mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-18 20:09:25 +00:00
4359e2f722
* add wrw reference
* start device
* raw not split version
* run simple example
* start to use atomic add
* simple transform result correct
* first version that can run
* fix atomic and set operator choice
* add check split-k
* format
* change input parameter
* add pad for t total
* rename example index
Co-authored-by: ltqin <letaoqin@amd.com>
[ROCm/composable_kernel commit: 7a9b93f4b6]
290 lines
13 KiB
C++
290 lines
13 KiB
C++
#include <iostream>
|
|
#include <numeric>
|
|
#include <initializer_list>
|
|
#include <cstdlib>
|
|
#include <stdlib.h>
|
|
#include <half.hpp>
|
|
#include "config.hpp"
|
|
#include "print.hpp"
|
|
#include "device.hpp"
|
|
#include "host_tensor.hpp"
|
|
#include "host_tensor_generator.hpp"
|
|
#include "device_tensor.hpp"
|
|
#include "tensor_layout.hpp"
|
|
#include "element_wise_operation.hpp"
|
|
#include "device_conv2d_backward_weight_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp"
|
|
#include "reference_conv_backward_weight.hpp"
|
|
|
|
using InDataType = ck::half_t;
|
|
using WeiDataType = ck::half_t;
|
|
using OutDataType = ck::half_t;
|
|
using AccDataType = float;
|
|
|
|
template <ck::index_t... Is>
|
|
using S = ck::Sequence<Is...>;
|
|
|
|
using InLayout = ck::tensor_layout::convolution::NHWC;
|
|
using WeiLayout = ck::tensor_layout::convolution::KYXC;
|
|
using OutLayout = ck::tensor_layout::convolution::NHWK;
|
|
|
|
using InElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
|
|
// clang-format off
|
|
using DeviceConvWrWInstance = ck::tensor_operation::device::
|
|
DeviceConv2dWrWXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<
|
|
InDataType, // InDataType
|
|
WeiDataType, // WeiDataType
|
|
OutDataType, // OutDataType
|
|
AccDataType, // AccDataType
|
|
InElementOp, // InElementwiseOperation
|
|
WeiElementOp, // WeiElementwiseOperation
|
|
OutElementOp, // OutElementwiseOperation
|
|
256, // BlockSize
|
|
128, // MPerBlock
|
|
128, // NPerBlock
|
|
4, // K0PerBlock
|
|
8, // K1
|
|
32, // MPerXdl
|
|
32, // NPerXdl
|
|
2, // MXdlPerWave
|
|
2, // NXdlPerWave
|
|
S<1, 4, 16, 4>, // ABlockTransferThreadClusterLengths_K0_M_K1
|
|
S<0, 3, 1, 2>, // ABlockTransferThreadClusterArrangeOrder
|
|
S<0, 2, 1, 3>, // ABlockTransferSrcAccessOrder
|
|
2, // ABlockTransferSrcVectorDim
|
|
8, // ABlockTransferSrcScalarPerVector
|
|
2, // ABlockTransferDstScalarPerVector_K1
|
|
true, // ABlockLdsAddExtraM
|
|
S<1, 4, 16, 4>, // BBlockTransferThreadClusterLengths_K0_N_K1
|
|
S<0, 3, 1, 2>, // BBlockTransferThreadClusterArrangeOrder
|
|
S<0, 2, 1, 3>, // BBlockTransferSrcAccessOrder
|
|
2, // BBlockTransferSrcVectorDim
|
|
8, // BBlockTransferSrcScalarPerVector
|
|
2, // BBlockTransferDstScalarPerVector_K1
|
|
true, // BBlockLdsAddExtraN
|
|
1, // CShuffleMXdlPerWavePerShuffle
|
|
1, // CShuffleNXdlPerWavePerShuffle
|
|
S<1, 32, 1, 4>, // CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
|
|
8>; // CBlockTransferScalarPerVector_NWaveNPerXdl
|
|
// clang-format on
|
|
|
|
using ReferenceConvWrwInstance = ck::tensor_operation::host::
|
|
ReferenceConvWrw<InDataType, WeiDataType, OutDataType, InElementOp, WeiElementOp, OutElementOp>;
|
|
|
|
int main(int argc, char* argv[])
|
|
{
|
|
bool do_verification = 0;
|
|
int init_method = 0;
|
|
int nrepeat = 5;
|
|
int do_log = 0;
|
|
int split_k = 4;
|
|
|
|
// Conv shape
|
|
ck::index_t N = 128;
|
|
ck::index_t K = 256;
|
|
ck::index_t C = 1024;
|
|
ck::index_t Y = 3;
|
|
ck::index_t X = 3;
|
|
ck::index_t Hi = 14;
|
|
ck::index_t Wi = 14;
|
|
ck::index_t conv_stride_h = 2;
|
|
ck::index_t conv_stride_w = 2;
|
|
ck::index_t conv_dilation_h = 1;
|
|
ck::index_t conv_dilation_w = 1;
|
|
ck::index_t in_left_pad_h = 0;
|
|
ck::index_t in_left_pad_w = 0;
|
|
ck::index_t in_right_pad_h = 0;
|
|
ck::index_t in_right_pad_w = 0;
|
|
|
|
if(argc == 6)
|
|
{
|
|
do_verification = std::stoi(argv[1]);
|
|
init_method = std::stoi(argv[2]);
|
|
nrepeat = std::stoi(argv[3]);
|
|
do_log = std::stoi(argv[4]);
|
|
split_k = std::stoi(argv[5]);
|
|
}
|
|
else if(argc == 21)
|
|
{
|
|
do_verification = std::stoi(argv[1]);
|
|
init_method = std::stoi(argv[2]);
|
|
nrepeat = std::stoi(argv[3]);
|
|
do_log = std::stoi(argv[4]);
|
|
split_k = std::stoi(argv[5]);
|
|
|
|
N = std::stoi(argv[6]);
|
|
K = std::stoi(argv[7]);
|
|
C = std::stoi(argv[8]);
|
|
Y = std::stoi(argv[9]);
|
|
X = std::stoi(argv[10]);
|
|
Hi = std::stoi(argv[11]);
|
|
Wi = std::stoi(argv[12]);
|
|
conv_stride_h = std::stoi(argv[13]);
|
|
conv_stride_w = std::stoi(argv[14]);
|
|
conv_dilation_h = std::stoi(argv[15]);
|
|
conv_dilation_w = std::stoi(argv[16]);
|
|
in_left_pad_h = std::stoi(argv[17]);
|
|
in_left_pad_w = std::stoi(argv[18]);
|
|
in_right_pad_h = std::stoi(argv[19]);
|
|
in_right_pad_w = std::stoi(argv[20]);
|
|
}
|
|
else
|
|
{
|
|
printf("arg1: verification (0=no, 1=yes)\n");
|
|
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
|
|
printf("arg3: run kernel # of times (>1)\n");
|
|
printf("arg4: is show log (0=no, 1=yes)\n");
|
|
printf("arg5: split-k \n");
|
|
printf("arg6 to 19: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
|
|
"RightPx\n");
|
|
exit(0);
|
|
}
|
|
|
|
const ck::index_t YEff = (Y - 1) * conv_dilation_h + 1;
|
|
const ck::index_t XEff = (X - 1) * conv_dilation_w + 1;
|
|
|
|
const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
|
|
const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
|
|
|
|
const std::vector<ck::index_t> conv_filter_strides{{conv_stride_h, conv_stride_w}};
|
|
const std::vector<ck::index_t> conv_filter_dilations{{conv_dilation_h, conv_dilation_w}};
|
|
const std::vector<ck::index_t> input_left_pads{{in_left_pad_h, in_left_pad_w}};
|
|
const std::vector<ck::index_t> input_right_pads{{in_right_pad_h, in_right_pad_w}};
|
|
|
|
// tensor layout
|
|
auto f_host_tensor_descriptor = [](std::size_t N_,
|
|
std::size_t C_,
|
|
std::size_t H,
|
|
std::size_t W,
|
|
auto layout) {
|
|
if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value ||
|
|
ck::is_same<decltype(layout), ck::tensor_layout::convolution::KCYX>::value ||
|
|
ck::is_same<decltype(layout), ck::tensor_layout::convolution::NKHW>::value)
|
|
{
|
|
return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
|
|
std::vector<std::size_t>({C_ * H * W, H * W, W, 1}));
|
|
}
|
|
else if constexpr(ck::is_same<decltype(layout),
|
|
ck::tensor_layout::convolution::NHWC>::value ||
|
|
ck::is_same<decltype(layout),
|
|
ck::tensor_layout::convolution::KYXC>::value ||
|
|
ck::is_same<decltype(layout),
|
|
ck::tensor_layout::convolution::NHWK>::value)
|
|
{
|
|
return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
|
|
std::vector<std::size_t>({C_ * H * W, 1, W * C_, C_}));
|
|
}
|
|
};
|
|
|
|
Tensor<InDataType> in_n_c_hi_wi(f_host_tensor_descriptor(N, C, Hi, Wi, InLayout{}));
|
|
Tensor<WeiDataType> wei_k_c_y_x_host_result(f_host_tensor_descriptor(K, C, Y, X, WeiLayout{}));
|
|
Tensor<WeiDataType> wei_k_c_y_x_device_result(
|
|
f_host_tensor_descriptor(K, C, Y, X, WeiLayout{}));
|
|
Tensor<OutDataType> out_n_k_ho_wo(f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
|
|
|
|
std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi.mDesc << std::endl;
|
|
std::cout << "wei_k_c_y_x: " << wei_k_c_y_x_host_result.mDesc << std::endl;
|
|
std::cout << "out_n_k_ho_wo: " << out_n_k_ho_wo.mDesc << std::endl;
|
|
|
|
switch(init_method)
|
|
{
|
|
case 0: break;
|
|
case 1:
|
|
in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
|
|
out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
|
|
break;
|
|
default:
|
|
in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_1<InDataType>{1});
|
|
out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_1<OutDataType>{1});
|
|
}
|
|
wei_k_c_y_x_device_result.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{0});
|
|
|
|
DeviceMem in_device_buf(sizeof(InDataType) * in_n_c_hi_wi.mDesc.GetElementSpace());
|
|
DeviceMem wei_device_buf(sizeof(WeiDataType) *
|
|
wei_k_c_y_x_device_result.mDesc.GetElementSpace());
|
|
DeviceMem out_device_buf(sizeof(OutDataType) * out_n_k_ho_wo.mDesc.GetElementSpace());
|
|
|
|
in_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
|
|
out_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
|
|
wei_device_buf.ToDevice(wei_k_c_y_x_device_result.mData.data());
|
|
|
|
// do GEMM
|
|
auto conv = DeviceConvWrWInstance{};
|
|
auto invoker = conv.MakeInvoker();
|
|
auto argument = conv.MakeArgument(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
|
|
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
|
|
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
|
|
N,
|
|
K,
|
|
C,
|
|
std::vector<ck::index_t>{{Hi, Wi}},
|
|
std::vector<ck::index_t>{{Y, X}},
|
|
std::vector<ck::index_t>{{Ho, Wo}},
|
|
conv_filter_strides,
|
|
conv_filter_dilations,
|
|
input_left_pads,
|
|
input_right_pads,
|
|
InElementOp{},
|
|
WeiElementOp{},
|
|
OutElementOp{},
|
|
split_k);
|
|
|
|
if(!conv.IsSupportedArgument(argument))
|
|
{
|
|
std::cout << "wrong! device_conv with the specified compilation parameters does "
|
|
"not support this Conv problem"
|
|
<< std::endl;
|
|
return 1;
|
|
}
|
|
|
|
float ave_time = invoker.Run(argument, nrepeat);
|
|
|
|
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;
|
|
|
|
std::size_t num_btype = sizeof(InDataType) * (N * C * Hi * Wi) +
|
|
sizeof(WeiDataType) * (K * C * Y * X) +
|
|
sizeof(OutDataType) * (N * K * Ho * Wo);
|
|
|
|
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
|
|
|
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
|
|
|
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
|
|
<< std::endl;
|
|
|
|
if(do_verification)
|
|
{
|
|
auto ref_conv = ReferenceConvWrwInstance{};
|
|
auto ref_invoker = ref_conv.MakeInvoker();
|
|
|
|
auto ref_argument = ref_conv.MakeArgument(in_n_c_hi_wi,
|
|
wei_k_c_y_x_host_result,
|
|
out_n_k_ho_wo,
|
|
conv_filter_strides,
|
|
conv_filter_dilations,
|
|
input_left_pads,
|
|
input_right_pads,
|
|
InElementOp{},
|
|
WeiElementOp{},
|
|
OutElementOp{});
|
|
|
|
ref_invoker.Run(ref_argument);
|
|
|
|
wei_device_buf.FromDevice(wei_k_c_y_x_device_result.mData.data());
|
|
|
|
if(do_log)
|
|
{
|
|
LogRangeAsType<float>(std::cout << "out: ", out_n_k_ho_wo.mData, ",") << std::endl;
|
|
LogRangeAsType<float>(std::cout << "in : ", in_n_c_hi_wi.mData, ",") << std::endl;
|
|
LogRangeAsType<float>(
|
|
std::cout << "wei_device(after): ", wei_k_c_y_x_device_result.mData, ",")
|
|
<< std::endl;
|
|
LogRangeAsType<float>(std::cout << "wei_host : ", wei_k_c_y_x_host_result.mData, ",")
|
|
<< std::endl;
|
|
}
|
|
check_error(wei_k_c_y_x_host_result, wei_k_c_y_x_device_result);
|
|
}
|
|
}
|