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Unverified Commit c7010716 authored by jakpiase's avatar jakpiase Committed by GitHub
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Add Grouped Gemm Multiple D SplitK TwoStage (#1212) 



* Support A/B/C elementwise ops.

* First part of GGEMM multiD splitk two stage.

* WIP - changes for debuggin.

* tmp save

* working version

* added bf16@int8 version

* fixes

* add reviewers sugestions

* pre-commited missing files

* switched to ifs from elseifs

---------
Co-authored-by: default avatarAdam Osewski <Adam.Osewski@amd.com>
parent a61e73bc
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example/15_grouped_gemm/grouped_gemm_multiple_d_splitk_xdl_fp16.cpp 0 → 100644
View file @ c7010716
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, 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_multiple_d_splitk_xdl_cshuffle_two_stage.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include <ck/utility/data_type.hpp>
#include <ck/utility/tuple.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_multiple_d.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
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 AddAdd = ck::tensor_operation::element_wise::AddAdd;
using ADataType = F16;
using BDataType = F16;
using AccDataType = F32;
using CShuffleDataType = F32;
using DDataType = F16;
using DsDataType = ck::Tuple<DDataType, DDataType>;
using EDataType = F32;
using ALayout = Row;
using BLayout = Col;
using DLayout = Row;
using DsLayout = ck::Tuple<DLayout, DLayout>;
using ELayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = AddAdd;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
static constexpr int NumDMatrices = 2;
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage
// 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, GemmMNKPadding, 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>, 4>;
// 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<std::vector<ck::index_t>> stride_Ds;
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 = 128;
bool time_kernel = true;
};
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;
std::vector<const void*> p_As;
std::vector<const void*> p_Bs;
std::vector<std::array<const void*, NumDMatrices>> p_Ds = {};
gemm_descs.reserve(group_count);
p_As.reserve(group_count);
p_Bs.reserve(group_count);
p_Ds.reserve(group_count);
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<std::array<Tensor<DDataType>, NumDMatrices>> d_tensors;
std::vector<Tensor<EDataType>> c_host_tensors;
std::vector<Tensor<EDataType>> c_device_result_tensors;
a_tensors.reserve(group_count);
b_tensors.reserve(group_count);
d_tensors.reserve(group_count);
c_host_tensors.reserve(group_count);
c_device_result_tensors.reserve(group_count);
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, c_tensors_device;
std::vector<std::vector<DeviceMemPtr>> d_tensors_device;
a_tensors_device.reserve(group_count);
b_tensors_device.reserve(group_count);
d_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++)
{
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{})));
auto d0_tensor = Tensor<DDataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], DLayout{}));
auto d1_tensor = Tensor<DDataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], DLayout{}));
std::array<Tensor<DDataType>, NumDMatrices> d_tens = {d0_tensor, d1_tensor};
d_tensors.push_back(d_tens);
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_result_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_result_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].GetElementSize() +
sizeof(BDataType) * b_tensors[i].GetElementSize() +
sizeof(DDataType) * d_tensors[i][0].GetElementSize() * NumDMatrices +
sizeof(EDataType) * c_device_result_tensors[i].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});
for(int j = 0; j < NumDMatrices; ++j)
{
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_2<DDataType>{-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});
for(int j = 0; j < NumDMatrices; ++j)
{
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
}
break;
default:
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
for(int j = 0; j < NumDMatrices; ++j)
{
d_tensors[i][j].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
}
}
}
for(int i = 0; i < group_count; i++)
{
a_tensors_device.emplace_back(
std::make_unique<DeviceMem>(a_tensors[i].GetElementSpaceSize() * sizeof(ADataType)));
b_tensors_device.emplace_back(
std::make_unique<DeviceMem>(b_tensors[i].GetElementSpaceSize() * sizeof(BDataType)));
c_tensors_device.emplace_back(std::make_unique<DeviceMem>(
c_device_result_tensors[i].GetElementSpaceSize() * sizeof(EDataType)));
for(int j = 0; j < NumDMatrices; ++j)
{
d_tensors_device[i].emplace_back(std::make_unique<DeviceMem>(
d_tensors[i][j].GetElementSpaceSize() * sizeof(DDataType)));
}
a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
for(int j = 0; j < NumDMatrices; ++j)
{
d_tensors_device[i][j]->ToDevice(d_tensors[i][j].mData.data());
}
c_tensors_device[i]->SetZero();
p_As.push_back(a_tensors_device[i]->GetDeviceBuffer());
p_Bs.push_back(b_tensors_device[i]->GetDeviceBuffer());
p_Ds.push_back(
{d_tensors_device[i][0]->GetDeviceBuffer(), d_tensors_device[i][1]->GetDeviceBuffer()});
p_Cs.push_back(c_tensors_device[i]->GetDeviceBuffer());
gemm_descs.push_back({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],
problem_size.stride_Ds[i]});
}
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
// do GEMM
auto argument = gemm.MakeArgument(
p_As, p_Bs, p_Ds, p_Cs, gemm_descs, a_element_op, b_element_op, cde_element_op);
gemm.SetKBatchSize(argument, config.k_batch);
if(!gemm.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_gemm with the specified compilation parameters does "
"not support this GEMM problem");
}
DeviceMem gemm_workspace_dev(gemm.GetWorkSpaceSize(&argument));
gemm.SetWorkSpacePointer(&argument, gemm_workspace_dev.GetDeviceBuffer());
DeviceMem gemm_arg_dev_mem(gemm.GetDeviceKernelArgSize(&argument));
gemm.SetDeviceKernelArgs(argument, gemm_arg_dev_mem.GetDeviceBuffer());
invoker.Run(argument, StreamConfig{nullptr, false, 1});
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::ReferenceGemmMultipleD<ADataType,
BDataType,
DsDataType,
EDataType,
AccDataType,
AElementOp,
BElementOp,
CDEElementOp>;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
auto karg = argument.gemm_kernel_args_[i].karg_;
auto dev_res_tensor =
Tensor<float>(f_host_tensor_descriptor(karg.M, karg.N, karg.StrideC, ELayout{}));
c_tensors_device[i]->FromDevice(c_device_result_tensors[i].mData.data(),
c_device_result_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],
d_tensors[i],
c_host_tensors[i],
a_element_op,
b_element_op,
cde_element_op);
ref_invoker.Run(ref_argument);
pass &= ck::utils::check_err(c_device_result_tensors[i], c_host_tensors[i]);
}
std::cout << "Verification: " << (pass ? "SUCCESS" : "FAILURE") << "!" << std::endl;
}
return pass;
}
std::vector<int> argToIntArray(char* input)
{
std::vector<int> out;
std::istringstream in(input);
std::string item;
while(std::getline(in, item, ','))
{
out.push_back(std::stoi(item));
}
return out;
}
int main(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
if(argc < 11)
{
std::vector<ck::index_t> Ms{64, 127, 255, 129, 260, 190, 77};
problem_size.group_count = Ms.size();
for(int i = 0; i < problem_size.group_count; i++)
{
problem_size.Ms.push_back(Ms[i]);
problem_size.Ns.push_back(252);
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]);
problem_size.stride_Ds.push_back({});
for(int j = 0; j < NumDMatrices; ++j)
{
problem_size.stride_Ds[i].push_back(problem_size.Ns[i]);
}
}
std::cout
<< "Usage:\n"
<< "arg1: verification (0=no, 1=yes)\n"
<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
<< "arg3: time kernel (0=n0, 1=yes)\n"
<< "arg4 to 9: Ms, Ns, Ks, StrideAs, StrideBs, StrideCs (e.g., 256,256 128,128 64,64 "
"64,64 64,64 128,128)\n"
<< "arg10: k_batch (> 0)\n"
<< "... setting default values." << std::endl;
}
else
{
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[10]);
problem_size.Ms = argToIntArray(argv[4]);
problem_size.Ns = argToIntArray(argv[5]);
problem_size.Ks = argToIntArray(argv[6]);
problem_size.stride_As = argToIntArray(argv[7]);
problem_size.stride_Bs = argToIntArray(argv[8]);
problem_size.stride_Cs = argToIntArray(argv[9]);
for(int j = 0; j < NumDMatrices; ++j)
{
problem_size.stride_Ds.push_back(problem_size.stride_Cs);
}
problem_size.group_count = problem_size.Ms.size();
}
return !run_grouped_gemm(problem_size, config);
}
include/ck/tensor_operation/gpu/device/device_grouped_gemm_multiple_d_splitk.hpp 0 → 100644
View file @ c7010716
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <array>
#include <iostream>
#include <vector>
#include <sstream>
#include "device_grouped_gemm.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
///
/// @brief Structure representing single GEMM problem arguments.
///
/// The pointer to the vector of those structures is passed to the GroupedGEMM entry
/// point kernel.
///
/// @tparam NumDTensor The number of D input tensors.
///
template <index_t NumDTensor = 0>
struct GroupedGemmMultipleDKernelArguments
{
__host__ __device__
GroupedGemmMultipleDKernelArguments(const void* p_a_grid_,
const void* p_b_grid_,
std::array<const void*, NumDTensor> p_ds_grid_,
void* p_e_grid_,
index_t M_,
index_t N_,
index_t K_,
index_t StrideA_,
index_t StrideB_,
std::array<index_t, NumDTensor> StrideDs_,
index_t StrideE_)
: p_a_grid{p_a_grid_},
p_b_grid{p_b_grid_},
p_ds_grid{p_ds_grid_},
p_e_grid{p_e_grid_},
M{M_},
N{N_},
K{K_},
StrideA{StrideA_},
StrideB{StrideB_},
StrideDs{StrideDs_},
StrideE{StrideE_}
{
}
const void* p_a_grid;
const void* p_b_grid;
std::array<const void*, NumDTensor> p_ds_grid;
void* p_e_grid;
index_t M;
index_t N;
index_t K;
index_t StrideA;
index_t StrideB;
std::array<index_t, NumDTensor> StrideDs;
index_t StrideE;
void Print() const
{
std::stringstream str;
for(auto sd : StrideDs)
str << sd << ",";
std::cout << "arg {"
<< "M:" << M << ", "
<< "N:" << N << ", "
<< "K:" << K << ", "
<< "SA:" << StrideA << ", "
<< "SB:" << StrideB << ", "
<< "SE:" << StrideE << ", "
<< "SDs: {" << str.str() << "}"
<< "}" << std::endl;
}
};
template <typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
typename ADataType,
typename BDataType,
typename DsDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation>
struct DeviceGroupedGemmMultipleDSplitK : public DeviceGroupedGemm<ALayout,
BLayout,
DsLayout,
ELayout,
ADataType,
BDataType,
DsDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation>
{
//----------------------------------------------------------------------------------------------
/// @brief Sets the k batch size.
///
/// @param p_arg Pointer to the Argument we're going to change.
/// @param[in] kbatch The kbatch value.
///
virtual void SetKBatchSize(BaseArgument* p_arg, index_t kbatch) const = 0;
//----------------------------------------------------------------------------------------------
/// @brief Sets the device kernel arguments pointer.
///
/// @param p_arg The pointer to the Argument we're going to update.
/// @param[in] p_dev_kernel_args The pointer to the device memory which contains kernel
/// arguments.
///
virtual void SetDeviceKernelArgs(BaseArgument* p_arg, void* p_dev_kernel_args) const = 0;
//----------------------------------------------------------------------------------------------
/// @brief Gets the device kernel argument size.
///
/// @param[in] p_arg The pointer to the Device op Argument.
///
/// @return The device kernel argument size.
///
virtual size_t GetDeviceKernelArgSize(const BaseArgument* p_arg) const = 0;
};
} // namespace device
} // namespace tensor_operation
} // namespace ck
include/ck/tensor_operation/gpu/device/impl/device_elementwise_impl.hpp
View file @ c7010716
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -22,10 +22,12 @@ namespace device {
template <typename InDataTypeTuple,
typename OutDataTypeTuple,
typename ElementwiseOperation,
index_t NumDim,
index_t MPerThread,
typename InScalarPerVectorSeq,
typename OutScalarPerVectorSeq>
index_t NumDim, // The max dim of input tensors
// the tensors descs have to be aligned, such that
// the innermost dim is the contiguous one.
index_t MPerThread, // How many elements per thread to read
typename InScalarPerVectorSeq, // Scalar per vec for each Input
typename OutScalarPerVectorSeq> // Scalar per vec for each Output
struct DeviceElementwiseImpl
: public DeviceElementwise<InDataTypeTuple, OutDataTypeTuple, ElementwiseOperation, NumDim>
{
......@@ -242,13 +244,13 @@ struct DeviceElementwiseImpl
static_for<0, NumInput, 1>{}([&](auto I) {
if(!IsScalarPerVectorValid(
arg.lengths_, arg.inStridesArray_[I.value], InScalarPerVectorSeq::At(I)))
valid = false;
valid = valid && false;
});
static_for<0, NumOutput, 1>{}([&](auto I) {
if(!IsScalarPerVectorValid(
arg.lengths_, arg.outStridesArray_[I.value], OutScalarPerVectorSeq::At(I)))
valid = false;
valid = valid && false;
});
return valid;
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multiple_d_splitk_xdl_cshuffle_two_stage.hpp 0 → 100644
View file @ c7010716
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <sstream>
#include <tuple>
#include "ck/ck.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"
#include "ck/host_utility/hip_check_error.hpp"
#include "ck/utility/common_header.hpp"
#include <ck/utility/loop_scheduler.hpp>
#include "ck/utility/tuple.hpp"
#include "ck/utility/sequence_helper.hpp"
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_multiple_d_splitk.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_elementwise_dynamic_vector_dims.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_splitk_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include <ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp>
#include <ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_selector.hpp>
namespace ck {
namespace tensor_operation {
namespace device {
template <typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
typename ADataType,
typename BDataType,
typename AccDataType,
typename CShuffleDataType,
typename DsDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
GemmSpecialization GemmSpec,
ck::index_t NumGemmKPrefetchStage,
ck::index_t BlockSize,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
ck::index_t KPerBlock,
ck::index_t AK1,
ck::index_t BK1,
ck::index_t MPerXDL,
ck::index_t NPerXDL,
ck::index_t MXdlPerWave,
ck::index_t NXdlPerWave,
typename ABlockTransferThreadClusterLengths_KBatch_AK0_M_AK1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
index_t ABlockTransferSrcVectorDim,
index_t ABlockTransferSrcScalarPerVector,
index_t ABlockTransferDstScalarPerVector_AK1,
index_t ABlockLdsExtraM,
typename BBlockTransferThreadClusterLengths_KBatch_BK0_N_BK1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
index_t BBlockTransferSrcVectorDim,
index_t BBlockTransferSrcScalarPerVector,
index_t BBlockTransferDstScalarPerVector_BK1,
index_t BBlockLdsExtraN,
index_t CShuffleMXdlPerWavePerShuffle,
index_t CShuffleNXdlPerWavePerShuffle,
typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
index_t CDEShuffleBlockTransferScalarPerVector_NPerBlock,
PipelineVersion PipelineVer = PipelineVersion::v1,
LoopScheduler LoopSched = make_default_loop_scheduler(),
typename ComputeDataType = EDataType,
// TODO: change gridwise_gemm_v2r4r2 to support AK1 & BK1
enable_if_t<AK1 == BK1, bool> = false>
struct DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage
: public DeviceGroupedGemmMultipleDSplitK<ALayout,
BLayout,
DsLayout,
ELayout,
ADataType,
BDataType,
DsDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation>
{
using DeviceOp = DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage;
static constexpr index_t NumDTensor = DsDataType::Size();
static constexpr auto I0 = Number<0>{};
static constexpr auto I1 = Number<1>{};
static constexpr auto I2 = Number<2>{};
static constexpr auto I3 = Number<3>{};
// TODO change GridwiseGEMM v2r4r2 to support separate AK1 & BK1
static constexpr index_t K0PerBlock = KPerBlock / AK1;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using WorkspaceDataType = float;
// First stage GridwiseGEMM kernel.
using GridwiseGemm = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2<
BlockSize,
ADataType,
BDataType,
AccDataType,
WorkspaceDataType,
ALayout,
BLayout,
ELayout,
AElementwiseOperation,
BElementwiseOperation,
PassThrough, // CElementwiseOperation
GemmSpec,
NumGemmKPrefetchStage,
MPerBlock,
NPerBlock,
K0PerBlock,
MPerXDL,
NPerXDL,
AK1,
MXdlPerWave,
NXdlPerWave,
ABlockTransferThreadClusterLengths_KBatch_AK0_M_AK1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_AK1,
false, // AThreadTransferSrcResetCoordinateAfterRun,
ABlockLdsExtraM,
BBlockTransferThreadClusterLengths_KBatch_BK0_N_BK1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_BK1,
false, // BThreadTransferSrcResetCoordinateAfterRun,
BBlockLdsExtraN,
CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
CDEShuffleBlockTransferScalarPerVector_NPerBlock,
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
LoopSched,
PipelineVer,
ComputeDataType>;
template <typename ELay>
static auto MakeEGridDescriptor_M_N(index_t M, index_t N, index_t StrideE)
{
const auto c_grid_desc_m_n = [&]() {
if constexpr(is_same<tensor_layout::gemm::RowMajor, ELay>::value)
{
return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideE, I1));
}
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ELay>::value)
{
return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideE));
}
}();
if constexpr(GemmSpec == GemmSpecialization::MNPadding)
{
const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
return transform_tensor_descriptor(
c_grid_desc_m_n,
make_tuple(make_right_pad_transform(M, PadM), make_right_pad_transform(N, PadN)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
}
else
{
return transform_tensor_descriptor(
c_grid_desc_m_n,
make_tuple(make_pass_through_transform(M), make_pass_through_transform(N)),
make_tuple(Sequence<0>{}, Sequence<1>{}),
make_tuple(Sequence<0>{}, Sequence<1>{}));
}
}
static auto MakeDsGridDescriptor_M_N(const std::array<index_t, NumDTensor>& MRaws,
const std::array<index_t, NumDTensor>& NRaws,
const std::array<index_t, NumDTensor>& DsStride)
{
return generate_tuple(
[&](auto i) {
using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
return MakeEGridDescriptor_M_N<DLayout>(MRaws[i], NRaws[i], DsStride[i]);
},
Number<NumDTensor>{});
}
static constexpr auto MakeDsGridPointer()
{
return generate_tuple(
[&](auto i) {
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
return static_cast<const DDataType*>(nullptr);
},
Number<NumDTensor>{});
}
static constexpr auto MakeElementwiseInputSequence()
{
return generate_sequence_v2(
[&]([[maybe_unused]] auto i) constexpr {
return Number<CDEShuffleBlockTransferScalarPerVector_NPerBlock>{};
},
Number<NumDTensor + 1>{});
}
using CGridDesc_M_N = typename GridwiseGemm::CGridDesc_M_N;
using EGridDesc_M_N = typename GridwiseGemm::CGridDesc_M_N;
using DsGridDesc_M_N = decltype(MakeDsGridDescriptor_M_N({}, {}, {}));
using DsGridPointer = decltype(MakeDsGridPointer());
using CDGridDesc_M_N = decltype(concat_tuple(ck::Tuple<CGridDesc_M_N>{}, DsGridDesc_M_N{}));
using CDDataTypes = decltype(concat_tuple(ck::Tuple<WorkspaceDataType*>{}, DsGridPointer{}));
using ElementwiseInputSequence = decltype(MakeElementwiseInputSequence());
static constexpr index_t ClusterLengthMPerBlock =
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock::At(1);
static constexpr index_t ClusterLengthNPerBlock =
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock::At(3);
using Block2ETileMapKSplit =
BlockToCTileMap_KSplit_M00_N0_M01Adapt<MPerBlock, NPerBlock, CGridDesc_M_N>;
using Block2TileMap = BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock>;
using GridwiseElementwise =
GridwiseElementwise<CDGridDesc_M_N,
ck::Tuple<EGridDesc_M_N>,
CDDataTypes,
ck::Tuple<EDataType*>,
Block2TileMap,
CDEElementwiseOperation,
BlockSize,
MPerBlock,
NPerBlock,
MPerBlock / ClusterLengthMPerBlock,
NPerBlock / ClusterLengthNPerBlock,
Sequence<0, 1>,
ElementwiseInputSequence,
ck::Sequence<CDEShuffleBlockTransferScalarPerVector_NPerBlock>,
true>;
// Block2CTileMap configuration parameter.
static constexpr index_t B2E_M01 = 8;
using GroupedGemmBlock2ETileMap = OffsettedBlockToCTileMap<Block2ETileMapKSplit>;
using GemmKernelArgument = typename GridwiseGemm::Argument;
struct GemmTransKernelArg
{
GemmKernelArgument karg_;
GroupedGemmBlock2ETileMap block_2_ctile_map_;
index_t block_start_, block_end_;
GemmTransKernelArg() = default;
GemmTransKernelArg(GemmKernelArgument&& karg,
GroupedGemmBlock2ETileMap&& b2c_map,
index_t block_start,
index_t block_end)
: karg_{karg},
block_2_ctile_map_{b2c_map},
block_start_{block_start},
block_end_{block_end}
{
}
};
static constexpr index_t DefaultKBatch = 1;
// Argument
struct Argument : public BaseArgument
{
Argument(std::vector<const void*>& p_As,
std::vector<const void*>& p_Bs,
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
std::vector<void*>& p_Es,
std::vector<GemmDesc>& gemm_descs,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op)
: Argument(p_As,
p_Bs,
p_Ds,
p_Es,
gemm_descs,
a_element_op,
b_element_op,
cde_element_op,
DefaultKBatch)
{
}
Argument(std::vector<const void*>& p_As,
std::vector<const void*>& p_Bs,
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
std::vector<void*>& p_Es,
std::vector<GemmDesc>& gemm_descs,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op,
index_t kbatch)
: K_BATCH{kbatch},
group_count_{0},
skipped_group_count_{0},
grid_size_{0},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
cde_element_op_{cde_element_op},
p_Ds_{p_Ds}
{
group_count_ = ck::type_convert<ck::index_t>(gemm_descs.size());
if(!(group_count_ == ck::type_convert<ck::index_t>(p_As.size()) &&
group_count_ == ck::type_convert<ck::index_t>(p_Bs.size()) &&
group_count_ == ck::type_convert<ck::index_t>(p_Es.size())))
{
throw std::runtime_error("Error! group_count_ != p_As/Bs/Ds/Es size");
}
gemm_kernel_args_.reserve(group_count_);
elementwise_c_grid_descs_m_n_.reserve(group_count_);
elementwise_d_grid_descs_m_n_.reserve(group_count_);
ds_grid_pointer_.reserve(group_count_);
group_grid_size_.reserve(group_count_);
for(std::size_t i = 0; i < gemm_descs.size(); ++i)
{
const index_t M = gemm_descs[i].M_;
const index_t N = gemm_descs[i].N_;
const index_t K = gemm_descs[i].K_;
if(M * N * K == 0)
{
skipped_group_count_++;
continue;
}
const index_t stride_a = gemm_descs[i].stride_A_;
const index_t stride_b = gemm_descs[i].stride_B_;
const index_t stride_e = gemm_descs[i].stride_C_;
const index_t m_padded = GridwiseGemm::CalculateMPadded(M);
const index_t n_padded = GridwiseGemm::CalculateNPadded(N);
const index_t k_padded = GridwiseGemm::CalculateKPadded(K, K_BATCH);
const index_t k0_padded = GridwiseGemm::CalculateK0Padded(K, K_BATCH);
const auto c_grid_desc_m_n = GridwiseGemm::MakeCGridDescriptor_M_N(M, N, stride_e);
DsGridDesc_M_N ds_grid_desc_m_n;
DsGridPointer p_ds_grid;
static_for<0, NumDTensor, 1>{}([&](auto j) {
using DLayout = remove_cvref_t<tuple_element_t<j.value, DsLayout>>;
using DDataType = remove_cvref_t<tuple_element_t<j.value, DsDataType>>;
p_ds_grid(j) = static_cast<const DDataType*>(p_Ds[i][j]);
ds_grid_desc_m_n(j) = DeviceOp::MakeEGridDescriptor_M_N<DLayout>(
M, N, gemm_descs[i].stride_Ds_[j]);
});
const auto local_b2c_tile_map =
Block2ETileMapKSplit{c_grid_desc_m_n, B2E_M01, K_BATCH};
const index_t grid_size_grp = local_b2c_tile_map.CalculateGridSize(c_grid_desc_m_n);
const index_t block_start = grid_size_;
const index_t block_end = grid_size_ + grid_size_grp;
grid_size_ += grid_size_grp;
group_grid_size_[i] = grid_size_grp;
// block-to-e-tile map
auto grouped_block_2_ctile_map =
GroupedGemmBlock2ETileMap(local_b2c_tile_map, block_start);
std::array<index_t, NumDTensor> stride_ds;
static_for<0, NumDTensor, 1>{}([&](auto j) {
if(gemm_descs[i].stride_Ds_.size() != NumDTensor)
{
throw std::runtime_error(
"Error! gemm_descs[i].stride_Ds_.size() does not match NumDTensor");
}
stride_ds[j] = gemm_descs[i].stride_Ds_[j];
});
stride_Ds_.emplace_back(std::move(stride_ds));
// We first set E pointer to actual operation output, but later on
// when workspace will be set, this will be updated to workspace memory.
auto karg = GemmKernelArgument{type_convert<const ADataType*>(p_As[i]),
type_convert<const BDataType*>(p_Bs[i]),
type_convert<WorkspaceDataType*>(p_Es[i]),
M,
N,
K,
stride_a,
stride_b,
stride_e,
m_padded,
n_padded,
k_padded,
k0_padded,
K_BATCH};
gemm_kernel_args_.emplace_back(
std::move(karg), std::move(grouped_block_2_ctile_map), block_start, block_end);
elementwise_c_grid_descs_m_n_.push_back(c_grid_desc_m_n);
elementwise_d_grid_descs_m_n_.push_back(ds_grid_desc_m_n);
ds_grid_pointer_.push_back(p_ds_grid);
}
// Store a copy of E pointers for elementwise kernel destination
e_ptrs_ = p_Es;
}
/**
* @brief Set new kbatch value.
*
* @param[in] kbatch The new splitK parameter value.
*/
void UpdateKBatch(index_t kbatch)
{
K_BATCH = kbatch;
grid_size_ = 0;
for(std::size_t i = 0; i < gemm_kernel_args_.size(); ++i)
{
auto& karg = gemm_kernel_args_[i].karg_;
const index_t k_padded = GridwiseGemm::CalculateKPadded(karg.K, K_BATCH);
const index_t k0_padded = GridwiseGemm::CalculateK0Padded(karg.K, K_BATCH);
const auto c_grid_desc_m_n =
GridwiseGemm::MakeCGridDescriptor_M_N(karg.M, karg.N, karg.StrideC);
const auto local_b2c_tile_map =
Block2ETileMapKSplit{c_grid_desc_m_n, B2E_M01, K_BATCH};
const index_t grid_size_grp = local_b2c_tile_map.CalculateGridSize(c_grid_desc_m_n);
const index_t block_start = grid_size_;
const index_t block_end = grid_size_ + grid_size_grp;
grid_size_ += grid_size_grp;
// block-to-e-tile map
auto grouped_block_2_ctile_map =
GroupedGemmBlock2ETileMap(local_b2c_tile_map, block_start);
group_grid_size_[i] = grid_size_grp;
karg.KPadded = k_padded;
karg.K0Padded = k0_padded;
karg.k_batch = K_BATCH;
gemm_kernel_args_[i].block_2_ctile_map_ = grouped_block_2_ctile_map;
gemm_kernel_args_[i].block_start_ = block_start;
gemm_kernel_args_[i].block_end_ = block_end;
#if DEBUG_LOG
index_t tiles = (block_end - block_start) / K_BATCH;
std::cout << "block_start: " << block_start << "\n"
<< "block_end: " << block_end << "\n"
<< "tiles: " << tiles << std::endl
<< std::endl;
std::cout << "KPadded: " << karg.KPadded << std::endl
<< "K0Padded: " << karg.K0Padded << std::endl
<< "KBatch: " << karg.k_batch << std::endl
<< "grid_size_: " << karg.KPadded << std::endl;
#endif
}
}
void UpdateEPointers()
{
// set-up each group E pointer to it's designated workspace memory.
WorkspaceDataType* p_workspace = reinterpret_cast<WorkspaceDataType*>(p_workspace_);
std::size_t offset = 0;
for(auto& arg : gemm_kernel_args_)
{
arg.karg_.p_c_grid = p_workspace + offset;
index_t tiles = (arg.block_end_ - arg.block_start_) / arg.karg_.k_batch;
offset += tiles * MPerBlock * NPerBlock;
#if DEBUG_LOG
std::cout << "block_start: " << arg.block_start_ << "\n"
<< "block_end: " << arg.block_end_ << "\n"
<< "tiles: " << tiles << "\n"
<< "offset: " << offset << std::endl;
#endif
}
}
std::size_t GetWorkspaceSizeBytes() const
{
std::size_t size_bytes{0};
for(const auto& arg : gemm_kernel_args_)
{
index_t tiles = (arg.block_end_ - arg.block_start_) / arg.karg_.k_batch;
size_bytes += tiles * MPerBlock * NPerBlock * sizeof(WorkspaceDataType);
}
return size_bytes;
}
std::size_t GetWorkspaceSize(std::size_t group) const
{
const auto& arg = gemm_kernel_args_[group];
index_t tiles = (arg.block_end_ - arg.block_start_) / arg.karg_.k_batch;
return tiles * MPerBlock * NPerBlock;
}
// private:
index_t K_BATCH;
index_t group_count_;
index_t skipped_group_count_;
index_t grid_size_;
// Pointer to device memory with GEMM kernel arguments.
const void* p_dev_gemm_args_;
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
CDEElementwiseOperation cde_element_op_;
std::vector<std::array<const void*, NumDTensor>>& p_Ds_;
std::vector<std::array<index_t, NumDTensor>> stride_Ds_;
std::vector<GemmTransKernelArg> gemm_kernel_args_;
std::vector<index_t> group_grid_size_;
std::vector<CGridDesc_M_N> elementwise_c_grid_descs_m_n_;
std::vector<DsGridDesc_M_N> elementwise_d_grid_descs_m_n_;
std::vector<DsGridPointer> ds_grid_pointer_;
std::vector<void*> e_ptrs_;
};
// Invoker
struct Invoker : public BaseInvoker
{
///
/// @brief Launch Grouped Gemm kernel.
///
/// @note This function overload is using user provided device buffer for kernel
/// arguments.
///
/// @param[in] arg The structure containing kernel arguments (in host
/// memory).
/// @param[in] dev_gemm_args The pointer to device memory with kernel arguments.
/// @param[in] dev_gemm_workspace The pointer to device memory for kernel auxiliary
/// workspace.
/// @param[in] stream_config The device stream configuration.
///
/// @return The average kernel execution time (if time measurement is enabled.)
///
float Run(const Argument& arg,
const void* dev_gemm_args,
void* dev_gemm_workspace,
const StreamConfig& stream_config = StreamConfig{})
{
auto [all_have_kbatch_gt_one, all_have_main_k_block_loop] =
CheckArgument(arg, stream_config);
if(dev_gemm_args == nullptr)
{
std::ostringstream err;
err << "The gemm arguments device buffer is not allocated!"
<< " In " << __FILE__ << ":" << __LINE__ << ", in function: " << __func__;
throw std::runtime_error(err.str());
}
if(dev_gemm_workspace == nullptr)
{
std::ostringstream err;
err << "The gemm workspace buffer is not allocated!"
<< " In " << __FILE__ << ":" << __LINE__ << ", in function: " << __func__;
throw std::runtime_error(err.str());
}
float ave_time = 0;
if(all_have_main_k_block_loop)
{
ave_time =
DispatchKernel<true>(arg, dev_gemm_args, dev_gemm_workspace, stream_config);
}
else
{
ave_time =
DispatchKernel<false>(arg, dev_gemm_args, dev_gemm_workspace, stream_config);
}
return ave_time;
}
///
/// @brief Launch Grouped Gemm kernel.
///
/// @note This function overload is using device buffers (for kernel arguments and
/// for kernel auxiliary workspace) provided with an argument. The user should
/// call @see GetDeviceKernelArgSize, @see GetWorkSpaceSize and @see
/// SetDeviceKernelArgs, @see SetWorkSpacePointer on arg parameter to properly
/// allocate those buffers.
///
/// @param[in] arg The structure containing kernel arguments (in host memory).
/// @param[in] stream_config The device stream configuration.
///
/// @return The average kernel execution time (if time measurement is enabled.)
///
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
if(arg.p_dev_gemm_args_ == nullptr)
{
std::ostringstream err;
err << "The gemm arguments device buffer is not allocated!"
<< " In " << __FILE__ << ":" << __LINE__ << ", in function: " << __func__;
throw std::runtime_error(err.str());
}
if(arg.p_workspace_ == nullptr)
{
std::ostringstream err;
err << "The gemm workspace buffer is not allocated!"
<< " In " << __FILE__ << ":" << __LINE__ << ", in function: " << __func__;
throw std::runtime_error(err.str());
}
return Run(arg, arg.p_dev_gemm_args_, arg.p_workspace_, stream_config);
}
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
private:
auto CheckArgument(const Argument& arg, const StreamConfig& stream_config) const
{
bool all_have_kbatch_gt_one, all_have_main_k_block_loop;
{
const auto a_grid_desc_kbatch_ak0_m_ak1 =
GridwiseGemm::MakeAGridDescriptor_KBatch_K0_M_K1(
arg.gemm_kernel_args_[0].karg_.M,
arg.gemm_kernel_args_[0].karg_.MPadded,
arg.gemm_kernel_args_[0].karg_.K,
arg.gemm_kernel_args_[0].karg_.StrideA,
arg.gemm_kernel_args_[0].karg_.k_batch,
arg.gemm_kernel_args_[0].karg_.K0Padded,
arg.gemm_kernel_args_[0].karg_.KPadded);
all_have_kbatch_gt_one = arg.K_BATCH > 1;
all_have_main_k_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(
a_grid_desc_kbatch_ak0_m_ak1.GetLength(I1) *
a_grid_desc_kbatch_ak0_m_ak1.GetLength(I3));
}
for(std::size_t i = 0; i < arg.gemm_kernel_args_.size(); ++i)
{
const auto& gemm_arg = arg.gemm_kernel_args_[i].karg_;
if(stream_config.log_level_ > 0)
{
gemm_arg.Print();
}
if(!GridwiseGemm::CheckValidity(gemm_arg))
{
std::ostringstream err;
err << "Group id: " << i << " has invalid GridwiseGemm settings!" << __FILE__
<< ":" << __LINE__ << ", in function: " << __func__;
throw std::runtime_error(err.str());
}
const auto a_grid_desc_kbatch_ak0_m_ak1 =
GridwiseGemm::MakeAGridDescriptor_KBatch_K0_M_K1(gemm_arg.M,
gemm_arg.MPadded,
gemm_arg.K,
gemm_arg.StrideA,
gemm_arg.k_batch,
gemm_arg.K0Padded,
gemm_arg.KPadded);
bool not_all_have_main_k_block_loop_same =
all_have_main_k_block_loop xor GridwiseGemm::CalculateHasMainK0BlockLoop(
a_grid_desc_kbatch_ak0_m_ak1.GetLength(I1) *
a_grid_desc_kbatch_ak0_m_ak1.GetLength(I3));
bool not_all_have_kbatch_value_same =
all_have_kbatch_gt_one xor (gemm_arg.k_batch > 1);
if(not_all_have_main_k_block_loop_same)
{
std::ostringstream err;
err << "Not all gemms have same value for main_k0_block_loop! in " << __FILE__
<< ":" << __LINE__ << ", in function: " << __func__;
throw std::runtime_error(err.str());
}
if(not_all_have_kbatch_value_same)
{
std::ostringstream err;
err << "Not all gemms have same kbatch value (=1 or >1)! "
<< "group [" << i << "], kbatch: " << gemm_arg.k_batch
<< ", group [0], kbatch: " << gemm_arg.k_batch << " in " << __FILE__ << ":"
<< __LINE__ << ", in function: " << __func__;
throw std::runtime_error(err.str());
}
}
return std::make_tuple(all_have_kbatch_gt_one, all_have_main_k_block_loop);
}
template <bool HasMainKBlockLoop>
float DispatchKernel(const Argument& arg,
const void* dev_gemm_args,
void* dev_gemm_workspace,
const StreamConfig& stream_config) const
{
const auto gemm_kernel =
kernel_grouped_gemm_xdl_splitk<GridwiseGemm,
GemmTransKernelArg,
HasMainKBlockLoop,
InMemoryDataOperationEnum::AtomicAdd,
AElementwiseOperation,
BElementwiseOperation,
PassThrough>;
const auto elementwise_kernel = kernel_elementwise<GridwiseElementwise,
CDGridDesc_M_N,
ck::Tuple<EGridDesc_M_N>,
CDDataTypes,
ck::Tuple<EDataType*>,
Block2TileMap,
CDEElementwiseOperation>;
return LaunchKernel(gemm_kernel,
elementwise_kernel,
arg,
dev_gemm_args,
dev_gemm_workspace,
stream_config);
}
template <typename KernelFunction, typename KernelFunction2>
float LaunchKernel(const KernelFunction& gemm_kernel,
const KernelFunction2& elementwise_kernel,
const Argument& arg,
const void* dev_gemm_args,
[[maybe_unused]] void* dev_gemm_workspace,
const StreamConfig& stream_config) const
{
float time{0.f};
auto preprocess = [&]() {
hip_check_error(hipMemsetAsync(
dev_gemm_workspace, 0, arg.GetWorkspaceSizeBytes(), stream_config.stream_id_));
};
// GEMM kernel
time = launch_and_time_kernel_with_preprocess(
stream_config,
preprocess,
gemm_kernel,
dim3(arg.grid_size_),
dim3(BlockSize),
0,
cast_pointer_to_constant_address_space(dev_gemm_args),
arg.group_count_,
arg.a_element_op_,
arg.b_element_op_,
PassThrough{});
// Elementwise kernels
for(int i = 0; i < arg.group_count_; ++i)
{
time += launch_and_time_kernel(
stream_config,
elementwise_kernel,
dim3(arg.group_grid_size_[i]),
dim3(BlockSize),
0,
concat_tuple(make_tuple(arg.elementwise_c_grid_descs_m_n_[i]),
arg.elementwise_d_grid_descs_m_n_[i]),
make_tuple(arg.elementwise_c_grid_descs_m_n_[i]),
concat_tuple(make_tuple(arg.gemm_kernel_args_[i].karg_.p_c_grid),
arg.ds_grid_pointer_[i]),
type_convert<EDataType*>(arg.e_ptrs_[i]),
Block2TileMap{arg.elementwise_c_grid_descs_m_n_[i].GetLength(I0),
arg.elementwise_c_grid_descs_m_n_[i].GetLength(I1)},
arg.cde_element_op_);
}
return time;
}
};
static constexpr bool IsValidCompilationParameter()
{
// TODO: properly implement this check
return true;
}
static bool IsSupportedArgument(const Argument& arg)
{
if(!ck::is_xdl_supported())
{
return false;
}
if((ck::type_convert<ck::index_t>(arg.gemm_kernel_args_.size()) +
arg.skipped_group_count_) != arg.group_count_)
{
#if DEBUG_LOG
std::cout << "The group count is not equal to sum of skipped groups "
"and kernel args size!"
<< std::endl;
#endif // DEBUG_LOG
return false;
}
bool supported = true;
for(std::size_t i = 0; i < arg.gemm_kernel_args_.size(); ++i)
{
const auto& gemm_arg = arg.gemm_kernel_args_[i].karg_;
bool group_arg_valid = GridwiseGemm::CheckValidity(gemm_arg);
if(not group_arg_valid)
{
#if DEBUG_LOG
std::cout << "[" << __func__ << "] group id: " << i
<< " has invalid GridwiseGemm settings!" << std::endl;
gemm_arg.Print();
#endif // DEBUG_LOG
}
supported = supported && group_arg_valid;
}
return supported;
}
bool IsSupportedArgument(const BaseArgument* p_arg) override
{
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
}
static auto MakeArgument(std::vector<const void*>& p_As,
std::vector<const void*>& p_Bs,
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
std::vector<void*>& p_Es,
std::vector<GemmDesc> gemm_descs,
AElementwiseOperation a_elementwise_op,
BElementwiseOperation b_elementwise_op,
CDEElementwiseOperation cde_elementwise_op)
{
return Argument{p_As,
p_Bs,
p_Ds,
p_Es,
gemm_descs,
a_elementwise_op,
b_elementwise_op,
cde_elementwise_op};
}
std::unique_ptr<BaseArgument>
MakeArgumentPointer(std::vector<const void*>& p_As,
std::vector<const void*>& p_Bs,
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
std::vector<void*>& p_Es,
std::vector<GemmDesc>& gemm_descs,
AElementwiseOperation a_elementwise_op,
BElementwiseOperation b_elementwise_op,
CDEElementwiseOperation cde_elementwise_op) override
{
return std::make_unique<Argument>(p_As,
p_Bs,
p_Ds,
p_Es,
gemm_descs,
a_elementwise_op,
b_elementwise_op,
cde_elementwise_op);
}
static auto MakeInvoker() { return Invoker{}; }
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
{
return std::make_unique<Invoker>(Invoker{});
}
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage"
<< "<"
<< std::string(ALayout::name)[0] << ","
<< std::string(BLayout::name)[0] << ","
<< std::string(ELayout::name)[0] << ","
<< BlockSize << ", "
<< MPerBlock << ", "
<< NPerBlock << ", "
<< KPerBlock << ", "
<< AK1 << ", "
<< BK1 << ", "
<< MPerXDL << ", "
<< NPerXDL << ", "
<< MXdlPerWave << ", "
<< NXdlPerWave << ", "
<< ABlockTransferSrcScalarPerVector << ", "
<< BBlockTransferSrcScalarPerVector << ", "
<< CShuffleMXdlPerWavePerShuffle << ", "
<< CShuffleNXdlPerWavePerShuffle << ", "
<< getGemmSpecializationString(GemmSpec) << ", "
<< ">";
// clang-format on
return str.str();
}
void SetDeviceKernelArgs(Argument& arg, void* p_dev_kernel_args) const
{
arg.p_dev_gemm_args_ = p_dev_kernel_args;
hip_check_error(hipMemcpy(p_dev_kernel_args,
arg.gemm_kernel_args_.data(),
GetDeviceKernelArgSize(&arg),
hipMemcpyHostToDevice));
}
void SetDeviceKernelArgs(BaseArgument* p_arg, void* p_dev_kernel_args) const override
{
return SetDeviceKernelArgs(*dynamic_cast<Argument*>(p_arg), p_dev_kernel_args);
}
size_t GetWorkSpaceSize(const BaseArgument* p_arg) const override
{
auto arg = dynamic_cast<const Argument*>(p_arg);
if(arg)
{
return arg->GetWorkspaceSizeBytes();
}
else
throw std::runtime_error(
"The argument pointer is not an object of "
"DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage::Argument structure!");
}
void SetWorkSpacePointer(
BaseArgument* p_arg,
void* p_workspace,
[[maybe_unused]] const StreamConfig& stream_config = StreamConfig{}) const override
{
auto p_arg_ = dynamic_cast<Argument*>(p_arg);
if(p_arg_)
{
p_arg_->p_workspace_ = p_workspace;
p_arg_->UpdateEPointers();
}
else
throw std::runtime_error(
"The argument pointer is not an object of "
"DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage::Argument structure!");
}
static void SetKBatchSize(Argument& arg, index_t kbatch) { arg.UpdateKBatch(kbatch); }
void SetKBatchSize(BaseArgument* p_arg, index_t kbatch) const override
{
return SetKBatchSize(*dynamic_cast<Argument*>(p_arg), kbatch);
}
size_t GetDeviceKernelArgSize(const BaseArgument* p_arg) const override
{
return dynamic_cast<const Argument*>(p_arg)->gemm_kernel_args_.size() *
sizeof(GemmTransKernelArg);
}
};
} // namespace device
} // namespace tensor_operation
} // namespace ck
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_splitk_cshuffle.hpp
View file @ c7010716
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -26,13 +26,19 @@ namespace device {
template <typename GridwiseGemm,
typename GemmDesc,
bool HasMainKBlockLoop,
InMemoryDataOperationEnum CGlobalMemoryDataOperation>
InMemoryDataOperationEnum CGlobalMemoryDataOperation,
typename AElementwiseOperation = ck::tensor_operation::element_wise::PassThrough,
typename BElementwiseOperation = ck::tensor_operation::element_wise::PassThrough,
typename CElementwiseOperation = ck::tensor_operation::element_wise::PassThrough>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
#endif
kernel_grouped_gemm_xdl_splitk(const void CK_CONSTANT_ADDRESS_SPACE* gemm_descs_const,
const index_t group_count)
const index_t group_count,
const AElementwiseOperation a_element_op,
const BElementwiseOperation b_element_op,
const CElementwiseOperation c_element_op)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
defined(__gfx94__))
......@@ -64,10 +70,16 @@ __global__ void
GridwiseGemm::template Run<HasMainKBlockLoop, CGlobalMemoryDataOperation>(
gemm_desc_ptr[group_id].karg_,
static_cast<void*>(p_shared),
gemm_desc_ptr[group_id].block_2_ctile_map_);
gemm_desc_ptr[group_id].block_2_ctile_map_,
a_element_op,
b_element_op,
c_element_op);
#else
ignore = gemm_descs_const;
ignore = group_count;
ignore = a_element_op;
ignore = b_element_op;
ignore = c_element_op;
#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
}
......@@ -193,7 +205,7 @@ struct DeviceGroupedGemmXdlSplitKCShuffle : public DeviceGroupedGemmSplitK<ALayo
static constexpr index_t B2E_M01 = 8;
using GroupedGemmBlock2ETileMap = OffsettedBlockToCTileMap<Block2ETileMapKSplit>;
using KernelArgument = typename GridwiseGemm::Argument;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
struct GemmTransKernelArg
{
KernelArgument karg_;
......@@ -437,7 +449,10 @@ struct DeviceGroupedGemmXdlSplitKCShuffle : public DeviceGroupedGemmSplitK<ALayo
dim3(BlockSize),
0,
cast_pointer_to_constant_address_space(arg.p_workspace_),
arg.gemm_kernel_args_.size());
arg.gemm_kernel_args_.size(),
PassThrough{},
PassThrough{},
PassThrough{});
};
if(all_have_main_k0_block_loop)
......
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm_multiple_d.hpp 0 → 100644
View file @ c7010716
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <sstream>
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/library/utility/host_tensor.hpp"
namespace ck {
namespace tensor_operation {
namespace host {
// assumption: every D matrix has the same layout and the same datatype
template <typename ADataType,
typename BDataType,
typename DsDataType,
typename CDataType,
typename AccDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
typename ComputeTypeA = ADataType,
typename ComputeTypeB = ComputeTypeA>
struct ReferenceGemmMultipleD : public device::BaseOperator
{
using DDataType = remove_cvref_t<tuple_element_t<0, DsDataType>>;
// Argument
struct Argument : public device::BaseArgument
{
Argument(const Tensor<ADataType>& a_m_k,
const Tensor<BDataType>& b_k_n,
const std::array<Tensor<DDataType>, DsDataType::Size()>& ds_m_n,
Tensor<CDataType>& c_m_n,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op)
: a_m_k_{a_m_k},
b_k_n_{b_k_n},
ds_m_n_{ds_m_n},
c_m_n_{c_m_n},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
cde_element_op_{cde_element_op}
{
}
const Tensor<ADataType>& a_m_k_;
const Tensor<BDataType>& b_k_n_;
const std::array<Tensor<DDataType>, DsDataType::Size()>& ds_m_n_;
Tensor<CDataType>& c_m_n_;
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
CDEElementwiseOperation cde_element_op_;
};
// Invoker
struct Invoker : public device::BaseInvoker
{
using Argument = ReferenceGemmMultipleD::Argument;
float Run(const Argument& arg)
{
auto f_mk_kn_mn = [&](auto m, auto n) {
const int K = arg.a_m_k_.mDesc.GetLengths()[1];
AccDataType v_acc = 0;
ComputeTypeA v_a = 0;
ComputeTypeB v_b = 0;
for(int k = 0; k < K; ++k)
{
// use PassThrough instead of ConvertBF16RTN for reference calculation
if constexpr(is_same_v<AElementwiseOperation,
ck::tensor_operation::element_wise::ConvertBF16RTN>)
{
ck::tensor_operation::element_wise::PassThrough{}(v_a, arg.a_m_k_(m, k));
}
else
{
arg.a_element_op_(v_a, arg.a_m_k_(m, k));
}
// same for B matrix
if constexpr(is_same_v<BElementwiseOperation,
ck::tensor_operation::element_wise::ConvertBF16RTN>)
{
ck::tensor_operation::element_wise::PassThrough{}(v_b, arg.b_k_n_(k, n));
}
else
{
arg.b_element_op_(v_b, arg.b_k_n_(k, n));
}
v_acc +=
ck::type_convert<AccDataType>(v_a) * ck::type_convert<AccDataType>(v_b);
}
CDataType v_c = 0;
if constexpr(DsDataType::Size() == 0)
{
arg.cde_element_op_(v_c, v_acc);
}
else if constexpr(DsDataType::Size() == 1)
{
arg.cde_element_op_(v_c, v_acc, arg.ds_m_n_[0](m, n));
}
else if constexpr(DsDataType::Size() == 2)
{
arg.cde_element_op_(v_c, v_acc, arg.ds_m_n_[0](m, n), arg.ds_m_n_[1](m, n));
}
arg.c_m_n_(m, n) = v_c;
};
make_ParallelTensorFunctor(
f_mk_kn_mn, arg.c_m_n_.mDesc.GetLengths()[0], arg.c_m_n_.mDesc.GetLengths()[1])(
std::thread::hardware_concurrency());
return 0;
}
float Run(const device::BaseArgument* p_arg,
const StreamConfig& /* stream_config */ = StreamConfig{}) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}
};
static constexpr bool IsValidCompilationParameter()
{
// TODO: properly implement this check
return true;
}
bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
static auto MakeArgument(const Tensor<ADataType>& a_m_k,
const Tensor<BDataType>& b_k_n,
const std::array<Tensor<DDataType>, DsDataType::Size()>& ds_m_n,
Tensor<CDataType>& c_m_n,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op)
{
return Argument{a_m_k, b_k_n, ds_m_n, c_m_n, a_element_op, b_element_op, cde_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
{
return std::make_unique<Invoker>(Invoker{});
}
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "ReferenceGemmMultipleD"
<< std::endl;
// clang-format on
return str.str();
}
};
} // namespace host
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/grouped_gemm.hpp
View file @ c7010716
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -146,6 +146,32 @@ void add_device_grouped_gemm_xdl_splitk_f8_f16_f16_mk_kn_mn_irregular_instances(
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_gemm_multiple_d_xdl_two_stage_f16_f16_f16_mk_kn_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemm<Row,
Row,
Empty_Tuple,
Row,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_gemm_multiple_d_xdl_two_stage_bf16_i8_bf16_mk_kn_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemm<Row,
Row,
Empty_Tuple,
Row,
BF16,
I8,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
template <typename ALayout,
typename BLayout,
typename ELayout,
......@@ -180,6 +206,7 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
#if defined(CK_ENABLE_FP16)
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
is_same_v<EDataType, half_t>)
{
......@@ -190,6 +217,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
add_device_grouped_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
add_device_grouped_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_irregular_instances(
op_ptrs);
add_device_grouped_gemm_multiple_d_xdl_two_stage_f16_f16_f16_mk_kn_mn_instances(
op_ptrs);
}
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<ELayout, Row>)
......@@ -210,7 +239,9 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(op_ptrs);
}
}
else if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, f8_t> &&
#endif
#if defined(CK_ENABLE_FP16) && defined(CK_ENABLE_FP8)
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, f8_t> &&
is_same_v<EDataType, half_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
......@@ -228,6 +259,19 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
add_device_grouped_gemm_xdl_splitk_f8_f16_f16_mk_kn_mn_irregular_instances(op_ptrs);
}
}
#endif
#if defined(CK_ENABLE_BF16) && defined(CK_ENABLE_INT8)
if constexpr(is_same_v<ADataType, bhalf_t> && is_same_v<BDataType, int8_t> &&
is_same_v<EDataType, bhalf_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_multiple_d_xdl_two_stage_bf16_i8_bf16_mk_kn_mn_instances(
op_ptrs);
}
}
#endif
return op_ptrs;
}
};
......
library/src/tensor_operation_instance/gpu/grouped_gemm/CMakeLists.txt
View file @ c7010716
......@@ -10,4 +10,6 @@ add_instance_library(device_grouped_gemm_instance
device_grouped_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_irregular_instance.cpp
device_grouped_gemm_xdl_splitk_f16_f8_f16_mk_kn_mn_irregular_instance.cpp
device_grouped_gemm_xdl_splitk_f8_f16_f16_mk_kn_mn_irregular_instance.cpp
device_grouped_gemm_multiple_d_splitk_xdl_two_stage_f16_f16_f16_mk_kn_mn_instance.cpp
device_grouped_gemm_multiple_d_splitk_xdl_two_stage_bf16_i8_bf16_mk_kn_mn_instance.cpp
)
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_multiple_d_splitk_xdl_two_stage_bf16_i8_bf16_mk_kn_mn_instance.cpp 0 → 100644
View file @ c7010716
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#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_multiple_d_splitk_xdl_cshuffle_two_stage.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using I8 = int8_t;
using BF16 = ck::bhalf_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using Empty_Tuple = ck::Tuple<>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
// Instances having AK1!=BK1 are temporarily disabled and will be re-enabled in future
// a[m, k] * b[k, n] = e[m, n]
using device_grouped_gemm_multiple_d_xdl_two_stage_bf16_i8_bf16_mk_kn_mn_generic_instances =
std::tuple<
// clang-format off
//#################################################| A| B| Ds| E| 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|
//#################################################| Layout| Layout| Layout| Layout| 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|
//#################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 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, 1, 8, 1, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>
// clang-format on
>;
using device_grouped_gemm_multiple_d_xdl_two_stage_bf16_i8_bf16_mk_kn_mn_instances = std::tuple<
// clang-format off
//#################################################| A| B| Ds| E| 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|
//#################################################| Layout| Layout| Layout| Layout| 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|
//#################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 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, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, 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, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 192, 64, 32, 8, 8, 32, 32, 3, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 64, 192, 32, 8, 8, 32, 32, 1, 3, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 48, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 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, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, 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, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 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, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 32, 192, 32, 8, 8, 32, 32, 1, 3, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 24, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 192, 32, 32, 8, 8, 32, 32, 3, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 32, 64, 32, 8, 8, 32, 32, 1, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 64, 32, 32, 8, 8, 32, 32, 1, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 64, 64, 32, 8, 8, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 16, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 16, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 16, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 16, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 16, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, BF16, I8, F32, BF16, Empty_Tuple, BF16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 16, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 16, 1, 4>, 4, PipelineVersion::v1>
// clang-format on
>;
void add_device_grouped_gemm_multiple_d_xdl_two_stage_bf16_i8_bf16_mk_kn_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemm<Row,
Row,
Empty_Tuple,
Row,
BF16,
I8,
Empty_Tuple,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances, device_grouped_gemm_multiple_d_xdl_two_stage_bf16_i8_bf16_mk_kn_mn_instances{});
add_device_operation_instances(
instances,
device_grouped_gemm_multiple_d_xdl_two_stage_bf16_i8_bf16_mk_kn_mn_generic_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_multiple_d_splitk_xdl_two_stage_f16_f16_f16_mk_kn_mn_instance.cpp 0 → 100644
View file @ c7010716
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#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_multiple_d_splitk_xdl_cshuffle_two_stage.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using Empty_Tuple = ck::Tuple<>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
// Instances having AK1!=BK1 are temporarily disabled and will be re-enabled in future
// a[m, k] * b[k, n] = e[m, n]
using device_grouped_gemm_multiple_d_xdl_two_stage_f16_f16_f16_mk_kn_mn_generic_instances =
std::tuple<
// clang-format off
//#################################################| A| B| Ds| E| 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|
//#################################################| Layout| Layout| Layout| Layout| 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|
//#################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 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, 1, 8, 1, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 1>
// clang-format on
>;
using device_grouped_gemm_multiple_d_xdl_two_stage_f16_f16_f16_mk_kn_mn_instances = std::tuple<
// clang-format off
//#################################################| A| B| Ds| E| 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|
//#################################################| Layout| Layout| Layout| Layout| 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|
//#################################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 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, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, 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, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 192, 64, 32, 8, 8, 32, 32, 3, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 64, 192, 32, 8, 8, 32, 32, 1, 3, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 48, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 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, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, 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, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 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, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 32, 192, 32, 8, 8, 32, 32, 1, 3, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 24, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 192, 32, 32, 8, 8, 32, 32, 3, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 32, 64, 32, 8, 8, 32, 32, 1, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 64, 32, 32, 8, 8, 32, 32, 1, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 64, 64, 32, 8, 8, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 16, 1, 8>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 16, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 16, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 16, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 16, 1, 4>, 4, PipelineVersion::v1>,
DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<1, 4, 16, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 16, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 16, 1, 4>, 4, PipelineVersion::v1>
// clang-format on
>;
void add_device_grouped_gemm_multiple_d_xdl_two_stage_f16_f16_f16_mk_kn_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemm<Row,
Row,
Empty_Tuple,
Row,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances, device_grouped_gemm_multiple_d_xdl_two_stage_f16_f16_f16_mk_kn_mn_instances{});
add_device_operation_instances(
instances,
device_grouped_gemm_multiple_d_xdl_two_stage_f16_f16_f16_mk_kn_mn_generic_instances{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
profiler/include/profiler/profile_grouped_gemm_two_stage_impl.hpp 0 → 100644
View file @ c7010716
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iomanip>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_splitk.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_multiple_d_splitk.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_gemm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/convolution_parameter.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/utility/fill.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
namespace ck {
namespace profiler {
template <typename ADataType,
typename BDataType,
typename CDataType,
typename AccDataType,
typename ALayout,
typename BLayout,
typename CLayout>
bool profile_grouped_gemm_two_stage_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
const std::vector<int>& Ms,
const std::vector<int>& Ns,
const std::vector<int>& Ks,
const std::vector<int>& StrideAs,
const std::vector<int>& StrideBs,
const std::vector<int>& StrideCs,
int kbatch = 1,
int n_warmup = 1,
int n_iter = 10)
{
bool pass = true;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
std::size_t group_count = Ms.size();
if(!(group_count == Ns.size() && group_count == Ks.size() && group_count == StrideAs.size() &&
group_count == StrideBs.size() && group_count == StrideCs.size()))
{
throw std::runtime_error("wrong! inconsistent M/N/Ks, StrideA/B/Cs size\n");
}
std::vector<Tensor<ADataType>> a_m_k;
std::vector<Tensor<BDataType>> b_k_n;
std::vector<Tensor<CDataType>> c_m_n_host_results;
std::vector<Tensor<CDataType>> c_m_n_device_results;
for(std::size_t i = 0; i < group_count; i++)
{
a_m_k.push_back(
Tensor<ADataType>(f_host_tensor_descriptor(Ms[i], Ks[i], StrideAs[i], ALayout{})));
b_k_n.push_back(
Tensor<BDataType>(f_host_tensor_descriptor(Ks[i], Ns[i], StrideBs[i], BLayout{})));
c_m_n_device_results.push_back(
Tensor<CDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{})));
c_m_n_host_results.push_back(
Tensor<CDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{})));
#if DEBUG_LOG
std::cout << "group: " << i << " a_m_k[" << i << "]:" << a_m_k[i].mDesc << ", b_k_n[" << i
<< "]:" << b_k_n[i].mDesc << ", c_m_n_device_results[" << i
<< "]:" << c_m_n_device_results[i].mDesc << std::endl;
#endif // DEBUG_LOG
std::size_t num_thread = 1;
switch(init_method)
{
case 0: break;
case 1:
a_m_k[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread);
b_k_n[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}, num_thread);
break;
default:
a_m_k[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}, num_thread);
b_k_n[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
}
}
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CElementOp = ck::tensor_operation::element_wise::PassThrough;
const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto c_element_op = CElementOp{};
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
std::vector<DeviceMemPtr> a_device_buf, b_device_buf, c_device_buf;
a_device_buf.reserve(group_count);
b_device_buf.reserve(group_count);
c_device_buf.reserve(group_count);
std::vector<const void*> p_a, p_b;
std::vector<void*> p_c;
p_a.reserve(group_count);
p_b.reserve(group_count);
p_c.reserve(group_count);
std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
gemm_descs.reserve(group_count);
for(std::size_t i = 0; i < group_count; i++)
{
a_device_buf.emplace_back(
std::make_unique<DeviceMem>(sizeof(ADataType) * a_m_k[i].mDesc.GetElementSpaceSize()));
b_device_buf.emplace_back(
std::make_unique<DeviceMem>(sizeof(BDataType) * b_k_n[i].mDesc.GetElementSpaceSize()));
c_device_buf.emplace_back(std::make_unique<DeviceMem>(
sizeof(CDataType) * c_m_n_device_results[i].mDesc.GetElementSpaceSize()));
a_device_buf[i]->ToDevice(a_m_k[i].mData.data());
b_device_buf[i]->ToDevice(b_k_n[i].mData.data());
gemm_descs.push_back({Ms[i], Ns[i], Ks[i], StrideAs[i], StrideBs[i], StrideCs[i], {}});
p_a.push_back(a_device_buf[i]->GetDeviceBuffer());
p_b.push_back(b_device_buf[i]->GetDeviceBuffer());
p_c.push_back(c_device_buf[i]->GetDeviceBuffer());
}
using DeviceOp = ck::tensor_operation::device::DeviceGroupedGemm<ALayout,
BLayout,
ck::Tuple<>,
CLayout,
ADataType,
BDataType,
ck::Tuple<>,
CDataType,
AElementOp,
BElementOp,
CElementOp>;
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
if(op_ptrs.size() <= 0)
{
throw std::runtime_error("wrong! no device GEMM instance found");
}
std::string best_gemm_name;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
float best_kbatch = 0;
auto p_ds = std::vector<std::array<const void*, 0>>{};
if(do_verification)
{
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp>;
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(a_m_k[i],
b_k_n[i],
c_m_n_host_results[i],
a_element_op,
b_element_op,
c_element_op);
ref_invoker.Run(ref_argument);
}
}
// profile device GEMM instances
for(auto& gemm_ptr : op_ptrs)
{
auto argument_ptr =
gemm_ptr->MakeArgumentPointer(p_a,
p_b,
p_ds,
p_c,
gemm_descs,
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{});
auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
DeviceMem gemm_desc_workspace(gemm_ptr->GetWorkSpaceSize(argument_ptr.get()));
gemm_ptr->SetWorkSpacePointer(argument_ptr.get(), gemm_desc_workspace.GetDeviceBuffer());
std::string gemm_name = gemm_ptr->GetTypeString();
using DeviceOpSplitK =
ck::tensor_operation::device::DeviceGroupedGemmMultipleDSplitK<ALayout,
BLayout,
ck::Tuple<>,
CLayout,
ADataType,
BDataType,
ck::Tuple<>,
CDataType,
AElementOp,
BElementOp,
CElementOp>;
// skip non-splitk grouped_gemm
if(dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get()) == nullptr)
{
continue;
}
std::vector<int> kbatch_list = {1, 2, 4, 8, 12, 16, 20, 24, 32, 48, 64};
if(kbatch > 0)
{
kbatch_list = {kbatch};
}
for(std::size_t j = 0; j < kbatch_list.size(); j++)
{
auto kbatch_curr = kbatch_list[j];
dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get())
->SetKBatchSize(argument_ptr.get(), kbatch_curr);
DeviceMem gemm_arg_dev_mem(dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get())
->GetDeviceKernelArgSize(argument_ptr.get()));
dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get())
->SetDeviceKernelArgs(argument_ptr.get(), gemm_arg_dev_mem.GetDeviceBuffer());
if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
{
gemm_desc_workspace.SetZero();
for(std::size_t i = 0; i < gemm_descs.size(); i++)
c_device_buf[i]->SetZero();
invoker_ptr->Run(argument_ptr.get(),
StreamConfig{nullptr, false, 0, n_warmup, n_iter});
if(do_verification)
{
bool instance_pass = true;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
c_device_buf[i]->FromDevice(c_m_n_device_results[i].mData.data());
if(std::is_same_v<CDataType, ck::half_t> && kbatch_curr > 1)
{
instance_pass =
instance_pass && ck::utils::check_err(c_m_n_device_results[i],
c_m_n_host_results[i],
"Error: Incorrect results!",
0.06);
}
else
{
instance_pass =
instance_pass && ck::utils::check_err(c_m_n_device_results[i],
c_m_n_host_results[i]);
}
if(do_log)
{
LogRangeAsType<float>(std::cout << "a : ", a_m_k[i].mData, ",")
<< std::endl;
LogRangeAsType<float>(std::cout << "b: ", b_k_n[i].mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "c_device: ", c_m_n_device_results[i].mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "c_host : ", c_m_n_host_results[i].mData, ",")
<< std::endl;
}
}
std::cout << "Instance: " << gemm_name << " verification "
<< (instance_pass ? "SUCCEED" : "FAILED") << std::endl;
pass = pass && instance_pass;
}
float ave_time = invoker_ptr->Run(
argument_ptr.get(), StreamConfig{nullptr, time_kernel, 0, n_warmup, n_iter});
if(time_kernel)
{
std::size_t flop = 0, num_btype = 0;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i];
num_btype += sizeof(ADataType) * Ms[i] * Ks[i] +
sizeof(BDataType) * Ks[i] * Ns[i] +
sizeof(CDataType) * Ms[i] * Ns[i];
}
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops
<< " TFlops, " << gb_per_sec << " GB/s, " << gemm_name << ", KBatch "
<< kbatch_curr << std::endl;
if(tflops > best_tflops)
{
best_gemm_name = gemm_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
best_kbatch = kbatch_curr;
}
}
}
else
{
std::cout << "Instance: " << gemm_name << ", does not support this GEMM problem"
<< std::endl;
}
}
}
if(time_kernel)
{
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_gemm_name << ", KBatch = " << best_kbatch
<< std::endl;
}
return pass;
}
} // namespace profiler
} // namespace ck
profiler/src/CMakeLists.txt
View file @ c7010716
......@@ -40,6 +40,7 @@ if(GPU_TARGETS MATCHES "gfx9")
list(APPEND PROFILER_SOURCES profile_gemm_add_silu.cpp)
list(APPEND PROFILER_SOURCES profile_gemm_add_relu_add_layernorm.cpp)
list(APPEND PROFILER_SOURCES profile_grouped_gemm_fixed_nk.cpp)
list(APPEND PROFILER_SOURCES profile_grouped_gemm_two_stage.cpp)
list(APPEND PROFILER_SOURCES profile_grouped_gemm_fastgelu.cpp)
endif()
list(APPEND PROFILER_SOURCES profile_gemm_multiply_add.cpp)
......
profiler/src/profile_grouped_gemm_two_stage.cpp 0 → 100644
View file @ c7010716
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "profiler/profile_grouped_gemm_two_stage_impl.hpp"
#include "profiler_operation_registry.hpp"
enum struct GemmMatrixLayout
{
MK_KN_MN, // 0
};
enum struct GemmDataType
{
F16_F16_F16, // 0
BF16_INT8_BF16 // 1
};
#define OP_NAME "grouped_gemm_two_stage"
#define OP_DESC "Grouped GEMM TwoStage"
namespace {
std::vector<int> argToIntArray(char* input)
{
std::vector<int> out;
std::istringstream in(input);
std::string item;
while(std::getline(in, item, ','))
{
out.push_back(std::stoi(item));
}
return out;
}
int profile_grouped_gemm_two_stage(int argc, char* argv[])
{
if(argc < 14)
{
std::cout
<< "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: fp16; 1: bf16@int8)\n"
<< "arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n]);\n"
<< "arg4: verification (0: no; 1: yes)\n"
<< "arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n"
<< "arg6: print tensor value (0: no; 1: yes)\n"
<< "arg7: time kernel (0=n0, 1=yes)\n"
<< "arg8 to 13: Ms, Ns, Ks, StrideAs, StrideBs, StrideCs (e.g., 256,256 128,128 64,64 "
"64,64 64,64 128,128)\n"
<< "arg15: kbatch value (default 1)\n"
<< "optional:\n"
<< "arg16: number of warm-up cycles (default 1)\n"
<< "arg17: number of iterations (default 10)\n"
<< std::endl;
exit(1);
}
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]);
const bool time_kernel = std::stoi(argv[7]);
const auto Ms = argToIntArray(argv[8]);
const auto Ns = argToIntArray(argv[9]);
const auto Ks = argToIntArray(argv[10]);
auto StrideAs = argToIntArray(argv[11]);
auto StrideBs = argToIntArray(argv[12]);
auto StrideCs = argToIntArray(argv[13]);
const int kbatch = argc == 15 ? std::stoi(argv[14]) : 1;
const int DefaultStrideA = Ks[0];
const int DefaultStrideB = Ns[0];
const int DefaultStrideC = Ns[0];
for(size_t i = 0; i < Ms.size(); ++i)
{
StrideAs[i] = StrideAs[i] == -1 ? DefaultStrideA : StrideAs[i];
StrideBs[i] = StrideBs[i] == -1 ? DefaultStrideB : StrideBs[i];
StrideCs[i] = StrideCs[i] == -1 ? DefaultStrideC : StrideCs[i];
}
int n_warmup = 1;
int n_iter = 10;
if(argc == 17)
{
n_warmup = std::stoi(argv[16]);
n_iter = std::stoi(argv[17]);
}
if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
{
ck::profiler::profile_grouped_gemm_two_stage_impl<ck::half_t,
ck::half_t,
ck::half_t,
float,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor>(
do_verification,
init_method,
do_log,
time_kernel,
Ms,
Ns,
Ks,
StrideAs,
StrideBs,
StrideCs,
kbatch,
n_warmup,
n_iter);
}
else if(data_type == GemmDataType::BF16_INT8_BF16 && layout == GemmMatrixLayout::MK_KN_MN)
{
ck::profiler::profile_grouped_gemm_two_stage_impl<ck::bhalf_t,
int8_t,
ck::bhalf_t,
float,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor,
ck::tensor_layout::gemm::RowMajor>(
do_verification,
init_method,
do_log,
time_kernel,
Ms,
Ns,
Ks,
StrideAs,
StrideBs,
StrideCs,
kbatch,
n_warmup,
n_iter);
}
else
{
throw std::runtime_error("wrong! this GEMM data_type & layout is not implemented");
}
return 0;
}
} // anonymous namespace
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_grouped_gemm_two_stage);
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