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TheRock/rocm-libraries/projects/miopen/test/cpu_prelu.hpp
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derek.holloway 7502bf64a4
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Flatten subprojects into a single base monorepo
2026-09-02 16:34:47 -07:00

101 lines
4.2 KiB
C++

/*******************************************************************************
*
* MIT License
*
* Copyright (c) 2024 Advanced Micro Devices, Inc.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*******************************************************************************/
#pragma once
#include "tensor_holder.hpp"
#include <miopen/tensor_view_utils.hpp>
template <class T>
void cpu_prelu_backward(const tensor<T> input,
const tensor<T> weight,
const tensor<T> output_grad,
tensor<T>& ref_input_grad,
tensor<T>& ref_weight_grad,
const bool has_dinput = true,
const bool has_dweight = true)
{
auto N = input.desc.GetElementSize();
auto input_tv = miopen::get_inner_expanded_tv<5>(input.desc);
auto weight_tv = miopen::get_inner_expanded_tv<1>(weight.desc);
auto output_grad_tv = miopen::get_inner_expanded_tv<5>(output_grad.desc);
auto input_grad_tv = miopen::get_inner_expanded_tv<5>(ref_input_grad.desc);
auto weight_grad_tv = miopen::get_inner_expanded_tv<1>(ref_weight_grad.desc);
auto weight_grad_collector = std::vector<float>(N);
miopen::par_ford(N)([&](int gid) {
auto tensor_layout = tensor_layout_t<5>(input_tv, gid);
float input_v = static_cast<float>(input[input_tv.get_tensor_view_idx(tensor_layout)]);
float grad_v =
static_cast<float>(output_grad[output_grad_tv.get_tensor_view_idx(tensor_layout)]);
if(has_dinput)
{
float weight_v;
weight_v = static_cast<float>(
weight[weight.desc.GetElementSize() == 1
? 0
: weight_tv.get_tensor_view_idx({tensor_layout.layout[1]})]);
float input_grad_v = input_v > 0 ? grad_v : weight_v * grad_v;
ref_input_grad[input_grad_tv.get_tensor_view_idx(tensor_layout)] =
static_cast<T>(input_grad_v);
}
if(has_dweight)
{
weight_grad_collector[gid] = input_v > 0 ? 0 : input_v * grad_v;
}
});
if(has_dweight)
{
if(weight.desc.GetElementSize() == 1)
{
double sum = 0;
for(int i = 0; i < N; ++i)
sum += static_cast<double>(weight_grad_collector[i]);
ref_weight_grad[0] = static_cast<T>(sum);
}
else
{
size_t inner_size = std::accumulate(
&input_tv.size[2], &input_tv.size[4], 1ul, std::multiplies<size_t>());
size_t outer_size = inner_size * input_tv.size[1];
miopen::par_ford(weight.desc.GetElementSize())([&](int i) {
double sum = 0;
miopen::ford(input_tv.size[0])([&](int j) {
miopen::ford(inner_size)([&](int k) {
sum += static_cast<double>(
weight_grad_collector[j * outer_size + i * inner_size + k]);
});
});
ref_weight_grad[weight_grad_tv.get_tensor_view_idx({i})] = static_cast<T>(sum);
});
}
}
}