wehub-resource-sync
90 lines
3.4 KiB
C++
90 lines
3.4 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/phi/kernels/selected_rows/hsigmoid_loss_grad_kernel.h"
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#include <set>
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/mixed_vector.h"
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#include "paddle/phi/kernels/cpu/hsigmoid_loss_grad.h"
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namespace phi::sr {
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static std::vector<int64_t> PathToRows(const DenseTensor& path) {
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std::set<int64_t> rows;
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const int64_t* paths = path.data<int64_t>();
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for (int64_t i = 0; i < path.numel(); ++i) {
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int64_t row = paths[i];
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if (row < 0) {
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continue;
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}
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rows.emplace(row);
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}
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return std::vector<int64_t>(rows.begin(), rows.end());
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}
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template <typename T, typename Context>
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void HSigmoidLossGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& w,
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const DenseTensor& label,
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const optional<DenseTensor>& path,
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const optional<DenseTensor>& code,
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const optional<DenseTensor>& bias,
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const DenseTensor& pre_out,
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const DenseTensor& out_grad,
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int num_classes,
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bool is_sparse,
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DenseTensor* x_grad,
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SelectedRows* w_grad,
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DenseTensor* bias_grad) {
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PADDLE_ENFORCE_NOT_NULL(
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path.get_ptr(),
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errors::NotFound("Custom tree must be set for sparse mode!"));
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Vector<int64_t> real_rows = PathToRows(*path);
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w_grad->set_rows(real_rows);
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// Build a map of id -> row_index to speed up finding the index of one id
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w_grad->set_height(w.dims()[0]);
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auto* w_grad_value = w_grad->mutable_value();
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DDim temp_dim(w.dims());
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temp_dim[0] = static_cast<int>(real_rows.size());
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w_grad_value->Resize(temp_dim);
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phi::HSigmoidLossGradKernelImpl<T>(dev_ctx,
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x,
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w,
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label,
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path,
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code,
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bias,
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pre_out,
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out_grad,
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num_classes,
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is_sparse,
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x_grad,
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w_grad_value,
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bias_grad,
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w_grad);
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}
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} // namespace phi::sr
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PD_REGISTER_KERNEL(hsigmoid_loss_grad_sr,
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CPU,
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ALL_LAYOUT,
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phi::sr::HSigmoidLossGradKernel,
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float,
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double) {}
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