mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 05:01:25 +00:00
ffe9775e70
-Added parameter to enable/disable verification and timing of kernel in various examples that missed it. -Added parameter to change number of groups to execute in grouped_gemm_examples. Signed-off-by: Michal Kulikowski <Michal.Kulikowski@amd.com>
318 lines
12 KiB
C++
318 lines
12 KiB
C++
// SPDX-License-Identifier: MIT
|
|
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
|
|
|
|
#pragma once
|
|
|
|
struct ProblemSize final
|
|
{
|
|
std::vector<ck::index_t> Ms;
|
|
std::vector<ck::index_t> Ns;
|
|
std::vector<ck::index_t> Ks;
|
|
|
|
std::vector<ck::index_t> stride_As;
|
|
std::vector<ck::index_t> stride_Bs;
|
|
std::vector<ck::index_t> stride_Cs;
|
|
|
|
ck::index_t group_count;
|
|
};
|
|
|
|
struct ExecutionConfig final
|
|
{
|
|
bool do_verification = true;
|
|
int init_method = 1;
|
|
bool time_kernel = false;
|
|
bool async_hargs = false;
|
|
};
|
|
|
|
bool run_grouped_gemm(const ProblemSize& 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));
|
|
static_assert(sizeof(ADataType) == sizeof(KernelADataType));
|
|
static_assert(sizeof(BDataType) == sizeof(KernelBDataType));
|
|
static_assert(sizeof(EDataType) == sizeof(KernelEDataType));
|
|
#endif
|
|
int group_count = problem_size.group_count;
|
|
|
|
// GEMM shape
|
|
std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
|
|
std::vector<const void*> p_a, p_b;
|
|
std::vector<void*> p_c;
|
|
|
|
gemm_descs.reserve(group_count);
|
|
|
|
for(int i = 0; i < group_count; i++)
|
|
{
|
|
int M = problem_size.Ms[i];
|
|
int N = problem_size.Ns[i];
|
|
int K = problem_size.Ks[i];
|
|
|
|
int stride_A = problem_size.stride_As[i];
|
|
int stride_B = problem_size.stride_Bs[i];
|
|
int stride_C = problem_size.stride_Cs[i];
|
|
|
|
gemm_descs.push_back({M, N, K, stride_A, stride_B, stride_C, {}});
|
|
}
|
|
|
|
auto f_host_tensor_descriptor =
|
|
[](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)
|
|
{
|
|
return HostTensorDescriptor({row, col}, {stride, 1_uz});
|
|
}
|
|
else
|
|
{
|
|
return HostTensorDescriptor({row, col}, {1_uz, stride});
|
|
}
|
|
};
|
|
|
|
std::vector<Tensor<ADataType>> a_tensors;
|
|
std::vector<Tensor<BDataType>> b_tensors;
|
|
std::vector<Tensor<EDataType>> c_host_tensors;
|
|
#ifdef BUILD_INT4_EXAMPLE
|
|
std::vector<Tensor<KernelEDataType>> c_device_tensors;
|
|
#else
|
|
std::vector<Tensor<EDataType>> c_device_tensors;
|
|
#endif
|
|
|
|
a_tensors.reserve(group_count);
|
|
b_tensors.reserve(group_count);
|
|
c_host_tensors.reserve(group_count);
|
|
c_device_tensors.reserve(group_count);
|
|
|
|
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
|
|
|
|
std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, c_tensors_device;
|
|
|
|
a_tensors_device.reserve(group_count);
|
|
b_tensors_device.reserve(group_count);
|
|
c_tensors_device.reserve(group_count);
|
|
|
|
std::size_t flop = 0, num_btype = 0;
|
|
|
|
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
|
{
|
|
a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
|
|
gemm_descs[i].M_, gemm_descs[i].K_, gemm_descs[i].stride_A_, ALayout{})));
|
|
b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor(
|
|
gemm_descs[i].K_, gemm_descs[i].N_, gemm_descs[i].stride_B_, BLayout{})));
|
|
c_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
|
|
gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_C_, ELayout{})));
|
|
#ifdef BUILD_INT4_EXAMPLE
|
|
c_device_tensors.push_back(Tensor<KernelEDataType>(f_host_tensor_descriptor(
|
|
gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_C_, ELayout{})));
|
|
#else
|
|
c_device_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
|
|
gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_C_, ELayout{})));
|
|
#endif
|
|
std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc
|
|
<< " b_k_n: " << b_tensors[i].mDesc << " c_m_n: " << c_device_tensors[i].mDesc
|
|
<< std::endl;
|
|
|
|
flop += std::size_t(2) * gemm_descs[i].M_ * gemm_descs[i].K_ * gemm_descs[i].N_;
|
|
num_btype += sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize() +
|
|
sizeof(BDataType) * b_tensors[i].mDesc.GetElementSize() +
|
|
sizeof(EDataType) * c_device_tensors[i].mDesc.GetElementSize();
|
|
|
|
switch(config.init_method)
|
|
{
|
|
case 0: break;
|
|
case 1:
|
|
a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
|
b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
|
break;
|
|
case 2:
|
|
a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
|
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
|
break;
|
|
default:
|
|
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<ADataType, 0>{});
|
|
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<BDataType, 1>{});
|
|
}
|
|
}
|
|
|
|
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
|
{
|
|
a_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
|
sizeof(ADataType) * a_tensors[i].mDesc.GetElementSpaceSize()));
|
|
b_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
|
sizeof(BDataType) * b_tensors[i].mDesc.GetElementSpaceSize()));
|
|
c_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
|
sizeof(EDataType) * c_device_tensors[i].mDesc.GetElementSpaceSize()));
|
|
|
|
#ifdef BUILD_INT4_EXAMPLE
|
|
const Tensor<KernelADataType> a_converted(a_tensors[i]);
|
|
const Tensor<KernelBDataType> b_converted(b_tensors[i]);
|
|
|
|
a_tensors_device[i]->ToDevice(a_converted.mData.data());
|
|
b_tensors_device[i]->ToDevice(b_converted.mData.data());
|
|
#else
|
|
a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
|
|
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
|
|
c_tensors_device[i]->SetZero();
|
|
#endif
|
|
|
|
p_a.push_back(a_tensors_device[i]->GetDeviceBuffer());
|
|
p_b.push_back(b_tensors_device[i]->GetDeviceBuffer());
|
|
p_c.push_back(c_tensors_device[i]->GetDeviceBuffer());
|
|
}
|
|
|
|
auto a_element_op = AElementOp{};
|
|
auto b_element_op = BElementOp{};
|
|
auto c_element_op = CDEElementOp{};
|
|
|
|
auto gemm = DeviceGemmInstance{};
|
|
auto invoker = gemm.MakeInvoker();
|
|
|
|
std::vector<std::array<const void*, 0>> p_Ds = {};
|
|
|
|
// do GEMM
|
|
auto argument = gemm.MakeArgument(
|
|
p_a, p_b, p_Ds, p_c, gemm_descs, a_element_op, b_element_op, c_element_op);
|
|
|
|
std::size_t workspace_size = gemm.GetWorkSpaceSize(&argument);
|
|
std::size_t kargs_size = gemm.GetDeviceKernelArgSize(&argument);
|
|
std::size_t hargs_size = gemm.GetHostKernelArgSize(&argument);
|
|
|
|
DeviceMem gemm_workspace, gemm_kargs;
|
|
void* gemm_hargs;
|
|
|
|
// The following is necessary since TwoStage kernel is using additional memory both
|
|
// for Workspace and kernel arguments.
|
|
if(kargs_size > 0)
|
|
{
|
|
gemm_kargs.Realloc(kargs_size);
|
|
gemm.SetDeviceKernelArgs(&argument, gemm_kargs.GetDeviceBuffer());
|
|
}
|
|
if(workspace_size > 0 && workspace_size != kargs_size)
|
|
{
|
|
gemm_workspace.Realloc(workspace_size);
|
|
gemm.SetWorkSpacePointer(&argument, gemm_workspace.GetDeviceBuffer());
|
|
}
|
|
if(config.async_hargs && hargs_size > 0)
|
|
{
|
|
hip_check_error(hipHostMalloc(&gemm_hargs, hargs_size));
|
|
gemm.SetHostKernelArgsPointer(&argument, gemm_hargs);
|
|
}
|
|
|
|
if(!gemm.IsSupportedArgument(argument))
|
|
{
|
|
throw std::runtime_error(
|
|
"wrong! device_gemm with the specified compilation parameters does "
|
|
"not support this GEMM problem");
|
|
}
|
|
|
|
if(!config.async_hargs)
|
|
{
|
|
invoker.Run(argument, StreamConfig{nullptr, false});
|
|
}
|
|
else
|
|
{
|
|
hipStream_t stream0 = nullptr;
|
|
hip_check_error(hipStreamCreate(&stream0));
|
|
|
|
hipEvent_t event0 = nullptr;
|
|
hip_check_error(hipEventCreate(&event0));
|
|
|
|
invoker.Run(argument, StreamConfig{nullptr, false}, stream0, event0);
|
|
|
|
hip_check_error(hipEventSynchronize(event0));
|
|
hip_check_error(hipStreamSynchronize(stream0));
|
|
}
|
|
|
|
bool pass = true;
|
|
if(config.do_verification)
|
|
{
|
|
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
|
BDataType,
|
|
EDataType,
|
|
AccDataType,
|
|
AElementOp,
|
|
BElementOp,
|
|
CDEElementOp>;
|
|
|
|
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
|
{
|
|
c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data());
|
|
auto ref_gemm = ReferenceGemmInstance{};
|
|
auto ref_invoker = ref_gemm.MakeInvoker();
|
|
|
|
auto ref_argument = ref_gemm.MakeArgument(a_tensors[i],
|
|
b_tensors[i],
|
|
c_host_tensors[i],
|
|
a_element_op,
|
|
b_element_op,
|
|
c_element_op);
|
|
|
|
ref_invoker.Run(ref_argument);
|
|
|
|
#ifdef BUILD_INT4_EXAMPLE
|
|
const Tensor<EDataType> c_device_result_converted(c_device_tensors[i]);
|
|
pass &= ck::utils::check_err(c_device_result_converted, c_host_tensors[i]);
|
|
|
|
#else
|
|
pass &= ck::utils::check_err(c_device_tensors[i], c_host_tensors[i]);
|
|
#endif
|
|
}
|
|
}
|
|
|
|
if(config.time_kernel)
|
|
{
|
|
float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
|
|
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_grouped_gemm_example(int argc, char* argv[])
|
|
{
|
|
ProblemSize problem_size;
|
|
ExecutionConfig config;
|
|
|
|
problem_size.group_count = 16;
|
|
|
|
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 == 6)
|
|
{
|
|
config.do_verification = std::stoi(argv[1]);
|
|
config.init_method = std::stoi(argv[2]);
|
|
config.time_kernel = std::stoi(argv[3]);
|
|
config.async_hargs = std::stoi(argv[4]);
|
|
problem_size.group_count = std::stoi(argv[5]);
|
|
}
|
|
else
|
|
{
|
|
printf("arg1: verification (0=no, 1=yes)\n");
|
|
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
|
|
printf("arg3: time kernel (0=n0, 1=yes)\n");
|
|
printf("arg4: async hargs (0=n0, 1=yes)\n");
|
|
printf("arg5: group count (default=16)");
|
|
exit(0);
|
|
}
|
|
|
|
for(int i = 0; i < problem_size.group_count; i++)
|
|
{
|
|
problem_size.Ms.push_back(256 + 256 * i);
|
|
problem_size.Ns.push_back(128 + 128 * i);
|
|
problem_size.Ks.push_back(128 + 64 * i);
|
|
|
|
problem_size.stride_As.push_back(problem_size.Ks[i]);
|
|
problem_size.stride_Bs.push_back(problem_size.Ks[i]);
|
|
problem_size.stride_Cs.push_back(problem_size.Ns[i]);
|
|
}
|
|
|
|
return run_grouped_gemm(problem_size, config);
|
|
}
|