Skip to content

GitLab

"test/convnd_fwd/convnd_fwd.cpp" did not exist on "85978e0201bb94bf6e59b325e1f5f19266845d08"
Unverified Commit cd167e49 authored by Chao Liu's avatar Chao Liu Committed by GitHub
Browse files

Compile for gfx908 and gfx90a (#130) 

* adding compilation for multiple targets

* fix build

* clean

* update Jekinsfile

* update readme

* update Jenkins

* use ck::half_t instead of ushort for bf16

* rename enum classes

* clean

* rename

* clean
parent ecf337ba
Hide whitespace changes
Inline Side-by-side
Showing with 155 additions and 109 deletions
library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_splitk_f32_f32_f32_km_kn_mn_instance.cpp
View file @ cd167e49
......@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// Compilation parameters for a[k, m] * b[k, n] = c[m, n]
using device_gemm_xdl_splitk_f32_f32_f32_km_kn_mn_instances = std::tuple<
......
library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instance.cpp
View file @ cd167e49
......@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// Compilation parameters for a[k, m] * b[n, k] = c[m, n]
using device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instances = std::tuple<
......
library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_splitk_f32_f32_f32_mk_kn_mn_instance.cpp
View file @ cd167e49
......@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmMNPadding = ck::tensor_operation::device::GemmSpecialization_t::MNPadding;
static constexpr auto GemmMNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
// Compilation parameters for a[m, k] * b[k, n] = c[m, n]
using device_gemm_xdl_splitk_f32_f32_f32_mk_kn_mn_instances = std::tuple<
......
library/src/tensor_operation_instance/gpu/gemm/device_gemm_xdl_splitk_f32_f32_f32_mk_nk_mn_instance.cpp
View file @ cd167e49
......@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// Compilation parameters for a[m, k] * b[n, k] = c[m, n]
using device_gemm_xdl_splitk_f32_f32_f32_mk_nk_mn_instances = std::tuple<
......
library/src/tensor_operation_instance/gpu/gemm_reduce/device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_kn_mn_instance.cpp
View file @ cd167e49
......@@ -23,7 +23,7 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ReduceSum = ck::tensor_operation::element_wise::ReduceSum;
using ReduceSquareSum = ck::tensor_operation::element_wise::ReduceSquareSum;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// c[m, n] = a[k, m] * b[k, n]
// d0[m] = reduce0(c[m, n])
......
library/src/tensor_operation_instance/gpu/gemm_reduce/device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_km_nk_mn_instance.cpp
View file @ cd167e49
......@@ -23,7 +23,7 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ReduceSum = ck::tensor_operation::element_wise::ReduceSum;
using ReduceSquareSum = ck::tensor_operation::element_wise::ReduceSquareSum;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// c[m, n] = a[k, m] * b[n, k]
// d0[m] = reduce0(c[m, n])
......
library/src/tensor_operation_instance/gpu/gemm_reduce/device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_kn_mn_instance.cpp
View file @ cd167e49
......@@ -23,7 +23,7 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ReduceSum = ck::tensor_operation::element_wise::ReduceSum;
using ReduceSquareSum = ck::tensor_operation::element_wise::ReduceSquareSum;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// c[m, n] = a[m, k] * b[n, k]
// d0[m] = reduce0(c[m, n])
......
library/src/tensor_operation_instance/gpu/gemm_reduce/device_gemm_reduce_xdl_cshuffle_f16_f16_f16_f32_f32_mk_nk_mn_instance.cpp
View file @ cd167e49
......@@ -23,7 +23,7 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ReduceSum = ck::tensor_operation::element_wise::ReduceSum;
using ReduceSquareSum = ck::tensor_operation::element_wise::ReduceSquareSum;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// c[m, n] = a[m, k] * b[n, k]
// d0[m] = reduce0(c[m, n])
......
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instance.cpp
View file @ cd167e49
......@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// Compilation parameters for a[k, m] * b[k, n] = c[m, n]
using device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances = std::tuple<
......
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instance.cpp
View file @ cd167e49
......@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// Compilation parameters for a[k, m] * b[n, k] = c[m, n]
using device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances = std::tuple<
......
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instance.cpp
View file @ cd167e49
......@@ -20,7 +20,7 @@ using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// Compilation parameters for a[m, k] * b[k, n] = c[m, n]
using device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances = std::tuple<
......
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instance.cpp
View file @ cd167e49
......@@ -20,8 +20,8 @@ using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization_t::Default;
static constexpr auto GemmMNPadding = ck::tensor_operation::device::GemmSpecialization_t::MNPadding;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr auto GemmMNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
// Compilation parameters for a[m, k] * b[n, k] = c[m, n]
using device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances = std::tuple<
......
profiler/src/README.md → profiler/README.md
View file @ cd167e49
## Docker script
```bash
docker run \
-it \
--rm \
--privileged \
--group-add sudo \
-w /root/workspace \
-v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace \
rocm/tensorflow:rocm4.3.1-tf2.6-dev \
/bin/bash
```
## Build ```ckProfiler```
```bash
mkdir build && cd build
```
```bash
# Need to Specify target ID, example below is gfx908
cmake \
-D BUILD_DEV=OFF \
-D CMAKE_BUILD_TYPE=Release \
-D CMAKE_CXX_FLAGS="-DCK_AMD_GPU_GFX908 --amdgpu-target=gfx908 -O3 " \
-D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc \
-D CMAKE_PREFIX_PATH=/opt/rocm \
..
```
```bash
make -j ckProfiler
```
## Profile GEMM kernels
```bash
#arg1: tensor operation (gemm=GEMM)
......@@ -42,8 +9,8 @@ cmake \
#arg7: run kernel # of times (>1)
#arg8 to 13: M, N, K, StrideA, StrideB, StrideC
##################### op datatype layout verify init log repeat M___ N___ K___ StrideA StrideB StrideC
./profiler/ckProfiler gemm 1 1 1 1 0 5 3840 4096 4096 4096 4096 4096
################ op datatype layout verify init log repeat M___ N___ K___ StrideA StrideB StrideC
./bin/ckProfiler gemm 1 1 1 1 0 5 3840 4096 4096 4096 4096 4096
```
Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
......@@ -55,7 +22,7 @@ c_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
Best Perf: 1.1933 ms, 107.977 TFlops, 79.0848 GB/s
```
## Profile forward convolution kernels
## Profile 2d forward convolution kernels
```bash
#arg1: tensor operation (conv=Convolution)
#arg2: data type (0=fp32, 1=fp16)
......@@ -67,8 +34,8 @@ Best Perf: 1.1933 ms, 107.977 TFlops, 79.0848 GB/s
#arg8: print matrix value (0=no, 1=yes)
#arg9: run kernel # of times (>1)
#arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx
##################### op datatype in_layout wei_layout out_layout verify init log repeat N__ K___ C___ Y X Hi__ Wi__ Strides Dilations LeftPads RightPads
./profiler/ckProfiler conv_fwd 1 1 1 1 1 1 0 5 128 256 192 3 3 71 71 2 2 1 1 1 1 1 1
################ op datatype in_layout wei_layout out_layout verify init log repeat N__ K___ C___ Y X Hi__ Wi__ Strides Dilations LeftPads RightPads
./bin/ckProfiler conv2d_fwd 1 1 1 1 1 1 0 5 128 256 192 3 3 71 71 2 2 1 1 1 1 1 1
```
Result (MI100 @ 1087Mhz, 133.5TFlops peak FP16)
......
profiler/include/profile_convnd_bwd_data_impl.hpp
View file @ cd167e49
......@@ -12,7 +12,7 @@
using F16 = ck::half_t;
using F32 = float;
using BF16 = ushort;
using BF16 = ck::bhalf_t;
using INT8 = int8_t;
namespace ck {
namespace tensor_operation {
......
profiler/include/profile_reduce_impl.hpp
View file @ cd167e49
......@@ -64,9 +64,9 @@ template <typename DescriptionType>
bool description_match(const DescriptionType& description,
int Rank,
const std::vector<int>& reduceDims,
ReduceTensorOp_t ReduceOpId,
NanPropagation_t NanOpt,
ReduceTensorIndices_t IndicesOpt)
ReduceTensorOp ReduceOpId,
NanPropagation NanOpt,
ReduceTensorIndices IndicesOpt)
{
if(description.Rank_ != Rank || description.ReduceOpId_ != static_cast<int>(ReduceOpId) ||
description.NanOpt_ != static_cast<int>(NanOpt) ||
......@@ -148,9 +148,9 @@ template <typename InDataType,
typename OutDataType,
int Rank,
int NumReduceDim,
ReduceTensorOp_t ReduceOpId,
NanPropagation_t NanOpt,
ReduceTensorIndices_t IndicesOpt>
ReduceTensorOp ReduceOpId,
NanPropagation NanOpt,
ReduceTensorIndices IndicesOpt>
void profile_reduce_impl_impl(bool do_verification,
int init_method,
bool do_log,
......@@ -166,17 +166,17 @@ void profile_reduce_impl_impl(bool do_verification,
using namespace ck::host_reduce;
constexpr bool op_support_indices =
(ReduceOpId == ReduceTensorOp_t::MIN || ReduceOpId == ReduceTensorOp_t::MAX ||
ReduceOpId == ReduceTensorOp_t::AMAX);
(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
ReduceOpId == ReduceTensorOp::AMAX);
constexpr bool NeedIndices =
(op_support_indices && (IndicesOpt != ReduceTensorIndices_t::NO_INDICES));
(op_support_indices && (IndicesOpt != ReduceTensorIndices::NO_INDICES));
constexpr bool PropagateNan = (NanOpt == NanPropagation_t::PROPAGATE_NAN);
constexpr bool PropagateNan = (NanOpt == NanPropagation::PROPAGATE_NAN);
constexpr bool out_support_atomic_add = std::is_same<OutDataType, float>::value;
constexpr bool op_support_atomic_add =
!op_support_indices && ReduceOpId != ReduceTensorOp_t::NORM2;
!op_support_indices && ReduceOpId != ReduceTensorOp::NORM2;
constexpr bool use_atomic_add = (out_support_atomic_add && op_support_atomic_add);
// 1) If InDataType is half_t, must use half_t as AccDataType for indexable reduction operations
......@@ -194,7 +194,7 @@ void profile_reduce_impl_impl(bool do_verification,
// 1) The indices can only be used when the reduction operation is indexable
constexpr bool invalid_reduce_3 =
(!op_support_indices && IndicesOpt != ReduceTensorIndices_t::NO_INDICES);
(!op_support_indices && IndicesOpt != ReduceTensorIndices::NO_INDICES);
// 1) If InDataType is int8_t, must use int8_t as AccDataType for indexable reduction operations
// 2) If InDataType is int8_t, must use int32_t as AccDataType for non-indexable reduction
......@@ -207,8 +207,8 @@ void profile_reduce_impl_impl(bool do_verification,
// 1) If InDataType is int8_t, the supported operation must be either indexable operations or
// ADD/AVG
constexpr bool invalid_reduce_5 = std::is_same<InDataType, int8_t>::value &&
(!op_support_indices && ReduceOpId != ReduceTensorOp_t::ADD &&
ReduceOpId != ReduceTensorOp_t::AVG);
(!op_support_indices && ReduceOpId != ReduceTensorOp::ADD &&
ReduceOpId != ReduceTensorOp::AVG);
// 1) If InDataType is bhalf_t, must use float as AccDataType for all reduction operations
constexpr bool invalid_reduce_6 =
......@@ -631,9 +631,9 @@ void profile_reduce_impl(bool do_verification,
int nrepeat,
const std::vector<size_t>& inLengths,
const std::vector<int>& reduceDims,
ReduceTensorOp_t ReduceOpId,
NanPropagation_t NanOpt,
ReduceTensorIndices_t IndicesOpt,
ReduceTensorOp ReduceOpId,
NanPropagation NanOpt,
ReduceTensorIndices IndicesOpt,
float alpha,
float beta)
{
......@@ -659,9 +659,9 @@ void profile_reduce_impl(bool do_verification,
OutDataType,
descType::Rank_,
descType::NumReduceDim_,
static_cast<ReduceTensorOp_t>(descType::ReduceOpId_),
static_cast<NanPropagation_t>(descType::NanOpt_),
static_cast<ReduceTensorIndices_t>(descType::IndicesOpt_)>(
static_cast<ReduceTensorOp>(descType::ReduceOpId_),
static_cast<NanPropagation>(descType::NanOpt_),
static_cast<ReduceTensorIndices>(descType::IndicesOpt_)>(
do_verification,
init_method,
do_log,
......
profiler/src/profile_batched_gemm_reduce.cpp
View file @ cd167e49
......@@ -9,7 +9,7 @@
int profile_batched_gemm_reduce(int argc, char* argv[])
{
enum struct GemmMatrixLayout_t
enum struct GemmMatrixLayout
{
MK_KN_MN, // 0
MK_NK_MN, // 1
......@@ -17,7 +17,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
KM_NK_MN, // 3
};
enum struct GemmReduceDataType_t
enum struct GemmReduceDataType
{
F32_F32_F32_F32_F32, // 0
F16_F16_F16_F32_F32, // 1
......@@ -40,8 +40,8 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
exit(1);
}
const auto data_type = static_cast<GemmReduceDataType_t>(std::stoi(argv[2]));
const auto layout = static_cast<GemmMatrixLayout_t>(std::stoi(argv[3]));
const auto data_type = static_cast<GemmReduceDataType>(std::stoi(argv[2]));
const auto layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[4]);
const int init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]);
......@@ -57,8 +57,7 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
const int BatchCount = std::stoi(argv[14]);
if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout_t::MK_KN_MN)
if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 && layout == GemmMatrixLayout::MK_KN_MN)
{
ck::profiler::profile_batched_gemm_reduce_impl<ck::half_t,
ck::half_t,
......@@ -79,8 +78,8 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
(StrideC < 0) ? N : StrideC,
BatchCount);
}
else if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout_t::MK_NK_MN)
else if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout::MK_NK_MN)
{
ck::profiler::profile_batched_gemm_reduce_impl<ck::half_t,
ck::half_t,
......@@ -101,8 +100,8 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
(StrideC < 0) ? N : StrideC,
BatchCount);
}
else if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout_t::KM_KN_MN)
else if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout::KM_KN_MN)
{
ck::profiler::profile_batched_gemm_reduce_impl<ck::half_t,
ck::half_t,
......@@ -123,8 +122,8 @@ int profile_batched_gemm_reduce(int argc, char* argv[])
(StrideC < 0) ? N : StrideC,
BatchCount);
}
else if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout_t::KM_NK_MN)
else if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout::KM_NK_MN)
{
ck::profiler::profile_batched_gemm_reduce_impl<ck::half_t,
ck::half_t,
......
profiler/src/profile_convnd_bwd_data.cpp
View file @ cd167e49
......@@ -7,7 +7,7 @@
#include "profile_convnd_bwd_data_impl.hpp"
enum ConvDataType
enum struct ConvDataType
{
F32_F32_F32, // 0
F16_F16_F16, // 1
......@@ -15,19 +15,19 @@ enum ConvDataType
INT8_INT8_INT8, // 3
};
enum ConvInputLayout
enum struct ConvInputLayout
{
NCHW, // 0
NHWC, // 1
};
enum ConvWeightLayout
enum struct ConvWeightLayout
{
KCYX, // 0
KYXC, // 1
};
enum ConvOutputLayout
enum struct ConvOutputLayout
{
NKHW, // 0
NHWK, // 1
......@@ -97,10 +97,10 @@ int profile_convnd_bwd_data(int argc, char* argv[], int num_dim_spatial)
return 1;
}
const int data_type = static_cast<ConvDataType>(std::stoi(argv[2]));
const int in_layout = static_cast<ConvInputLayout>(std::stoi(argv[3]));
const int wei_layout = static_cast<ConvWeightLayout>(std::stoi(argv[4]));
const int out_layout = static_cast<ConvOutputLayout>(std::stoi(argv[5]));
const auto data_type = static_cast<ConvDataType>(std::stoi(argv[2]));
const auto in_layout = static_cast<ConvInputLayout>(std::stoi(argv[3]));
const auto wei_layout = static_cast<ConvWeightLayout>(std::stoi(argv[4]));
const auto out_layout = static_cast<ConvOutputLayout>(std::stoi(argv[5]));
const bool do_verification = std::stoi(argv[6]);
const int init_method = std::stoi(argv[7]);
const bool do_log = std::stoi(argv[8]);
......
profiler/src/profile_gemm_reduce.cpp
View file @ cd167e49
......@@ -8,7 +8,7 @@
int profile_gemm_reduce(int argc, char* argv[])
{
enum struct GemmMatrixLayout_t
enum struct GemmMatrixLayout
{
MK_KN_MN, // 0
MK_NK_MN, // 1
......@@ -16,7 +16,7 @@ int profile_gemm_reduce(int argc, char* argv[])
KM_NK_MN, // 3
};
enum struct GemmReduceDataType_t
enum struct GemmReduceDataType
{
F32_F32_F32_F32_F32, // 0
F16_F16_F16_F32_F32, // 1
......@@ -39,8 +39,8 @@ int profile_gemm_reduce(int argc, char* argv[])
exit(1);
}
const auto data_type = static_cast<GemmReduceDataType_t>(std::stoi(argv[2]));
const auto layout = static_cast<GemmMatrixLayout_t>(std::stoi(argv[3]));
const auto data_type = static_cast<GemmReduceDataType>(std::stoi(argv[2]));
const auto layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[4]);
const int init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]);
......@@ -54,8 +54,7 @@ int profile_gemm_reduce(int argc, char* argv[])
const int StrideB = std::stoi(argv[12]);
const int StrideC = std::stoi(argv[13]);
if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout_t::MK_KN_MN)
if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 && layout == GemmMatrixLayout::MK_KN_MN)
{
ck::profiler::profile_gemm_reduce_impl<ck::half_t,
ck::half_t,
......@@ -75,8 +74,8 @@ int profile_gemm_reduce(int argc, char* argv[])
(StrideB < 0) ? N : StrideB,
(StrideC < 0) ? N : StrideC);
}
else if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout_t::MK_NK_MN)
else if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout::MK_NK_MN)
{
ck::profiler::profile_gemm_reduce_impl<ck::half_t,
ck::half_t,
......@@ -96,8 +95,8 @@ int profile_gemm_reduce(int argc, char* argv[])
(StrideB < 0) ? K : StrideB,
(StrideC < 0) ? N : StrideC);
}
else if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout_t::KM_KN_MN)
else if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout::KM_KN_MN)
{
ck::profiler::profile_gemm_reduce_impl<ck::half_t,
ck::half_t,
......@@ -117,8 +116,8 @@ int profile_gemm_reduce(int argc, char* argv[])
(StrideB < 0) ? N : StrideB,
(StrideC < 0) ? N : StrideC);
}
else if(data_type == GemmReduceDataType_t::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout_t::KM_NK_MN)
else if(data_type == GemmReduceDataType::F16_F16_F16_F32_F32 &&
layout == GemmMatrixLayout::KM_NK_MN)
{
ck::profiler::profile_gemm_reduce_impl<ck::half_t,
ck::half_t,
......
profiler/src/profile_grouped_gemm.cpp
View file @ cd167e49
......@@ -6,7 +6,7 @@
#include <half.hpp>
#include "profile_grouped_gemm_impl.hpp"
enum GemmMatrixLayout
enum struct GemmMatrixLayout
{
MK_KN_MN, // 0
MK_NK_MN, // 1
......@@ -18,7 +18,7 @@ enum GemmMatrixLayout
KM_NK_NM, // 7
};
enum GemmDataType
enum struct GemmDataType
{
F32_F32_F32, // 0
F16_F16_F16, // 1
......@@ -61,8 +61,8 @@ int profile_grouped_gemm(int argc, char* argv[])
exit(1);
}
const int data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
const int layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
const auto data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
const auto layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[4]);
const int init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]);
......
profiler/src/profile_reduce.cpp
View file @ cd167e49
......@@ -20,9 +20,9 @@
using namespace std;
using ck::NanPropagation_t;
using ck::ReduceTensorIndices_t;
using ck::ReduceTensorOp_t;
using ck::NanPropagation;
using ck::ReduceTensorIndices;
using ck::ReduceTensorOp;
static struct option long_options[] = {{"inLengths", required_argument, nullptr, 'D'},
{"reduceDims", required_argument, nullptr, 'R'},
......@@ -84,7 +84,7 @@ static std::vector<T> getTypeValuesFromString(const char* cstr_values)
return (values);
}
enum struct appDataType_t
enum struct AppDataType
{
appHalf = 0,
appFloat = 1,
......@@ -130,18 +130,18 @@ class AppArgs
std::vector<float> scales;
ReduceTensorOp_t reduceOp = ReduceTensorOp_t::ADD;
appDataType_t compTypeId = appDataType_t::appFloat;
appDataType_t outTypeId = appDataType_t::appFloat;
ReduceTensorOp reduceOp = ReduceTensorOp::ADD;
AppDataType compTypeId = AppDataType::appFloat;
AppDataType outTypeId = AppDataType::appFloat;
bool compType_assigned = false;
bool outType_assigned = false;
NanPropagation_t nanOpt = NanPropagation_t::NOT_PROPAGATE_NAN;
ReduceTensorIndices_t indicesOpt = ReduceTensorIndices_t::NO_INDICES;
bool do_log = false;
bool do_verification = false;
bool do_dumpout = false;
NanPropagation nanOpt = NanPropagation::NOT_PROPAGATE_NAN;
ReduceTensorIndices indicesOpt = ReduceTensorIndices::NO_INDICES;
bool do_log = false;
bool do_verification = false;
bool do_dumpout = false;
int init_method;
int nrepeat;
......@@ -213,33 +213,33 @@ class AppArgs
if(!optarg)
throw std::runtime_error("Invalid option format!");
reduceOp = static_cast<ReduceTensorOp_t>(std::atoi(optarg));
reduceOp = static_cast<ReduceTensorOp>(std::atoi(optarg));
break;
case 'C':
if(!optarg)
throw std::runtime_error("Invalid option format!");
compTypeId = static_cast<appDataType_t>(std::atoi(optarg));
compTypeId = static_cast<AppDataType>(std::atoi(optarg));
compType_assigned = true;
break;
case 'W':
if(!optarg)
throw std::runtime_error("Invalid option format!");
outTypeId = static_cast<appDataType_t>(std::atoi(optarg));
outTypeId = static_cast<AppDataType>(std::atoi(optarg));
outType_assigned = true;
break;
case 'N':
if(!optarg)
throw std::runtime_error("Invalid option format!");
nanOpt = static_cast<NanPropagation_t>(std::atoi(optarg));
nanOpt = static_cast<NanPropagation>(std::atoi(optarg));
break;
case 'I':
if(!optarg)
throw std::runtime_error("Invalid option format!");
indicesOpt = static_cast<ReduceTensorIndices_t>(std::atoi(optarg));
indicesOpt = static_cast<ReduceTensorIndices>(std::atoi(optarg));
break;
case 'S':
if(!optarg)
......@@ -303,10 +303,10 @@ class AppArgs
scales.push_back(0.0f);
};
if(reduceOp == ReduceTensorOp_t::MIN || reduceOp == ReduceTensorOp_t::MAX ||
reduceOp == ReduceTensorOp_t::AMAX)
if(reduceOp == ReduceTensorOp::MIN || reduceOp == ReduceTensorOp::MAX ||
reduceOp == ReduceTensorOp::AMAX)
{
if(indicesOpt != ReduceTensorIndices_t::NO_INDICES)
if(indicesOpt != ReduceTensorIndices::NO_INDICES)
need_indices = true;
// for indexable operations, no need to assign compType and outType, just let them be
......@@ -333,22 +333,22 @@ int profile_reduce(int argc, char* argv[])
check_reduce_dims(rank, args.reduceDims);
if(args.reduceOp == ReduceTensorOp_t::MUL || args.reduceOp == ReduceTensorOp_t::NORM1)
if(args.reduceOp == ReduceTensorOp::MUL || args.reduceOp == ReduceTensorOp::NORM1)
throw std::runtime_error("MUL and NORM1 are not supported by composable kernel!");
if(args.use_half)
{
if(!args.compType_assigned)
args.compTypeId = appDataType_t::appHalf;
args.compTypeId = AppDataType::appHalf;
if(args.outType_assigned &&
(args.outTypeId != appDataType_t::appHalf && args.outTypeId != appDataType_t::appFloat))
args.outTypeId = appDataType_t::appFloat;
(args.outTypeId != AppDataType::appHalf && args.outTypeId != AppDataType::appFloat))
args.outTypeId = AppDataType::appFloat;
if(!args.outType_assigned)
args.outTypeId = appDataType_t::appHalf;
args.outTypeId = AppDataType::appHalf;
if(args.compTypeId == appDataType_t::appHalf)
if(args.compTypeId == AppDataType::appHalf)
{
profile_reduce_impl<ck::half_t, ck::half_t, ck::half_t>(args.do_verification,
args.init_method,
......@@ -363,7 +363,7 @@ int profile_reduce(int argc, char* argv[])
args.scales[0],
args.scales[1]);
}
else if(args.compTypeId == appDataType_t::appFloat)
else if(args.compTypeId == AppDataType::appFloat)
{
profile_reduce_impl<ck::half_t, float, ck::half_t>(args.do_verification,
args.init_method,
......@@ -399,16 +399,16 @@ int profile_reduce(int argc, char* argv[])
else if(args.use_int8)
{
if(!args.compType_assigned)
args.compTypeId = appDataType_t::appInt8;
args.compTypeId = AppDataType::appInt8;
if(args.outType_assigned &&
(args.outTypeId != appDataType_t::appInt8 && args.outTypeId != appDataType_t::appInt32))
args.outTypeId = appDataType_t::appInt32;
(args.outTypeId != AppDataType::appInt8 && args.outTypeId != AppDataType::appInt32))
args.outTypeId = AppDataType::appInt32;
if(!args.outType_assigned)
args.outTypeId = appDataType_t::appInt8;
args.outTypeId = AppDataType::appInt8;
if(args.compTypeId == appDataType_t::appInt8)
if(args.compTypeId == AppDataType::appInt8)
{
profile_reduce_impl<int8_t, int8_t, int8_t>(args.do_verification,
args.init_method,
......@@ -423,7 +423,7 @@ int profile_reduce(int argc, char* argv[])
args.scales[0],
args.scales[1]);
}
else if(args.compTypeId == appDataType_t::appInt32)
else if(args.compTypeId == AppDataType::appInt32)
{
profile_reduce_impl<int8_t, int32_t, int8_t>(args.do_verification,
args.init_method,
......@@ -443,12 +443,12 @@ int profile_reduce(int argc, char* argv[])
}
else if(args.use_bf16)
{
if(args.outType_assigned && (args.outTypeId != appDataType_t::appBFloat16 &&
args.outTypeId != appDataType_t::appFloat))
args.outTypeId = appDataType_t::appFloat;
if(args.outType_assigned &&
(args.outTypeId != AppDataType::appBFloat16 && args.outTypeId != AppDataType::appFloat))
args.outTypeId = AppDataType::appFloat;
if(!args.outType_assigned)
args.outTypeId = appDataType_t::appBFloat16;
args.outTypeId = AppDataType::appBFloat16;
profile_reduce_impl<ck::bhalf_t, float, ck::bhalf_t>(args.do_verification,
args.init_method,
......@@ -465,7 +465,7 @@ int profile_reduce(int argc, char* argv[])
}
else
{
if(args.compTypeId == appDataType_t::appFloat)
if(args.compTypeId == AppDataType::appFloat)
{
profile_reduce_impl<float, float, float>(args.do_verification,
args.init_method,
......@@ -480,7 +480,7 @@ int profile_reduce(int argc, char* argv[])
args.scales[0],
args.scales[1]);
}
else if(args.compTypeId == appDataType_t::appDouble)
else if(args.compTypeId == AppDataType::appDouble)
{
profile_reduce_impl<float, double, float>(args.do_verification,
args.init_method,
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment