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composable_kernel/experimental/gemm_benchmark/run_gemm_example_v2.inc
Illia Silin 717f2efef7 [rocm-libraries] ROCm/rocm-libraries#6978 (commit e58096d) 
[CK] add composable kernel support on gfx1250 (#6978)

## Motivation

Add composable kernel support on gfx1250.

## Technical Details

<!-- Explain the changes along with any relevant GitHub links. -->

## Test Plan

<!-- Explain any relevant testing done to verify this PR. -->

## Test Result

<!-- Briefly summarize test outcomes. -->

## Submission Checklist

- [ ] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.

---------

Co-authored-by: Qun Lin <qlin@amd.com>
Co-authored-by: jialuo12_amdeng <jia.luo@amd.com>
Co-authored-by: Andriy Roshchenko <andriy.roshchenko@amd.com>
Co-authored-by: hsivasun_amdeng <haresh.sivasuntharampillai@amd.com>
2026-05-15 06:46:51 -07:00

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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
template <bool BPreShuffle, typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{
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 KBatch = problem_size.KBatch;
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 || stride == 0)
{
// 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);
};
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{});
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:
default:
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;
}
Tensor<float> a_m_k_f32({M, K});
Tensor<float> b_k_n_f32({K, N});
Tensor<float> a_m_k_gfx9_f32({M, K});
Tensor<float> b_k_n_gfx9_f32({K, N});
if constexpr(ck::is_same_v<ADataType, ck::pk_i4_t>)
{
permute_a_pk_i4(a_m_k, M, K, a_m_k_f32, a_m_k_gfx9_f32);
}
if constexpr(ck::is_same_v<BDataType, ck::pk_i4_t>)
{
permute_b_pk_i4(b_k_n, N, K, b_k_n_f32, b_k_n_gfx9_f32);
}
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;
auto gemm = DeviceGemmV2Instance{};
auto invoker = gemm.MakeInvoker();
Tensor<BDataType> b_preshuffled(
f_host_tensor_descriptor(K, N, StrideB, BLayout{})); // use laout only for size
if constexpr(BPreShuffle)
{
static_assert(std::is_same_v<BLayout, ck::tensor_layout::gemm::RowMajor> == false);
static constexpr ck::index_t KPack = ck::math::max(
static_cast<ck::index_t>(16 / sizeof(BDataType)), static_cast<ck::index_t>(8));
constexpr int NPerXdl = 16;
preShuffleBuffer(b_k_n.mData.data(), b_preshuffled.mData.data(), N, K, NPerXdl, KPack);
}
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());
if constexpr(BPreShuffle)
{
b_k_n_device_buf.ToDevice(b_preshuffled.mData.data());
}
else
{
b_k_n_device_buf.ToDevice(b_k_n.mData.data());
}
DeviceMem workspace;
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto c_element_op = CElementOp{};
// do GEMM
float ave_time = 0;
auto argument = gemm.MakeArgument(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()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
KBatch,
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 == 1) || (config.do_verification == 3))
{
if constexpr(ck::is_same_v<ADataType, ck::pk_i4_t> && ck::is_same_v<BDataType, ck::pk_i4_t>)
{
bool useGfx9Result =
(ck::is_same_v<ADataType, ck::pk_i4_t> && ck::is_same_v<BDataType, ck::pk_i4_t> &&
(gemm.GetKPerBlock() & DisableGfx9I4ToF32) == 0);
using PkInt4ReferenceGemmInstance =
ck::tensor_operation::host::ReferenceGemm<float,
float,
CDataType,
AccDataType,
PassThrough,
PassThrough,
PassThrough>;
auto ref_gemm = PkInt4ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(useGfx9Result ? a_m_k_gfx9_f32 : a_m_k_f32,
useGfx9Result ? b_k_n_gfx9_f32 : b_k_n_f32,
c_m_n_host_result,
PassThrough{},
PassThrough{},
PassThrough{});
ref_invoker.Run(ref_argument);
}
else
{
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, a_element_op, b_element_op, c_element_op);
ref_invoker.Run(ref_argument);
}
ave_time = invoker.Run(argument, StreamConfig{nullptr, false, 1});
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>());
}
if(config.time_kernel)
{
ave_time = invoker.Run(argument,
StreamConfig{nullptr,
config.time_kernel,
0,
config.cold_niters,
config.nrepeat,
true,
config.rotating_count});
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;
}
template <bool BPreShuffle = false>
bool run_gemm_splitk_example(int argc, char* argv[])
{
ProblemSizeSplitK problem_size;
ExecutionConfig config;
return !parse_cmd_args(argc, argv, problem_size, config) ||
run_gemm<BPreShuffle>(problem_size, config);
}
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