mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-04 13:41:24 +00:00
ad57f6ef0b
* Wrap ck host utitlies in CK namespace. The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace. Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace. There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate kernels from either template library. * Add using declarations to test code. After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace. * Add using declarations to client examples.
201 lines
8.3 KiB
C++
201 lines
8.3 KiB
C++
// SPDX-License-Identifier: MIT
|
|
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
|
|
|
|
#include <cstdlib>
|
|
#include <iostream>
|
|
#include <numeric>
|
|
#include <type_traits>
|
|
|
|
#include "ck/ck.hpp"
|
|
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
|
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
|
|
|
#include "ck/library/utility/algorithm.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/convolution_parameter.hpp"
|
|
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
|
|
#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
|
|
|
|
using ::ck::DeviceMem;
|
|
using ::ck::HostTensorDescriptor;
|
|
using ::ck::Tensor;
|
|
|
|
void print_helper_msg()
|
|
{
|
|
std::cout << "arg1: verification (0=no, 1=yes)\n"
|
|
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
|
|
<< "arg3: time kernel (0=no, 1=yes)\n"
|
|
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
|
|
}
|
|
|
|
template <ck::index_t NDimSpatial,
|
|
typename InDataType,
|
|
typename WeiDataType,
|
|
typename DsDataType,
|
|
typename OutDataType,
|
|
typename InElementOp,
|
|
typename WeiElementOp,
|
|
typename OutElementOp,
|
|
typename DeviceConvNDFwdInstance>
|
|
bool run_grouped_conv_fwd_dl(bool do_verification,
|
|
int init_method,
|
|
bool time_kernel,
|
|
const ck::utils::conv::ConvParam& conv_param,
|
|
const HostTensorDescriptor& in_g_n_c_wis_desc,
|
|
const HostTensorDescriptor& wei_g_k_c_xs_desc,
|
|
const HostTensorDescriptor& out_g_n_k_wos_desc,
|
|
const InElementOp& in_element_op,
|
|
const WeiElementOp& wei_element_op,
|
|
const OutElementOp& out_element_op)
|
|
{
|
|
using DDataType = ck::remove_cvref_t<ck::tuple_element_t<0, DsDataType>>;
|
|
Tensor<InDataType> in(in_g_n_c_wis_desc);
|
|
Tensor<WeiDataType> wei(wei_g_k_c_xs_desc);
|
|
Tensor<DDataType> bias(out_g_n_k_wos_desc);
|
|
Tensor<OutDataType> out_host(out_g_n_k_wos_desc);
|
|
Tensor<OutDataType> out_device(out_g_n_k_wos_desc);
|
|
|
|
std::cout << "in: " << in.mDesc << std::endl;
|
|
std::cout << "wei: " << wei.mDesc << std::endl;
|
|
std::cout << "out: " << out_host.mDesc << std::endl;
|
|
|
|
switch(init_method)
|
|
{
|
|
case 0: break;
|
|
case 1:
|
|
in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-2, 3});
|
|
wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-2, 3});
|
|
bias.GenerateTensorValue(GeneratorTensor_2<DDataType>{-2, 3});
|
|
break;
|
|
case 2:
|
|
in.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
|
|
wei.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
|
|
bias.GenerateTensorValue(GeneratorTensor_3<DDataType>{-0.5, 0.5});
|
|
break;
|
|
default:
|
|
in.GenerateTensorValue(GeneratorTensor_1<InDataType>{1});
|
|
wei.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{-1});
|
|
bias.GenerateTensorValue(GeneratorTensor_1<DDataType>{1});
|
|
}
|
|
|
|
DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
|
|
DeviceMem wei_device_buf(sizeof(WeiDataType) * wei.mDesc.GetElementSpaceSize());
|
|
DeviceMem bias_device_buf(sizeof(DDataType) * bias.mDesc.GetElementSpaceSize());
|
|
DeviceMem out_device_buf(sizeof(OutDataType) * out_device.mDesc.GetElementSpaceSize());
|
|
|
|
in_device_buf.ToDevice(in.mData.data());
|
|
wei_device_buf.ToDevice(wei.mData.data());
|
|
bias_device_buf.ToDevice(bias.mData.data());
|
|
|
|
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_lengths{};
|
|
std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_strides{};
|
|
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
|
|
std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
|
|
std::array<ck::index_t, NDimSpatial + 3> d_g_n_k_wos_lengths{};
|
|
std::array<ck::index_t, NDimSpatial + 3> d_g_n_k_wos_strides{};
|
|
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_lengths{};
|
|
std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_strides{};
|
|
std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
|
|
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
|
|
std::array<ck::index_t, NDimSpatial> input_left_pads{};
|
|
std::array<ck::index_t, NDimSpatial> input_right_pads{};
|
|
|
|
auto copy = [](auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
|
|
|
|
copy(in_g_n_c_wis_desc.GetLengths(), a_g_n_c_wis_lengths);
|
|
copy(in_g_n_c_wis_desc.GetStrides(), a_g_n_c_wis_strides);
|
|
copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
|
|
copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
|
|
copy(out_g_n_k_wos_desc.GetLengths(), d_g_n_k_wos_lengths);
|
|
copy(out_g_n_k_wos_desc.GetStrides(), d_g_n_k_wos_strides);
|
|
copy(out_g_n_k_wos_desc.GetLengths(), e_g_n_k_wos_lengths);
|
|
copy(out_g_n_k_wos_desc.GetStrides(), e_g_n_k_wos_strides);
|
|
copy(conv_param.conv_filter_strides_, conv_filter_strides);
|
|
copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
|
|
copy(conv_param.input_left_pads_, input_left_pads);
|
|
copy(conv_param.input_right_pads_, input_right_pads);
|
|
|
|
// do Conv
|
|
auto conv = DeviceConvNDFwdInstance{};
|
|
auto invoker = conv.MakeInvoker();
|
|
auto argument = conv.MakeArgument(
|
|
in_device_buf.GetDeviceBuffer(),
|
|
wei_device_buf.GetDeviceBuffer(),
|
|
std::array<const void*, 1>{bias_device_buf.GetDeviceBuffer()},
|
|
out_device_buf.GetDeviceBuffer(),
|
|
a_g_n_c_wis_lengths,
|
|
a_g_n_c_wis_strides,
|
|
b_g_k_c_xs_lengths,
|
|
b_g_k_c_xs_strides,
|
|
std::array<std::array<ck::index_t, NDimSpatial + 3>, 1>{{d_g_n_k_wos_lengths}},
|
|
std::array<std::array<ck::index_t, NDimSpatial + 3>, 1>{{d_g_n_k_wos_strides}},
|
|
e_g_n_k_wos_lengths,
|
|
e_g_n_k_wos_strides,
|
|
conv_filter_strides,
|
|
conv_filter_dilations,
|
|
input_left_pads,
|
|
input_right_pads,
|
|
in_element_op,
|
|
wei_element_op,
|
|
out_element_op);
|
|
|
|
if(!conv.IsSupportedArgument(argument))
|
|
{
|
|
std::cout << "wrong! device_conv with the specified compilation parameters does not "
|
|
"support this Conv problem"
|
|
<< std::endl;
|
|
return true;
|
|
}
|
|
|
|
float avg_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
|
|
|
std::size_t flop = conv_param.GetFlops();
|
|
std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>();
|
|
|
|
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
|
|
float gb_per_sec = num_btype / 1.E6 / avg_time;
|
|
std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
|
<< conv.GetTypeString() << std::endl;
|
|
|
|
if(do_verification)
|
|
{
|
|
auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<
|
|
NDimSpatial,
|
|
InDataType,
|
|
WeiDataType,
|
|
OutDataType,
|
|
InElementOp,
|
|
WeiElementOp,
|
|
ck::tensor_operation::element_wise::PassThrough>();
|
|
|
|
auto ref_invoker = ref_conv.MakeInvoker();
|
|
auto ref_argument =
|
|
ref_conv.MakeArgument(in,
|
|
wei,
|
|
out_host,
|
|
conv_param.conv_filter_strides_,
|
|
conv_param.conv_filter_dilations_,
|
|
conv_param.input_left_pads_,
|
|
conv_param.input_right_pads_,
|
|
in_element_op,
|
|
wei_element_op,
|
|
ck::tensor_operation::element_wise::PassThrough{});
|
|
|
|
ref_invoker.Run(ref_argument);
|
|
|
|
// cde_elementwise
|
|
out_host.ForEach(
|
|
[&](auto&, auto idx) { out_element_op(out_host(idx), out_host(idx), bias(idx)); });
|
|
|
|
out_device_buf.FromDevice(out_device.mData.data());
|
|
|
|
return ck::utils::check_err(
|
|
out_device.mData, out_host.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
|
|
}
|
|
|
|
return true;
|
|
}
|