mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 10:09:41 +00:00
Add fp16/fp8 support into Grouped gemm FixedNK (#874)
* move all arguments into device
* add b2c_tile_map
* add examples
* add SetDeviceKernelArgs
* dedicated fixed_nk solution
* init client api
* add grouped_gemm_bias example
* add a instance
* add instances
* formatting
* fixed cmake
* Update EnableCompilerWarnings.cmake
* Update cmake-ck-dev.sh
* clean; fixed comments
* fixed comment
* add instances for fp32 output
* add instances for fp32 output
* add fp32 out client example
* fixed CI
* init commit for kbatch
* add splitk gridwise
* format
* fixed
* clean deviceop
* clean code
* finish splitk
* fixed instances
* change m_loops to tile_loops
* add setkbatch
* clean code
* add splitK+bias
* add instances
* opt mk_nk instances
* clean examples
* fixed CI
* remove zero
* finished non-zero
* clean
* clean code
* optimized global_barrier
* fixed ci
* fixed CI
* instance and client
* removed AddBias
* format
* fixed CI
* fixed CI
* move 20_grouped_gemm to 21_grouped_gemm
* clean
* formatting
* clean
* clean
* fixed computeType
---------
Co-authored-by: Jing Zhang <jizha@amd.com>
[ROCm/composable_kernel commit: f9d0eddb90]
This commit is contained in:
zjing14
@@ -25,6 +25,11 @@ if(DTYPES MATCHES "int8" OR NOT DEFINED DTYPES)
|
||||
add_example_executable(example_grouped_gemm_xdl_int8 grouped_gemm_xdl_int8.cpp)
|
||||
add_dependencies(example_grouped_gemm_xdl example_grouped_gemm_xdl_int8)
|
||||
endif()
|
||||
if(DTYPES MATCHES "f8" OR NOT DEFINED DTYPES)
|
||||
add_example_executable(example_grouped_gemm_xdl_fixed_nk_fp8 grouped_gemm_xdl_fixed_nk_fp8.cpp)
|
||||
add_dependencies(example_grouped_gemm_xdl example_grouped_gemm_xdl_fixed_nk_fp8)
|
||||
endif()
|
||||
|
||||
if(USE_BITINT_EXTENSION_INT4)
|
||||
add_example_executable(example_grouped_gemm_xdl_int4 grouped_gemm_xdl_int4.cpp)
|
||||
add_dependencies(example_grouped_gemm_xdl example_grouped_gemm_xdl_int4)
|
||||
|
||||
330
example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_fp8.cpp
Normal file
330
example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_fp8.cpp
Normal file
@@ -0,0 +1,330 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include <iostream>
|
||||
#include <numeric>
|
||||
#include <initializer_list>
|
||||
#include <cstdlib>
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_fixed_nk.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
#include "ck/library/utility/device_memory.hpp"
|
||||
#include "ck/library/utility/host_tensor.hpp"
|
||||
#include "ck/library/utility/host_tensor_generator.hpp"
|
||||
#include "ck/library/utility/literals.hpp"
|
||||
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using F8 = ck::f8_t;
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
using ADataType = F16;
|
||||
using BDataType = F8;
|
||||
using AccDataType = F32;
|
||||
using CShuffleDataType = F32;
|
||||
using DsDataType = ck::Tuple<>;
|
||||
using EDataType = F16;
|
||||
|
||||
using ALayout = Row;
|
||||
using BLayout = Col;
|
||||
using DsLayout = ck::Tuple<>;
|
||||
using ELayout = Row;
|
||||
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = PassThrough;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNPadding;
|
||||
|
||||
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemm_Xdl_Fixed_NK
|
||||
// clang-format off
|
||||
//######| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//######| | | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
|
||||
//######| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
|
||||
// clang-format on
|
||||
|
||||
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;
|
||||
int k_batch = 1;
|
||||
bool time_kernel = false;
|
||||
};
|
||||
|
||||
bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
|
||||
{
|
||||
auto group_count = problem_size.group_count;
|
||||
|
||||
// GEMM shape
|
||||
std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
|
||||
std::vector<void*> p_Cs;
|
||||
|
||||
gemm_descs.reserve(group_count);
|
||||
|
||||
int sum_of_m = 0;
|
||||
|
||||
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;
|
||||
std::vector<Tensor<EDataType>> c_device_tensors;
|
||||
|
||||
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(int i = 0; i < group_count; i++)
|
||||
{
|
||||
sum_of_m += problem_size.Ms[i];
|
||||
a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
|
||||
problem_size.Ms[i], problem_size.Ks[i], problem_size.stride_As[i], ALayout{})));
|
||||
b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor(
|
||||
problem_size.Ks[i], problem_size.Ns[i], problem_size.stride_Bs[i], BLayout{})));
|
||||
c_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
|
||||
problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
|
||||
c_device_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
|
||||
problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
|
||||
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) * problem_size.Ms[i] * problem_size.Ks[i] * problem_size.Ns[i];
|
||||
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<0>{});
|
||||
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
|
||||
}
|
||||
}
|
||||
|
||||
using GroupedGemmKernelArgument = ck::tensor_operation::device::GroupedGemmKernelArgument<>;
|
||||
|
||||
std::vector<GroupedGemmKernelArgument> grouped_gemm_kernel_args_;
|
||||
grouped_gemm_kernel_args_.reserve(group_count);
|
||||
|
||||
for(int i = 0; i < group_count; i++)
|
||||
{
|
||||
a_tensors_device.emplace_back(
|
||||
std::make_unique<DeviceMem>(sizeof(ADataType) * sum_of_m * problem_size.Ks[i]));
|
||||
|
||||
b_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(BDataType) * problem_size.Ns[i] * problem_size.Ks[i]));
|
||||
|
||||
c_tensors_device.emplace_back(
|
||||
std::make_unique<DeviceMem>(sizeof(EDataType) * sum_of_m * problem_size.Ns[i]));
|
||||
|
||||
a_tensors_device[i]->ToDevice(a_tensors[i].mData.data(),
|
||||
a_tensors[i].mDesc.GetElementSpaceSize() * sizeof(ADataType));
|
||||
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data(),
|
||||
b_tensors[i].mDesc.GetElementSpaceSize() * sizeof(BDataType));
|
||||
c_tensors_device[i]->SetZero();
|
||||
|
||||
p_Cs.push_back(c_tensors_device[i]->GetDeviceBuffer());
|
||||
|
||||
gemm_descs.push_back({sum_of_m,
|
||||
problem_size.Ns[i],
|
||||
problem_size.Ks[i],
|
||||
1,
|
||||
problem_size.stride_Bs[i],
|
||||
1,
|
||||
{}});
|
||||
|
||||
grouped_gemm_kernel_args_.push_back({a_tensors_device[i]->GetDeviceBuffer(),
|
||||
b_tensors_device[i]->GetDeviceBuffer(),
|
||||
{},
|
||||
c_tensors_device[i]->GetDeviceBuffer(),
|
||||
problem_size.Ms[i],
|
||||
problem_size.Ns[i],
|
||||
problem_size.Ks[i],
|
||||
problem_size.stride_As[i],
|
||||
problem_size.stride_Bs[i],
|
||||
{},
|
||||
problem_size.stride_Cs[i]});
|
||||
}
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto c_element_op = CDEElementOp{};
|
||||
|
||||
auto gemm = DeviceGemmInstance{};
|
||||
auto invoker = gemm.MakeInvoker();
|
||||
|
||||
std::vector<const void*> p_As = {};
|
||||
std::vector<const void*> p_Bs = {};
|
||||
std::vector<std::array<const void*, 0>> p_Ds = {};
|
||||
|
||||
// do GEMM
|
||||
auto argument = gemm.MakeArgument(
|
||||
p_As, p_Bs, p_Ds, p_Cs, gemm_descs, a_element_op, b_element_op, c_element_op);
|
||||
|
||||
DeviceMem gemm_arg_dev_mem(gemm.GetDeviceKernelArgSize(&argument));
|
||||
DeviceMem gemm_workspace_dev(gemm.GetWorkSpaceSize(&argument));
|
||||
|
||||
gemm.SetWorkSpacePointer(&argument, gemm_workspace_dev.GetDeviceBuffer());
|
||||
|
||||
hip_check_error(hipMemcpy(gemm_arg_dev_mem.GetDeviceBuffer(),
|
||||
grouped_gemm_kernel_args_.data(),
|
||||
gemm.GetDeviceKernelArgSize(&argument),
|
||||
hipMemcpyHostToDevice));
|
||||
|
||||
if(!gemm.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_gemm with the specified compilation parameters does "
|
||||
"not support this GEMM problem");
|
||||
}
|
||||
|
||||
gemm.SetDeviceKernelArgs(argument, gemm_arg_dev_mem.GetDeviceBuffer());
|
||||
gemm.SetKBatch(argument, config.k_batch);
|
||||
|
||||
invoker.Run(argument, StreamConfig{nullptr, false});
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
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(),
|
||||
c_device_tensors[i].mDesc.GetElementSize() *
|
||||
sizeof(EDataType));
|
||||
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);
|
||||
|
||||
pass &= ck::utils::check_err(c_device_tensors[i], c_host_tensors[i]);
|
||||
}
|
||||
}
|
||||
|
||||
return pass;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
ProblemSize problem_size;
|
||||
ExecutionConfig config;
|
||||
|
||||
problem_size.group_count = 16;
|
||||
|
||||
problem_size.Ms = {
|
||||
167, 183, 177, 181, 153, 139, 156, 173, 163, 150, 204, 184, 168, 156, 168, 148};
|
||||
|
||||
for(int i = 0; i < problem_size.group_count; i++)
|
||||
{
|
||||
problem_size.Ns.push_back(768);
|
||||
problem_size.Ks.push_back(4608);
|
||||
|
||||
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]);
|
||||
}
|
||||
|
||||
if(argc == 5)
|
||||
{
|
||||
config.do_verification = std::stoi(argv[1]);
|
||||
config.init_method = std::stoi(argv[2]);
|
||||
config.time_kernel = std::stoi(argv[3]);
|
||||
config.k_batch = std::stoi(argv[4]);
|
||||
}
|
||||
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: k_batch (> 0)\n");
|
||||
exit(0);
|
||||
}
|
||||
|
||||
return !run_grouped_gemm(problem_size, config);
|
||||
}
|
||||
Reference in New Issue
Block a user