wehub-resource-sync
136 lines
4.0 KiB
C++
136 lines
4.0 KiB
C++
// Copyright (c) 2024 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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#pragma once
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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namespace phi {
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template <typename T>
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void CvmComputeKernel(const bool use_cvm,
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const int64_t item_width,
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const T** X,
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T** Y) {
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const auto cvm_offset = use_cvm ? 0 : 2;
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std::memcpy(*Y, *X + cvm_offset, (item_width - cvm_offset) * sizeof(T));
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if (use_cvm) {
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(*Y)[0] = log((*Y)[0] + 1);
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(*Y)[1] = log((*Y)[1] + 1) - (*Y)[0];
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}
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(*X) += item_width;
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(*Y) += item_width - cvm_offset;
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}
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template <typename T>
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void CvmGradComputeKernel(const bool use_cvm,
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const int64_t item_width,
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const T& CVM,
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const T** DY,
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T** DX) {
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const auto cvm_offset = use_cvm ? 0 : 2;
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std::memcpy(*DX + cvm_offset, *DY, (item_width - cvm_offset) * sizeof(T));
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(*DX)[0] = (&CVM)[0];
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(*DX)[1] = (&CVM)[1];
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(*DX) += item_width;
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(*DY) += item_width - cvm_offset;
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}
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template <typename T, typename Context>
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void CVMOpKernel(const Context& dev_ctx,
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const DenseTensor& x_in,
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const DenseTensor& cvm,
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bool use_cvm,
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DenseTensor* out) {
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const auto* x = &x_in;
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const T* x_data = x->data<T>();
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auto batch_size = x->dims()[0];
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auto item_size = x->numel() / batch_size;
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auto* y = out;
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T* y_data = dev_ctx.template Alloc<T>(y);
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// for Input X do not have Lod Information.
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if (x->NumLevels() == 0) {
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if (use_cvm) {
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for (int i = 0; i < batch_size; i++) {
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int64_t cursor = i * item_size;
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y_data[cursor] = log(x_data[cursor] + 1);
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y_data[cursor + 1] = log(x_data[cursor + 1] + 1) - y_data[cursor];
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for (int j = 2; j < item_size; j++) {
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y_data[cursor + j] = x_data[cursor + j];
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}
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}
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} else {
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for (int i = 0; i < batch_size; i++) {
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CvmComputeKernel(use_cvm, item_size, &x_data, &y_data);
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}
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}
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} else {
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auto lod = x->lod()[0];
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for (size_t i = 0; i < lod.size() - 1; ++i) {
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for (size_t j = 0; j < lod[i + 1] - lod[i]; ++j) {
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CvmComputeKernel(use_cvm, item_size, &x_data, &y_data);
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}
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}
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}
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}
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template <typename T, typename Context>
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void CVMGradOpKernel(const Context& dev_ctx,
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const DenseTensor& x_in,
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const DenseTensor& cvm_in,
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const DenseTensor& out_grad,
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bool use_cvm,
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DenseTensor* x_grad) {
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auto* dx = x_grad;
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T* dx_data = dev_ctx.template Alloc<T>(dx);
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const DenseTensor* cvm = &cvm_in;
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const T* cvm_data = cvm->data<T>();
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const auto* dOut = &out_grad;
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const T* dout_data = dOut->data<T>();
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auto offset = 2;
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auto batch_size = dx->dims()[0];
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auto item_size = dx->numel() / batch_size;
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// for Input X do not have Lod Information.
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if (dx->NumLevels() == 0) {
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for (int x = 0; x < batch_size; ++x) {
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CvmGradComputeKernel(use_cvm, item_size, *cvm_data, &dout_data, &dx_data);
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cvm_data += offset;
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}
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} else {
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auto lod = dx->lod()[0];
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int seq_num = static_cast<int>(lod.size()) - 1;
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for (int i = 0; i < seq_num; ++i) {
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for (size_t j = 0; j < lod[i + 1] - lod[i]; ++j) {
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CvmGradComputeKernel(
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use_cvm, item_size, *cvm_data, &dout_data, &dx_data);
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}
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cvm_data += offset;
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}
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}
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}
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} // namespace phi
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