Files
composable_kernel/example/51_avgpool3d_bwd/avgpool3d_bwd_common.hpp
rocking 578142db3a Average pool backward deviceOP and example (#797)
* Add avgpool bwd reference code

* Refine naming

* Fix invalid in_element op in ref_conv

* Add example (only reference now)

* Add the full example of avgpool bwd

* Fix copyright

* Imitate MakeDescriptor from  transform_conv_bwd_data_to_gemm_v1.hpp

* rename channel to c from k

* Arrange the code

* Imitate the argument from conv bwd

* Implement invoker

* Fix order of parameter in example

* Refactor reference code for different dimension

* Support different stride

* Check if argument is valid

* Fix kernel parameter for NDHWC, fastest dimension C is not reduced

* Add more data type in example

* Fix bug in example

* calculate Do Ho Wo according to the dilation

* Remove useless header

* Add comment in reference code

* Add layout parameter

* Remove layout in derived class

* Refine reference comment
2023-08-10 12:04:35 +08:00

148 lines
6.2 KiB
C++

// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include "ck/ck.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_avgpool_bwd.hpp"
template <typename TensorLayout>
std::vector<ck::index_t> f_tensor_strides_ncdhw(ck::index_t N_,
ck::index_t C_,
ck::index_t D,
ck::index_t H,
ck::index_t W,
TensorLayout layout)
{
using namespace ck::literals;
(void)N_;
if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCDHW>::value)
return {C_ * D * H * W, D * H * W, H * W, W, 1_uz};
else if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NDHWC>::value)
return {D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_};
};
template <typename TensorLayout>
HostTensorDescriptor f_host_tensor_descriptor(std::size_t N_,
std::size_t C_,
std::size_t D,
std::size_t H,
std::size_t W,
TensorLayout layout)
{
using namespace ck::literals;
if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCDHW>::value)
{
return HostTensorDescriptor({N_, C_, D, H, W}, {C_ * D * H * W, D * H * W, H * W, W, 1_uz});
}
else if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NDHWC>::value)
{
return HostTensorDescriptor({N_, C_, D, H, W},
{D * C_ * H * W, 1_uz, C_ * H * W, W * C_, C_});
}
};
template <typename DevicePoolBwdInstance,
typename DOutDataType,
typename DInDataType,
typename DOutLayout,
typename DInLayout>
bool pool3d_bwd_test(bool do_verification,
bool time_kernel,
ck::index_t N,
ck::index_t C,
ck::index_t Di,
ck::index_t Hi,
ck::index_t Wi,
std::vector<ck::index_t> window_lengths,
std::vector<ck::index_t> window_strides,
std::vector<ck::index_t> window_dilations,
std::vector<ck::index_t> dinput_left_pads,
std::vector<ck::index_t> dinput_right_pads)
{
auto OutSpatialLength = [&](auto InSpatialLength, int index) {
ck::index_t left_pad = dinput_left_pads[index];
ck::index_t right_pad = dinput_right_pads[index];
ck::index_t window_len = window_lengths[index];
ck::index_t stride = window_strides[index];
ck::index_t dilation = window_dilations[index];
ck::index_t eff = (window_len - 1) * dilation + 1;
return (InSpatialLength + left_pad + right_pad - eff) / stride + 1;
};
ck::index_t Do = OutSpatialLength(Di, 0);
ck::index_t Ho = OutSpatialLength(Hi, 1);
ck::index_t Wo = OutSpatialLength(Wi, 2);
Tensor<DOutDataType> dout(f_host_tensor_descriptor(N, C, Do, Ho, Wo, DOutLayout{}));
Tensor<DInDataType> din_dev(f_host_tensor_descriptor(N, C, Di, Hi, Wi, DInLayout{}));
Tensor<DInDataType> din_host(f_host_tensor_descriptor(N, C, Di, Hi, Wi, DInLayout{}));
std::cout << "dout: " << dout.mDesc << std::endl;
std::cout << "din_host: " << din_host.mDesc << std::endl;
dout.GenerateTensorValue(GeneratorTensor_3<DOutDataType>{0.0, 1.0});
DeviceMem dout_device_buf(sizeof(DOutDataType) * dout.mDesc.GetElementSpaceSize());
DeviceMem din_device_buf(sizeof(DInDataType) * din_dev.mDesc.GetElementSpaceSize());
dout_device_buf.ToDevice(dout.mData.data());
din_device_buf.SetZero();
auto pool = DevicePoolBwdInstance{};
auto invoker_ptr = pool.MakeInvokerPointer();
auto argument_ptr =
pool.MakeArgumentPointer(static_cast<DOutDataType*>(dout_device_buf.GetDeviceBuffer()),
static_cast<DInDataType*>(din_device_buf.GetDeviceBuffer()),
{N, C, Do, Ho, Wo},
{N, C, Di, Hi, Wi},
f_tensor_strides_ncdhw(N, C, Do, Ho, Wo, DOutLayout{}),
f_tensor_strides_ncdhw(N, C, Di, Hi, Wi, DInLayout{}),
window_lengths,
window_strides,
window_dilations,
dinput_left_pads,
dinput_right_pads);
if(!pool.IsSupportedArgument(argument_ptr.get()))
{
throw std::runtime_error("wrong! device_op with the specified compilation parameters does "
"not support this problem");
}
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::cout << "Perf: " << ave_time << std::endl;
bool pass = true;
if(do_verification)
{
auto ref_pool =
ck::tensor_operation::host::ReferenceAvgPoolBwd<3, DInDataType, DOutDataType>();
auto ref_invoker = ref_pool.MakeInvoker();
auto ref_argument = ref_pool.MakeArgument(din_host,
dout,
window_lengths,
window_strides,
window_dilations,
dinput_left_pads,
dinput_right_pads);
ref_invoker.Run(ref_argument);
din_device_buf.FromDevice(din_dev.mData.data());
pass = ck::utils::check_err(din_dev, din_host);
}
return pass;
}