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Commit 3552041a authored by danyao12's avatar danyao12
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Merge branch 'develop' into ck_tile/fa_bwd_opt

parents e8927110 733f33af
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example/01_gemm/gemm_xdl_fp8_v3.cpp
View file @ 3552041a
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp" #include "common.hpp"
...@@ -28,14 +28,14 @@ using DeviceGemmV2Instance = ...@@ -28,14 +28,14 @@ using DeviceGemmV2Instance =
ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,
PassThrough, PassThrough, PassThrough, GemmDefault, PassThrough, PassThrough, PassThrough, GemmDefault,
256, 256,
128, 256, 224, 256,
128, 16, 16, 128, 16, 16,
16, 16, 16, 16,
4, 8, 7, 8,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 16, 16, 1, 2, 16, 16, 0,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 16, 16, 1, 2, 16, 16, 0,
1, 2, S<1, 32, 1, 8>, 8, 1, 2, S<1, 32, 1, 8>, 8,
ck::BlockGemmPipelineScheduler::Intrawave,ck::BlockGemmPipelineVersion::v3, ck::f8_t>; ck::BlockGemmPipelineScheduler::Intrawave,ck::BlockGemmPipelineVersion::v3, ck::f8_t>;
// clang-format on // clang-format on
......
example/01_gemm/run_gemm_example_streamk_v2.inc 0 → 100644
View file @ 3552041a
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
template <typename DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 1e-1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 1.5e-1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 16.1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 8192.1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{
#if defined(BUILD_INT4_EXAMPLE) && defined(CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4)
static_assert(sizeof(ck::int4_t) == sizeof(int8_t));
#endif
using namespace ck::literals;
auto M = problem_size.M;
auto N = problem_size.N;
auto K = problem_size.K;
auto StrideA = problem_size.StrideA;
auto StrideB = problem_size.StrideB;
auto StrideC = problem_size.StrideC;
auto Grid_size = problem_size.Grid_size;
auto Streamk_sel = problem_size.Streamk_sel;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
auto f_get_default_stride =
[](std::size_t row, std::size_t col, ck::index_t stride, auto layout) {
if(stride == -1)
{
// give a chance if stride is -1, return a default packed stride
if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
{
return static_cast<std::size_t>(col);
}
else
{
return static_cast<std::size_t>(row);
}
}
else
return static_cast<std::size_t>(stride);
};
auto f_get_default_streamk_policy = [](ck::index_t streamk_sel) {
if(streamk_sel == -1)
{
return static_cast<std::size_t>(4);
}
else
return static_cast<std::size_t>(streamk_sel);
};
StrideA = f_get_default_stride(M, K, StrideA, ALayout{});
StrideB = f_get_default_stride(K, N, StrideB, BLayout{});
StrideC = f_get_default_stride(M, N, StrideC, CLayout{});
Streamk_sel = f_get_default_streamk_policy(Streamk_sel);
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
switch(config.init_method)
{
case 0:
a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
break;
case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
break;
case 2:
a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
break;
case 3:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
}
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{}));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
#ifdef BUILD_INT4_EXAMPLE
DeviceMem a_m_k_device_buf(sizeof(KernelADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(KernelBDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(KernelCDataType) *
c_m_n_device_result.mDesc.GetElementSpaceSize());
const Tensor<KernelADataType> a_m_k_converted(a_m_k);
const Tensor<KernelBDataType> b_k_n_converted(b_k_n);
a_m_k_device_buf.ToDevice(a_m_k_converted.mData.data());
b_k_n_device_buf.ToDevice(b_k_n_converted.mData.data());
#else
DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_m_k_device_buf.ToDevice(a_m_k.mData.data());
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
#endif
DeviceMem workspace;
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto c_element_op = CElementOp{};
// do GEMM
auto gemm = DeviceGemmV2_Streamk_Instance{};
auto invoker = gemm.MakeInvoker();
float ave_time = 0;
auto argument = gemm.MakeArgument(
#ifdef BUILD_INT4_EXAMPLE
static_cast<KernelADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<KernelBDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<KernelCDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
#else
static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
#endif
M,
N,
K,
StrideA,
StrideB,
StrideC,
Streamk_sel,
Grid_size,
a_element_op,
b_element_op,
c_element_op);
if(!gemm.IsSupportedArgument(argument))
{
std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
return true;
}
bool pass = true;
if(config.do_verification)
{
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, PassThrough{}, PassThrough{}, PassThrough{});
ref_invoker.Run(ref_argument);
ave_time = invoker.Run(argument, StreamConfig{nullptr, false, 1});
#ifdef BUILD_INT4_EXAMPLE
Tensor<CDataType> c_m_n_device_result_converted(c_m_n_host_result.mDesc);
c_m_n_device_buf.FromDevice(c_m_n_device_result_converted.mData.data());
c_m_n_device_result = c_m_n_device_result_converted.CopyAsType<CDataType>();
return ck::utils::check_err(c_m_n_device_result_converted, c_m_n_host_result);
#else
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
pass &= ck::utils::check_err(c_m_n_device_result,
c_m_n_host_result,
"Error: Incorrect results!",
get_rtol<CDataType>(),
get_atol<CDataType>());
#endif
}
if(config.time_kernel)
{
ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
std::size_t flop = 2_uz * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * 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;
}
return pass;
}
bool run_gemm_universal_streamk_example(int argc, char* argv[])
{
ProblemSizeStreamK_universal problem_size;
ExecutionConfig config;
return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm(problem_size, config);
}
example/02_gemm_bilinear/gemm_bilinear_wmma_fp16.cpp
View file @ 3552041a
...@@ -17,6 +17,7 @@ ...@@ -17,6 +17,7 @@
#include "ck/library/utility/literals.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"
#include "ck/host_utility/device_prop.hpp"
struct AlphaBetaAdd struct AlphaBetaAdd
{ {
...@@ -175,6 +176,14 @@ int main(int argc, char* argv[]) ...@@ -175,6 +176,14 @@ int main(int argc, char* argv[])
exit(0); exit(0);
} }
bool is_supported = ck::is_gfx11_supported();
if(!is_supported)
{
std::cout << "WARNING: wmma example not supported on the platform " << ck::get_device_name()
<< std::endl;
return 0;
}
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; using namespace ck::literals;
......
example/02_gemm_bilinear/gemm_bilinear_wmma_int8.cpp
View file @ 3552041a
...@@ -17,6 +17,7 @@ ...@@ -17,6 +17,7 @@
#include "ck/library/utility/literals.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"
#include "ck/host_utility/device_prop.hpp"
struct AlphaBetaAdd struct AlphaBetaAdd
{ {
...@@ -175,6 +176,14 @@ int main(int argc, char* argv[]) ...@@ -175,6 +176,14 @@ int main(int argc, char* argv[])
exit(0); exit(0);
} }
bool is_supported = ck::is_gfx11_supported();
if(!is_supported)
{
std::cout << "WARNING: wmma example not supported on the platform " << ck::get_device_name()
<< std::endl;
return 0;
}
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; using namespace ck::literals;
......
example/12_reduce/CMakeLists.txt
View file @ 3552041a
add_example_executable(example_reduce_blockwise reduce_blockwise.cpp) add_example_executable(example_reduce_blockwise reduce_blockwise.cpp)
add_example_executable(example_reduce_threadwise_multi_d reduce_threadwise_multi_d.cpp)
add_example_executable(example_reduce_multiblock_atomic_add reduce_multiblock_atomic_add.cpp) add_example_executable(example_reduce_multiblock_atomic_add reduce_multiblock_atomic_add.cpp)
add_example_executable(example_reduce_blockwise_two_call reduce_blockwise_two_call.cpp) add_example_executable(example_reduce_blockwise_two_call reduce_blockwise_two_call.cpp)
example/12_reduce/reduce_threadwise_multi_d.cpp 0 → 100644
View file @ 3552041a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <initializer_list>
#include <cstdlib>
#include <getopt.h>
#include "ck/utility/reduction_enums.hpp"
#include "reduce_threadwise_multi_d_impl.hpp"
#include "reduce_example_common.hpp"
using namespace ck;
using namespace ck::tensor_operation::device;
static struct option long_options[] = {{"inLengths", required_argument, nullptr, 'D'},
{"verify", required_argument, nullptr, 'v'},
{"help", no_argument, nullptr, '?'},
{nullptr, 0, nullptr, 0}};
class SimpleAppArgs
{
private:
int option_index = 0;
public:
std::vector<size_t> inLengths = {16, 64, 32, 16};
std::vector<int> reduceDims = {0};
std::vector<float> scales = {1.0f, 0.0f};
bool do_verification = true;
int data_type = 1;
int init_method = 2;
bool time_kernel = true;
public:
void show_usage(const char* cmd)
{
std::cout << "Usage of " << cmd << std::endl;
std::cout << "--inLengths or -D, comma separated list of input tensor dimension lengths"
<< std::endl;
std::cout << "--reduceDims or -R, comma separated list of to-reduce dimensions"
<< std::endl;
std::cout << "--verify or -v, 1/0 to indicate whether to verify the reduction result by "
"comparing with the host-based reduction"
<< std::endl;
std::cout << "Arg1: data type (0: fp16, 1: fp32, 3: int8, 5: bp16, 6: fp64, 7: int4)"
<< std::endl;
std::cout << "Arg2 -- init method (0=no init, 1=single integer value, 2=scope integer "
"value, 3=decimal value)"
<< std::endl;
std::cout << "Arg3 -- time kernel (0=no, 1=yes)" << std::endl;
};
int processArgs(int argc, char* argv[])
{
using ck::host_common::getTypeValuesFromString;
int ch;
while(1)
{
ch = getopt_long(argc, argv, "D:R:v:l:", long_options, &option_index);
if(ch == -1)
break;
switch(ch)
{
case 'D':
if(!optarg)
throw std::runtime_error("Invalid option format!");
inLengths = getTypeValuesFromString<size_t>(optarg);
break;
case 'R':
if(!optarg)
throw std::runtime_error("Invalid option format!");
reduceDims = getTypeValuesFromString<int>(optarg);
break;
case 'v':
if(!optarg)
throw std::runtime_error("Invalid option format!");
do_verification = static_cast<bool>(std::atoi(optarg));
break;
case '?':
if(std::string(long_options[option_index].name) == "help")
{
show_usage(argv[0]);
return (-1);
};
break;
default: show_usage(argv[0]); return (-1);
};
};
if(optind + 3 > argc)
{
throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
};
data_type = std::atoi(argv[optind++]);
init_method = std::atoi(argv[optind++]);
time_kernel = static_cast<bool>(std::atoi(argv[optind]));
if(scales.empty())
{
scales.push_back(1.0f);
scales.push_back(0.0f);
};
return (0);
};
};
template <typename InOutDataType,
typename AccDataType,
ReduceTensorOp ReduceOpId,
index_t PropagateNan,
index_t OutputIndex>
bool reduce_threadwise_multi_d_test(bool do_verification,
int init_method,
bool time_kernel,
const std::vector<size_t>& inLengths,
const std::vector<int>& reduceDims,
float alpha,
float beta)
{
bool matched = false;
int result = 0;
const auto tuple_object = reduce_shape_instances{};
static_for<0, std::tuple_size<reduce_shape_instances>::value, 1>{}([&](auto i) {
if(matched)
return;
using ShapeType = remove_cvref_t<decltype(std::get<i>(tuple_object))>;
if(ShapeType::Rank_ != inLengths.size() || ShapeType::NumReduceDim_ != reduceDims.size())
return;
std::array<int, ShapeType::NumReduceDim_> arrReduceDims;
ck::ranges::copy(reduceDims, arrReduceDims.begin());
result = reduce_threadwise_multi_d_impl<InOutDataType,
AccDataType,
ReduceOpId,
ShapeType::Rank_,
ShapeType::NumReduceDim_,
PropagateNan,
OutputIndex>(
do_verification, init_method, time_kernel, inLengths, arrReduceDims, alpha, beta);
matched = true;
});
return (result == 0) ? true : false;
};
constexpr ReduceTensorOp ReduceOpId = ReduceTensorOp::AVG;
constexpr bool PropagateNan = true;
constexpr bool OutputIndex = false;
int main(int argc, char* argv[])
{
bool pass = true;
if(argc > 1)
{
SimpleAppArgs arg;
if(arg.processArgs(argc, argv) < 0)
return (-1);
if(arg.data_type == 0)
{
pass = reduce_threadwise_multi_d_test<ck::half_t,
float,
ReduceOpId,
PropagateNan,
OutputIndex>(arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
else if(arg.data_type == 1)
{
pass =
reduce_threadwise_multi_d_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
arg.do_verification,
arg.init_method,
arg.time_kernel,
arg.inLengths,
arg.reduceDims,
arg.scales[0],
arg.scales[1]);
}
}
else
{
// for testing half_t
pass = pass && reduce_threadwise_multi_d_test<ck::half_t,
float,
ReduceOpId,
PropagateNan,
OutputIndex>(
true, 2, true, {16, 64, 32, 960}, {0}, 1.0f, 0.0f);
// for testing float
pass = pass &&
reduce_threadwise_multi_d_test<float, float, ReduceOpId, PropagateNan, OutputIndex>(
true, 2, true, {16, 64, 32, 960}, {0}, 1.0f, 0.0f);
// for testing bhalf_t
pass = pass && reduce_threadwise_multi_d_test<ck::bhalf_t,
float,
ReduceOpId,
PropagateNan,
OutputIndex>(
true, 2, true, {16, 64, 32, 960}, {0}, 1.0f, 0.0f);
}
return (pass ? 0 : 1);
};
example/12_reduce/reduce_threadwise_multi_d_impl.hpp 0 → 100644
View file @ 3552041a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include "ck/ck.hpp"
#include "ck/utility/reduction_enums.hpp"
#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_reduce_threadwise_multi_d.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_reduce.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/host_common_util.hpp"
#include "reduce_example_common.hpp"
template <typename InOutDataType,
typename AccDataType,
ck::ReduceTensorOp ReduceOpId,
ck::index_t Rank,
ck::index_t NumReduceDim,
bool PropagateNan,
bool OutputIndex>
int reduce_threadwise_multi_d_impl(bool do_verification,
int init_method,
bool time_kernel,
const std::vector<size_t>& inLengths,
const std::array<int, NumReduceDim>& reduceDims,
float alpha,
float beta)
{
using namespace ck;
using namespace ck::tensor_operation::device;
constexpr index_t NumOutDim = (Rank - NumReduceDim == 0) ? 1 : Rank - NumReduceDim;
constexpr bool op_support_indices =
(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
ReduceOpId == ReduceTensorOp::AMAX);
constexpr bool invalid_reduce_1 = OutputIndex && !op_support_indices;
// 1) If InOutDataType is half_t, must use half_t as AccDataType for indexable reduction
// operations 2) If InOutDataType is half_t, must use float as AccDataType for non-indexable
// reduction operations
constexpr bool invalid_reduce_2 =
std::is_same<InOutDataType, half_t>::value &&
((!op_support_indices && !std::is_same<AccDataType, float>::value) ||
(op_support_indices && !std::is_same<AccDataType, half_t>::value));
// 1) If InOutDataType is float, must use float as AccDataType for indexable reduction
// operations
constexpr bool invalid_reduce_3 =
std::is_same<InOutDataType, float>::value &&
(op_support_indices && !std::is_same<AccDataType, float>::value);
// 1) If InOutDataType is int8_t or int4_t, must use int8_t as AccDataType for indexable
// reduction operations 2) If InOutDataType is int8_t or int4_t, must use int32_t as AccDataType
// for non-indexable reduction operations
constexpr bool invalid_reduce_4 =
std::is_same<InOutDataType, int8_t>::value &&
((!op_support_indices && !std::is_same<AccDataType, int32_t>::value) ||
(op_support_indices && !std::is_same<AccDataType, int8_t>::value));
// 1) If InOutDataType is int8_t or int4_t, the supported operation must be either indexable
// operations or ADD/AVG
constexpr bool invalid_reduce_5 = std::is_same<InOutDataType, int8_t>::value &&
(!op_support_indices && ReduceOpId != ReduceTensorOp::ADD &&
ReduceOpId != ReduceTensorOp::AVG);
// 1) If InOutDataType is bhalf_t, must use float as AccDataType for all reduction operations
constexpr bool invalid_reduce_6 =
std::is_same<InOutDataType, bhalf_t>::value && !std::is_same<AccDataType, float>::value;
constexpr bool invalid_reduce = (invalid_reduce_1 || invalid_reduce_2 || invalid_reduce_3 ||
invalid_reduce_4 || invalid_reduce_5 || invalid_reduce_6);
if constexpr(invalid_reduce)
{
std::cerr << "The reduction setting is invalid, exiting!" << std::endl;
return (-1);
};
using PassThrough = tensor_operation::element_wise::PassThrough;
using Add = tensor_operation::element_wise::Add;
using ReduceOperation = typename reduce_binary_operator<ReduceOpId>::opType;
using InElementwiseOperation = PassThrough;
using OutElementwiseOperation = Add;
using InOutDataTypeInDevice = InOutDataType;
using DeviceReduceInstance =
ck::tensor_operation::device::DeviceReduceThreadWiseMultiD<InOutDataTypeInDevice,
ck::Tuple<InOutDataTypeInDevice>,
AccDataType,
InOutDataTypeInDevice,
Rank,
NumReduceDim,
ReduceOperation,
InElementwiseOperation,
OutElementwiseOperation,
256, // BlockSize
4, // MThreadSliceSize
1, // KThreadSliceSize
0, // InSrcVectorDim
1, // InSrceVectorSize
1,
Sequence<1>>; // OutDstVectorSize
Tensor<InOutDataType> in(inLengths);
std::vector<size_t> outLengths;
auto invariantDims = get_invariant_dims<Rank, NumReduceDim>(reduceDims);
if(invariantDims.empty())
outLengths.push_back(1);
else
for(auto dim : invariantDims)
outLengths.push_back(inLengths[dim]);
Tensor<InOutDataType> out_ref(outLengths);
Tensor<InOutDataType> out(outLengths);
Tensor<InOutDataType> d0(outLengths);
Tensor<int> out_indices_ref(outLengths);
Tensor<int> out_indices(outLengths);
auto inStrides = in.mDesc.GetStrides();
auto outStrides = out.mDesc.GetStrides();
size_t invariant_total_length = out.mDesc.GetElementSize();
size_t reduce_total_length = in.mDesc.GetElementSize() / invariant_total_length;
std::size_t num_thread = 1;
if(do_verification)
{
switch(init_method)
{
case 0: break;
case 1:
in.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
d0.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
if(beta != 0.0f)
out_ref.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
break;
case 2:
in.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
d0.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
if(beta != 0.0f)
out_ref.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
break;
default:
in.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-5.0, 5.0}, num_thread);
d0.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-5.0, 5.0}, num_thread);
if(beta != 0.0f)
out_ref.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-5.0, 5.0},
num_thread);
}
if(beta != 0.0f)
for(size_t i = 0; i < out_ref.mDesc.GetElementSpaceSize(); i++)
out.mData[i] = out_ref.mData[i];
};
// these buffers are usually provided by the user application
DeviceMem in_dev(sizeof(InOutDataTypeInDevice) * in.mDesc.GetElementSpaceSize());
DeviceMem d0_dev(sizeof(InOutDataTypeInDevice) * d0.mDesc.GetElementSpaceSize());
DeviceMem out_dev(sizeof(InOutDataTypeInDevice) * out.mDesc.GetElementSpaceSize());
in_dev.ToDevice(in.mData.data());
d0_dev.ToDevice(d0.mData.data());
if(beta != 0.0f)
{
out_dev.ToDevice(out.mData.data());
};
size_t indicesSizeInBytes = OutputIndex ? out.mDesc.GetElementSize() * sizeof(int32_t) : 0;
DeviceMem out_index_dev(indicesSizeInBytes);
InElementwiseOperation in_elementwise_op;
OutElementwiseOperation out_elementwise_op;
std::array<index_t, Rank> arrInLengths;
std::array<index_t, Rank> arrInStrides;
std::array<index_t, NumOutDim> arrOutLengths;
std::array<index_t, NumOutDim> arrOutStrides;
ck::ranges::copy(inLengths, arrInLengths.begin());
ck::ranges::copy(inStrides, arrInStrides.begin());
ck::ranges::copy(outLengths, arrOutLengths.begin());
ck::ranges::copy(outStrides, arrOutStrides.begin());
if(do_verification)
{
using ReferenceReduceInstance =
ck::tensor_operation::host::ReferenceReduce<InOutDataType,
AccDataType,
InOutDataType,
Rank,
NumReduceDim,
ReduceOperation,
InElementwiseOperation,
PassThrough,
PropagateNan,
OutputIndex>;
auto reduce_ref = ReferenceReduceInstance{};
auto argument_ptr_ref = reduce_ref.MakeArgumentPointer(arrInLengths,
arrInStrides,
arrOutLengths,
arrOutStrides,
reduceDims,
static_cast<double>(alpha),
static_cast<double>(beta),
in.mData.data(),
nullptr,
out_ref.mData.data(),
out_indices_ref.mData.data(),
in_elementwise_op,
PassThrough{});
if(!reduce_ref.IsSupportedArgument(argument_ptr_ref.get()))
{
std::cout << "The runtime parameters not supported by the reduce reference, exiting!"
<< std::endl;
return (false);
};
auto invoker_ptr_ref = reduce_ref.MakeInvokerPointer();
invoker_ptr_ref->Run(argument_ptr_ref.get());
for(std::size_t i = 0; i < out_ref.GetElementSize(); i++)
out_elementwise_op(out_ref.mData[i], out_ref.mData[i], d0.mData[i]);
};
auto reduce = DeviceReduceInstance{};
auto argument_ptr = reduce.MakeArgumentPointer(arrInLengths,
arrInStrides,
{arrOutLengths},
{arrOutStrides},
arrOutLengths,
arrOutStrides,
reduceDims,
in_dev.GetDeviceBuffer(),
{d0_dev.GetDeviceBuffer()},
out_dev.GetDeviceBuffer(),
in_elementwise_op,
out_elementwise_op);
if(!reduce.IsSupportedArgument(argument_ptr.get()))
{
std::cerr << "The runtime parameters not supported by the DeviceReduce instance, exiting!"
<< std::endl;
return (-2);
};
std::string reduce_name = reduce.GetTypeString();
auto invoker_ptr = reduce.MakeInvokerPointer();
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t num_bytes = invariant_total_length * reduce_total_length * sizeof(InOutDataType) +
invariant_total_length * sizeof(InOutDataType);
float gb_per_sec = num_bytes / 1.E6 / avg_time;
std::cout << "Perf: " << avg_time << " ms, " << gb_per_sec << " GB/s, " << reduce_name
<< std::endl;
bool pass = true;
if(do_verification)
{
out_dev.FromDevice(out.mData.data());
pass = pass && ck::utils::check_err(out, out_ref);
if(OutputIndex)
{
out_index_dev.FromDevice(out_indices.mData.data());
pass = pass && ck::utils::check_err(out_indices, out_indices_ref);
};
};
return (pass ? 0 : 1);
}
example/30_grouped_conv_fwd_multiple_d/grouped_conv_fwd_bias_relu_add_wmma_fp16.cpp
View file @ 3552041a
...@@ -2,6 +2,7 @@ ...@@ -2,6 +2,7 @@
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "common_wmma.hpp" #include "common_wmma.hpp"
#include "ck/host_utility/device_prop.hpp"
// kernel data types // kernel data types
using InKernelDataType = FP16; using InKernelDataType = FP16;
...@@ -23,4 +24,14 @@ using OutElementOp = ck::tensor_operation::element_wise::AddReluAdd; ...@@ -23,4 +24,14 @@ using OutElementOp = ck::tensor_operation::element_wise::AddReluAdd;
#include "run_grouped_conv_fwd_bias_relu_add_wmma_example.inc" #include "run_grouped_conv_fwd_bias_relu_add_wmma_example.inc"
int main(int argc, char* argv[]) { return !run_grouped_conv_fwd_bias_relu_add_example(argc, argv); } int main(int argc, char* argv[])
{
bool is_supported = ck::is_gfx11_supported();
if(!is_supported)
{
std::cout << "WARNING: wmma example not supported on the platform " << ck::get_device_name()
<< std::endl;
return 0;
}
return !run_grouped_conv_fwd_bias_relu_add_example(argc, argv);
}
example/30_grouped_conv_fwd_multiple_d/grouped_conv_fwd_bias_relu_add_wmma_int8.cpp
View file @ 3552041a
...@@ -2,6 +2,7 @@ ...@@ -2,6 +2,7 @@
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "common_wmma.hpp" #include "common_wmma.hpp"
#include "ck/host_utility/device_prop.hpp"
// kernel data types // kernel data types
using InKernelDataType = I8; using InKernelDataType = I8;
...@@ -23,4 +24,14 @@ using OutElementOp = ck::tensor_operation::element_wise::AddReluAdd; ...@@ -23,4 +24,14 @@ using OutElementOp = ck::tensor_operation::element_wise::AddReluAdd;
#include "run_grouped_conv_fwd_bias_relu_add_wmma_example.inc" #include "run_grouped_conv_fwd_bias_relu_add_wmma_example.inc"
int main(int argc, char* argv[]) { return !run_grouped_conv_fwd_bias_relu_add_example(argc, argv); } int main(int argc, char* argv[])
{
bool is_supported = ck::is_gfx11_supported();
if(!is_supported)
{
std::cout << "WARNING: wmma example not supported on the platform " << ck::get_device_name()
<< std::endl;
return 0;
}
return !run_grouped_conv_fwd_bias_relu_add_example(argc, argv);
}
example/32_batched_gemm_scale_softmax_gemm/batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16.cpp
View file @ 3552041a
...@@ -27,6 +27,7 @@ Gemm + Softmax + Gemm fused operation. Computes C_g_m_n = Softmax(A_g_m_k * B0_g ...@@ -27,6 +27,7 @@ Gemm + Softmax + Gemm fused operation. Computes C_g_m_n = Softmax(A_g_m_k * B0_g
#include "ck/library/utility/literals.hpp" #include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
#include "ck/host_utility/device_prop.hpp"
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
...@@ -163,4 +164,14 @@ using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm< ...@@ -163,4 +164,14 @@ using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<
#include "run_batched_gemm_scale_softmax_gemm_permute_wmma.inc" #include "run_batched_gemm_scale_softmax_gemm_permute_wmma.inc"
int main(int argc, char* argv[]) { return run(argc, argv); } int main(int argc, char* argv[])
{
bool is_supported = ck::is_gfx11_supported();
if(!is_supported)
{
std::cout << "WARNING: wmma example not supported on the platform " << ck::get_device_name()
<< std::endl;
return 0;
}
return run(argc, argv);
}
example/32_batched_gemm_scale_softmax_gemm/batched_gemm_scale_softmax_gemm_permute_wmma_fp16.cpp
View file @ 3552041a
...@@ -27,6 +27,7 @@ Gemm + Softmax + Gemm fused operation. Computes C_g_m_n = Softmax(A_g_m_k * B0_g ...@@ -27,6 +27,7 @@ Gemm + Softmax + Gemm fused operation. Computes C_g_m_n = Softmax(A_g_m_k * B0_g
#include "ck/library/utility/literals.hpp" #include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
#include "ck/host_utility/device_prop.hpp"
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
...@@ -285,4 +286,14 @@ using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm< ...@@ -285,4 +286,14 @@ using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<
#include "run_batched_gemm_scale_softmax_gemm_permute_wmma.inc" #include "run_batched_gemm_scale_softmax_gemm_permute_wmma.inc"
int main(int argc, char* argv[]) { return run(argc, argv); } int main(int argc, char* argv[])
{
bool is_supported = ck::is_gfx11_supported();
if(!is_supported)
{
std::cout << "WARNING: wmma example not supported on the platform " << ck::get_device_name()
<< std::endl;
return 0;
}
return run(argc, argv);
}
example/32_batched_gemm_scale_softmax_gemm/cross_attention_forward_wmma_fp16.cpp
View file @ 3552041a
...@@ -27,6 +27,7 @@ Gemm + Softmax + Gemm fused operation. Computes C_g_m_n = Softmax(A_g_m_k * B0_g ...@@ -27,6 +27,7 @@ Gemm + Softmax + Gemm fused operation. Computes C_g_m_n = Softmax(A_g_m_k * B0_g
#include "ck/library/utility/literals.hpp" #include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
#include "ck/host_utility/device_prop.hpp"
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
...@@ -351,4 +352,14 @@ using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm< ...@@ -351,4 +352,14 @@ using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<
#include "run_cross_attention_wmma.inc" #include "run_cross_attention_wmma.inc"
int main(int argc, char* argv[]) { return run(argc, argv); } int main(int argc, char* argv[])
{
bool is_supported = ck::is_gfx11_supported();
if(!is_supported)
{
std::cout << "WARNING: wmma example not supported on the platform " << ck::get_device_name()
<< std::endl;
return 0;
}
return run(argc, argv);
}
example/32_batched_gemm_scale_softmax_gemm/grouped_query_attention_forward_wmma_fp16.cpp
View file @ 3552041a
...@@ -28,6 +28,7 @@ Example is GQA-4 ...@@ -28,6 +28,7 @@ Example is GQA-4
#include "ck/library/utility/literals.hpp" #include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
#include "ck/host_utility/device_prop.hpp"
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
...@@ -299,4 +300,14 @@ using ReferenceGemm1Instance = ...@@ -299,4 +300,14 @@ using ReferenceGemm1Instance =
#include "run_grouped_query_attention_forward_wmma.inc" #include "run_grouped_query_attention_forward_wmma.inc"
int main(int argc, char* argv[]) { return run(argc, argv); } int main(int argc, char* argv[])
{
bool is_supported = ck::is_gfx11_supported();
if(!is_supported)
{
std::cout << "WARNING: wmma example not supported on the platform " << ck::get_device_name()
<< std::endl;
return 0;
}
return run(argc, argv);
}
example/32_batched_gemm_scale_softmax_gemm/multi_query_attention_forward_wmma_fp16.cpp
View file @ 3552041a
...@@ -26,6 +26,7 @@ Shazeer, Noam. “Fast Transformer Decoding: One Write-Head Is All You Need.” ...@@ -26,6 +26,7 @@ Shazeer, Noam. “Fast Transformer Decoding: One Write-Head Is All You Need.”
#include "ck/library/utility/literals.hpp" #include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
#include "ck/host_utility/device_prop.hpp"
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
...@@ -284,4 +285,14 @@ using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm_ ...@@ -284,4 +285,14 @@ using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm_
#include "run_multi_query_attention_forward_wmma.inc" #include "run_multi_query_attention_forward_wmma.inc"
int main(int argc, char* argv[]) { return run(argc, argv); } int main(int argc, char* argv[])
{
bool is_supported = ck::is_gfx11_supported();
if(!is_supported)
{
std::cout << "WARNING: wmma example not supported on the platform " << ck::get_device_name()
<< std::endl;
return 0;
}
return run(argc, argv);
}
example/32_batched_gemm_scale_softmax_gemm/self_attention_forward_wmma_fp16.cpp
View file @ 3552041a
...@@ -27,6 +27,7 @@ Gemm + Softmax + Gemm fused operation. Computes C_g_m_n = Softmax(A_g_m_k * B0_g ...@@ -27,6 +27,7 @@ Gemm + Softmax + Gemm fused operation. Computes C_g_m_n = Softmax(A_g_m_k * B0_g
#include "ck/library/utility/literals.hpp" #include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
#include "ck/host_utility/device_prop.hpp"
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
...@@ -329,4 +330,14 @@ using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm< ...@@ -329,4 +330,14 @@ using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<
#include "run_self_attention_wmma.inc" #include "run_self_attention_wmma.inc"
int main(int argc, char* argv[]) { return run(argc, argv); } int main(int argc, char* argv[])
{
bool is_supported = ck::is_gfx11_supported();
if(!is_supported)
{
std::cout << "WARNING: wmma example not supported on the platform " << ck::get_device_name()
<< std::endl;
return 0;
}
return run(argc, argv);
}
example/35_splitK_gemm/CMakeLists.txt
View file @ 3552041a
...@@ -21,3 +21,9 @@ if(USE_BITINT_EXTENSION_INT4) ...@@ -21,3 +21,9 @@ if(USE_BITINT_EXTENSION_INT4)
add_example_executable(example_splitK_gemm_xdl_int4 splitK_gemm_xdl_int4.cpp) add_example_executable(example_splitK_gemm_xdl_int4 splitK_gemm_xdl_int4.cpp)
add_example_dependencies(example_splitK_gemm_xdl example_splitK_gemm_xdl_int4) add_example_dependencies(example_splitK_gemm_xdl example_splitK_gemm_xdl_int4)
endif() endif()
add_example_executable(example_gemm_xdl_splitk_reduce_multi_d_fp16 gemm_xdl_splitk_reduce_multi_d_fp16.cpp)
add_example_executable(example_gemm_xdl_splitk_reduce_multi_d_bf16 gemm_xdl_splitk_reduce_multi_d_bf16.cpp)
add_example_executable(example_gemm_xdl_splitk_reduce_bf16A_i8B gemm_xdl_splitk_reduce_bf16A_i8B.cpp)
add_example_executable(example_gemm_xdl_splitk_reduce_bfp16 gemm_xdl_splitk_reduce_bf16.cpp)
example/35_splitK_gemm/common.hpp 0 → 100644
View file @ 3552041a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include <iostream>
#include <initializer_list>
#include <numeric>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/fill.hpp"
#include "ck/library/utility/host_tensor.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_multiple_d.hpp"
struct ProblemSizeSplitK final
{
ck::index_t M = 256;
ck::index_t N = 1024;
ck::index_t K = 512;
ck::index_t StrideA = K;
ck::index_t StrideB = N;
ck::index_t StrideC = N;
ck::index_t KBatch = 2;
};
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 2;
bool time_kernel = true;
};
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Add = ck::tensor_operation::element_wise::Add;
bool parse_cmd_args(int argc,
char* argv[],
ProblemSizeSplitK& problem_size,
ExecutionConfig& config)
{
if(argc == 1)
{
// use default case
}
else if(argc == 4)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
}
else if(argc >= 10)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
problem_size.M = std::stoi(argv[4]);
problem_size.N = std::stoi(argv[5]);
problem_size.K = std::stoi(argv[6]);
problem_size.StrideA = std::stoi(argv[7]);
problem_size.StrideB = std::stoi(argv[8]);
problem_size.StrideC = std::stoi(argv[9]);
if(argc >= 11)
{
problem_size.KBatch = std::stoi(argv[10]);
}
}
else
{
std::cerr << "arg1: verification (0=no, 1=yes)" << std::endl
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)"
<< std::endl
<< "arg3: time kernel (0=no, 1=yes)" << std::endl
<< "arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC" << std::endl
<< "arg10: KBatch" << std::endl;
return false;
}
return true;
}
example/35_splitK_gemm/gemm_xdl_splitk_reduce_bf16.cpp 0 → 100644
View file @ 3552041a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3r1.hpp"
using ADataType = ck::bhalf_t;
using BDataType = ck::bhalf_t;
using AccDataType = float;
using CShuffleDataType = ck::bhalf_t;
using CDataType = ck::bhalf_t;
using ReduceDataType = ck::bhalf_t;
using D0DataType = ck::bhalf_t;
using DsDataType = ck::Tuple<>;
using ALayout = Row;
using BLayout = Row;
using CLayout = Row;
using D0Layout = CLayout;
using DsLayout = ck::Tuple<>;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNPadding;
// clang-format off
using DeviceGemmV2Instance =
ck::tensor_operation::device::DeviceGemm_Xdl_CShuffleV3R1<
ALayout, BLayout, DsLayout, CLayout,
ADataType, BDataType, DsDataType, CDataType, AccDataType, CShuffleDataType,
AElementOp, BElementOp, CDEElementOp, GemmDefault,
256,
128, 128, 64,
8, 4,
32, 32,
2, 2,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>,
1, 8, 4, 0,
1, 1, S<1, 32, 1, 8>, 8,
ck::BlockGemmPipelineScheduler::Intrawave,ck::BlockGemmPipelineVersion::v3>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
PassThrough>;
#include "run_gemm_splitk_reduce_multi_d_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }
example/35_splitK_gemm/gemm_xdl_splitk_reduce_bf16A_i8B.cpp 0 → 100644
View file @ 3552041a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3r1.hpp"
using ADataType = ck::bhalf_t;
using BDataType = int8_t;
using AccDataType = float;
using CShuffleDataType = ck::bhalf_t;
using CDataType = ck::bhalf_t;
using ReduceDataType = float;
using D0DataType = ck::bhalf_t;
using DsDataType = ck::Tuple<>;
using ALayout = Row;
using BLayout = Row;
using CLayout = Row;
using D0Layout = Row;
using DsLayout = ck::Tuple<>;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNPadding;
// clang-format off
using DeviceGemmV2Instance =
ck::tensor_operation::device::DeviceGemm_Xdl_CShuffleV3R1<
ALayout, BLayout, DsLayout, CLayout,
ADataType, BDataType, DsDataType, CDataType, AccDataType, CShuffleDataType,
AElementOp, BElementOp, CDEElementOp, GemmDefault,
256,
128, 128, 64,
8, 4,
32, 32,
2, 2,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>,
1, 8, 4, 0,
1, 1, S<1, 32, 1, 8>, 8,
ck::BlockGemmPipelineScheduler::Intrawave,ck::BlockGemmPipelineVersion::v3, ReduceDataType>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
PassThrough>;
#include "run_gemm_splitk_reduce_multi_d_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }
example/35_splitK_gemm/gemm_xdl_splitk_reduce_multi_d_bf16.cpp 0 → 100644
View file @ 3552041a
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#include "common.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3r1.hpp"
using ADataType = ck::bhalf_t;
using BDataType = ck::bhalf_t;
using AccDataType = float;
using CShuffleDataType = ck::bhalf_t;
using CDataType = ck::bhalf_t;
using ReduceDataType = float;
using D0DataType = ck::bhalf_t;
using DsDataType = ck::Tuple<D0DataType>;
using ALayout = Row;
using BLayout = Row;
using CLayout = Row;
using D0Layout = CLayout;
using DsLayout = ck::Tuple<D0Layout>;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = Add;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNPadding;
// clang-format off
using DeviceGemmV2Instance =
ck::tensor_operation::device::DeviceGemm_Xdl_CShuffleV3R1<
ALayout, BLayout, DsLayout, CLayout,
ADataType, BDataType, DsDataType, CDataType, AccDataType, CShuffleDataType,
AElementOp, BElementOp, CDEElementOp, GemmDefault,
256,
128, 128, 64,
8, 4,
32, 32,
2, 2,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>,
2, 8, 8, 0,
S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>,
1, 8, 4, 0,
1, 1, S<1, 32, 1, 8>, 8,
ck::BlockGemmPipelineScheduler::Intrawave,ck::BlockGemmPipelineVersion::v3, ReduceDataType>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
PassThrough>;
#include "run_gemm_splitk_reduce_multi_d_example.inc"
int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }
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