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Conv bwd data multiple d (#404)

Browse Source 
* init commit of convnd bwd data

* begin compiling example

* have a first version that produce a right result

* refine device level launch kernel code

* add more instances in example and get right results

* clang-format

* format example file

* add more instances

* fix instances

* adding conv_bwd_data multile_d

* adding conv_bwd_data multile_d

* adding conv_bwd multiple d

* adding conv_bwd multiple d

* adding conv_bwd multiple d

* refactor

* refactor

* adding conv bwd data multiple d

* adding conv bwd data multiple d

* adding conv bwd data multiple d

* adding conv bwd data multiple d

* adding conv bwd data multiple d

* adding conv bwd data multiple d

* adding conv bwd data multiple d

* refactor

* update conv fwd's bias impl

* refactor

* reorg file

* clean up cmake

* clean

* clean

* clean

Co-authored-by: Chao Liu <lc.roy86@gmail.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>

[ROCm/composable_kernel commit: 27858374ac]
This commit is contained in:
Shaojie WANG
2022-09-20 00:25:28 +08:00
committed by GitHub
parent c1cfd4c894
commit 207398ef99
28 changed files with 2262 additions and 1161 deletions
Download Patch File Download Diff File Expand all files Collapse all files

258
example/24_batched_gemm_e_permute/batched_gemm_e_permute_xdl_fp16.cpp
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@@ -1,258 +0,0 @@
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_e_permute_xdl.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.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/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = F16;
using BDataType = F16;
using AccDataType = F32;
using CShuffleDataType = F16;
using EDataType = F16;
using ALayout = Row;
using BLayout = Col;
using ELayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceBatchedGemmEPermuteXdl
// clang-format off
//######| ALayout| BLayout| ELayout| AData| BData| AccData| CShuffle| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//######| | | | Type| Type| Type| DataType| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//######| | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
// clang-format on
using ReferenceBatchedGemmInstance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
BDataType,
EDataType,
AccDataType,
AElementOp,
BElementOp,
CDEElementOp>;
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
const int M = 256;
const int N = 128;
const int K = 64;
const int stride_A = K;
const int stride_B = K;
const int batch_stride_A = M * K;
const int batch_stride_B = K * N;
const int G0 = 16;
const int G1 = 8;
const int batch_count = G0 * G1;
// output layout - [G0, M, G1, N]
const int stride_G0 = M * G1 * N;
const int stride_G1 = N;
const int stride_M = G1 * N;
const int stride_N = 1;
if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
exit(0);
}
// GEMM shape
ck::tensor_operation::device::BatchedGemmEPermuteDesc batched_gemm_e_permute_desc{
G0, G1, M, N, stride_G0, stride_G1, stride_M, stride_N};
auto f_host_tensor_descriptor = [](std::size_t batch_count_,
std::size_t row,
std::size_t col,
std::size_t stride,
std::size_t batch_stride,
auto layout) {
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count_, row, col}),
std::vector<std::size_t>({batch_stride, stride, 1}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({batch_count_, row, col}),
std::vector<std::size_t>({batch_stride, 1, stride}));
}
};
Tensor<ADataType> a_g_m_k(
f_host_tensor_descriptor(batch_count, M, K, stride_A, batch_stride_A, ALayout{}));
Tensor<BDataType> b_g_k_n(
f_host_tensor_descriptor(batch_count, K, N, stride_B, batch_stride_B, BLayout{}));
auto f_host_e_tensor_descriptor = [](std::size_t G0_,
std::size_t G1_,
std::size_t M_,
std::size_t N_,
std::size_t stride_G0_,
std::size_t stride_G1_,
std::size_t stride_M_,
std::size_t stride_N_) {
return HostTensorDescriptor(
std::vector<std::size_t>({G0_, G1_, M_, N_}),
std::vector<std::size_t>({stride_G0_, stride_G1_, stride_M_, stride_N_}));
};
Tensor<EDataType> e_g0_g1_m_n_host_result(
f_host_e_tensor_descriptor(G0, G1, M, N, stride_G0, stride_G1, stride_M, stride_N));
Tensor<EDataType> e_g0_g1_m_n_device_result(
f_host_e_tensor_descriptor(G0, G1, M, N, stride_G0, stride_G1, stride_M, stride_N));
std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;
std::cout << "e_g0_g1_m_n: " << e_g0_g1_m_n_host_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
break;
default:
a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_g_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
}
DeviceMem a_device_buf(sizeof(ADataType) * a_g_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_g_k_n.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(EDataType) *
e_g0_g1_m_n_device_result.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a_g_m_k.mData.data());
b_device_buf.ToDevice(b_g_k_n.mData.data());
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
// do GEM
auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
static_cast<EDataType*>(e_device_buf.GetDeviceBuffer()),
M,
N,
K,
stride_A,
stride_B,
batch_stride_A,
batch_stride_B,
batched_gemm_e_permute_desc,
batch_count,
a_element_op,
b_element_op,
cde_element_op);
if(!gemm.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_gemm with the specified compilation parameters does "
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * batch_count * M * N * K;
std::size_t num_btype = sizeof(ADataType) * batch_count * M * K +
sizeof(BDataType) * batch_count * K * N +
sizeof(EDataType) * batch_count * M * N;
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, "
<< gemm.GetTypeString() << std::endl;
bool pass = true;
if(do_verification)
{
e_device_buf.FromDevice(e_g0_g1_m_n_device_result.mData.data());
auto ref_batched_gemm = ReferenceBatchedGemmInstance{};
auto ref_invoker = ref_batched_gemm.MakeInvoker();
Tensor<EDataType> c_g_m_n_host_result = HostTensorDescriptor(
std::vector<std::size_t>({batch_count, M, N}), std::vector<std::size_t>({M * N, N, 1}));
auto ref_argument = ref_batched_gemm.MakeArgument(
a_g_m_k, b_g_k_n, c_g_m_n_host_result, a_element_op, b_element_op, cde_element_op);
ref_invoker.Run(ref_argument);
for(int g0 = 0; g0 < G0; g0++)
{
for(int g1 = 0; g1 < G1; g1++)
{
for(int m = 0; m < M; m++)
{
for(int n = 0; n < N; n++)
{
int g = g0 * G1 + g1;
e_g0_g1_m_n_host_result(g0, g1, m, n) = c_g_m_n_host_result(g, m, n);
}
}
}
}
pass = ck::utils::check_err(e_g0_g1_m_n_host_result.mData,
e_g0_g1_m_n_device_result.mData,
"Error: Incorrect results c");
}
return pass ? 0 : 1;
}

6
example/30_grouped_convnd_fwd_bias_relu_add/grouped_convnd_fwd_bias_relu_add_xdl_bf16.cpp
View File

@@ -137,7 +137,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NW_C;
using WeiLayout = ctc::G_K_X_C;
using BiasLayout = ctc::G_NW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NW_K;
using OutLayout = ctc::G_NW_K;
@@ -220,7 +220,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NHW_C;
using WeiLayout = ctc::G_K_YX_C;
using BiasLayout = ctc::G_NHW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NHW_K;
using OutLayout = ctc::G_NHW_K;
@@ -332,7 +332,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NDHW_C;
using WeiLayout = ctc::G_K_ZYX_C;
using BiasLayout = ctc::G_NDHW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NDHW_K;
using OutLayout = ctc::G_NDHW_K;

6
example/30_grouped_convnd_fwd_bias_relu_add/grouped_convnd_fwd_bias_relu_add_xdl_fp16.cpp
View File

@@ -137,7 +137,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NW_C;
using WeiLayout = ctc::G_K_X_C;
using BiasLayout = ctc::G_NW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NW_K;
using OutLayout = ctc::G_NW_K;
@@ -220,7 +220,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NHW_C;
using WeiLayout = ctc::G_K_YX_C;
using BiasLayout = ctc::G_NHW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NHW_K;
using OutLayout = ctc::G_NHW_K;
@@ -332,7 +332,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NDHW_C;
using WeiLayout = ctc::G_K_ZYX_C;
using BiasLayout = ctc::G_NDHW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NDHW_K;
using OutLayout = ctc::G_NDHW_K;

6
example/30_grouped_convnd_fwd_bias_relu_add/grouped_convnd_fwd_bias_relu_add_xdl_fp32.cpp
View File

@@ -137,7 +137,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NW_C;
using WeiLayout = ctc::G_K_X_C;
using BiasLayout = ctc::G_NW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NW_K;
using OutLayout = ctc::G_NW_K;
@@ -220,7 +220,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NHW_C;
using WeiLayout = ctc::G_K_YX_C;
using BiasLayout = ctc::G_NHW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NHW_K;
using OutLayout = ctc::G_NHW_K;
@@ -332,7 +332,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NDHW_C;
using WeiLayout = ctc::G_K_ZYX_C;
using BiasLayout = ctc::G_NDHW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NDHW_K;
using OutLayout = ctc::G_NDHW_K;

6
example/30_grouped_convnd_fwd_bias_relu_add/grouped_convnd_fwd_bias_relu_add_xdl_int4.cpp
View File

@@ -137,7 +137,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NW_C;
using WeiLayout = ctc::G_K_X_C;
using BiasLayout = ctc::G_NW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NW_K;
using OutLayout = ctc::G_NW_K;
@@ -220,7 +220,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NHW_C;
using WeiLayout = ctc::G_K_YX_C;
using BiasLayout = ctc::G_NHW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NHW_K;
using OutLayout = ctc::G_NHW_K;
@@ -332,7 +332,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NDHW_C;
using WeiLayout = ctc::G_K_ZYX_C;
using BiasLayout = ctc::G_NDHW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NDHW_K;
using OutLayout = ctc::G_NDHW_K;

6
example/30_grouped_convnd_fwd_bias_relu_add/grouped_convnd_fwd_bias_relu_add_xdl_int8.cpp
View File

@@ -137,7 +137,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NW_C;
using WeiLayout = ctc::G_K_X_C;
using BiasLayout = ctc::G_NW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NW_K;
using OutLayout = ctc::G_NW_K;
@@ -220,7 +220,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NHW_C;
using WeiLayout = ctc::G_K_YX_C;
using BiasLayout = ctc::G_NHW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NHW_K;
using OutLayout = ctc::G_NHW_K;
@@ -332,7 +332,7 @@ int main(int argc, char* argv[])
{
using InLayout = ctc::G_NDHW_C;
using WeiLayout = ctc::G_K_ZYX_C;
using BiasLayout = ctc::G_NDHW_K;
using BiasLayout = ctc::G_K;
using ResidualLayout = ctc::G_NDHW_K;
using OutLayout = ctc::G_NDHW_K;

1
example/38_grouped_conv_bwd_data_bias_relu/CMakeLists.txt Normal file
View File

@@ -0,0 +1 @@
add_example_executable(example_grouped_conv_bwd_data_bias_relu_fp16 grouped_conv_bwd_data_bias_relu_fp16.cpp)

199
example/38_grouped_conv_bwd_data_bias_relu/grouped_conv_bwd_data_bias_relu_common.hpp Normal file
View File

@@ -0,0 +1,199 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#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/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_bwd_data.hpp"
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 OutDataType,
typename WeiDataType,
typename BiasDataType,
typename InDataType,
typename OutElementOp,
typename WeiElementOp,
typename InElementOp,
typename DeviceInstance>
int run_conv_bwd_data_bias_relu(bool do_verification,
int init_method,
bool time_kernel,
const ck::utils::conv::ConvParam& conv_param,
const HostTensorDescriptor& out_g_n_k_wos_desc,
const HostTensorDescriptor& wei_g_k_c_xs_desc,
const HostTensorDescriptor& bias_g_n_c_wis_desc,
const HostTensorDescriptor& in_g_n_c_wis_desc,
const OutElementOp& out_element_op,
const WeiElementOp& wei_element_op,
const InElementOp& in_element_op)
{
Tensor<OutDataType> out(out_g_n_k_wos_desc);
Tensor<WeiDataType> wei(wei_g_k_c_xs_desc);
Tensor<BiasDataType> bias(bias_g_n_c_wis_desc);
Tensor<InDataType> in_host(in_g_n_c_wis_desc);
Tensor<InDataType> in_device(in_g_n_c_wis_desc);
std::cout << "out: " << out.mDesc << std::endl;
std::cout << "wei: " << wei.mDesc << std::endl;
std::cout << "bias: " << bias.mDesc << std::endl;
std::cout << "in: " << in_host.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
out.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
wei.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
bias.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-5, 5});
break;
default:
out.GenerateTensorValue(GeneratorTensor_3<OutDataType>{0.0, 1.0});
wei.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
bias.GenerateTensorValue(GeneratorTensor_3<BiasDataType>{0.0, 1.0});
}
DeviceMem out_device_buf(sizeof(OutDataType) * out.mDesc.GetElementSpaceSize());
DeviceMem wei_device_buf(sizeof(WeiDataType) * wei.mDesc.GetElementSpaceSize());
DeviceMem bias_device_buf(sizeof(BiasDataType) * bias.mDesc.GetElementSpaceSize());
DeviceMem in_device_buf(sizeof(InDataType) * in_device.mDesc.GetElementSpaceSize());
out_device_buf.ToDevice(out.mData.data());
wei_device_buf.ToDevice(wei.mData.data());
bias_device_buf.ToDevice(bias.mData.data());
// reset input to zero
in_device_buf.SetZero();
std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_lengths{};
std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_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> d0_g_n_c_wis_lengths{};
std::array<ck::index_t, NDimSpatial + 3> d0_g_n_c_wis_strides{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_c_wis_lengths{};
std::array<ck::index_t, NDimSpatial + 3> e_g_n_c_wis_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) { std::copy(x.begin(), x.end(), y.begin()); };
copy(out_g_n_k_wos_desc.GetLengths(), a_g_n_k_wos_lengths);
copy(out_g_n_k_wos_desc.GetStrides(), a_g_n_k_wos_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(bias_g_n_c_wis_desc.GetLengths(), d0_g_n_c_wis_lengths);
copy(bias_g_n_c_wis_desc.GetStrides(), d0_g_n_c_wis_strides);
copy(in_g_n_c_wis_desc.GetLengths(), e_g_n_c_wis_lengths);
copy(in_g_n_c_wis_desc.GetStrides(), e_g_n_c_wis_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 = DeviceInstance{};
auto invoker = conv.MakeInvoker();
auto argument = conv.MakeArgument(
out_device_buf.GetDeviceBuffer(),
wei_device_buf.GetDeviceBuffer(),
std::array<const void*, 1>{bias_device_buf.GetDeviceBuffer()},
in_device_buf.GetDeviceBuffer(),
a_g_n_k_wos_lengths,
a_g_n_k_wos_strides,
b_g_k_c_xs_lengths,
b_g_k_c_xs_strides,
std::array<std::array<ck::index_t, NDimSpatial + 3>, 1>{d0_g_n_c_wis_lengths},
std::array<std::array<ck::index_t, NDimSpatial + 3>, 1>{d0_g_n_c_wis_strides},
e_g_n_c_wis_lengths,
e_g_n_c_wis_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
out_element_op,
wei_element_op,
in_element_op);
if(!conv.IsSupportedArgument(argument))
{
printf("wrong! device_conv with the specified compilation parameters does "
"not support this Conv problem\n");
return 1;
}
float ave_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 / 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)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
// c doesn't physically exist, any layout is fine
Tensor<float> c_host(in_g_n_c_wis_desc);
auto ref_conv = ck::tensor_operation::host::ReferenceConvBwdData<NDimSpatial,
float,
WeiDataType,
OutDataType,
PassThrough,
WeiElementOp,
OutElementOp>();
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(c_host,
wei,
out,
conv_param.conv_filter_strides_,
conv_param.conv_filter_dilations_,
conv_param.input_left_pads_,
conv_param.input_right_pads_,
PassThrough{},
wei_element_op,
out_element_op);
ref_invoker.Run(ref_argument);
// TODO: implement elementwise operation for host
in_host.ForEach(
[&](auto&, auto idx) { in_element_op(in_host(idx), c_host(idx), bias(idx)); });
in_device_buf.FromDevice(in_device.mData.data());
return ck::utils::check_err(in_device.mData, in_host.mData) ? 0 : 1;
}
return 0;
}

174
example/38_grouped_conv_bwd_data_bias_relu/grouped_conv_bwd_data_bias_relu_fp16.cpp Normal file
View File

@@ -0,0 +1,174 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "grouped_conv_bwd_data_bias_relu_common.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_bwd_data_multiple_d.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_data_multiple_d_xdl_cshuffle_v1.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using OutDataType = ck::half_t;
using WeiDataType = ck::half_t;
using AccDataType = float;
using CShuffleDataType = ck::half_t;
using BiasDataType = ck::half_t; // bias
using InDataType = ck::half_t;
using OutLayout = ck::tensor_layout::convolution::GNHWK;
using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using BiasLayout = ck::tensor_layout::convolution::G_C;
using InLayout = ck::tensor_layout::convolution::GNHWC;
using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
using CBiasInElementOp = ck::tensor_operation::element_wise::AddRelu;
static constexpr auto ConvBwdDataDefault =
ck::tensor_operation::device::ConvolutionBackwardDataSpecialization::Default;
template <ck::index_t NDimSpatial>
using DeviceConvNdBwdDataInstance =
ck::tensor_operation::device::DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1<
NDimSpatial,
OutLayout,
WeiLayout,
ck::Tuple<BiasLayout>,
InLayout,
OutDataType,
WeiDataType,
AccDataType,
CShuffleDataType,
ck::Tuple<BiasDataType>,
InDataType,
OutElementOp,
WeiElementOp,
CBiasInElementOp,
ConvBwdDataDefault,
true, // DoPadGemmM
true, // DoPadGemmN
1,
256,
128,
256,
32,
8,
2,
32,
32,
2,
4,
S<4, 64, 1>,
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
1,
S<4, 64, 1>,
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
0,
1,
1,
S<1, 32, 1, 8>,
8>;
int main(int argc, char* argv[])
{
namespace ctc = ck::tensor_layout::convolution;
print_helper_msg();
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
ck::utils::conv::ConvParam conv_param{
2, 2, 128, 256, 256, {3, 3}, {14, 14}, {2, 2}, {1, 1}, {1, 1}, {1, 1}};
if(argc == 1)
{
// use default
}
else if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
const ck::index_t num_dim_spatial = std::stoi(argv[4]);
conv_param = ck::utils::conv::parse_conv_param(num_dim_spatial, 5, argv);
}
const auto in_element_op = CBiasInElementOp{};
const auto wei_element_op = WeiElementOp{};
const auto out_element_op = OutElementOp{};
if(conv_param.num_dim_spatial_ == 2)
{
// output image: GNHWK
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
conv_param);
// weight: GKYXC
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
conv_param);
// input image bias: G_C
const auto bias_g_n_c_wis_desc =
HostTensorDescriptor({conv_param.G_,
conv_param.N_,
conv_param.C_,
conv_param.input_spatial_lengths_[0],
conv_param.input_spatial_lengths_[1]},
{
conv_param.C_, // g
0, // n
1, // c
0, // hi
0 // wi
});
// input image: GNHWC
const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
conv_param);
using DeviceInstance = DeviceConvNdBwdDataInstance<2>;
run_conv_bwd_data_bias_relu<2,
OutDataType,
WeiDataType,
BiasDataType,
InDataType,
OutElementOp,
WeiElementOp,
CBiasInElementOp,
DeviceInstance>(do_verification,
init_method,
time_kernel,
conv_param,
out_g_n_k_wos_desc,
wei_g_k_c_xs_desc,
bias_g_n_c_wis_desc,
in_g_n_c_wis_desc,
wei_element_op,
out_element_op,
in_element_op);
}
return 0;
}

40
example/CMakeLists.txt
View File

@@ -21,36 +21,10 @@ function(add_example_executable_no_testing EXAMPLE_NAME FILE_NAME)
add_dependencies(examples ${EXAMPLE_NAME})
endfunction(add_example_executable_no_testing EXAMPLE_NAME)
add_subdirectory(01_gemm)
add_subdirectory(02_gemm_bilinear)
add_subdirectory(03_gemm_bias_relu)
add_subdirectory(04_gemm_add_add_fastgelu)
add_subdirectory(09_convnd_fwd)
add_subdirectory(10_convnd_fwd_multiple_d_multiple_reduce)
add_subdirectory(12_reduce)
add_subdirectory(13_pool2d_fwd)
add_subdirectory(14_gemm_xdl_requant_relu_requant)
add_subdirectory(15_grouped_gemm)
add_subdirectory(16_gemm_multi_d_multi_reduces)
add_subdirectory(17_convnd_bwd_data)
add_subdirectory(18_batched_gemm_reduce)
add_subdirectory(19_binary_elementwise)
add_subdirectory(20_convnd_bwd_weight)
add_subdirectory(21_gemm_layernorm)
add_subdirectory(22_cgemm)
add_subdirectory(23_softmax)
add_subdirectory(24_batched_gemm)
add_subdirectory(25_gemm_bias_e_permute)
add_subdirectory(26_contraction)
add_subdirectory(27_layernorm)
add_subdirectory(28_grouped_gemm_bias_e_permute)
add_subdirectory(29_batched_gemm_bias_e_permute)
add_subdirectory(30_grouped_convnd_fwd_bias_relu_add)
add_subdirectory(31_batched_gemm_gemm)
add_subdirectory(32_batched_gemm_scale_softmax_gemm)
add_subdirectory(33_multiple_reduce)
add_subdirectory(34_batchnorm)
add_subdirectory(35_splitK_gemm)
add_subdirectory(36_sparse_embedding)
add_subdirectory(37_batched_gemm_add_add_relu_gemm_add)
add_subdirectory(41_grouped_conv_conv_fwd)
# add all example subdir
file(GLOB dir_list LIST_DIRECTORIES true *)
FOREACH(subdir ${dir_list})
IF(IS_DIRECTORY "${subdir}")
add_subdirectory(${subdir})
ENDIF()
ENDFOREACH()