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Commit 16f02f76 authored by Astha Rai's avatar Astha Rai
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Merge branch 'gridwise_2d' of github.com:ROCmSoftwarePlatform/composable_kernel into gridwise_2d

parents 7d653017 9b3365e1
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example/20_convnd_bwd_weight/convnd_bwd_weight_xdl_fp16.cpp deleted 100644 → 0
View file @ 7d653017
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "convnd_bwd_weight_common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_convnd_bwd_weight_nwc_kxc_nwk_xdl_cshuffle.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 InElementOp = ck::tensor_operation::element_wise::PassThrough;
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdWeightDefault =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
template <ck::index_t NDimSpatial>
using DeviceConvndBwdWeightInstance =
ck::tensor_operation::device::DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle<
NDimSpatial, // NDimSpatial
InDataType, // InDataType
WeiDataType, // WeiDataType
OutDataType, // OutDataType
AccDataType, // AccDataType
InElementOp, // InElementwiseOperation
WeiElementOp, // WeiElementwiseOperation
OutElementOp, // OutElementwiseOperation
ConvBwdWeightDefault, // ConvolutionBackwardWeightSpecialization
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
int main(int argc, char* argv[])
{
namespace ctc = ck::tensor_layout::convolution;
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
ck::utils::conv::ConvParam conv_param{
2, 1, 32, 256, 1024, {3, 3}, {14, 14}, {2, 2}, {1, 1}, {1, 1}, {1, 1}};
ck::index_t split_k = 4;
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);
split_k = std::stoi(argv[5 + 3 + 6 * num_dim_spatial - 1]);
split_k = std::max(1, split_k);
}
const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{};
const auto out_element_op = OutElementOp{};
if(conv_param.num_dim_spatial_ == 1)
{
using InLayout = ctc::GNWC;
using WeiLayout = ctc::GKXC;
using OutLayout = ctc::GNWK;
const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
conv_param);
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
conv_param);
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
conv_param);
return run_conv_bwd_weight<1,
InDataType,
WeiDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
DeviceConvndBwdWeightInstance<1>>(do_verification,
init_method,
time_kernel,
conv_param,
in_g_n_c_wis_desc,
wei_g_k_c_xs_desc,
out_g_n_k_wos_desc,
in_element_op,
wei_element_op,
out_element_op,
split_k);
}
else if(conv_param.num_dim_spatial_ == 2)
{
using InLayout = ctc::GNHWC;
using WeiLayout = ctc::GKYXC;
using OutLayout = ctc::GNHWK;
const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
conv_param);
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
conv_param);
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
conv_param);
return run_conv_bwd_weight<2,
InDataType,
WeiDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
DeviceConvndBwdWeightInstance<2>>(do_verification,
init_method,
time_kernel,
conv_param,
in_g_n_c_wis_desc,
wei_g_k_c_xs_desc,
out_g_n_k_wos_desc,
in_element_op,
wei_element_op,
out_element_op,
split_k);
}
else if(conv_param.num_dim_spatial_ == 3)
{
using InLayout = ctc::GNDHWC;
using WeiLayout = ctc::GKZYXC;
using OutLayout = ctc::GNDHWK;
const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
conv_param);
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
conv_param);
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
conv_param);
return run_conv_bwd_weight<3,
InDataType,
WeiDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp,
DeviceConvndBwdWeightInstance<3>>(do_verification,
init_method,
time_kernel,
conv_param,
in_g_n_c_wis_desc,
wei_g_k_c_xs_desc,
out_g_n_k_wos_desc,
in_element_op,
wei_element_op,
out_element_op,
split_k);
}
return 0;
}
example/20_grouped_conv_bwd_weight/CMakeLists.txt 0 → 100644
View file @ 16f02f76
add_custom_target(example_grouped_conv_bwd_weight)
add_example_executable(example_grouped_conv_bwd_weight_xdl_fp16 grouped_conv_bwd_weight_xdl_fp16.cpp)
add_example_executable(example_grouped_conv_bwd_weight_xdl_bf16 grouped_conv_bwd_weight_xdl_bf16.cpp)
add_dependencies(example_grouped_conv_bwd_weight example_grouped_conv_bwd_weight_xdl_fp16
example_grouped_conv_bwd_weight_xdl_bf16)
example/20_grouped_conv_bwd_weight/common.hpp 0 → 100644
View file @ 16f02f76
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <algorithm>
#include <iostream>
#include <iterator>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/convolution_backward_weight_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_gnwc_gkxc_gnwk_xdl_cshuffle.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_weight.hpp"
using BF16 = ck::bhalf_t;
using F16 = ck::half_t;
using F32 = float;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvBwdWeightDefault =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
template <typename InputLay, typename WeightLay, typename OutputLay>
struct CommonLayoutSetting
{
using InputLayout = InputLay;
using WeightLayout = WeightLay;
using OutputLayout = OutputLay;
};
template <ck::index_t NDimSpatial>
struct CommonLayoutSettingSelector;
namespace ctl = ck::tensor_layout::convolution;
template <>
struct CommonLayoutSettingSelector<1> final : CommonLayoutSetting<ctl::GNWC, ctl::GKXC, ctl::GNWK>
{
};
template <>
struct CommonLayoutSettingSelector<2> final
: CommonLayoutSetting<ctl::GNHWC, ctl::GKYXC, ctl::GNHWK>
{
};
template <>
struct CommonLayoutSettingSelector<3> final
: CommonLayoutSetting<ctl::GNDHWC, ctl::GKZYXC, ctl::GNDHWK>
{
};
template <ck::index_t NDimSpatial>
using InputLayout = typename CommonLayoutSettingSelector<NDimSpatial>::InputLayout;
template <ck::index_t NDimSpatial>
using WeightLayout = typename CommonLayoutSettingSelector<NDimSpatial>::WeightLayout;
template <ck::index_t NDimSpatial>
using OutputLayout = typename CommonLayoutSettingSelector<NDimSpatial>::OutputLayout;
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
};
#define DefaultConvParam \
ck::utils::conv::ConvParam \
{ \
2, 4, 1, 128, 256, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, { 1, 1 } \
}
inline void print_help_msg()
{
std::cerr << "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;
}
inline bool parse_cmd_args(int argc,
char* argv[],
ExecutionConfig& config,
ck::utils::conv::ConvParam& conv_param)
{
constexpr int num_execution_config_args =
3; // arguments for do_verification, init_method, time_kernel
constexpr int num_conv_param_leading_args = 5; // arguments for num_dim_spatial_, G_, N_, K_, C_
constexpr int threshold_to_catch_partial_args = 1 + num_execution_config_args;
constexpr int threshold_to_catch_all_args =
threshold_to_catch_partial_args + num_conv_param_leading_args;
if(argc == 1)
{
// use default
}
// catch only ExecutionConfig arguments
else if(argc == threshold_to_catch_partial_args)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
}
// catch both ExecutionConfig & ConvParam arguments
else if(threshold_to_catch_all_args < argc && ((argc - threshold_to_catch_all_args) % 3 == 0))
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.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, threshold_to_catch_partial_args, argv);
}
else
{
print_help_msg();
return false;
}
return true;
}
example/20_grouped_conv_bwd_weight/grouped_conv_bwd_weight_xdl_bf16.cpp 0 → 100644
View file @ 16f02f76
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
using InDataType = BF16;
// bf16 kernel use fp32 atomic add to accumulate Weight tensor into global memory
using WeiDataType = F32;
using OutDataType = BF16;
using AccDataType = F32;
using InElementOp = PassThrough;
using WeiElementOp = PassThrough;
using OutElementOp = PassThrough;
#include "run_grouped_conv_bwd_weight_example.inc"
int main(int argc, char* argv[]) { return !run_grouped_conv_bwd_weight_example(argc, argv); }
example/20_grouped_conv_bwd_weight/grouped_conv_bwd_weight_xdl_fp16.cpp 0 → 100644
View file @ 16f02f76
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
using InDataType = F16;
using WeiDataType = F16;
using OutDataType = F16;
using AccDataType = F32;
using InElementOp = PassThrough;
using WeiElementOp = PassThrough;
using OutElementOp = PassThrough;
#include "run_grouped_conv_bwd_weight_example.inc"
int main(int argc, char* argv[]) { return !run_grouped_conv_bwd_weight_example(argc, argv); }
example/20_convnd_bwd_weight/convnd_bwd_weight_common.hpp → example/20_grouped_conv_bwd_weight/run_grouped_conv_bwd_weight_example.inc
View file @ 16f02f76
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream> template <ck::index_t NDimSpatial>
#include <numeric> using DeviceConvBwdWeightInstance =
#include <initializer_list> ck::tensor_operation::device::DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Xdl_CShuffle<
#include <cstdlib> NDimSpatial, // NDimSpatial
InDataType, // InDataType
#include "ck/ck.hpp" WeiDataType, // WeiDataType
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" OutDataType, // OutDataType
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" AccDataType, // AccDataType
InElementOp, // InElementwiseOperation
#include "ck/library/utility/check_err.hpp" WeiElementOp, // WeiElementwiseOperation
#include "ck/library/utility/device_memory.hpp" OutElementOp, // OutElementwiseOperation
#include "ck/library/utility/host_tensor.hpp" ConvBwdWeightDefault, // ConvolutionBackwardWeightSpecialization
#include "ck/library/utility/host_tensor_generator.hpp" 256, // BlockSize
#include "ck/library/utility/convolution_parameter.hpp" 128, // MPerBlock
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp" 128, // NPerBlock
#include "ck/library/reference_tensor_operation/cpu/reference_conv_bwd_weight.hpp" 4, // K0PerBlock
8, // K1
void print_helper_msg() 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
128 / (sizeof(WeiDataType) * CHAR_BIT)>; // CBlockTransferScalarPerVector_NWaveNPerXdl
template <ck::index_t NDimSpatial>
using HostConvBwdWeightInstance = ck::tensor_operation::host::ReferenceConvBwdWeight<NDimSpatial,
InDataType,
WeiDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp>;
template <ck::index_t NDimSpatial>
bool run_grouped_conv_bwd_weight(const ExecutionConfig& config,
const ck::utils::conv::ConvParam& conv_param)
{ {
std::cout << "arg1: verification (0=no, 1=yes)\n" constexpr ck::index_t split_k = 2;
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: time kernel (0=no, 1=yes)\n" const auto in_g_n_c_wis_desc =
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl; ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<
} InputLayout<NDimSpatial>>(conv_param);
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<
WeightLayout<NDimSpatial>>(conv_param);
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<
OutputLayout<NDimSpatial>>(conv_param);
template <ck::index_t NDimSpatial,
typename InDataType,
typename WeiDataType,
typename OutDataType,
typename InElementOp,
typename WeiElementOp,
typename OutElementOp,
typename DeviceConvBwdWeightInstance>
int run_conv_bwd_weight(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,
ck::index_t split_k)
{
Tensor<InDataType> in(in_g_n_c_wis_desc); Tensor<InDataType> in(in_g_n_c_wis_desc);
Tensor<WeiDataType> wei_host_result(wei_g_k_c_xs_desc); Tensor<WeiDataType> wei_host_result(wei_g_k_c_xs_desc);
Tensor<WeiDataType> wei_device_result(wei_g_k_c_xs_desc); Tensor<WeiDataType> wei_device_result(wei_g_k_c_xs_desc);
...@@ -55,7 +77,7 @@ int run_conv_bwd_weight(bool do_verification, ...@@ -55,7 +77,7 @@ int run_conv_bwd_weight(bool do_verification,
std::cout << "wei: " << wei_host_result.mDesc << std::endl; std::cout << "wei: " << wei_host_result.mDesc << std::endl;
std::cout << "out: " << out.mDesc << std::endl; std::cout << "out: " << out.mDesc << std::endl;
switch(init_method) switch(config.init_method)
{ {
case 0: break; case 0: break;
case 1: case 1:
...@@ -77,36 +99,55 @@ int run_conv_bwd_weight(bool do_verification, ...@@ -77,36 +99,55 @@ int run_conv_bwd_weight(bool do_verification,
// init to 0 // init to 0
wei_device_buf.SetZero(); wei_device_buf.SetZero();
std::array<ck::index_t, NDimSpatial> input_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{};
std::array<ck::index_t, NDimSpatial> output_spatial_lengths{};
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 range_copy = [](const auto& from, auto to) { std::copy(begin(from), end(from), to); };
range_copy(conv_param.input_spatial_lengths_, begin(input_spatial_lengths));
range_copy(conv_param.filter_spatial_lengths_, begin(filter_spatial_lengths));
range_copy(conv_param.output_spatial_lengths_, begin(output_spatial_lengths));
range_copy(conv_param.conv_filter_strides_, begin(conv_filter_strides));
range_copy(conv_param.conv_filter_dilations_, begin(conv_filter_dilations));
range_copy(conv_param.input_left_pads_, begin(input_left_pads));
range_copy(conv_param.input_right_pads_, begin(input_right_pads));
// do GEMM // do GEMM
auto conv = DeviceConvBwdWeightInstance{}; auto conv = DeviceConvBwdWeightInstance<NDimSpatial>{};
auto invoker = conv.MakeInvoker(); auto invoker = conv.MakeInvoker();
auto argument = conv.MakeArgument(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()), auto argument = conv.MakeArgument(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()), static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()), static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
conv_param.G_,
conv_param.N_, conv_param.N_,
conv_param.K_, conv_param.K_,
conv_param.C_, conv_param.C_,
conv_param.input_spatial_lengths_, input_spatial_lengths,
conv_param.filter_spatial_lengths_, filter_spatial_lengths,
conv_param.output_spatial_lengths_, output_spatial_lengths,
conv_param.conv_filter_strides_, conv_filter_strides,
conv_param.conv_filter_dilations_, conv_filter_dilations,
conv_param.input_left_pads_, input_left_pads,
conv_param.input_right_pads_, input_right_pads,
in_element_op, InElementOp{},
wei_element_op, WeiElementOp{},
out_element_op, OutElementOp{},
split_k); split_k);
if(!conv.IsSupportedArgument(argument)) if(!conv.IsSupportedArgument(argument))
{ {
std::cout << "wrong! device_conv with the specified compilation parameters does " std::cerr << "wrong! device_conv with the specified compilation parameters does "
"not support this Conv problem" "not support this Conv problem"
<< std::endl; << std::endl;
return 1; return false;
} }
float avg_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel}); float avg_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = conv_param.GetFlops(); std::size_t flop = conv_param.GetFlops();
std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>(); std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>();
...@@ -115,21 +156,14 @@ int run_conv_bwd_weight(bool do_verification, ...@@ -115,21 +156,14 @@ int run_conv_bwd_weight(bool do_verification,
float gb_per_sec = num_btype / 1.E6 / 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" std::cerr << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
<< conv.GetTypeString() << std::endl; << std::endl
<< "DeviceOp: " << conv.GetTypeString() << std::endl;
if(do_verification) if(config.do_verification)
{ {
auto ref_conv = ck::tensor_operation::host::ReferenceConvBwdWeight<NDimSpatial, auto ref_conv = HostConvBwdWeightInstance<NDimSpatial>{};
InDataType, auto ref_invoker = ref_conv.MakeInvoker();
WeiDataType,
OutDataType,
InElementOp,
WeiElementOp,
OutElementOp>{};
auto ref_invoker = ref_conv.MakeInvoker();
auto ref_argument = ref_conv.MakeArgument(in, auto ref_argument = ref_conv.MakeArgument(in,
wei_host_result, wei_host_result,
out, out,
...@@ -145,8 +179,28 @@ int run_conv_bwd_weight(bool do_verification, ...@@ -145,8 +179,28 @@ int run_conv_bwd_weight(bool do_verification,
wei_device_buf.FromDevice(wei_device_result.mData.data()); wei_device_buf.FromDevice(wei_device_result.mData.data());
return ck::utils::check_err(wei_device_result.mData, wei_host_result.mData) ? 0 : 1; return ck::utils::check_err(wei_device_result.mData, wei_host_result.mData);
}
return true;
}
bool run_grouped_conv_bwd_weight_example(int argc, char* argv[])
{
ExecutionConfig config;
ck::utils::conv::ConvParam conv_param = DefaultConvParam;
if(!parse_cmd_args(argc, argv, config, conv_param))
{
return false;
}
switch(conv_param.num_dim_spatial_)
{
case 1: return run_grouped_conv_bwd_weight<1>(config, conv_param);
case 2: return run_grouped_conv_bwd_weight<2>(config, conv_param);
case 3: return run_grouped_conv_bwd_weight<3>(config, conv_param);
} }
return 0; return false;
} }
example/21_gemm_layernorm/gemm_bias_relu_add_layernorm_xdl_fp16.cpp
View file @ 16f02f76
...@@ -16,6 +16,7 @@ ...@@ -16,6 +16,7 @@
#include "ck/library/utility/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/utility/check_err.hpp" #include "ck/library/utility/check_err.hpp"
...@@ -108,21 +109,20 @@ using DeviceNormalizeInstance = ck::tensor_operation::device::DeviceElementwise< ...@@ -108,21 +109,20 @@ using DeviceNormalizeInstance = ck::tensor_operation::device::DeviceElementwise<
ck::Sequence<8>>; // scalarPerVector: y(layerNorm_out) ck::Sequence<8>>; // scalarPerVector: y(layerNorm_out)
auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) { auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
return HostTensorDescriptor(std::vector<std::size_t>({len}), return HostTensorDescriptor({len}, {stride});
std::vector<std::size_t>({stride}));
}; };
auto f_host_tensor_descriptor2d = auto f_host_tensor_descriptor2d =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) { [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value) if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{ {
return HostTensorDescriptor(std::vector<std::size_t>({row, col}), return HostTensorDescriptor({row, col}, {stride, 1_uz});
std::vector<std::size_t>({stride, 1}));
} }
else else
{ {
return HostTensorDescriptor(std::vector<std::size_t>({row, col}), return HostTensorDescriptor({row, col}, {1_uz, stride});
std::vector<std::size_t>({1, stride}));
} }
}; };
...@@ -372,8 +372,8 @@ int main() ...@@ -372,8 +372,8 @@ int main()
N); N);
layerNorm_device_buf.FromDevice(layerNorm_m_n.mData.data()); layerNorm_device_buf.FromDevice(layerNorm_m_n.mData.data());
pass &= ck::utils::check_err(layerNorm_m_n.mData, pass &= ck::utils::check_err(layerNorm_m_n,
host_layerNorm_m_n.mData, host_layerNorm_m_n,
"Error: Incorrect results layerNorm_m_n", "Error: Incorrect results layerNorm_m_n",
1e-2, 1e-2,
1e-2); 1e-2);
......
example/21_gemm_layernorm/gemm_layernorm_xdl_fp16.cpp
View file @ 16f02f76
...@@ -16,6 +16,7 @@ ...@@ -16,6 +16,7 @@
#include "ck/library/utility/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/utility/check_err.hpp" #include "ck/library/utility/check_err.hpp"
...@@ -107,21 +108,20 @@ using DeviceNormalizeInstance = ck::tensor_operation::device::DeviceElementwise< ...@@ -107,21 +108,20 @@ using DeviceNormalizeInstance = ck::tensor_operation::device::DeviceElementwise<
ck::Sequence<8>>; // scalarPerVector: y(layerNorm_out) ck::Sequence<8>>; // scalarPerVector: y(layerNorm_out)
auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) { auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
return HostTensorDescriptor(std::vector<std::size_t>({len}), return HostTensorDescriptor({len}, {stride});
std::vector<std::size_t>({stride}));
}; };
auto f_host_tensor_descriptor2d = auto f_host_tensor_descriptor2d =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) { [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value) if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{ {
return HostTensorDescriptor(std::vector<std::size_t>({row, col}), return HostTensorDescriptor({row, col}, {stride, 1_uz});
std::vector<std::size_t>({stride, 1}));
} }
else else
{ {
return HostTensorDescriptor(std::vector<std::size_t>({row, col}), return HostTensorDescriptor({row, col}, {1_uz, stride});
std::vector<std::size_t>({1, stride}));
} }
}; };
...@@ -346,11 +346,8 @@ int main() ...@@ -346,11 +346,8 @@ int main()
N); N);
layerNorm_device_buf.FromDevice(layerNorm_m_n.mData.data()); layerNorm_device_buf.FromDevice(layerNorm_m_n.mData.data());
pass &= ck::utils::check_err(layerNorm_m_n.mData, pass &= ck::utils::check_err(
host_layerNorm_m_n.mData, layerNorm_m_n, host_layerNorm_m_n, "Error: Incorrect results d1", 1e-3, 1e-3);
"Error: Incorrect results d1",
1e-3,
1e-3);
} }
{ {
......
example/21_gemm_layernorm/gemm_xdl_layernorm_single_kernel_fp16.cpp
View file @ 16f02f76
...@@ -10,6 +10,7 @@ ...@@ -10,6 +10,7 @@
#include "ck/library/utility/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_layernorm_cshuffle.hpp" #include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_layernorm_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
...@@ -132,15 +133,15 @@ int main(int argc, char* argv[]) ...@@ -132,15 +133,15 @@ int main(int argc, char* argv[])
auto f_host_tensor_descriptor = auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) { [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value) if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{ {
return HostTensorDescriptor(std::vector<std::size_t>({row, col}), return HostTensorDescriptor({row, col}, {stride, 1_uz});
std::vector<std::size_t>({stride, 1}));
} }
else else
{ {
return HostTensorDescriptor(std::vector<std::size_t>({row, col}), return HostTensorDescriptor({row, col}, {1_uz, stride});
std::vector<std::size_t>({1, stride}));
} }
}; };
...@@ -149,10 +150,10 @@ int main(int argc, char* argv[]) ...@@ -149,10 +150,10 @@ int main(int argc, char* argv[])
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{})); Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{})); Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<AccDataType> acc_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{})); Tensor<AccDataType> acc_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<C0DataType> c0_n_bias(HostTensorDescriptor(std::vector<size_t>({size_t(N)}))); Tensor<C0DataType> c0_n_bias({N});
Tensor<C0DataType> c0_m_n_add(f_host_tensor_descriptor(M, N, StrideC, CLayout{})); Tensor<C0DataType> c0_m_n_add(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
Tensor<C0DataType> c0_n_gamma(HostTensorDescriptor(std::vector<size_t>({size_t(N)}))); Tensor<C0DataType> c0_n_gamma({N});
Tensor<C0DataType> c0_n_beta(HostTensorDescriptor(std::vector<size_t>({size_t(N)}))); Tensor<C0DataType> c0_n_beta({N});
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl; std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl; std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
...@@ -274,15 +275,12 @@ int main(int argc, char* argv[]) ...@@ -274,15 +275,12 @@ int main(int argc, char* argv[])
if constexpr(std::is_same<CShuffleDataType, F32>::value) if constexpr(std::is_same<CShuffleDataType, F32>::value)
{ {
pass &= ck::utils::check_err( pass &= ck::utils::check_err(
c_m_n_device_result.mData, c_m_n_host_result.mData, "Error: Incorrect results c"); c_m_n_device_result, c_m_n_host_result, "Error: Incorrect results c");
} }
else if constexpr(std::is_same<CShuffleDataType, F16>::value) else if constexpr(std::is_same<CShuffleDataType, F16>::value)
{ {
pass &= ck::utils::check_err(c_m_n_device_result.mData, pass &= ck::utils::check_err(
c_m_n_host_result.mData, c_m_n_device_result, c_m_n_host_result, "Error: Incorrect results c", 1e-2, 1e-2);
"Error: Incorrect results c",
1e-2,
1e-2);
} }
} }
return pass ? 0 : 1; return pass ? 0 : 1;
......
example/22_cgemm/cgemm_xdl_common.hpp
View file @ 16f02f76
...@@ -11,6 +11,7 @@ ...@@ -11,6 +11,7 @@
#include "ck/library/utility/device_memory.hpp" #include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
template <ck::index_t... Is> template <ck::index_t... Is>
...@@ -62,15 +63,15 @@ bool run_cgemm_xdl(ck::index_t M, ...@@ -62,15 +63,15 @@ bool run_cgemm_xdl(ck::index_t M,
auto f_host_tensor_descriptor = auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) { [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value) if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{ {
return HostTensorDescriptor(std::vector<std::size_t>({row, col}), return HostTensorDescriptor({row, col}, {stride, 1_uz});
std::vector<std::size_t>({stride, 1}));
} }
else else
{ {
return HostTensorDescriptor(std::vector<std::size_t>({row, col}), return HostTensorDescriptor({row, col}, {1_uz, stride});
std::vector<std::size_t>({1, stride}));
} }
}; };
...@@ -219,14 +220,14 @@ bool run_cgemm_xdl(ck::index_t M, ...@@ -219,14 +220,14 @@ bool run_cgemm_xdl(ck::index_t M,
const Tensor<CDataType> c_m_n_real_device_result_converted(c_m_n_real_device_result); const Tensor<CDataType> c_m_n_real_device_result_converted(c_m_n_real_device_result);
const Tensor<CDataType> c_m_n_imag_device_result_converted(c_m_n_imag_device_result); const Tensor<CDataType> c_m_n_imag_device_result_converted(c_m_n_imag_device_result);
result = ck::utils::check_err(c_m_n_real_device_result_converted.mData, result = ck::utils::check_err(c_m_n_real_device_result_converted,
c_m_n_real_host_result.mData, c_m_n_real_host_result,
"Verification error: incorrect results in real part!", "Verification error: incorrect results in real part!",
1e-2f, 1e-2f,
1e-1f); 1e-1f);
result = result && ck::utils::check_err( result = result && ck::utils::check_err(
c_m_n_imag_device_result_converted.mData, c_m_n_imag_device_result_converted,
c_m_n_imag_host_result.mData, c_m_n_imag_host_result,
"Verification error: incorrect results in imaginary part!", "Verification error: incorrect results in imaginary part!",
1e-2f, 1e-2f,
1e-1f); 1e-1f);
...@@ -234,14 +235,14 @@ bool run_cgemm_xdl(ck::index_t M, ...@@ -234,14 +235,14 @@ bool run_cgemm_xdl(ck::index_t M,
else else
#endif // CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4 #endif // CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
{ {
result = ck::utils::check_err(c_m_n_real_device_result.mData, result = ck::utils::check_err(c_m_n_real_device_result,
c_m_n_real_host_result.mData, c_m_n_real_host_result,
"Verification error: incorrect results in real part!", "Verification error: incorrect results in real part!",
1e-2f, 1e-2f,
1e-1f); 1e-1f);
result = result && ck::utils::check_err( result = result && ck::utils::check_err(
c_m_n_imag_device_result.mData, c_m_n_imag_device_result,
c_m_n_imag_host_result.mData, c_m_n_imag_host_result,
"Verification error: incorrect results in imaginary part!", "Verification error: incorrect results in imaginary part!",
1e-2f, 1e-2f,
1e-1f); 1e-1f);
......
example/23_softmax/softmax_blockwise.cpp
View file @ 16f02f76
...@@ -246,7 +246,7 @@ int main(int argc, char* argv[]) ...@@ -246,7 +246,7 @@ int main(int argc, char* argv[])
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false}); invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
out_dev.FromDevice(out.mData.data()); out_dev.FromDevice(out.mData.data());
// LogRangeAsType<float>(std::cout << "tensor out: " , out.mData, ",") << std::endl; // LogRangeAsType<float>(std::cout << "tensor out: " , out.mData, ",") << std::endl;
pass = pass && ck::utils::check_err(out.mData, out_ref.mData); pass = pass && ck::utils::check_err(out, out_ref);
}; };
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, args.time_kernel}); float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, args.time_kernel});
......
example/24_batched_gemm/run_batched_gemm_example.inc
View file @ 16f02f76
...@@ -55,15 +55,15 @@ bool run_batched_gemm(const ProblemSize& problem_size, const ExecutionConfig& co ...@@ -55,15 +55,15 @@ bool run_batched_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
std::size_t stride, std::size_t stride,
std::size_t batch_stride, std::size_t batch_stride,
auto layout) { auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value) if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{ {
return HostTensorDescriptor(std::vector<std::size_t>({batch_count_, row, col}), return HostTensorDescriptor({batch_count_, row, col}, {batch_stride, stride, 1_uz});
std::vector<std::size_t>({batch_stride, stride, 1}));
} }
else else
{ {
return HostTensorDescriptor(std::vector<std::size_t>({batch_count_, row, col}), return HostTensorDescriptor({batch_count_, row, col}, {batch_stride, 1_uz, stride});
std::vector<std::size_t>({batch_stride, 1, stride}));
} }
}; };
...@@ -174,11 +174,11 @@ bool run_batched_gemm(const ProblemSize& problem_size, const ExecutionConfig& co ...@@ -174,11 +174,11 @@ bool run_batched_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
#ifdef BUILD_INT4_EXAMPLE #ifdef BUILD_INT4_EXAMPLE
const Tensor<EDataType> e_device_result_converted(e_g_m_n_device_result); const Tensor<EDataType> e_device_result_converted(e_g_m_n_device_result);
pass &= ck::utils::check_err(e_device_result_converted.mData, e_g_m_n_host_result.mData); pass &= ck::utils::check_err(e_device_result_converted, e_g_m_n_host_result);
#else #else
pass = ck::utils::check_err( pass = ck::utils::check_err(
e_g_m_n_device_result.mData, e_g_m_n_host_result.mData, "Error: Incorrect results c"); e_g_m_n_device_result, e_g_m_n_host_result, "Error: Incorrect results c");
#endif #endif
} }
......
example/25_gemm_bias_e_permute/gemm_bias_e_permute_g1m2n3k1_xdl_fp16.cpp
View file @ 16f02f76
...@@ -246,21 +246,11 @@ int main(int argc, char* argv[]) ...@@ -246,21 +246,11 @@ int main(int argc, char* argv[])
exit(0); exit(0);
} }
Tensor<ADataType> a_gs_ms_ks( Tensor<ADataType> a_gs_ms_ks(a_gs_ms_ks_lengths, a_gs_ms_ks_strides);
std::vector<std::size_t>(a_gs_ms_ks_lengths.begin(), a_gs_ms_ks_lengths.end()), Tensor<BDataType> b_gs_ns_ks(b_gs_ns_ks_lengths, b_gs_ns_ks_strides);
std::vector<std::size_t>(a_gs_ms_ks_strides.begin(), a_gs_ms_ks_strides.end())); Tensor<DDataType> d_gs_ms_ns(d_gs_ms_ns_lengths, d_gs_ms_ns_strides);
Tensor<BDataType> b_gs_ns_ks( Tensor<EDataType> e_gs_ms_ns_host_result(e_gs_ms_ns_lengths, e_gs_ms_ns_strides);
std::vector<std::size_t>(b_gs_ns_ks_lengths.begin(), b_gs_ns_ks_lengths.end()), Tensor<EDataType> e_gs_ms_ns_device_result(e_gs_ms_ns_lengths, e_gs_ms_ns_strides);
std::vector<std::size_t>(b_gs_ns_ks_strides.begin(), b_gs_ns_ks_strides.end()));
Tensor<DDataType> d_gs_ms_ns(
std::vector<std::size_t>(d_gs_ms_ns_lengths.begin(), d_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(d_gs_ms_ns_strides.begin(), d_gs_ms_ns_strides.end()));
Tensor<EDataType> e_gs_ms_ns_host_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
Tensor<EDataType> e_gs_ms_ns_device_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl; std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl;
std::cout << "b_gs_ns_ks: " << b_gs_ns_ks.mDesc << std::endl; std::cout << "b_gs_ns_ks: " << b_gs_ns_ks.mDesc << std::endl;
...@@ -357,9 +347,7 @@ int main(int argc, char* argv[]) ...@@ -357,9 +347,7 @@ int main(int argc, char* argv[])
if(do_verification) if(do_verification)
{ {
Tensor<CShuffleDataType> c_gs_ms_ns_host_result( Tensor<CShuffleDataType> c_gs_ms_ns_host_result(e_gs_ms_ns_lengths, e_gs_ms_ns_strides);
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
using ReferenceOpInstance = ReferenceContraction_G1_M2_N3_K1<NumDimM, using ReferenceOpInstance = ReferenceContraction_G1_M2_N3_K1<NumDimM,
NumDimN, NumDimN,
...@@ -407,9 +395,7 @@ int main(int argc, char* argv[]) ...@@ -407,9 +395,7 @@ int main(int argc, char* argv[])
} }
} }
return ck::utils::check_err(e_gs_ms_ns_device_result.mData, e_gs_ms_ns_host_result.mData) return ck::utils::check_err(e_gs_ms_ns_device_result, e_gs_ms_ns_host_result) ? 0 : 1;
? 0
: 1;
} }
return 0; return 0;
......
example/25_gemm_bias_e_permute/gemm_bias_e_permute_g1m3n2k1_xdl_fp16.cpp
View file @ 16f02f76
...@@ -246,21 +246,11 @@ int main(int argc, char* argv[]) ...@@ -246,21 +246,11 @@ int main(int argc, char* argv[])
exit(0); exit(0);
} }
Tensor<ADataType> a_gs_ms_ks( Tensor<ADataType> a_gs_ms_ks(a_gs_ms_ks_lengths, a_gs_ms_ks_strides);
std::vector<std::size_t>(a_gs_ms_ks_lengths.begin(), a_gs_ms_ks_lengths.end()), Tensor<BDataType> b_gs_ns_ks(b_gs_ns_ks_lengths, b_gs_ns_ks_strides);
std::vector<std::size_t>(a_gs_ms_ks_strides.begin(), a_gs_ms_ks_strides.end())); Tensor<DDataType> d_gs_ms_ns(d_gs_ms_ns_lengths, d_gs_ms_ns_strides);
Tensor<BDataType> b_gs_ns_ks( Tensor<EDataType> e_gs_ms_ns_host_result(e_gs_ms_ns_lengths, e_gs_ms_ns_strides);
std::vector<std::size_t>(b_gs_ns_ks_lengths.begin(), b_gs_ns_ks_lengths.end()), Tensor<EDataType> e_gs_ms_ns_device_result(e_gs_ms_ns_lengths, e_gs_ms_ns_strides);
std::vector<std::size_t>(b_gs_ns_ks_strides.begin(), b_gs_ns_ks_strides.end()));
Tensor<DDataType> d_gs_ms_ns(
std::vector<std::size_t>(d_gs_ms_ns_lengths.begin(), d_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(d_gs_ms_ns_strides.begin(), d_gs_ms_ns_strides.end()));
Tensor<EDataType> e_gs_ms_ns_host_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
Tensor<EDataType> e_gs_ms_ns_device_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl; std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl;
std::cout << "b_gs_ns_ks: " << b_gs_ns_ks.mDesc << std::endl; std::cout << "b_gs_ns_ks: " << b_gs_ns_ks.mDesc << std::endl;
...@@ -357,9 +347,7 @@ int main(int argc, char* argv[]) ...@@ -357,9 +347,7 @@ int main(int argc, char* argv[])
if(do_verification) if(do_verification)
{ {
Tensor<CShuffleDataType> c_gs_ms_ns_host_result( Tensor<CShuffleDataType> c_gs_ms_ns_host_result(e_gs_ms_ns_lengths, e_gs_ms_ns_strides);
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
using ReferenceOpInstance = ReferenceContraction_G1_M3_N2_K1<NumDimG, using ReferenceOpInstance = ReferenceContraction_G1_M3_N2_K1<NumDimG,
NumDimM, NumDimM,
...@@ -408,9 +396,7 @@ int main(int argc, char* argv[]) ...@@ -408,9 +396,7 @@ int main(int argc, char* argv[])
} }
} }
return ck::utils::check_err(e_gs_ms_ns_device_result.mData, e_gs_ms_ns_host_result.mData) return ck::utils::check_err(e_gs_ms_ns_device_result, e_gs_ms_ns_host_result) ? 0 : 1;
? 0
: 1;
} }
return 0; return 0;
......
example/26_contraction/contraction_bilinear_xdl_fp32.cpp
View file @ 16f02f76
...@@ -288,21 +288,11 @@ int main(int argc, char* argv[]) ...@@ -288,21 +288,11 @@ int main(int argc, char* argv[])
exit(0); exit(0);
} }
Tensor<ADataType> a_ms_ks( Tensor<ADataType> a_ms_ks(a_ms_ks_lengths, a_ms_ks_strides);
std::vector<std::size_t>(a_ms_ks_lengths.begin(), a_ms_ks_lengths.end()), Tensor<BDataType> b_ns_ks(b_ns_ks_lengths, b_ns_ks_strides);
std::vector<std::size_t>(a_ms_ks_strides.begin(), a_ms_ks_strides.end())); Tensor<EDataType> d_ms_ns(d_ms_ns_lengths, d_ms_ns_strides);
Tensor<BDataType> b_ns_ks( Tensor<EDataType> e_ms_ns_host_result(e_ms_ns_lengths, e_ms_ns_strides);
std::vector<std::size_t>(b_ns_ks_lengths.begin(), b_ns_ks_lengths.end()), Tensor<EDataType> e_ms_ns_device_result(e_ms_ns_lengths, e_ms_ns_strides);
std::vector<std::size_t>(b_ns_ks_strides.begin(), b_ns_ks_strides.end()));
Tensor<EDataType> d_ms_ns(
std::vector<std::size_t>(d_ms_ns_lengths.begin(), d_ms_ns_lengths.end()),
std::vector<std::size_t>(d_ms_ns_strides.begin(), d_ms_ns_strides.end()));
Tensor<EDataType> e_ms_ns_host_result(
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
Tensor<EDataType> e_ms_ns_device_result(
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
std::cout << "a_ms_ks: " << a_ms_ks.mDesc << std::endl; std::cout << "a_ms_ks: " << a_ms_ks.mDesc << std::endl;
std::cout << "b_ns_ks: " << b_ns_ks.mDesc << std::endl; std::cout << "b_ns_ks: " << b_ns_ks.mDesc << std::endl;
...@@ -398,9 +388,7 @@ int main(int argc, char* argv[]) ...@@ -398,9 +388,7 @@ int main(int argc, char* argv[])
if(do_verification) if(do_verification)
{ {
Tensor<CShuffleDataType> c_ms_ns_host_result( Tensor<CShuffleDataType> c_ms_ns_host_result(e_ms_ns_lengths, e_ms_ns_strides);
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
using ReferenceOpInstance = ReferenceContraction_M2_N2_K2<NumDimM, using ReferenceOpInstance = ReferenceContraction_M2_N2_K2<NumDimM,
NumDimN, NumDimN,
...@@ -437,7 +425,7 @@ int main(int argc, char* argv[]) ...@@ -437,7 +425,7 @@ int main(int argc, char* argv[])
} }
} }
return ck::utils::check_err(e_ms_ns_device_result.mData, e_ms_ns_host_result.mData) ? 0 : 1; return ck::utils::check_err(e_ms_ns_device_result, e_ms_ns_host_result) ? 0 : 1;
} }
return 0; return 0;
......
example/26_contraction/contraction_scale_xdl_fp32.cpp
View file @ 16f02f76
...@@ -277,18 +277,10 @@ int main(int argc, char* argv[]) ...@@ -277,18 +277,10 @@ int main(int argc, char* argv[])
exit(0); exit(0);
} }
Tensor<ADataType> a_ms_ks( Tensor<ADataType> a_ms_ks(a_ms_ks_lengths, a_ms_ks_strides);
std::vector<std::size_t>(a_ms_ks_lengths.begin(), a_ms_ks_lengths.end()), Tensor<BDataType> b_ns_ks(b_ns_ks_lengths, b_ns_ks_strides);
std::vector<std::size_t>(a_ms_ks_strides.begin(), a_ms_ks_strides.end())); Tensor<EDataType> e_ms_ns_host_result(e_ms_ns_lengths, e_ms_ns_strides);
Tensor<BDataType> b_ns_ks( Tensor<EDataType> e_ms_ns_device_result(e_ms_ns_lengths, e_ms_ns_strides);
std::vector<std::size_t>(b_ns_ks_lengths.begin(), b_ns_ks_lengths.end()),
std::vector<std::size_t>(b_ns_ks_strides.begin(), b_ns_ks_strides.end()));
Tensor<EDataType> e_ms_ns_host_result(
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
Tensor<EDataType> e_ms_ns_device_result(
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
std::cout << "a_ms_ks: " << a_ms_ks.mDesc << std::endl; std::cout << "a_ms_ks: " << a_ms_ks.mDesc << std::endl;
std::cout << "b_ns_ks: " << b_ns_ks.mDesc << std::endl; std::cout << "b_ns_ks: " << b_ns_ks.mDesc << std::endl;
...@@ -379,9 +371,7 @@ int main(int argc, char* argv[]) ...@@ -379,9 +371,7 @@ int main(int argc, char* argv[])
if(do_verification) if(do_verification)
{ {
Tensor<CShuffleDataType> c_ms_ns_host_result( Tensor<CShuffleDataType> c_ms_ns_host_result(e_ms_ns_lengths, e_ms_ns_strides);
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
using ReferenceOpInstance = ReferenceContraction_M2_N2_K2<NumDimM, using ReferenceOpInstance = ReferenceContraction_M2_N2_K2<NumDimM,
NumDimN, NumDimN,
...@@ -417,7 +407,7 @@ int main(int argc, char* argv[]) ...@@ -417,7 +407,7 @@ int main(int argc, char* argv[])
} }
} }
return ck::utils::check_err(e_ms_ns_device_result.mData, e_ms_ns_host_result.mData) ? 0 : 1; return ck::utils::check_err(e_ms_ns_device_result, e_ms_ns_host_result) ? 0 : 1;
} }
return 0; return 0;
......
example/27_layernorm/layernorm_blockwise.cpp
View file @ 16f02f76
...@@ -17,6 +17,7 @@ ...@@ -17,6 +17,7 @@
#include "ck/library/utility/host_common_util.hpp" #include "ck/library/utility/host_common_util.hpp"
#include "ck/library/utility/host_tensor.hpp" #include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp" #include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_layernorm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_layernorm.hpp"
using XDataType = ck::half_t; using XDataType = ck::half_t;
...@@ -60,13 +61,13 @@ int main() ...@@ -60,13 +61,13 @@ int main()
ck::index_t Stride = N; ck::index_t Stride = N;
auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) { auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
return HostTensorDescriptor(std::vector<std::size_t>({len}), return HostTensorDescriptor({len}, {stride});
std::vector<std::size_t>({stride}));
}; };
auto f_host_tensor_descriptor2d = [](std::size_t row, std::size_t col, std::size_t stride) { auto f_host_tensor_descriptor2d = [](std::size_t row, std::size_t col, std::size_t stride) {
return HostTensorDescriptor(std::vector<std::size_t>({row, col}), using namespace ck::literals;
std::vector<std::size_t>({stride, 1}));
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}; };
Tensor<XDataType> x(f_host_tensor_descriptor2d(M, N, Stride)); Tensor<XDataType> x(f_host_tensor_descriptor2d(M, N, Stride));
...@@ -132,8 +133,7 @@ int main() ...@@ -132,8 +133,7 @@ int main()
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
y_dev.FromDevice(y.mData.data()); y_dev.FromDevice(y.mData.data());
pass &= pass &= ck::utils::check_err(y, host_y, "Error: Incorrect results d1", 1e-3, 1e-3);
ck::utils::check_err(y.mData, host_y.mData, "Error: Incorrect results d1", 1e-3, 1e-3);
} }
return (pass ? 0 : 1); return (pass ? 0 : 1);
} }
example/28_grouped_gemm_bias_e_permute/grouped_gemm_bias_e_permute_xdl_fp16.cpp
View file @ 16f02f76
...@@ -297,18 +297,10 @@ int main(int argc, char* argv[]) ...@@ -297,18 +297,10 @@ int main(int argc, char* argv[])
const auto e_ms_ns_lengths = contraction_descs[i].e_ms_ns_lengths; const auto e_ms_ns_lengths = contraction_descs[i].e_ms_ns_lengths;
const auto e_ms_ns_strides = contraction_descs[i].e_ms_ns_strides; const auto e_ms_ns_strides = contraction_descs[i].e_ms_ns_strides;
Tensor<ADataType> a_ms_ks( Tensor<ADataType> a_ms_ks(a_ms_ks_lengths, a_ms_ks_strides);
std::vector<std::size_t>(a_ms_ks_lengths.begin(), a_ms_ks_lengths.end()), Tensor<BDataType> b_ns_ks(b_ns_ks_lengths, b_ns_ks_strides);
std::vector<std::size_t>(a_ms_ks_strides.begin(), a_ms_ks_strides.end())); Tensor<DDataType> d_ms_ns(d_ms_ns_lengths, d_ms_ns_strides);
Tensor<BDataType> b_ns_ks( Tensor<EDataType> e_ms_ns_device_result(e_ms_ns_lengths, e_ms_ns_strides);
std::vector<std::size_t>(b_ns_ks_lengths.begin(), b_ns_ks_lengths.end()),
std::vector<std::size_t>(b_ns_ks_strides.begin(), b_ns_ks_strides.end()));
Tensor<DDataType> d_ms_ns(
std::vector<std::size_t>(d_ms_ns_lengths.begin(), d_ms_ns_lengths.end()),
std::vector<std::size_t>(d_ms_ns_strides.begin(), d_ms_ns_strides.end()));
Tensor<EDataType> e_ms_ns_device_result(
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
ck::index_t M_ = std::accumulate(e_ms_ns_lengths.begin(), ck::index_t M_ = std::accumulate(e_ms_ns_lengths.begin(),
e_ms_ns_lengths.begin() + NumDimM, e_ms_ns_lengths.begin() + NumDimM,
...@@ -423,13 +415,9 @@ int main(int argc, char* argv[]) ...@@ -423,13 +415,9 @@ int main(int argc, char* argv[])
const auto e_ms_ns_lengths = contraction_descs[i].e_ms_ns_lengths; const auto e_ms_ns_lengths = contraction_descs[i].e_ms_ns_lengths;
const auto e_ms_ns_strides = contraction_descs[i].e_ms_ns_strides; const auto e_ms_ns_strides = contraction_descs[i].e_ms_ns_strides;
Tensor<EDataType> c_ms_ns_host_result( Tensor<EDataType> c_ms_ns_host_result(e_ms_ns_lengths, e_ms_ns_strides);
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
Tensor<EDataType> e_ms_ns_host_result( Tensor<EDataType> e_ms_ns_host_result(e_ms_ns_lengths, e_ms_ns_strides);
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
e_tensors_device[i]->FromDevice(e_device_tensors[i].mData.data()); e_tensors_device[i]->FromDevice(e_device_tensors[i].mData.data());
...@@ -475,7 +463,7 @@ int main(int argc, char* argv[]) ...@@ -475,7 +463,7 @@ int main(int argc, char* argv[])
} }
} }
pass &= ck::utils::check_err(e_device_tensors[i].mData, e_ms_ns_host_result.mData); pass &= ck::utils::check_err(e_device_tensors[i], e_ms_ns_host_result);
} }
} }
......
example/29_batched_gemm_bias_e_permute/batched_gemm_bias_e_permute_xdl_fp16.cpp
View file @ 16f02f76
...@@ -246,21 +246,11 @@ int main(int argc, char* argv[]) ...@@ -246,21 +246,11 @@ int main(int argc, char* argv[])
exit(0); exit(0);
} }
Tensor<ADataType> a_gs_ms_ks( Tensor<ADataType> a_gs_ms_ks(a_gs_ms_ks_lengths, a_gs_ms_ks_strides);
std::vector<std::size_t>(a_gs_ms_ks_lengths.begin(), a_gs_ms_ks_lengths.end()), Tensor<BDataType> b_gs_ns_ks(b_gs_ns_ks_lengths, b_gs_ns_ks_strides);
std::vector<std::size_t>(a_gs_ms_ks_strides.begin(), a_gs_ms_ks_strides.end())); Tensor<DDataType> d_gs_ms_ns(d_gs_ms_ns_lengths, d_gs_ms_ns_strides);
Tensor<BDataType> b_gs_ns_ks( Tensor<EDataType> e_gs_ms_ns_host_result(e_gs_ms_ns_lengths, e_gs_ms_ns_strides);
std::vector<std::size_t>(b_gs_ns_ks_lengths.begin(), b_gs_ns_ks_lengths.end()), Tensor<EDataType> e_gs_ms_ns_device_result(e_gs_ms_ns_lengths, e_gs_ms_ns_strides);
std::vector<std::size_t>(b_gs_ns_ks_strides.begin(), b_gs_ns_ks_strides.end()));
Tensor<DDataType> d_gs_ms_ns(
std::vector<std::size_t>(d_gs_ms_ns_lengths.begin(), d_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(d_gs_ms_ns_strides.begin(), d_gs_ms_ns_strides.end()));
Tensor<EDataType> e_gs_ms_ns_host_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
Tensor<EDataType> e_gs_ms_ns_device_result(
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl; std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl;
std::cout << "b_gs_ns_ks: " << b_gs_ns_ks.mDesc << std::endl; std::cout << "b_gs_ns_ks: " << b_gs_ns_ks.mDesc << std::endl;
...@@ -362,9 +352,7 @@ int main(int argc, char* argv[]) ...@@ -362,9 +352,7 @@ int main(int argc, char* argv[])
if(do_verification) if(do_verification)
{ {
Tensor<CShuffleDataType> c_ms_ns_host_result( Tensor<CShuffleDataType> c_ms_ns_host_result(e_gs_ms_ns_lengths, e_gs_ms_ns_strides);
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
using ReferenceOpInstance = ReferenceContraction_G2_M2_N2_K1<NumDimG, using ReferenceOpInstance = ReferenceContraction_G2_M2_N2_K1<NumDimG,
NumDimM, NumDimM,
...@@ -409,9 +397,7 @@ int main(int argc, char* argv[]) ...@@ -409,9 +397,7 @@ int main(int argc, char* argv[])
} }
} }
return ck::utils::check_err(e_gs_ms_ns_device_result.mData, e_gs_ms_ns_host_result.mData) return ck::utils::check_err(e_gs_ms_ns_device_result, e_gs_ms_ns_host_result) ? 0 : 1;
? 0
: 1;
} }
return 0; return 0;
......
example/30_grouped_conv_fwd_multiple_d/CMakeLists.txt 0 → 100644
View file @ 16f02f76
add_custom_target(example_grouped_conv_fwd_multiple_d)
add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_fp16 grouped_conv_fwd_bias_relu_add_xdl_fp16.cpp)
add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_fp32 grouped_conv_fwd_bias_relu_add_xdl_fp32.cpp)
add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_bf16 grouped_conv_fwd_bias_relu_add_xdl_bf16.cpp)
add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_int8 grouped_conv_fwd_bias_relu_add_xdl_int8.cpp)
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_fp16)
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_fp32)
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_bf16)
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_int8)
if(USE_BITINT_EXTENSION_INT4)
add_example_executable(example_grouped_conv_fwd_bias_relu_add_xdl_int4 grouped_conv_fwd_bias_relu_add_xdl_int4.cpp)
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_bias_relu_add_xdl_int4)
endif() # USE_BITINT_EXTENSION_INT4
add_example_executable(example_grouped_conv_fwd_xdl_fp16 grouped_conv_fwd_xdl_fp16.cpp)
add_dependencies(example_grouped_conv_fwd_multiple_d example_grouped_conv_fwd_xdl_fp16)
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