i = 0; i < decrease_axis.size(); ++i) {
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vec_origin_out_shape[decrease_axis[i]] = 1;
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}
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int index = 0;
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for (size_t i = 0; i < vec_origin_out_shape.size(); ++i) {
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if (vec_origin_out_shape[i] == -1) {
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vec_origin_out_shape[i] = out_dims[index];
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++index;
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}
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}
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out->Resize(framework::make_ddim(vec_origin_out_shape));
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}
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}
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out->mutable_data<T>(context.GetPlace());
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auto new_out_dims = out->dims();
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auto offsets = Eigen::array<int, D>();
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auto extents = Eigen::array<int, D>();
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for (size_t i = 0; i < D; ++i) {
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offsets[i] = 0;
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extents[i] = new_out_dims[i];
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}
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int start;
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for (size_t i = 0; i < axes.size(); ++i) {
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start = starts[i];
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if (start < 0) {
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start = (start + in_dims[axes[i]]);
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}
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start = std::max(start, 0);
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offsets[axes[i]] = start;
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}
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auto in_t =
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framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
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*in);
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auto out_t =
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framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
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*out, new_out_dims);
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out_t.device(place) = in_t.slice(offsets, extents);
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out->Resize(out_dims);
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}
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};
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template <typename DeviceContext, typename T>
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class SliceGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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size_t rank = ctx.Input<framework::Tensor>("Input")->dims().size();
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switch (rank) {
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case 1:
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SliceCompute<1>(ctx);
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break;
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case 2:
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SliceCompute<2>(ctx);
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break;
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case 3:
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SliceCompute<3>(ctx);
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break;
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case 4:
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SliceCompute<4>(ctx);
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break;
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case 5:
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SliceCompute<5>(ctx);
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break;
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case 6:
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SliceCompute<6>(ctx);
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break;
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}
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}
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private:
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template <size_t D>
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void SliceCompute(const framework::ExecutionContext& context) const {
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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auto* d_out =
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context.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* d_input =
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context.Output<framework::Tensor>(framework::GradVarName("Input"));
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d_input->mutable_data<T>(context.GetPlace());
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auto out_dims = d_out->dims();
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auto in_dims = d_input->dims();
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auto axes = context.Attr<std::vector<int>>("axes");
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auto starts = context.Attr<std::vector<int>>("starts");
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auto ends = context.Attr<std::vector<int>>("ends");
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auto list_new_ends_tensor =
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context.MultiInput<framework::Tensor>("EndsTensorList");
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auto list_new_starts_tensor =
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context.MultiInput<framework::Tensor>("StartsTensorList");
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if (list_new_starts_tensor.size() > 0) {
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starts = get_new_data_from_tensorlist(list_new_starts_tensor);
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} else if (context.HasInput("StartsTensor")) {
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auto* starts_tensor = context.Input<framework::Tensor>("StartsTensor");
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starts = get_new_data_from_tensor(starts_tensor);
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}
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if (list_new_ends_tensor.size() > 0) {
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ends = get_new_data_from_tensorlist(list_new_ends_tensor);
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} else if (context.HasInput("EndsTensor")) {
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auto* ends_tensor = context.Input<framework::Tensor>("EndsTensor");
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ends = get_new_data_from_tensor(ends_tensor);
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}
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auto decrease_axis = context.Attr<std::vector<int>>("decrease_axis");
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if (decrease_axis.size() > 0) {
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if (decrease_axis.size() == (size_t)in_dims.size()) {
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// all dims decrease
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std::vector<int> vec_origin_out_shape(decrease_axis.size(), 1);
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out_dims = framework::make_ddim(vec_origin_out_shape);
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} else {
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std::vector<int> vec_origin_out_shape(
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out_dims.size() + decrease_axis.size(), -1);
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for (size_t i = 0; i < decrease_axis.size(); ++i) {
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vec_origin_out_shape[decrease_axis[i]] = 1;
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}
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int index = 0;
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for (size_t i = 0; i < vec_origin_out_shape.size(); ++i) {
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if (vec_origin_out_shape[i] == -1) {
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vec_origin_out_shape[i] = out_dims[index];
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++index;
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}
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}
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out_dims = framework::make_ddim(vec_origin_out_shape);
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}
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}
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auto offsets = Eigen::array<int, D>();
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auto extents = Eigen::array<int, D>();
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for (size_t i = 0; i < D; ++i) {
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offsets[i] = 0;
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extents[i] = out_dims[i];
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}
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int start;
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for (size_t i = 0; i < axes.size(); ++i) {
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start = starts[i];
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if (start < 0) {
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start = (start + in_dims[axes[i]]);
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}
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start = std::max(start, 0);
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offsets[axes[i]] = start;
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}
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Eigen::array<std::pair<int, int>, D> paddings;
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for (size_t i = 0; i < paddings.size(); ++i) {
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paddings[i].first = offsets[i];
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paddings[i].second = (in_dims[i] - out_dims[i]) - offsets[i];
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}
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auto d_in_t =
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framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
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*d_input);
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auto d_out_t =
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framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
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*d_out, out_dims);
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d_in_t.device(place) = d_out_t.pad(paddings, 0);
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}
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};
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} // namespace operators
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} // namespace paddle
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