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96 lines
2.8 KiB
96 lines
2.8 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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#pragma once
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#include <functional>
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#include <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/math_function.h"
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#include "paddle/fluid/platform/for_range.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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struct WhereFunctor {
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WhereFunctor(const T& true_index, int true_num, const T& stride, int rank,
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int64_t* out)
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: true_index_(true_index),
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true_num_(true_num),
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stride_(stride),
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rank_(rank),
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out_ptr_(out) {}
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HOSTDEVICE void operator()(size_t idx) const {
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int index = true_index_[idx];
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for (int j = 0; j < rank_; j++) {
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out_ptr_[idx * rank_ + j] = index / stride_[j];
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index -= out_ptr_[idx * rank_ + j] * stride_[j];
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}
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}
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const T true_index_;
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int true_num_;
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const T stride_;
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int rank_;
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int64_t* out_ptr_;
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};
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using CPUDeviceContext = paddle::platform::CPUDeviceContext;
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template <typename T>
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class CPUWhereKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* condition = context.Input<framework::Tensor>("Condition");
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auto* out = context.Output<framework::Tensor>("Out");
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const bool* cond_data = condition->data<bool>();
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auto numel = condition->numel();
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auto dims = condition->dims();
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const int rank = dims.size();
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std::vector<int> true_index;
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for (auto i = 0; i < numel; i++) {
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if (cond_data[i]) {
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true_index.push_back(i);
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}
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}
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auto true_num = true_index.size();
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out->Resize(framework::make_ddim({static_cast<int64_t>(true_num), rank}));
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auto out_ptr = out->mutable_data<T>(context.GetPlace());
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if (true_num == 0) {
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return;
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}
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std::vector<int> stride(rank);
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stride[rank - 1] = 1;
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for (int i = rank - 2; i >= 0; i--) {
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stride[i] = stride[i + 1] * dims[i + 1];
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}
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auto& dev_ctx = context.template device_context<CPUDeviceContext>();
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WhereFunctor<int*> functor(true_index.data(), true_num, stride.data(), rank,
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out_ptr);
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platform::ForRange<CPUDeviceContext> for_range(dev_ctx, true_num);
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for_range(functor);
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}
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};
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} // namespace operators
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} // namespace paddle
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