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Merge commit '3e777217551c82a47eb9540791fb5542f2704e63' into develop

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assistant-librarian[bot]
2026-02-02 23:16:03 +00:00
parent b0b7f95d6e
commit 9c38bf0527
35 changed files with 3790 additions and 328 deletions
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2
client_example/31_grouped_gemm_bf16Aint8B/grouped_gemm_bias_fastgelu_xdl_bf16_i8.cpp
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@@ -15,6 +15,8 @@
#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_multi_abd_fixed_nk.hpp"
#include "ck/host_utility/hip_check_error.hpp"
using ::ck::hip_check_error;
template <ck::index_t... Is>

8
example/59_grouped_gemm_multi_ABD/CMakeLists.txt
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@@ -8,3 +8,11 @@ add_example_dependencies(example_grouped_gemm_xdl_multi_abd example_grouped_gemm
add_example_executable(example_grouped_gemm_multi_abd_xdl_fixed_nk_bias_bf16_i8 grouped_gemm_multi_abd_xdl_fixed_nk_bias_bf16_i8.cpp)
add_example_dependencies(example_grouped_gemm_xdl_multi_abd example_grouped_gemm_multi_abd_xdl_fixed_nk_bias_bf16_i8)
add_custom_target(example_grouped_gemm_wmma_multi_abd)
add_example_executable(example_grouped_gemm_multi_abd_wmma_fixed_nk_bias_fp16 grouped_gemm_multi_abd_wmma_fixed_nk_bias_fp16.cpp)
add_example_dependencies(example_grouped_gemm_wmma_multi_abd example_grouped_gemm_multi_abd_wmma_fixed_nk_bias_fp16)
add_example_executable(example_grouped_gemm_multi_abd_wmma_fixed_nk_bias_bf16_i8 grouped_gemm_multi_abd_wmma_fixed_nk_bias_bf16_i8.cpp)
add_example_dependencies(example_grouped_gemm_wmma_multi_abd example_grouped_gemm_multi_abd_wmma_fixed_nk_bias_bf16_i8)

400
example/59_grouped_gemm_multi_ABD/grouped_gemm_multi_abd_wmma_fixed_nk_bias_bf16_i8.cpp Normal file
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@@ -0,0 +1,400 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#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_multi_abd_wmma_fixed_nk.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/host_utility/hip_check_error.hpp"
using ::ck::DeviceMem;
using ::ck::hip_check_error;
using ::ck::HostTensorDescriptor;
using ::ck::Tensor;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using BF16 = ck::bhalf_t;
using I8 = int8_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using Bypass = ck::tensor_layout::BypassLayoutVerification;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Add = ck::tensor_operation::element_wise::Add;
using A0DataType = BF16;
using AsDataType = ck::Tuple<A0DataType>;
using B0DataType = I8;
using B1DataType = BF16;
using BsDataType = ck::Tuple<B0DataType, B1DataType>;
using AccDataType = F32;
using CShuffleDataType = BF16;
using D0DataType = BF16;
using DsDataType = ck::Tuple<D0DataType>;
using EDataType = BF16;
using A0Layout = Row;
using AsLayout = ck::Tuple<A0Layout>;
using B0Layout = Col;
using B1Layout = B0Layout;
using BsLayout = ck::Tuple<B0Layout, B1Layout>;
using DsLayout = ck::Tuple<Row>;
using ELayout = Row;
using Multiply = ck::tensor_operation::element_wise::Multiply;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
using AElementOp = PassThrough;
using BElementOp = Multiply;
using CDEElementOp = AddFastGelu;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK
// clang-format off
///######| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| 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| 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| | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
///######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 128, 32, 128, 32, 8, 8, 16, 16, 1, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 8>, 1>;
// clang-format on
struct ProblemSize final
{
std::vector<ck::index_t> Ms;
std::vector<ck::index_t> Ns;
std::vector<ck::index_t> Ks;
std::vector<ck::index_t> stride_As;
std::vector<ck::index_t> stride_Bs;
std::vector<ck::index_t> stride_Cs;
ck::index_t group_count;
};
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int k_batch = 1;
};
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::GemmMultiABDDesc> gemm_descs;
gemm_descs.reserve(group_count);
int sum_of_m = 0;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz}, Bypass{});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride}, Bypass{});
}
};
std::vector<Tensor<A0DataType>> a0_tensors;
std::vector<Tensor<B1DataType>> b_tensors;
std::vector<Tensor<B0DataType>> b0_tensors;
std::vector<Tensor<B1DataType>> b1_tensors;
std::vector<Tensor<D0DataType>> d0_tensors;
std::vector<Tensor<EDataType>> c_host_tensors;
std::vector<Tensor<EDataType>> c_device_tensors;
a0_tensors.reserve(group_count);
b_tensors.reserve(group_count);
b0_tensors.reserve(group_count);
b1_tensors.reserve(group_count);
d0_tensors.reserve(group_count);
c_host_tensors.reserve(group_count);
c_device_tensors.reserve(group_count);
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
std::vector<DeviceMemPtr> a0_tensors_device, b0_tensors_device, b1_tensors_device,
d0_tensors_device, c_tensors_device;
a0_tensors_device.reserve(group_count);
b0_tensors_device.reserve(group_count);
b1_tensors_device.reserve(group_count);
d0_tensors_device.reserve(group_count);
c_tensors_device.reserve(group_count);
std::size_t flop = 0, num_btype = 0;
for(int i = 0; i < group_count; i++)
{
sum_of_m += problem_size.Ms[i];
a0_tensors.push_back(Tensor<A0DataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ks[i], problem_size.stride_As[i], A0Layout{})));
b_tensors.push_back(Tensor<B1DataType>(f_host_tensor_descriptor(
problem_size.Ks[i], problem_size.Ns[i], problem_size.stride_Bs[i], B0Layout{})));
b0_tensors.push_back(Tensor<B0DataType>(f_host_tensor_descriptor(
problem_size.Ks[i], problem_size.Ns[i], problem_size.stride_Bs[i], B0Layout{})));
b1_tensors.push_back(Tensor<B1DataType>(
f_host_tensor_descriptor(problem_size.Ks[i], problem_size.Ns[i], 0, B1Layout{})));
d0_tensors.push_back(Tensor<D0DataType>(
f_host_tensor_descriptor(problem_size.Ms[i], problem_size.Ns[i], 0, ELayout{})));
c_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
c_device_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
std::cout << "gemm[" << i << "] a_m_k: " << a0_tensors[i].mDesc
<< " b_k_n: " << b0_tensors[i].mDesc << " d_m_n: " << d0_tensors[i].mDesc
<< " c_m_n: " << c_device_tensors[i].mDesc << std::endl;
flop += std::size_t(2) * problem_size.Ms[i] * problem_size.Ks[i] * problem_size.Ns[i];
num_btype += sizeof(A0DataType) * a0_tensors[i].mDesc.GetElementSize() +
sizeof(B0DataType) * b0_tensors[i].mDesc.GetElementSize() +
sizeof(B1DataType) * b1_tensors[i].mDesc.GetElementSize() +
sizeof(D0DataType) * d0_tensors[i].mDesc.GetElementSize() +
sizeof(EDataType) * c_device_tensors[i].mDesc.GetElementSize();
switch(config.init_method)
{
case 0: break;
case 1:
a0_tensors[i].GenerateTensorValue(GeneratorTensor_2<A0DataType>{-5, 5});
b0_tensors[i].GenerateTensorValue(GeneratorTensor_2<B0DataType>{-5, 5});
b1_tensors[i].GenerateTensorValue(GeneratorTensor_2<B1DataType>{0, 5});
break;
case 2:
a0_tensors[i].GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
b0_tensors[i].GenerateTensorValue(GeneratorTensor_3<B0DataType>{-5, 5});
b1_tensors[i].GenerateTensorValue(GeneratorTensor_3<B1DataType>{-0.5, 0.5});
break;
default:
a0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<A0DataType, 0>{});
b0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<B0DataType, 1>{});
b1_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<B1DataType, 1>{});
}
d0_tensors[i].GenerateTensorValue(GeneratorTensor_3<D0DataType>{-0.5, 0.5});
}
constexpr ck::index_t NumATensor = 1;
constexpr ck::index_t NumBTensor = 2;
constexpr ck::index_t NumDTensor = 1;
using GroupedGemmKernelArgument = ck::tensor_operation::device::
GroupedGemmMultiABDKernelArgument<NumATensor, NumBTensor, NumDTensor>;
std::vector<GroupedGemmKernelArgument> grouped_gemm_kernel_args_;
grouped_gemm_kernel_args_.reserve(group_count);
for(int i = 0; i < group_count; i++)
{
a0_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(A0DataType) * problem_size.Ms[i] * problem_size.Ks[i]));
b0_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(B0DataType) * problem_size.Ns[i] * problem_size.Ks[i]));
b1_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(B1DataType) * problem_size.Ns[i] * problem_size.Ks[i]));
d0_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(D0DataType) * problem_size.Ns[i]));
c_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(EDataType) * problem_size.Ms[i] * problem_size.Ns[i]));
a0_tensors_device[i]->ToDevice(a0_tensors[i].mData.data());
b0_tensors_device[i]->ToDevice(b0_tensors[i].mData.data());
b1_tensors_device[i]->ToDevice(b1_tensors[i].mData.data());
d0_tensors_device[i]->ToDevice(d0_tensors[i].mData.data());
c_tensors_device[i]->SetZero();
gemm_descs.push_back(
{sum_of_m, problem_size.Ns[i], problem_size.Ks[i], {1}, {1, 1}, {0}, 1});
grouped_gemm_kernel_args_.push_back(
{std::array<const void*, NumATensor>{a0_tensors_device[i]->GetDeviceBuffer()},
std::array<const void*, NumBTensor>{b0_tensors_device[i]->GetDeviceBuffer(),
b1_tensors_device[i]->GetDeviceBuffer()},
std::array<const void*, NumDTensor>{d0_tensors_device[i]->GetDeviceBuffer()},
c_tensors_device[i]->GetDeviceBuffer(),
problem_size.Ms[i],
problem_size.Ns[i],
problem_size.Ks[i],
std::array<ck::index_t, NumATensor>{problem_size.stride_As[i]},
std::array<ck::index_t, NumBTensor>{problem_size.stride_Bs[i], 0},
std::array<ck::index_t, NumDTensor>{0},
problem_size.stride_Cs[i]});
}
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
std::vector<std::array<const void*, NumATensor>> p_As = {};
std::vector<std::array<const void*, NumBTensor>> p_Bs = {};
std::vector<std::array<const void*, NumDTensor>> p_Ds = {};
std::vector<void*> p_Cs = {};
// do GEMM
auto argument = gemm.MakeArgument(p_As, p_Bs, p_Ds, p_Cs, gemm_descs);
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_kernel_args_dev(gemm.GetDeviceKernelArgSize(&argument));
hip_check_error(hipMemcpy(gemm_kernel_args_dev.GetDeviceBuffer(),
grouped_gemm_kernel_args_.data(),
gemm.GetDeviceKernelArgSize(&argument),
hipMemcpyHostToDevice));
gemm.SetDeviceKernelArgs(argument, gemm_kernel_args_dev.GetDeviceBuffer());
gemm.SetKBatch(argument, config.k_batch);
gemm.SetElementwiseOps(argument, a_element_op, b_element_op, cde_element_op);
invoker.Run(&argument, StreamConfig{nullptr, false});
if(config.time_kernel)
{
float ave_time = invoker.Run(&argument, StreamConfig{nullptr, config.time_kernel});
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< " GB/s, " << gemm.GetTypeString() << std::endl;
}
bool pass = true;
if(config.do_verification)
{
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<A0DataType,
B1DataType,
EDataType,
AccDataType,
PassThrough,
PassThrough,
PassThrough>;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
for(int n = 0; n < problem_size.Ns[i]; ++n)
{
for(int k = 0; k < problem_size.Ks[i]; ++k)
{
b_element_op(b_tensors[i](k, n), b0_tensors[i](k, n), b1_tensors[i](k, n));
}
}
c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data(),
c_device_tensors[i].mDesc.GetElementSize() *
sizeof(EDataType));
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(a0_tensors[i],
b_tensors[i],
c_host_tensors[i],
PassThrough{},
PassThrough{},
PassThrough{});
ref_invoker.Run(ref_argument);
for(int m = 0; m < problem_size.Ms[i]; ++m)
{
for(int n = 0; n < problem_size.Ns[i]; ++n)
{
cde_element_op(
c_host_tensors[i](m, n), c_host_tensors[i](m, n), d0_tensors[i](m, n));
}
}
pass &= ck::utils::check_err(c_device_tensors[i], c_host_tensors[i]);
}
}
return pass;
}
int main(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
problem_size.group_count = 16;
for(int i = 0; i < problem_size.group_count; i++)
{
problem_size.Ms.push_back(32 + rand() % 32);
problem_size.Ns.push_back(1024);
problem_size.Ks.push_back(512);
problem_size.stride_As.push_back(problem_size.Ks[i]);
problem_size.stride_Bs.push_back(problem_size.Ks[i]);
problem_size.stride_Cs.push_back(problem_size.Ns[i]);
}
if(argc == 5)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
config.k_batch = std::stoi(argv[4]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4: k_batch (>0)\n");
exit(0);
}
return !run_grouped_gemm(problem_size, config);
}

396
example/59_grouped_gemm_multi_ABD/grouped_gemm_multi_abd_wmma_fixed_nk_bias_fp16.cpp Normal file
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@@ -0,0 +1,396 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#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_multi_abd_wmma_fixed_nk.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp"
#include "ck/tensor_operation/gpu/element/combined_element_wise_operation.hpp"
#include "ck/utility/scheduler_enum.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/host_utility/hip_check_error.hpp"
using ::ck::DeviceMem;
using ::ck::hip_check_error;
using ::ck::HostTensorDescriptor;
using ::ck::Tensor;
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 Bypass = ck::tensor_layout::BypassLayoutVerification;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Add = ck::tensor_operation::element_wise::Add;
using Scale = ck::tensor_operation::element_wise::Scale;
using AddScale = ck::tensor_operation::element_wise::BinaryWithUnaryCombinedOp<Add, Scale, Scale>;
using A0DataType = F16;
using A1DataType = F32;
using AsDataType = ck::Tuple<A0DataType, A1DataType>;
using B0DataType = F16;
using BsDataType = ck::Tuple<B0DataType>;
using AccDataType = F32;
using CShuffleDataType = F32;
using D0DataType = F16;
using DsDataType = ck::Tuple<D0DataType>;
using EDataType = F16;
using A0Layout = Row;
using A1Layout = Row;
using AsLayout = ck::Tuple<A0Layout, A1Layout>;
using B0Layout = Col;
using BsLayout = ck::Tuple<B0Layout>;
using D0Layout = Row;
using DsLayout = ck::Tuple<D0Layout>;
using ELayout = Row;
using AElementOp = AddScale;
using BElementOp = PassThrough;
using CDEElementOp = Add;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK
// clang-format off
///######| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| 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| 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| | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
///######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 128, 32, 128, 32, 8, 8, 16, 16, 1, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 1, 1, 1, 1, 1, S<1, 16, 1, 8>, 1>;
// clang-format on
struct ProblemSize final
{
std::vector<ck::index_t> Ms;
std::vector<ck::index_t> Ns;
std::vector<ck::index_t> Ks;
std::vector<ck::index_t> stride_As;
std::vector<ck::index_t> stride_Bs;
std::vector<ck::index_t> stride_Cs;
ck::index_t group_count;
};
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
int k_batch = 1;
};
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::GemmMultiABDDesc> gemm_descs;
gemm_descs.reserve(group_count);
int sum_of_m = 0;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz}, Bypass{});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride}, Bypass{});
}
};
std::vector<Tensor<A0DataType>> a0_tensors;
std::vector<Tensor<A1DataType>> a1_tensors;
std::vector<Tensor<B0DataType>> b_tensors;
std::vector<Tensor<D0DataType>> d0_tensors;
std::vector<Tensor<EDataType>> e_host_tensors;
std::vector<Tensor<EDataType>> e_device_tensors;
a0_tensors.reserve(group_count);
a1_tensors.reserve(group_count);
b_tensors.reserve(group_count);
d0_tensors.reserve(group_count);
e_host_tensors.reserve(group_count);
e_device_tensors.reserve(group_count);
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
std::vector<DeviceMemPtr> a0_tensors_device, a1_tensors_device, b_tensors_device,
d0_tensors_device, c_tensors_device;
a0_tensors_device.reserve(group_count);
a1_tensors_device.reserve(group_count);
b_tensors_device.reserve(group_count);
d0_tensors_device.reserve(group_count);
c_tensors_device.reserve(group_count);
std::size_t flop = 0, num_btype = 0;
for(int i = 0; i < group_count; i++)
{
sum_of_m += problem_size.Ms[i];
a0_tensors.push_back(Tensor<A0DataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ks[i], problem_size.stride_As[i], A0Layout{})));
a1_tensors.push_back(Tensor<A1DataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ks[i], problem_size.stride_As[i], A1Layout{})));
b_tensors.push_back(Tensor<B0DataType>(f_host_tensor_descriptor(
problem_size.Ks[i], problem_size.Ns[i], problem_size.stride_Bs[i], B0Layout{})));
d0_tensors.push_back(Tensor<D0DataType>(
f_host_tensor_descriptor(problem_size.Ms[i], problem_size.Ns[i], 0, ELayout{})));
e_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
e_device_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
std::cout << "gemm[" << i << "] a_m_k: " << a0_tensors[i].mDesc
<< " b_k_n: " << b_tensors[i].mDesc << " d_m_n: " << d0_tensors[i].mDesc
<< " c_m_n: " << e_device_tensors[i].mDesc << std::endl;
flop += std::size_t(2) * problem_size.Ms[i] * problem_size.Ks[i] * problem_size.Ns[i];
num_btype += sizeof(A0DataType) * a0_tensors[i].mDesc.GetElementSize() +
sizeof(A1DataType) * a1_tensors[i].mDesc.GetElementSize() +
sizeof(B0DataType) * b_tensors[i].mDesc.GetElementSize() +
sizeof(D0DataType) * d0_tensors[i].mDesc.GetElementSize() +
sizeof(EDataType) * e_device_tensors[i].mDesc.GetElementSize();
switch(config.init_method)
{
case 0: break;
case 1:
a0_tensors[i].GenerateTensorValue(GeneratorTensor_2<A0DataType>{-5, 5});
a1_tensors[i].GenerateTensorValue(GeneratorTensor_2<A1DataType>{-5, 5});
b_tensors[i].GenerateTensorValue(GeneratorTensor_2<B0DataType>{-5, 5});
break;
case 2:
a0_tensors[i].GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
a1_tensors[i].GenerateTensorValue(GeneratorTensor_3<A1DataType>{0.0, 1.0});
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
break;
default:
a0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<A0DataType, 0>{});
a1_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<A1DataType, 0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<B0DataType, 1>{});
}
d0_tensors[i].GenerateTensorValue(GeneratorTensor_3<D0DataType>{-0.5, 0.5});
}
constexpr ck::index_t NumATensor = 2;
constexpr ck::index_t NumBTensor = 1;
constexpr ck::index_t NumDTensor = 1;
using GroupedGemmKernelArgument = ck::tensor_operation::device::
GroupedGemmMultiABDKernelArgument<NumATensor, NumBTensor, NumDTensor>;
std::vector<GroupedGemmKernelArgument> grouped_gemm_kernel_args_;
grouped_gemm_kernel_args_.reserve(group_count);
for(int i = 0; i < group_count; i++)
{
a0_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(A0DataType) * problem_size.Ms[i] * problem_size.Ks[i]));
a1_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(A1DataType) * problem_size.Ms[i] * problem_size.Ks[i]));
b_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(B0DataType) * problem_size.Ns[i] * problem_size.Ks[i]));
d0_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(D0DataType) * problem_size.Ns[i]));
c_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(EDataType) * problem_size.Ms[i] * problem_size.Ns[i]));
a0_tensors_device[i]->ToDevice(a0_tensors[i].mData.data());
a1_tensors_device[i]->ToDevice(a1_tensors[i].mData.data());
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
d0_tensors_device[i]->ToDevice(d0_tensors[i].mData.data());
c_tensors_device[i]->SetZero();
gemm_descs.push_back({sum_of_m,
problem_size.Ns[i],
problem_size.Ks[i],
{1, 1},
{problem_size.stride_Bs[i]},
{0},
1});
grouped_gemm_kernel_args_.push_back(
{std::array<const void*, NumATensor>{a0_tensors_device[i]->GetDeviceBuffer(),
a1_tensors_device[i]->GetDeviceBuffer()},
std::array<const void*, NumBTensor>{b_tensors_device[i]->GetDeviceBuffer()},
std::array<const void*, NumDTensor>{d0_tensors_device[i]->GetDeviceBuffer()},
c_tensors_device[i]->GetDeviceBuffer(),
problem_size.Ms[i],
problem_size.Ns[i],
problem_size.Ks[i],
std::array<ck::index_t, NumATensor>{problem_size.stride_As[i],
problem_size.stride_As[i]},
std::array<ck::index_t, NumBTensor>{problem_size.stride_Bs[i]},
std::array<ck::index_t, NumDTensor>{0},
problem_size.stride_Cs[i]});
}
constexpr float scale = 1.f;
auto a_element_op = AElementOp{Add{}, Scale{scale}, Scale{scale}};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
std::vector<std::array<const void*, NumATensor>> p_As = {};
std::vector<std::array<const void*, NumBTensor>> p_Bs = {};
std::vector<std::array<const void*, NumDTensor>> p_Ds = {};
std::vector<void*> p_Cs = {};
// do GEMM
auto argument = gemm.MakeArgument(p_As, p_Bs, p_Ds, p_Cs, gemm_descs);
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_kernel_args_dev(gemm.GetDeviceKernelArgSize(&argument));
hip_check_error(hipMemcpy(gemm_kernel_args_dev.GetDeviceBuffer(),
grouped_gemm_kernel_args_.data(),
gemm.GetDeviceKernelArgSize(&argument),
hipMemcpyHostToDevice));
gemm.SetDeviceKernelArgs(argument, gemm_kernel_args_dev.GetDeviceBuffer());
gemm.SetKBatch(argument, config.k_batch);
gemm.SetElementwiseOps(argument, a_element_op, b_element_op, cde_element_op);
invoker.Run(&argument, StreamConfig{nullptr, false});
if(config.time_kernel)
{
float ave_time = invoker.Run(&argument, StreamConfig{nullptr, config.time_kernel});
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< " GB/s, " << gemm.GetTypeString() << std::endl;
}
bool pass = true;
if(config.do_verification)
{
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<A0DataType,
B0DataType,
EDataType,
AccDataType,
PassThrough,
BElementOp,
PassThrough>;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
for(int m = 0; m < problem_size.Ms[i]; ++m)
{
for(int k = 0; k < problem_size.Ks[i]; ++k)
{
a_element_op(a0_tensors[i](m, k), a0_tensors[i](m, k), a1_tensors[i](m, k));
}
}
c_tensors_device[i]->FromDevice(e_device_tensors[i].mData.data(),
e_device_tensors[i].mDesc.GetElementSize() *
sizeof(EDataType));
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(a0_tensors[i],
b_tensors[i],
e_host_tensors[i],
PassThrough{},
b_element_op,
PassThrough{});
ref_invoker.Run(ref_argument);
for(int m = 0; m < problem_size.Ms[i]; ++m)
{
for(int n = 0; n < problem_size.Ns[i]; ++n)
{
cde_element_op(
e_host_tensors[i](m, n), e_host_tensors[i](m, n), d0_tensors[i](m, n));
}
}
pass &= ck::utils::check_err(e_device_tensors[i], e_host_tensors[i]);
}
}
return pass;
}
int main(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
problem_size.group_count = 16;
for(int i = 0; i < problem_size.group_count; i++)
{
problem_size.Ms.push_back(32 + rand() % 32);
problem_size.Ns.push_back(64);
problem_size.Ks.push_back(64);
problem_size.stride_As.push_back(problem_size.Ks[i]);
problem_size.stride_Bs.push_back(problem_size.Ks[i]);
problem_size.stride_Cs.push_back(problem_size.Ns[i]);
}
if(argc == 5)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
config.k_batch = std::stoi(argv[4]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4: k_batch (>0)\n");
exit(0);
}
return !run_grouped_gemm(problem_size, config);
}

27
example/59_grouped_gemm_multi_ABD/grouped_gemm_multi_abd_xdl_fixed_nk_bias_bf16_i8.cpp
View File

@@ -20,6 +20,8 @@
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/host_utility/hip_check_error.hpp"
using ::ck::DeviceMem;
using ::ck::hip_check_error;
using ::ck::HostTensorDescriptor;
@@ -220,8 +222,8 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
for(int i = 0; i < group_count; i++)
{
a0_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(A0DataType) * sum_of_m * problem_size.Ks[i]));
a0_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(A0DataType) * problem_size.Ms[i] * problem_size.Ks[i]));
b0_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(B0DataType) * problem_size.Ns[i] * problem_size.Ks[i]));
@@ -232,21 +234,12 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
d0_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(D0DataType) * problem_size.Ns[i]));
c_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(EDataType) * sum_of_m * problem_size.Ns[i]));
a0_tensors_device[i]->ToDevice(a0_tensors[i].mData.data(),
a0_tensors[i].mDesc.GetElementSpaceSize() *
sizeof(A0DataType));
b0_tensors_device[i]->ToDevice(b0_tensors[i].mData.data(),
b0_tensors[i].mDesc.GetElementSpaceSize() *
sizeof(B0DataType));
b1_tensors_device[i]->ToDevice(b1_tensors[i].mData.data(),
b1_tensors[i].mDesc.GetElementSpaceSize() *
sizeof(B1DataType));
c_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(EDataType) * problem_size.Ms[i] * problem_size.Ns[i]));
a0_tensors_device[i]->ToDevice(a0_tensors[i].mData.data());
b0_tensors_device[i]->ToDevice(b0_tensors[i].mData.data());
b1_tensors_device[i]->ToDevice(b1_tensors[i].mData.data());
d0_tensors_device[i]->ToDevice(d0_tensors[i].mData.data());
c_tensors_device[i]->SetZero();
@@ -398,7 +391,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4: k_batch (>0)\n");
exit(0);
}

33
example/59_grouped_gemm_multi_ABD/grouped_gemm_multi_abd_xdl_fixed_nk_bias_fp16.cpp
View File

@@ -20,6 +20,8 @@
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/host_utility/hip_check_error.hpp"
using ::ck::DeviceMem;
using ::ck::hip_check_error;
using ::ck::HostTensorDescriptor;
@@ -47,9 +49,9 @@ using B0DataType = F16;
using BsDataType = ck::Tuple<B0DataType>;
using AccDataType = F32;
using CShuffleDataType = F32;
using D0DataType = F32;
using D0DataType = F16;
using DsDataType = ck::Tuple<D0DataType>;
using EDataType = F32;
using EDataType = F16;
using A0Layout = Row;
using A1Layout = Row;
@@ -210,11 +212,11 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
for(int i = 0; i < group_count; i++)
{
a0_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(A0DataType) * sum_of_m * problem_size.Ks[i]));
a0_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(A0DataType) * problem_size.Ms[i] * problem_size.Ks[i]));
a1_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(A1DataType) * sum_of_m * problem_size.Ks[i]));
a1_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(A1DataType) * problem_size.Ms[i] * problem_size.Ks[i]));
b_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(B0DataType) * problem_size.Ns[i] * problem_size.Ks[i]));
@@ -222,19 +224,12 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
d0_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(D0DataType) * problem_size.Ns[i]));
c_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(EDataType) * sum_of_m * problem_size.Ns[i]));
c_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(EDataType) * problem_size.Ms[i] * problem_size.Ns[i]));
a0_tensors_device[i]->ToDevice(a0_tensors[i].mData.data(),
a0_tensors[i].mDesc.GetElementSpaceSize() *
sizeof(A0DataType));
a1_tensors_device[i]->ToDevice(a1_tensors[i].mData.data(),
a1_tensors[i].mDesc.GetElementSpaceSize() *
sizeof(A1DataType));
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data(),
b_tensors[i].mDesc.GetElementSpaceSize() *
sizeof(B0DataType));
a0_tensors_device[i]->ToDevice(a0_tensors[i].mData.data());
a1_tensors_device[i]->ToDevice(a1_tensors[i].mData.data());
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
d0_tensors_device[i]->ToDevice(d0_tensors[i].mData.data());
c_tensors_device[i]->SetZero();
@@ -394,7 +389,7 @@ int main(int argc, char* argv[])
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4: k_batch (>0)\n");
exit(0);
}

2
example/ck_tile/38_block_scale_gemm/gemm_bquant_quantgrouped_bf8.cpp
View File

@@ -4,7 +4,7 @@
#include "run_gemm_quant_example.inc"
template <typename T>
using GemmConfig = GemmConfigQuantPrefill<T>;
using GemmConfig = GemmConfigQuantDecode<T>;
#define RUN_GEMM_EXAMPLE_PREC_TYPE \
run_gemm_example_prec_type<GemmConfig<ck_tile::bf8_t>, \

2
example/ck_tile/38_block_scale_gemm/gemm_bquant_quantgrouped_bf8i4.cpp
View File

@@ -4,7 +4,7 @@
#include "run_gemm_quant_example.inc"
template <typename T>
using GemmConfig = GemmConfigQuantPrefill<T>;
using GemmConfig = GemmConfigQuantDecode<T>;
#define RUN_GEMM_EXAMPLE_PREC_TYPE \
run_gemm_example_prec_type<GemmConfig<ck_tile::bf8_t>, \

2
example/ck_tile/38_block_scale_gemm/gemm_bquant_quantgrouped_fp8.cpp
View File

@@ -4,7 +4,7 @@
#include "run_gemm_quant_example.inc"
template <typename T>
using GemmConfig = GemmConfigQuantPrefill<T>;
using GemmConfig = GemmConfigQuantDecode<T>;
#define RUN_GEMM_EXAMPLE_PREC_TYPE \
run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t>, \

2
example/ck_tile/38_block_scale_gemm/gemm_bquant_quantgrouped_fp8i4.cpp
View File

@@ -4,7 +4,7 @@
#include "run_gemm_quant_example.inc"
template <typename T>
using GemmConfig = GemmConfigQuantPrefill<T>;
using GemmConfig = GemmConfigQuantDecode<T>;
#define RUN_GEMM_EXAMPLE_PREC_TYPE \
run_gemm_example_prec_type<GemmConfig<ck_tile::fp8_t>, \

183
example/ck_tile/38_block_scale_gemm/run_gemm_quant_example.inc
View File

@@ -215,11 +215,8 @@ float gemm_calc_quant(const ck_tile::QuantGemmHostArgs& args, const ck_tile::str
const dim3 grids = Kernel::GridSize(args.M, args.N, args.k_batch);
const dim3 blocks = Kernel::BlockSize();
if(args.k_batch != 1)
{
throw std::runtime_error("split-k is not supported yet!");
}
// Split-K validation is handled by Kernel::IsSupportedArgument
// Split-K is only supported for BQuantGrouped without preshuffle
if(!Kernel::IsSupportedArgument(kargs))
{
throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
@@ -661,182 +658,6 @@ int run_gemm_example_with_layouts(const ck_tile::ArgParser& arg_parser,
}
}
}
else if(init_method == 3)
{
if constexpr(QuantMode == ck_tile::QuantType::BQuantGrouped)
{
ck_tile::FillConstant<ADataType>{static_cast<ADataType>(0x38)}(a_m_k);
ck_tile::FillConstant<BDataType>{static_cast<BDataType>(0x22)}(b_k_n);
ck_tile::FillConstant<BQDataType>{static_cast<BQDataType>(0.5f)}(*bq_tensor_ptr);
}
else if constexpr(QuantMode == ck_tile::QuantType::ABQuantGrouped)
{
ck_tile::FillConstant<ADataType>{static_cast<ADataType>(0x38)}(a_m_k);
ck_tile::FillConstant<BDataType>{static_cast<BDataType>(0x22)}(b_k_n);
ck_tile::FillConstant<AQDataType>{static_cast<AQDataType>(0.5f)}(*aq_tensor_ptr);
ck_tile::FillConstant<BQDataType>{static_cast<BQDataType>(0.5f)}(*bq_tensor_ptr);
}
else
{
ck_tile::FillConstant<ADataType>{static_cast<ADataType>(0x22)}(a_m_k);
ck_tile::FillConstant<AQDataType>{static_cast<AQDataType>(2.0f)}(*aq_tensor_ptr);
ck_tile::FillConstant<BDataType>{static_cast<BDataType>(0x38)}(b_k_n);
if constexpr(QuantMode == ck_tile::QuantType::RowColQuant)
{
ck_tile::FillConstant<BQDataType>{static_cast<BQDataType>(0.5f)}(*bq_tensor_ptr);
}
}
}
else if(init_method == 4)
{
if constexpr(QuantMode == ck_tile::QuantType::BQuantGrouped)
{
if constexpr(std::is_same_v<BDataType, ck_tile::pk_int4_t>)
{
ck_tile::FillUniformDistribution<ck_tile::pk_int4_t>{-5.0f, 5.0f, fill_seed(gen)}(
b_k_n);
ck_tile::FillUniformDistribution<BQDataType>{-2.0f, 2.0f, fill_seed(gen)}(
*bq_tensor_ptr);
}
else if constexpr(std::is_same_v<BDataType, ck_tile::pk_fp4_raw_t>)
{
ck_tile::FillUniformDistribution<BDataType>{-5.0f, 5.0f, fill_seed(gen)}(b_k_n);
ck_tile::FillUniformDistribution<BQDataType>{125.f, 130.f, fill_seed(gen)}(
*bq_tensor_ptr);
}
else
{
ck_tile::FillUniformDistribution<BDataType>{-2.0f, 3.0f, fill_seed(gen)}(b_k_n);
ck_tile::FillUniformDistribution<BQDataType>{-2.0f, 2.0f, fill_seed(gen)}(
*bq_tensor_ptr);
}
ck_tile::FillUniformDistribution<ADataType>{-5.0f, 5.0f, fill_seed(gen)}(a_m_k);
}
else if constexpr(QuantMode == ck_tile::QuantType::AQuantGrouped)
{
if constexpr(std::is_same_v<ADataType, ck_tile::pk_int4_t>)
{
ck_tile::FillUniformDistribution<ck_tile::pk_int4_t>{-5.0f, 5.0f, fill_seed(gen)}(
a_m_k);
}
else
{
ck_tile::FillUniformDistribution<ADataType>{-2.0f, 3.0f, fill_seed(gen)}(a_m_k);
}
ck_tile::FillUniformDistribution<AQDataType>{2.0f, 2.0f, fill_seed(gen)}(
*aq_tensor_ptr);
ck_tile::FillUniformDistribution<BDataType>{-5.0f, 5.0f, fill_seed(gen)}(b_k_n);
}
else if constexpr(QuantMode == ck_tile::QuantType::ABQuantGrouped)
{
if constexpr(std::is_same_v<ADataType, ck_tile::pk_int4_t> ||
std::is_same_v<ADataType, ck_tile::pk_fp4_t>)
{
ck_tile::FillUniformDistribution<ADataType>{-5.0f, 5.0f, fill_seed(gen)}(a_m_k);
ck_tile::FillUniformDistribution<BDataType>{-5.0f, 5.0f, fill_seed(gen)}(b_k_n);
}
else
{
ck_tile::FillUniformDistribution<ADataType>{-2.0f, 3.0f, fill_seed(gen)}(a_m_k);
ck_tile::FillUniformDistribution<BDataType>{-2.0f, 3.0f, fill_seed(gen)}(b_k_n);
}
ck_tile::FillUniformDistribution<AQDataType>{-2.0f, 2.0f, fill_seed(gen)}(
*aq_tensor_ptr);
ck_tile::FillUniformDistribution<BQDataType>{-2.0f, 2.0f, fill_seed(gen)}(
*bq_tensor_ptr);
}
else
{
ck_tile::FillUniformDistribution<ADataType>{-2.0f, 2.0f, fill_seed(gen)}(a_m_k);
ck_tile::FillUniformDistribution<BDataType>{-2.0f, 2.0f, fill_seed(gen)}(b_k_n);
ck_tile::FillUniformDistribution<AQDataType>{-2.0f, 2.0f, fill_seed(gen)}(
*aq_tensor_ptr);
ck_tile::FillUniformDistribution<BQDataType>{-2.0f, 2.0f, fill_seed(gen)}(
*bq_tensor_ptr);
}
}
else if(init_method == 5)
{
if constexpr(QuantMode == ck_tile::QuantType::BQuantGrouped)
{
if constexpr(std::is_same_v<BDataType, ck_tile::pk_int4_t>)
{
ck_tile::FillUniformDistribution<ck_tile::pk_int4_t>{-5.0f, 5.0f, fill_seed(gen)}(
b_k_n);
ck_tile::FillUniformDistribution<BQDataType>{-2.0f, 2.0f, fill_seed(gen)}(
*bq_tensor_ptr);
}
else if constexpr(std::is_same_v<BDataType, ck_tile::pk_fp4_raw_t>)
{
ck_tile::FillUniformDistribution<BDataType>{-5.0f, 5.0f, fill_seed(gen)}(b_k_n);
ck_tile::FillUniformDistribution<BQDataType>{125.f, 130.f, fill_seed(gen)}(
*bq_tensor_ptr);
}
else
{
ck_tile::FillUniformDistribution<BDataType>{-2.0f, 3.0f, fill_seed(gen)}(b_k_n);
ck_tile::FillUniformDistribution<BQDataType>{-2.0f, 2.0f, fill_seed(gen)}(
*bq_tensor_ptr);
}
ck_tile::FillUniformDistribution<ADataType>{-5.0f, 5.0f, fill_seed(gen)}(a_m_k);
}
else if constexpr(QuantMode == ck_tile::QuantType::AQuantGrouped)
{
if constexpr(std::is_same_v<ADataType, ck_tile::pk_int4_t>)
{
ck_tile::FillUniformDistribution<ck_tile::pk_int4_t>{-5.0f, 5.0f, fill_seed(gen)}(
a_m_k);
}
else
{
ck_tile::FillUniformDistribution<ADataType>{1.0f, 1.0f, fill_seed(gen)}(a_m_k);
}
// Fill aquant such that column j has value 2^j (1, 2, 4, 8, ...)
for(ck_tile::index_t row = 0;
row < static_cast<ck_tile::index_t>(aq_tensor_ptr->get_length(0));
++row)
{
for(ck_tile::index_t col = 0;
col < static_cast<ck_tile::index_t>(aq_tensor_ptr->get_length(1));
++col)
{
(*aq_tensor_ptr)(row, col) = static_cast<AQDataType>(col + 1);
}
}
// std::cout << "aq_tensor_ptr: " << *aq_tensor_ptr << std::endl;
ck_tile::FillUniformDistribution<BDataType>{1.0f, 1.0f, fill_seed(gen)}(b_k_n);
}
else if constexpr(QuantMode == ck_tile::QuantType::ABQuantGrouped)
{
if constexpr(std::is_same_v<ADataType, ck_tile::pk_int4_t> ||
std::is_same_v<ADataType, ck_tile::pk_fp4_t>)
{
ck_tile::FillUniformDistribution<ADataType>{-5.0f, 5.0f, fill_seed(gen)}(a_m_k);
ck_tile::FillUniformDistribution<BDataType>{-5.0f, 5.0f, fill_seed(gen)}(b_k_n);
}
else
{
ck_tile::FillUniformDistribution<ADataType>{-2.0f, 3.0f, fill_seed(gen)}(a_m_k);
ck_tile::FillUniformDistribution<BDataType>{-2.0f, 3.0f, fill_seed(gen)}(b_k_n);
}
ck_tile::FillUniformDistribution<AQDataType>{-2.0f, 2.0f, fill_seed(gen)}(
*aq_tensor_ptr);
ck_tile::FillUniformDistribution<BQDataType>{-2.0f, 2.0f, fill_seed(gen)}(
*bq_tensor_ptr);
}
else
{
ck_tile::FillUniformDistribution<ADataType>{-2.0f, 2.0f, fill_seed(gen)}(a_m_k);
ck_tile::FillUniformDistribution<BDataType>{-2.0f, 2.0f, fill_seed(gen)}(b_k_n);
ck_tile::FillUniformDistribution<AQDataType>{-2.0f, 2.0f, fill_seed(gen)}(
*aq_tensor_ptr);
ck_tile::FillUniformDistribution<BQDataType>{-2.0f, 2.0f, fill_seed(gen)}(
*bq_tensor_ptr);
}
}
else
{
a_m_k.SetZero();

899
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multi_abd_wmma_fixed_nk.hpp Normal file
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@@ -0,0 +1,899 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include <iostream>
#include <sstream>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
#include "ck/utility/env.hpp"
#include "ck/host_utility/hip_check_error.hpp"
#include "ck/utility/common_header.hpp"
#include "ck/utility/tuple.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/utility/common_header.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_multi_abd_fixed_nk.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_abd_wmma_cshuffle_v3.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "ck/host_utility/kernel_launch.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
template <typename GridwiseGemm,
typename GemmDesc,
bool HasMainKBlockLoop,
InMemoryDataOperationEnum EGlobalMemoryDataOperation,
typename AsLayout,
typename BsLayout,
typename DsLayout,
typename ELayout,
typename Block2ETileMap,
typename GroupedGemmBlock2ETileMap,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
GemmSpecialization GemmSpec,
index_t MinimumOccupancy = 1,
TailNumber TailNum = TailNumber::Full>
__global__ void
#if CK_USE_LAUNCH_BOUNDS
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, MinimumOccupancy)
#endif
kernel_grouped_gemm_wmma_fixed_nk(const void CK_CONSTANT_ADDRESS_SPACE* gemm_descs_const,
const index_t group_count,
const index_t grid_size_grp,
const AElementwiseOperation a_element_op,
const BElementwiseOperation b_element_op,
const CDEElementwiseOperation cde_element_op)
{
#if defined(__gfx11__) || defined(__gfx12__)
__shared__ char p_shared[GridwiseGemm::template GetSharedMemoryNumberOfByte<
typename GridwiseGemm::EpilogueCShuffle>()];
const index_t KBatch = 1;
const index_t block_id = get_block_1d_id();
const auto gemm_desc_ptr =
reinterpret_cast<const GemmDesc*>(cast_pointer_to_generic_address_space(gemm_descs_const));
const index_t group_id = block_id / grid_size_grp;
if(group_id >= group_count)
return;
auto karg = gemm_desc_ptr[group_id];
if(karg.M == 0 || karg.N == 0 || karg.K == 0)
return;
#if defined(__gfx11__)
// gfx11 does not support *_atomic_pk_add_f16/bf16 instructions
if constexpr(!(EGlobalMemoryDataOperation == InMemoryDataOperationEnum::AtomicAdd &&
(std::is_same_v<typename GridwiseGemm::EDataType_, ck::half_t> ||
std::is_same_v<typename GridwiseGemm::EDataType_, ck::bhalf_t>)))
#endif
{
typename GridwiseGemm::Problem problem(karg.M,
karg.N,
karg.K,
karg.StrideAs,
karg.StrideBs,
karg.StrideDs,
karg.StrideE,
KBatch);
const auto e_grid_desc_m_n = GridwiseGemm::template MakeDEGridDescriptor_M_N<ELayout>(
problem.M, problem.MPadded, problem.N, problem.NPadded, problem.StrideE);
const index_t BlockStart = group_id * grid_size_grp;
const auto local_b2e_tile_map = Block2ETileMap{e_grid_desc_m_n, KBatch};
const auto local_grid_size = local_b2e_tile_map.CalculateGridSize(e_grid_desc_m_n);
constexpr auto NumATensor = GridwiseGemm::AsGridPointer::Size();
constexpr auto NumBTensor = GridwiseGemm::BsGridPointer::Size();
constexpr auto NumDTensor = GridwiseGemm::DsGridPointer::Size();
typename GridwiseGemm::AsGridPointer p_as_grid_;
typename GridwiseGemm::BsGridPointer p_bs_grid_;
typename GridwiseGemm::DsGridPointer p_ds_grid_;
static_for<0, NumATensor, 1>{}([&](auto i) {
using ADataType = remove_cvref_t<decltype(p_as_grid_(i))>;
p_as_grid_(i) = static_cast<ADataType>(karg.p_as_grid[i]);
});
static_for<0, NumBTensor, 1>{}([&](auto i) {
using BDataType = remove_cvref_t<decltype(p_bs_grid_(i))>;
p_bs_grid_(i) = static_cast<BDataType>(karg.p_bs_grid[i]);
});
static_for<0, NumDTensor, 1>{}([&](auto i) {
using DDataType = remove_cvref_t<decltype(p_ds_grid_(i))>;
p_ds_grid_(i) = static_cast<DDataType>(karg.p_ds_grid[i]);
});
index_t id_off = 0;
index_t id_local = get_block_1d_id() - BlockStart;
while(id_local < local_grid_size)
{
const auto block_2_etile_map =
GroupedGemmBlock2ETileMap(local_b2e_tile_map, BlockStart, id_off);
auto epilogue_args = typename GridwiseGemm::EpilogueCShuffle{};
GridwiseGemm::template Run<HasMainKBlockLoop,
EGlobalMemoryDataOperation,
TailNum,
decltype(block_2_etile_map),
decltype(epilogue_args),
1,
2>(
p_as_grid_,
p_bs_grid_,
p_ds_grid_,
static_cast<typename GridwiseGemm::EDataType_*>(karg.p_e_grid),
p_shared,
problem,
block_2_etile_map,
a_element_op,
b_element_op,
cde_element_op,
epilogue_args);
id_off += grid_size_grp;
id_local += grid_size_grp;
}
}
#else
ignore = gemm_descs_const;
ignore = group_count;
ignore = grid_size_grp;
ignore = a_element_op;
ignore = b_element_op;
ignore = cde_element_op;
#endif
}
template <typename AsLayout,
typename BsLayout,
typename DsLayout,
typename ELayout,
typename AsDataType,
typename BsDataType,
typename AccDataType,
typename CShuffleDataType,
typename DsDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
GemmSpecialization GemmSpec,
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 MPerWmma,
ck::index_t NPerWmma,
ck::index_t MRepeat,
ck::index_t NRepeat,
typename ABlockTransferThreadClusterLengths_K0_M_K1,
typename ABlockTransferThreadClusterArrangeOrder,
typename ABlockTransferSrcAccessOrder,
ck::index_t ABlockTransferSrcVectorDim,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t ABlockTransferDstScalarPerVector_K1,
bool ABlockLdsExtraM,
typename BBlockTransferThreadClusterLengths_K0_N_K1,
typename BBlockTransferThreadClusterArrangeOrder,
typename BBlockTransferSrcAccessOrder,
ck::index_t BBlockTransferSrcVectorDim,
ck::index_t BBlockTransferSrcScalarPerVector,
ck::index_t BBlockTransferDstScalarPerVector_K1,
bool BBlockLdsExtraN,
index_t CShuffleMRepeatPerShuffle,
index_t CShuffleNRepeatPerShuffle,
typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
index_t CDEBlockTransferScalarPerVector_NPerBlock,
BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v1,
typename ComputeTypeA = EDataType,
typename ComputeTypeB = ComputeTypeA,
bool PermuteA = false,
bool PermuteB = false>
struct DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK
: public DeviceGroupedGemmMultiABDFixedNK<AsLayout,
BsLayout,
DsLayout,
ELayout,
AsDataType,
BsDataType,
DsDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation>
{
using DeviceOp = DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK;
static constexpr index_t NumATensor = AsDataType::Size();
static constexpr index_t NumBTensor = BsDataType::Size();
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>{};
// Note: Pass multiple layout but then using only the first one
// This is to replicate xdl functionality but it should be extended
using ALayout = remove_cvref_t<tuple_element_t<0, AsLayout>>;
using BLayout = remove_cvref_t<tuple_element_t<0, BsLayout>>;
using GridwiseGemm = GridwiseGemm_wmma_cshuffle_v3<
ALayout,
BLayout,
DsLayout,
ELayout,
AsDataType,
BsDataType,
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation,
GemmSpec,
BlockSize,
MPerBlock,
NPerBlock,
KPerBlock,
AK1,
BK1,
MPerWmma,
NPerWmma,
MRepeat,
NRepeat,
ABlockTransferThreadClusterLengths_K0_M_K1,
ABlockTransferThreadClusterArrangeOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1,
false,
ABlockLdsExtraM,
BBlockTransferThreadClusterLengths_K0_N_K1,
BBlockTransferThreadClusterArrangeOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1,
false,
BBlockLdsExtraN,
CShuffleMRepeatPerShuffle,
CShuffleNRepeatPerShuffle,
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
typename uniform_sequence_gen<NumDTensor + 1,
CDEBlockTransferScalarPerVector_NPerBlock>::type,
BlkGemmPipeSched,
BlkGemmPipelineVer,
ComputeTypeA,
ComputeTypeB,
false,
false>;
// TODO: Block to tile mappings could potentially moved out to avoid code duplications between
// different device implementations.
template <typename UnderlyingBlockToCTileMap>
struct OffsettedBlockToCTileMapMLoops
{
using underlying_type = UnderlyingBlockToCTileMap;
__host__ __device__ OffsettedBlockToCTileMapMLoops(
UnderlyingBlockToCTileMap block_to_ctile_map, index_t block_start, index_t id_off = 0)
{
block_to_ctile_map_ = block_to_ctile_map;
block_start_ = block_start;
id_off_ = id_off;
}
template <typename TopIdx>
__host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
{
auto idx_bot = block_to_ctile_map_.CalculateBottomIndex(
make_multi_index(idx_top[Number<0>{}] - block_start_ + id_off_));
return make_tuple(idx_bot[Number<0>{}], idx_bot[Number<1>{}], idx_bot[Number<2>{}]);
}
template <typename CTileIdx, typename CTileDim>
__host__ __device__ bool ValidCTileIndex(const CTileIdx& c_tile_idx,
const CTileDim& c_tile_dim) const
{
return block_to_ctile_map_.ValidCTileIndex(c_tile_idx, c_tile_dim);
}
template <typename CGridDesc_M_N>
__host__ bool CheckValidity(const CGridDesc_M_N& c_grid_desc_m_n) const
{
return block_to_ctile_map_.CheckValidity(c_grid_desc_m_n);
}
template <typename CGridDesc_M_N>
__host__ constexpr index_t CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
{
return block_to_ctile_map_.CalculateGridSize(c_grid_desc_m_n);
}
UnderlyingBlockToCTileMap block_to_ctile_map_;
index_t block_start_;
index_t id_off_;
};
template <index_t MPerBlock_, index_t NPerBlock_>
struct BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops
{
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops() = default;
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
const BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&) = default;
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&&) = default;
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&
operator=(const BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&) = default;
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&
operator=(BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&&) = default;
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(index_t M,
index_t N,
index_t KBatch,
index_t M01 = 8)
: M_(M), N_(N), KBatch_(KBatch), M01_(M01)
{
}
template <typename CGridDesc_M_N>
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
const CGridDesc_M_N& c_grid_desc_m_n, index_t KBatch, index_t M01 = 8)
: BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1), KBatch, M01)
{
}
__host__ __device__ constexpr index_t CalculateGridSize(index_t M, index_t N) const
{
const auto M0 = math::integer_divide_ceil(M, MPerBlock);
const auto N0 = math::integer_divide_ceil(N, NPerBlock);
return M0 * N0 * KBatch_;
}
template <typename CGridDesc_M_N>
__host__ __device__ constexpr index_t
CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
{
return CalculateGridSize(c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1));
}
template <typename CGridDesc_M_N>
__host__ bool CheckValidity(const CGridDesc_M_N& /* c_grid_desc_m_n */) const
{
return true;
}
template <typename TopIdx>
__host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
{
auto block_1d_id = idx_top[I0];
const auto M0 = math::integer_divide_ceil(M_, MPerBlock_);
const auto N0 = math::integer_divide_ceil(N_, NPerBlock_);
block_1d_id = block_1d_id % (M0 * N0 * KBatch_); // hide groups
const index_t idx_ksplit = block_1d_id / (M0 * N0);
block_1d_id = block_1d_id % (M0 * N0);
index_t idx_N0 = block_1d_id % N0;
index_t idx_M0 = block_1d_id / N0;
const auto M01_adapt = (idx_M0 < M0 - M0 % M01_) ? M01_ : M0 % M01_;
index_t idx_M00 = idx_M0 / M01_;
index_t idx_M01 = idx_M0 % M01_;
index_t idx_N0_M01_local = idx_N0 + idx_M01 * N0;
return make_tuple(idx_ksplit,
idx_N0_M01_local % M01_adapt + idx_M00 * M01_,
idx_N0_M01_local / M01_adapt);
}
template <typename CTileIdx, typename CTileDim>
__host__ __device__ bool ValidCTileIndex(const CTileIdx& /* c_tile_idx */,
const CTileDim& /* c_tile_dim */) const
{
return true; // always valid provided that user gets grid size from CalculateGridSize()
}
private:
index_t M_;
index_t N_;
index_t KBatch_;
index_t M01_;
};
using Block2ETileMap = BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops<MPerBlock, NPerBlock>;
using GroupedGemmBlock2ETileMap = OffsettedBlockToCTileMapMLoops<Block2ETileMap>;
static constexpr index_t DefaultKBatch = 1; // implementation only supports KBatch == 1
using KernelArgument = typename GridwiseGemm::Argument;
using GemmTransKernelArg =
GroupedGemmMultiABDKernelArgument<NumATensor, NumBTensor, NumDTensor>;
static constexpr bool CalculateHasMainKBlockLoop(const GemmTransKernelArg& karg,
index_t k_batch)
{
index_t k_grain = k_batch * KPerBlock;
index_t K_split = (karg.K + k_grain - 1) / k_batch;
return GridwiseGemm::CalculateHasMainKBlockLoop(K_split);
}
// Argument
struct Argument : public BaseArgument
{
Argument(std::vector<std::array<const void*, NumATensor>>& p_As,
std::vector<std::array<const void*, NumBTensor>>& p_Bs,
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
std::vector<void*>& p_Es,
std::vector<GemmMultiABDDesc>& gemm_descs,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation c_element_op)
: Argument(p_As,
p_Bs,
p_Ds,
p_Es,
gemm_descs,
a_element_op,
b_element_op,
c_element_op,
DefaultKBatch)
{
// TODO: use occupancy api to calculate appropriate batch size.
}
// Client is expected to manually copy the kernel arguments to the device therefore there is
// no point in setting tensor device pointers for the argument structure.
Argument(std::vector<std::array<const void*, NumATensor>>&,
std::vector<std::array<const void*, NumBTensor>>&,
std::vector<std::array<const void*, NumDTensor>>&,
std::vector<void*>&,
std::vector<GemmMultiABDDesc>& gemm_descs,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation c_element_op,
index_t kbatch)
: group_count_{ck::type_convert<ck::index_t>(gemm_descs.size())},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
c_element_op_{c_element_op},
grouped_gemm_kernel_args_dev{nullptr},
gemm_kernel_host_args_{nullptr},
grid_size_{0},
k_batch_{kbatch}
{
gemm_desc_kernel_arg_.reserve(group_count_);
index_t group_id = 0;
sum_of_m = gemm_descs[0].M_;
const index_t AverM = math::integer_divide_ceil(sum_of_m, group_count_);
const index_t fixed_N = gemm_descs[0].N_;
const index_t fixed_K = gemm_descs[0].K_;
for(std::size_t g = 0; g < gemm_descs.size(); g++)
{
const index_t M = gemm_descs[g].M_;
const index_t N = gemm_descs[g].N_;
const index_t K = gemm_descs[g].K_;
if(M != sum_of_m || N != fixed_N || K != fixed_K)
{
throw std::runtime_error("wrong! M/N/K is not identical");
}
a_mtx_mraw_kraw_.emplace_back(sum_of_m, K);
b_mtx_nraw_kraw_.emplace_back(N, K);
// pointer
std::array<const void*, NumATensor> p_as_grid;
std::array<const void*, NumBTensor> p_bs_grid;
std::array<const void*, NumDTensor> p_ds_grid;
static_for<0, NumATensor, 1>{}([&](auto i) { p_as_grid[i] = nullptr; });
static_for<0, NumBTensor, 1>{}([&](auto i) { p_bs_grid[i] = nullptr; });
static_for<0, NumDTensor, 1>{}([&](auto i) { p_ds_grid[i] = nullptr; });
std::array<index_t, NumATensor> StrideAs;
std::array<index_t, NumBTensor> StrideBs;
std::array<index_t, NumDTensor> StrideDs;
const index_t StrideE = gemm_descs[g].stride_C_;
if(gemm_descs[g].stride_As_.size() != NumATensor)
{
throw std::runtime_error(
"wrong! gemm_descs[i].stride_As_.size() does not match NumATensor");
}
static_for<0, NumATensor, 1>{}(
[&](auto j) { StrideAs[j] = gemm_descs[g].stride_As_[j]; });
if(gemm_descs[g].stride_Bs_.size() != NumBTensor)
{
throw std::runtime_error(
"wrong! gemm_descs[i].stride_Bs_.size() does not match NumBTensor");
}
static_for<0, NumBTensor, 1>{}(
[&](auto j) { StrideBs[j] = gemm_descs[g].stride_Bs_[j]; });
if(gemm_descs[g].stride_Ds_.size() != NumDTensor)
{
throw std::runtime_error(
"wrong! gemm_descs[i].stride_Ds_.size() does not match NumDTensor");
}
static_for<0, NumDTensor, 1>{}(
[&](auto j) { StrideDs[j] = gemm_descs[g].stride_Ds_[j]; });
const auto e_grid_desc_m_n =
GridwiseGemm::template MakeDEGridDescriptor_M_N<ELayout>(
AverM, AverM, N, N, StrideE);
// block-to-e-tile map
const auto local_b2c_tile_map = Block2ETileMap{e_grid_desc_m_n, k_batch_};
grid_size_grp_ = local_b2c_tile_map.CalculateGridSize(e_grid_desc_m_n);
if(group_id * grid_size_grp_ != grid_size_)
{
throw std::runtime_error("wrong! grid_size_grp_ is not identical!");
}
const index_t block_start = grid_size_;
grid_size_ += grid_size_grp_;
if(!local_b2c_tile_map.CheckValidity(e_grid_desc_m_n))
{
throw std::runtime_error("wrong! block_2_etile_map validation failed");
}
auto grouped_block_2_ctile_map =
GroupedGemmBlock2ETileMap(local_b2c_tile_map, block_start);
auto karg = GemmTransKernelArg({p_as_grid,
p_bs_grid,
p_ds_grid,
nullptr,
AverM,
N,
K,
StrideAs,
StrideBs,
StrideDs,
StrideE});
gemm_desc_kernel_arg_.emplace_back(std::move(karg));
group_id++;
}
}
void UpdateKBatch(index_t) {}
index_t group_count_;
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
CDEElementwiseOperation c_element_op_;
std::vector<GemmTransKernelArg> gemm_desc_kernel_arg_;
std::vector<Tuple<index_t, index_t>> a_mtx_mraw_kraw_;
std::vector<Tuple<index_t, index_t>> b_mtx_nraw_kraw_;
const void* grouped_gemm_kernel_args_dev;
void* gemm_kernel_host_args_;
index_t grid_size_;
index_t grid_size_grp_;
index_t sum_of_m;
index_t k_batch_;
};
// Invoker
struct Invoker : public BaseInvoker
{
using Argument = DeviceOp::Argument;
float RunImp(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
{
if(arg.grouped_gemm_kernel_args_dev == nullptr)
{
throw std::runtime_error("wrong! grouped_gemm_kernel_args_dev is nullptr");
}
if(arg.k_batch_ != 1)
{
throw std::runtime_error("Split K functionality is not supported for wmma multi "
"abd fixed nk implementation.");
}
float ave_time = 0;
auto launch_kernel = [&](auto e_global_memory_operation_) {
const auto kernel = kernel_grouped_gemm_wmma_fixed_nk<GridwiseGemm,
GemmTransKernelArg,
true, // has_main_k_block_loop
e_global_memory_operation_,
AsLayout,
BsLayout,
DsLayout,
ELayout,
Block2ETileMap,
GroupedGemmBlock2ETileMap,
AElementwiseOperation,
BElementwiseOperation,
CDEElementwiseOperation,
GemmSpec>;
return launch_and_time_kernel(
stream_config,
kernel,
dim3(arg.grid_size_),
dim3(BlockSize),
0,
cast_pointer_to_constant_address_space(arg.grouped_gemm_kernel_args_dev),
arg.gemm_desc_kernel_arg_.size(),
arg.grid_size_grp_,
arg.a_element_op_,
arg.b_element_op_,
arg.c_element_op_);
};
constexpr auto Set = InMemoryDataOperationEnum::Set;
ave_time = launch_kernel(integral_constant<InMemoryDataOperationEnum, Set>{});
return ave_time;
}
// polymorphic
float Run(const BaseArgument* p_arg,
const StreamConfig& stream_config = StreamConfig{}) override
{
return RunImp(*dynamic_cast<const Argument*>(p_arg), stream_config);
}
};
static bool IsSupportedArgument(const Argument& arg)
{
if(!ck::is_gfx11_supported() && !ck::is_gfx12_supported())
{
return false;
}
if(ck::type_convert<ck::index_t>(arg.gemm_desc_kernel_arg_.size()) != arg.group_count_)
{
return false;
}
bool supported = true;
// If we use padding we do not support vector loads for dimensions not divisible by
// vector load size.
if constexpr(GemmSpec != GemmSpecialization::Default)
{
// [A|B]BlockTransferSrcVectorDim value define dimension in the block {K0,M,K1} layout,
// thus we have to adapt it to the {M,K} or {N,K} layout.
const auto a_raw_vector_dim = ABlockTransferSrcVectorDim != 1 ? 1 : 0;
const auto b_raw_vector_dim = BBlockTransferSrcVectorDim != 1 ? 1 : 0;
for(index_t i = 0; i < arg.group_count_; ++i)
{
const auto a_vector_dim = arg.a_mtx_mraw_kraw_[i].At(Number<a_raw_vector_dim>{});
const auto b_vector_dim = arg.b_mtx_nraw_kraw_[i].At(Number<b_raw_vector_dim>{});
supported = supported & (a_vector_dim % ABlockTransferSrcScalarPerVector == 0);
supported = supported & (b_vector_dim % BBlockTransferSrcScalarPerVector == 0);
}
}
for(index_t i = 0; i < arg.group_count_; i++)
{
if(CalculateHasMainKBlockLoop(arg.gemm_desc_kernel_arg_[i], arg.k_batch_) != true)
{
supported = false;
}
}
return supported;
}
// polymorphic
bool IsSupportedArgument(const BaseArgument* p_arg) override
{
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
}
static auto MakeArgument(std::vector<std::array<const void*, NumATensor>>& p_As,
std::vector<std::array<const void*, NumBTensor>>& p_Bs,
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
std::vector<void*>& p_Es,
std::vector<GemmMultiABDDesc> gemm_descs,
AElementwiseOperation a_element_op = AElementwiseOperation{},
BElementwiseOperation b_element_op = BElementwiseOperation{},
CDEElementwiseOperation c_element_op = CDEElementwiseOperation{})
{
return Argument{
p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, c_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
// polymorphic
std::unique_ptr<BaseArgument>
MakeArgumentPointer(std::vector<std::array<const void*, NumATensor>>& p_As,
std::vector<std::array<const void*, NumBTensor>>& p_Bs,
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
std::vector<void*>& p_Es,
std::vector<GemmMultiABDDesc>& gemm_descs,
AElementwiseOperation a_element_op = AElementwiseOperation{},
BElementwiseOperation b_element_op = BElementwiseOperation{},
CDEElementwiseOperation c_element_op = CDEElementwiseOperation{}) override
{
return std::make_unique<Argument>(
p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, c_element_op);
}
// polymorphic
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
{
return std::make_unique<Invoker>(Invoker{});
}
// polymorphic
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "DeviceGroupedGemm_Wmma_Fixed_Nk"
<< "<"
<< BlockSize << ", "
<< MPerBlock << ", "
<< NPerBlock << ", "
<< KPerBlock << ", "
<< AK1 << ", "
<< BK1 << ", "
<< MPerWmma << ", "
<< NPerWmma << ", "
<< ABlockTransferSrcScalarPerVector << ", "
<< BBlockTransferSrcScalarPerVector << ", "
<< CShuffleMRepeatPerShuffle << ", "
<< CShuffleNRepeatPerShuffle << ", "
<< getGemmSpecializationString(GemmSpec)
<< ">";
// clang-format on
return str.str();
}
static void SetElementwiseOps(Argument& arg,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation c_element_op)
{
arg.a_element_op_ = a_element_op;
arg.b_element_op_ = b_element_op;
arg.c_element_op_ = c_element_op;
}
// polymorphic
void SetElementwiseOps(BaseArgument* p_arg,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation c_element_op) const override
{
SetElementwiseOps(
*dynamic_cast<Argument*>(p_arg), a_element_op, b_element_op, c_element_op);
}
static void SetDeviceKernelArgs(Argument& arg, const void* kernel_args)
{
arg.grouped_gemm_kernel_args_dev = kernel_args;
}
// polymorphic
void SetDeviceKernelArgs(BaseArgument* p_arg, const void* kernel_args) const override
{
return SetDeviceKernelArgs(*dynamic_cast<Argument*>(p_arg), kernel_args);
}
size_t GetDeviceKernelArgSize(const BaseArgument* p_arg) const override
{
auto arg = *dynamic_cast<const Argument*>(p_arg);
return arg.group_count_ *
sizeof(GroupedGemmMultiABDKernelArgument<NumATensor, NumBTensor, NumDTensor>);
}
size_t GetWorkSpaceSize(const BaseArgument* p_arg) const override
{
auto p_arg_ = dynamic_cast<const Argument*>(p_arg);
if(p_arg_)
{
return p_arg_->gemm_desc_kernel_arg_.size() * sizeof(GemmTransKernelArg);
}
else
throw std::runtime_error(
"The argument pointer is not an object of "
"DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK::Argument structure!");
}
void SetWorkSpacePointer(BaseArgument* p_arg,
void* p_workspace,
const StreamConfig& stream_config = StreamConfig{}) const override
{
auto p_arg_ = dynamic_cast<Argument*>(p_arg);
p_arg_->p_workspace_ = p_workspace;
hip_check_error(
hipMemsetAsync(p_workspace, 0, GetWorkSpaceSize(p_arg), stream_config.stream_id_));
}
static void SetKBatch(Argument& arg, index_t k_batch) { arg.UpdateKBatch(k_batch); }
// polymorphic
void SetKBatch(BaseArgument* p_arg, index_t k_batch) const override
{
return SetKBatch(*dynamic_cast<Argument*>(p_arg), k_batch);
}
void SetHostKernelArgsPointer(BaseArgument* p_arg, void* p_host_kernel_args) const
{
Argument* pArg_ = dynamic_cast<Argument*>(p_arg);
if(!pArg_)
{
throw std::runtime_error("Failed to cast argument pointer!");
}
pArg_->gemm_kernel_host_args_ = p_host_kernel_args;
std::copy(pArg_->gemm_desc_kernel_arg_.begin(),
pArg_->gemm_desc_kernel_arg_.end(),
static_cast<GemmTransKernelArg*>(pArg_->gemm_kernel_host_args_));
}
};
} // namespace device
} // namespace tensor_operation
} // namespace ck

27
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multi_abd_xdl_fixed_nk.hpp
View File

@@ -605,7 +605,7 @@ struct DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK
if(arg.grouped_gemm_kernel_args_dev == nullptr)
{
throw std::runtime_error("wrong! grouped_gemm_kernel_args_dev is nullpr");
throw std::runtime_error("wrong! grouped_gemm_kernel_args_dev is nullptr");
}
float ave_time = 0;
@@ -688,6 +688,11 @@ struct DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK
static bool IsSupportedArgument(const Argument& arg)
{
if(!ck::is_xdl_wmma_supported<ComputeType, ComputeType, MPerXDL, NPerXDL>())
{
return false;
}
// Split-K autodeduction is not supported
if(arg.k_batch_ < 1)
{
@@ -720,6 +725,26 @@ struct DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK
}
}
for(index_t i = 0; i < arg.group_count_; i++)
{
if(get_warp_size() == 64)
{
if(GridwiseGemm64::CalculateHasMainKBlockLoop(arg.gemm_desc_kernel_arg_[i].K_) !=
true)
{
supported = false;
}
}
else
{
if(GridwiseGemm32::CalculateHasMainKBlockLoop(arg.gemm_desc_kernel_arg_[i].K_) !=
true)
{
supported = false;
}
}
}
return supported;
}

2
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_fixed_nk.hpp
View File

@@ -696,7 +696,7 @@ struct DeviceGroupedGemm_Xdl_Fixed_NK : public DeviceGroupedGemmFixedNK<ALayout,
if(arg.grouped_gemm_kernel_args_dev == nullptr)
{
throw std::runtime_error("wrong! grouped_gemm_kernel_args_dev is nullpr");
throw std::runtime_error("wrong! grouped_gemm_kernel_args_dev is nullptr");
}
float ave_time = 0;

1
include/ck/tensor_operation/gpu/grid/gridwise_gemm_wmma_cshuffle_v3.hpp
View File

@@ -333,6 +333,7 @@ struct GridwiseGemm_wmma_cshuffle_v3
using typename Base::DsGridPointer;
using AsDataType_ = AsDataType;
using BsDataType_ = BsDataType;
using EDataType_ = EDataType;
struct Problem
{

9
include/ck/utility/tuple_helper.hpp
View File

@@ -48,6 +48,15 @@ __host__ __device__ constexpr auto concat_tuple_of_reference(const Tuple<X&...>&
ty);
}
template <typename... X, typename... Y>
auto concat_tuple_of_reference(ck::Tuple<X&...>& tx, ck::Tuple<Y&...>& ty)
{
return ck::unpack2(
[&](auto&&... zs) { return ck::Tuple<decltype(zs)...>{ck::forward<decltype(zs)>(zs)...}; },
tx,
ty);
}
template <typename... X, typename... Y>
__host__ __device__ constexpr auto concat_tuple(const Tuple<X...>& tx, const Tuple<Y...>& ty)
{

130
include/ck_tile/ops/gemm_quant/kernel/gemm_quant_kernel.hpp
View File

@@ -380,9 +380,18 @@ struct QuantGemmKernel
__device__ SplitKBatchOffset(const QuantGemmKernelArgs& kargs,
const std::size_t k_id = blockIdx.z)
{
constexpr auto K1 = GemmPipeline::BlockGemmShape::WarpTile::at(I2);
const index_t K_t = amd_wave_read_first_lane(kargs.k_batch * K1);
const index_t KRead = amd_wave_read_first_lane((kargs.K + K_t - 1) / K_t * K1);
constexpr auto K1 =
GemmPipeline::BlockGemmShape::WarpTile::at(I2); // smallest unit of K work per block
const index_t K_t = amd_wave_read_first_lane(
kargs.k_batch * K1); // amount of K elements consumed if every split-K batch
// performs exactly one "unit" (K1)
const index_t KRead = amd_wave_read_first_lane(
(kargs.K + K_t - 1) / K_t * K1); // total k elements to be read in this batch
// offset not necessarily = KRead, because B can have packed elements (e.g. fp8i4)
constexpr index_t BPackedSize =
ck_tile::numeric_traits<remove_cvref_t<BDataType>>::PackedSize;
const index_t b_k_offset_elements =
amd_wave_read_first_lane(k_id * KRead / BPackedSize);
if constexpr(std::is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
{
@@ -395,11 +404,11 @@ struct QuantGemmKernel
if constexpr(std::is_same_v<tensor_layout::gemm::RowMajor, BLayout>)
{
b_k_split_offset = amd_wave_read_first_lane(k_id * KRead * kargs.stride_B);
b_k_split_offset = amd_wave_read_first_lane(b_k_offset_elements * kargs.stride_B);
}
else if constexpr(std::is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
{
b_k_split_offset = amd_wave_read_first_lane(k_id * KRead);
b_k_split_offset = amd_wave_read_first_lane(b_k_offset_elements);
}
if(k_id < static_cast<uint32_t>(kargs.k_batch - 1))
@@ -410,10 +419,47 @@ struct QuantGemmKernel
{
splitted_k = amd_wave_read_first_lane(kargs.K - KRead * (kargs.k_batch - 1));
}
// Compute BQ offset for BQuantGrouped mode (non-preshuffle only)
// Note: With the alignment validation in IsSupportedArgument, KRead is always
// a multiple of BQuantGroupSize::kK, so bq_k_split_offset will be correctly aligned.
if constexpr(kQuantType == QuantType::BQuantGrouped && !BPreshuffleQuant)
{
using BQuantGroupSize = remove_cvref_t<typename GemmPipeline::BQuantGroupSize>;
// Compute the K offset for this batch (in terms of K elements)
const index_t k_offset = amd_wave_read_first_lane(k_id * KRead);
// Convert K offset to BQ group offset (logical offset in K/kK dimension)
bq_group_offset = amd_wave_read_first_lane(k_offset / BQuantGroupSize::kK);
// BQ tensor layout:
// RowMajor: [K/kK, N/kN] with stride [N/kN, 1]
// ColumnMajor: [N/kN, K/kK] with stride [K/kK, 1]
if constexpr(std::is_same_v<tensor_layout::gemm::RowMajor, BQLayout>)
{
// For RowMajor BQ, K is the row dimension
// offset = bq_group_offset * stride_BQ
const index_t stride_bq =
amd_wave_read_first_lane(integer_divide_ceil(kargs.N, BQuantGroupSize::kN));
bq_k_split_offset = amd_wave_read_first_lane(bq_group_offset * stride_bq);
}
else if constexpr(std::is_same_v<tensor_layout::gemm::ColumnMajor, BQLayout>)
{
// For ColumnMajor BQ, K is the column dimension
// offset = bq_group_offset
bq_k_split_offset = amd_wave_read_first_lane(bq_group_offset);
}
}
else
{
bq_group_offset = 0;
bq_k_split_offset = 0;
}
}
index_t a_k_split_offset;
index_t b_k_split_offset;
index_t bq_group_offset; // Logical offset in K-groups (K/kK dimension)
index_t bq_k_split_offset; // Memory pointer offset (accounting for layout/stride)
index_t splitted_k;
};
@@ -805,10 +851,13 @@ struct QuantGemmKernel
CK_TILE_DEVICE static auto MakeBQBlockWindow(const BQDataType* bq_ptr,
const QuantGemmKernelArgs& kargs,
const index_t bq_group_offset,
const index_t i_m,
const index_t i_n)
{
// Step 1: Create tensor view for BQ
// Note: For split-K, the bq_ptr is already offset by bq_k_split_offset (pointer offset).
// The dimension should use the remaining K-groups from this offset position.
const auto& bq_tensor_view = [&]() {
if constexpr(kQuantType == QuantType::RowColQuant)
{
@@ -850,11 +899,12 @@ struct QuantGemmKernel
"ABQuantGrouped requires ColumnMajor BQ layout");
}
using BQuantGroupSize = remove_cvref_t<typename GemmPipeline::BQuantGroupSize>;
if constexpr(std::is_same_v<BQLayout, tensor_layout::gemm::RowMajor>)
{
return make_naive_tensor_view<address_space_enum::global>(
bq_ptr,
make_tuple(integer_divide_ceil(kargs.K, BQuantGroupSize::kK),
make_tuple(kargs.QK_B - bq_group_offset,
integer_divide_ceil(kargs.N, BQuantGroupSize::kN)),
make_tuple(integer_divide_ceil(kargs.N, BQuantGroupSize::kN), 1),
number<GemmPipeline::GetVectorSizeBQ()>{},
@@ -865,8 +915,8 @@ struct QuantGemmKernel
return make_naive_tensor_view<address_space_enum::global>(
bq_ptr,
make_tuple(integer_divide_ceil(kargs.N, BQuantGroupSize::kN),
integer_divide_ceil(kargs.K, BQuantGroupSize::kK)),
make_tuple(integer_divide_ceil(kargs.K, BQuantGroupSize::kK), 1),
kargs.QK_B - bq_group_offset),
make_tuple(kargs.QK_B, 1),
number<GemmPipeline::GetVectorSizeBQ()>{},
number<1>{});
}
@@ -1047,13 +1097,61 @@ struct QuantGemmKernel
CK_TILE_HOST static bool IsSupportedArgument(const QuantGemmKernelArgs& kargs)
{
// Split-K is supported for BQuantGrouped mode without preshuffle
if(kargs.k_batch != 1)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
constexpr bool is_bquant_non_preshuffle =
(kQuantType == QuantType::BQuantGrouped) && !BPreshuffleQuant;
if constexpr(!is_bquant_non_preshuffle)
{
CK_TILE_ERROR("Conditions not met for Kbatch >1 !");
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("Conditions not met for Kbatch >1 ! "
"Split-K only supported for BQuantGrouped without preshuffle.");
}
return false;
}
else
{
using BQuantGroupSize = remove_cvref_t<typename GemmPipeline::BQuantGroupSize>;
constexpr auto K1 = GemmPipeline::BlockGemmShape::WarpTile::at(I2);
const index_t K_t = kargs.k_batch * K1;
const index_t KRead = (kargs.K + K_t - 1) / K_t * K1;
constexpr index_t BPackedSize =
ck_tile::numeric_traits<remove_cvref_t<BDataType>>::PackedSize;
// Constraint 1: KRead must align with B packing requirements.
// For packed data types, multiple K elements are stored in each storage unit.
// Split-K advances the B pointer by (KRead / BPackedSize) storage units per batch.
// If KRead is not divisible by BPackedSize, this division produces a fractional
// offset, making it impossible to start reading from a valid storage unit boundary.
if(KRead % BPackedSize != 0)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("KRead must be a multiple of B packed size for split-K!");
}
return false;
}
// Constraint 2: KRead must align with quantization group boundaries.
// Each split-K batch reads KRead consecutive K elements. If KRead is not
// a multiple of BQuantGroupSize::kK, the batch will span partial quantization
// groups, requiring split access to a quantization scale. This violates the
// atomic processing requirement where each batch must work with complete groups.
if(KRead % BQuantGroupSize::kK != 0)
{
if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
{
CK_TILE_ERROR("Split-K batch size must be aligned with quantization group "
"size! KRead=" +
std::to_string(KRead) +
" is not divisible by BQuantGroupSize::kK=" +
std::to_string(BQuantGroupSize::kK));
}
return false;
}
}
return false;
}
if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
@@ -1215,7 +1313,10 @@ struct QuantGemmKernel
const auto& b_block_window =
MakeBBlockWindow(b_ptr, kargs, splitk_batch_offset.splitted_k, block_idx_n);
const auto& aq_block_window = MakeAQBlockWindow(aq_ptr, kargs, block_idx_m, block_idx_n);
const auto& bq_block_window = MakeBQBlockWindow(bq_ptr, kargs, block_idx_m, block_idx_n);
// Note: Pass bq_group_offset so the tensor view dimension reflects
// the remaining K-groups from the split-K offset position.
const auto& bq_block_window = MakeBQBlockWindow(
bq_ptr, kargs, splitk_batch_offset.bq_group_offset, block_idx_m, block_idx_n);
const index_t num_loop =
amd_wave_read_first_lane(TilePartitioner::GetLoopNum(splitk_batch_offset.splitted_k));
@@ -1343,8 +1444,9 @@ struct QuantGemmKernel
const BDataType* b_ptr =
static_cast<const BDataType*>(kargs.b_ptr) + splitk_batch_offset.b_k_split_offset;
const AQDataType* aq_ptr = static_cast<const AQDataType*>(kargs.aq_ptr);
const BQDataType* bq_ptr = static_cast<const BQDataType*>(kargs.bq_ptr);
CDataType* c_ptr = static_cast<CDataType*>(kargs.c_ptr);
const BQDataType* bq_ptr =
static_cast<const BQDataType*>(kargs.bq_ptr) + splitk_batch_offset.bq_k_split_offset;
CDataType* c_ptr = static_cast<CDataType*>(kargs.c_ptr);
// allocate LDS
__shared__ char smem_ptr[GetSmemSize()];

8
include/ck_tile/ops/gemm_quant/kernel/grouped_gemm_quant_kernel.hpp
View File

@@ -387,8 +387,8 @@ struct QuantGroupedGemmKernel
Base::MakeABlockWindow(a_ptr, kargs, splitk_batch_offset.splitted_k, block_idx_m);
const auto& b_block_window =
Base::MakeBBlockWindow(b_ptr, kargs, splitk_batch_offset.splitted_k, block_idx_n);
const auto& bq_block_window =
Base::MakeBQBlockWindow(bq_ptr, kargs, block_idx_m, block_idx_n);
const auto& bq_block_window = Base::MakeBQBlockWindow(
bq_ptr, kargs, splitk_batch_offset.bq_group_offset, block_idx_m, block_idx_n);
const index_t num_loop = __builtin_amdgcn_readfirstlane(
TilePartitioner::GetLoopNum(splitk_batch_offset.splitted_k));
@@ -453,8 +453,8 @@ struct QuantGroupedGemmKernel
Base::MakeBBlockWindow(b_ptr, kargs, splitk_batch_offset.splitted_k, block_idx_n);
const auto& aq_block_window =
Base::MakeAQBlockWindow(aq_ptr, kargs, block_idx_m, block_idx_n);
const auto& bq_block_window =
Base::MakeBQBlockWindow(bq_ptr, kargs, block_idx_m, block_idx_n);
const auto& bq_block_window = Base::MakeBQBlockWindow(
bq_ptr, kargs, splitk_batch_offset.bq_group_offset, block_idx_m, block_idx_n);
// Get hot-loop and tail configuration
const index_t num_loop = __builtin_amdgcn_readfirstlane(

194
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm_multi_abd.hpp Normal file
View File

@@ -0,0 +1,194 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#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"
#include "ck/utility/functional4.hpp"
#include "ck/utility/tuple_helper.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
namespace ck {
namespace tensor_operation {
namespace host {
template <typename AsTensorTuple,
typename BsTensorTuple,
typename DsTensorTuple,
typename EDataType,
typename AccDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
typename AComputeType,
typename BComputeType>
struct ReferenceGemmMultiABD : public device::BaseOperator
{
// Argument
struct Argument : public device::BaseArgument
{
Argument(const AsTensorTuple& as_m_k,
const BsTensorTuple& bs_k_n,
const DsTensorTuple& ds_m_n,
Tensor<EDataType>& e_m_n,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op)
: as_m_k_{as_m_k},
bs_k_n_{bs_k_n},
ds_m_n_{ds_m_n},
e_m_n_{e_m_n},
a_element_op_{a_element_op},
b_element_op_{b_element_op},
cde_element_op_{cde_element_op}
{
}
const AsTensorTuple& as_m_k_;
const BsTensorTuple& bs_k_n_;
const DsTensorTuple& ds_m_n_;
Tensor<EDataType>& e_m_n_;
AElementwiseOperation a_element_op_;
BElementwiseOperation b_element_op_;
CDEElementwiseOperation cde_element_op_;
};
// Invoker
struct Invoker : public device::BaseInvoker
{
using Argument = ReferenceGemmMultiABD::Argument;
float Run(const Argument& arg)
{
static constexpr index_t NumATensor = AsTensorTuple::Size();
static constexpr index_t NumBTensor = BsTensorTuple::Size();
static constexpr index_t NumDTensor = DsTensorTuple::Size();
const int M = arg.as_m_k_[Number<0>{}].mDesc.GetLengths()[0];
const int K = arg.as_m_k_[Number<0>{}].mDesc.GetLengths()[1];
const int N = arg.bs_k_n_[Number<0>{}].mDesc.GetLengths()[1];
Tensor<AComputeType> a_m_k({M, K});
for(int m = 0; m < M; ++m)
{
for(int k = 0; k < K; ++k)
{
// result
auto data_refs1 = ck::tie(a_m_k(m, k));
// inputs
auto data_refs2 = generate_tie(
[&](auto i) -> auto& { return arg.as_m_k_[Number<i>{}](m, k); },
Number<NumATensor>{});
auto data_refs = concat_tuple_of_reference(data_refs1, data_refs2);
unpack(arg.a_element_op_, data_refs);
}
}
Tensor<BComputeType> b_k_n({K, N});
for(int k = 0; k < K; ++k)
{
for(int n = 0; n < N; ++n)
{
// result
auto data_refs1 = ck::tie(b_k_n(k, n));
// inputs
auto data_refs2 = generate_tie(
[&](auto i) -> auto& { return arg.bs_k_n_[Number<i>{}](k, n); },
Number<NumBTensor>{});
auto data_refs = concat_tuple_of_reference(data_refs1, data_refs2);
unpack(arg.b_element_op_, data_refs);
}
}
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
Tensor<AccDataType> c_m_n({M, N});
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<AComputeType,
BComputeType,
AccDataType,
AccDataType,
PassThrough,
PassThrough,
PassThrough>;
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a_m_k, b_k_n, c_m_n, PassThrough{}, PassThrough{}, PassThrough{});
ref_invoker.Run(ref_argument);
for(int m = 0; m < M; ++m)
{
for(int n = 0; n < N; ++n)
{
// compulsory
auto data_refs1 = ck::tie(arg.e_m_n_(m, n), c_m_n(m, n));
// optional (if multiple Ds)
auto data_refs2 = generate_tie(
[&](auto i) -> auto& { return arg.ds_m_n_[Number<i>{}](m, n); },
Number<NumDTensor>{});
auto data_refs = concat_tuple_of_reference(data_refs1, data_refs2);
unpack(arg.cde_element_op_, data_refs);
}
}
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 AsTensorTuple& as_m_k,
const BsTensorTuple& bs_k_n,
const DsTensorTuple& ds_m_n,
Tensor<EDataType>& e_m_n,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CDEElementwiseOperation cde_element_op)
{
return Argument{as_m_k, bs_k_n, ds_m_n, e_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 << "ReferenceGemmMultiABD"
<< std::endl;
// clang-format on
return str.str();
}
};
} // namespace host
} // namespace tensor_operation
} // namespace ck

295
library/include/ck/library/tensor_operation_instance/gpu/grouped_gemm_multi_abd_fixed_nk.hpp
View File

@@ -10,7 +10,6 @@
#include "ck/ck.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd_fixed_nk.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multi_abd_xdl_fixed_nk.hpp"
namespace ck {
namespace tensor_operation {
@@ -21,6 +20,7 @@ using Multiply = ck::tensor_operation::element_wise::Multiply;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
#if defined(CK_USE_XDL)
// RRR
void add_device_grouped_gemm_xdl_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_bias_gelu_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<ck::Tuple<Row>,
@@ -179,6 +179,167 @@ void add_device_grouped_gemm_xdl_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_instan
PassThrough,
Multiply,
PassThrough>>>& instances);
#endif
#if defined(CK_USE_WMMA)
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_bias_gelu_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<ck::Tuple<Row>,
ck::Tuple<Row, Row>,
ck::Tuple<Row>,
Row,
ck::Tuple<BF16>,
ck::Tuple<I8, BF16>,
ck::Tuple<BF16>,
BF16,
PassThrough,
Multiply,
AddFastGelu>>>& instances);
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_bias_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<ck::Tuple<Row>,
ck::Tuple<Row, Row>,
ck::Tuple<Row>,
Row,
ck::Tuple<BF16>,
ck::Tuple<I8, BF16>,
ck::Tuple<BF16>,
BF16,
PassThrough,
Multiply,
Add>>>& instances);
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_gelu_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<ck::Tuple<Row>,
ck::Tuple<Row, Row>,
ck::Tuple<>,
Row,
ck::Tuple<BF16>,
ck::Tuple<I8, BF16>,
ck::Tuple<>,
BF16,
PassThrough,
Multiply,
FastGelu>>>& instances);
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<ck::Tuple<Row>,
ck::Tuple<Row, Row>,
ck::Tuple<>,
Row,
ck::Tuple<BF16>,
ck::Tuple<I8, BF16>,
ck::Tuple<>,
BF16,
PassThrough,
Multiply,
PassThrough>>>& instances);
// RCR
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_bias_gelu_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<ck::Tuple<Row>,
ck::Tuple<Col, Col>,
ck::Tuple<Row>,
Row,
ck::Tuple<BF16>,
ck::Tuple<I8, BF16>,
ck::Tuple<BF16>,
BF16,
PassThrough,
Multiply,
AddFastGelu>>>& instances);
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_bias_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<ck::Tuple<Row>,
ck::Tuple<Col, Col>,
ck::Tuple<Row>,
Row,
ck::Tuple<BF16>,
ck::Tuple<I8, BF16>,
ck::Tuple<BF16>,
BF16,
PassThrough,
Multiply,
Add>>>& instances);
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_gelu_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<ck::Tuple<Row>,
ck::Tuple<Col, Col>,
ck::Tuple<>,
Row,
ck::Tuple<BF16>,
ck::Tuple<I8, BF16>,
ck::Tuple<>,
BF16,
PassThrough,
Multiply,
FastGelu>>>& instances);
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<ck::Tuple<Row>,
ck::Tuple<Col, Col>,
ck::Tuple<>,
Row,
ck::Tuple<BF16>,
ck::Tuple<I8, BF16>,
ck::Tuple<>,
BF16,
PassThrough,
Multiply,
PassThrough>>>& instances);
// CRR
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_bias_gelu_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<ck::Tuple<Col>,
ck::Tuple<Row, Row>,
ck::Tuple<Row>,
Row,
ck::Tuple<BF16>,
ck::Tuple<I8, BF16>,
ck::Tuple<BF16>,
BF16,
PassThrough,
Multiply,
AddFastGelu>>>& instances);
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_bias_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<ck::Tuple<Col>,
ck::Tuple<Row, Row>,
ck::Tuple<Row>,
Row,
ck::Tuple<BF16>,
ck::Tuple<I8, BF16>,
ck::Tuple<BF16>,
BF16,
PassThrough,
Multiply,
Add>>>& instances);
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_gelu_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<ck::Tuple<Col>,
ck::Tuple<Row, Row>,
ck::Tuple<>,
Row,
ck::Tuple<BF16>,
ck::Tuple<I8, BF16>,
ck::Tuple<>,
BF16,
PassThrough,
Multiply,
FastGelu>>>& instances);
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<ck::Tuple<Col>,
ck::Tuple<Row, Row>,
ck::Tuple<>,
Row,
ck::Tuple<BF16>,
ck::Tuple<I8, BF16>,
ck::Tuple<>,
BF16,
PassThrough,
Multiply,
PassThrough>>>& instances);
#endif // CK_USE
// GEMM + Add + Gelu
template <typename AsLayout,
@@ -218,6 +379,7 @@ struct DeviceOperationInstanceFactory<
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
#if defined(CK_USE_XDL)
if constexpr(is_same_v<AsDataType, ck::Tuple<BF16>> &&
is_same_v<BsDataType, ck::Tuple<I8, BF16>> &&
is_same_v<DsDataType, ck::Tuple<BF16>> && is_same_v<EDataType, BF16>)
@@ -246,6 +408,38 @@ struct DeviceOperationInstanceFactory<
op_ptrs);
}
}
#endif // CK_USE_XDL
#if defined(CK_USE_WMMA)
if constexpr(is_same_v<AsDataType, ck::Tuple<BF16>> &&
is_same_v<BsDataType, ck::Tuple<I8, BF16>> &&
is_same_v<DsDataType, ck::Tuple<BF16>> && is_same_v<EDataType, BF16>)
{
if constexpr(is_same_v<AsLayout, ck::Tuple<Row>> &&
is_same_v<BsLayout, ck::Tuple<Row, Row>> &&
is_same_v<DsLayout, ck::Tuple<Row>> && is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_bias_gelu_instances(
op_ptrs);
}
if constexpr(is_same_v<AsLayout, ck::Tuple<Col>> &&
is_same_v<BsLayout, ck::Tuple<Row, Row>> &&
is_same_v<DsLayout, ck::Tuple<Row>> && is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_bias_gelu_instances(
op_ptrs);
}
if constexpr(is_same_v<AsLayout, ck::Tuple<Row>> &&
is_same_v<BsLayout, ck::Tuple<Col, Col>> &&
is_same_v<DsLayout, ck::Tuple<Row>> && is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_bias_gelu_instances(
op_ptrs);
}
}
#endif // CK_USE_WMMA
return op_ptrs;
}
@@ -289,6 +483,7 @@ struct DeviceOperationInstanceFactory<
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
#if defined(CK_USE_XDL)
if constexpr(is_same_v<AsDataType, ck::Tuple<BF16>> &&
is_same_v<BsDataType, ck::Tuple<I8, BF16>> &&
is_same_v<DsDataType, ck::Tuple<BF16>> && is_same_v<EDataType, BF16>)
@@ -317,6 +512,38 @@ struct DeviceOperationInstanceFactory<
op_ptrs);
}
}
#endif // CK_USE_XDL
#if defined(CK_USE_WMMA)
if constexpr(is_same_v<AsDataType, ck::Tuple<BF16>> &&
is_same_v<BsDataType, ck::Tuple<I8, BF16>> &&
is_same_v<DsDataType, ck::Tuple<BF16>> && is_same_v<EDataType, BF16>)
{
if constexpr(is_same_v<AsLayout, ck::Tuple<Row>> &&
is_same_v<BsLayout, ck::Tuple<Row, Row>> &&
is_same_v<DsLayout, ck::Tuple<Row>> && is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_bias_instances(
op_ptrs);
}
if constexpr(is_same_v<AsLayout, ck::Tuple<Col>> &&
is_same_v<BsLayout, ck::Tuple<Row, Row>> &&
is_same_v<DsLayout, ck::Tuple<Row>> && is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_bias_instances(
op_ptrs);
}
if constexpr(is_same_v<AsLayout, ck::Tuple<Row>> &&
is_same_v<BsLayout, ck::Tuple<Col, Col>> &&
is_same_v<DsLayout, ck::Tuple<Row>> && is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_bias_instances(
op_ptrs);
}
}
#endif // CK_USE_WMMA
return op_ptrs;
}
@@ -360,6 +587,7 @@ struct DeviceOperationInstanceFactory<
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
#if defined(CK_USE_XDL)
if constexpr(is_same_v<AsDataType, ck::Tuple<BF16>> &&
is_same_v<BsDataType, ck::Tuple<I8, BF16>> &&
is_same_v<DsDataType, ck::Tuple<>> && is_same_v<EDataType, BF16>)
@@ -388,6 +616,38 @@ struct DeviceOperationInstanceFactory<
op_ptrs);
}
}
#endif // CK_USE_XDL
#if defined(CK_USE_WMMA)
if constexpr(is_same_v<AsDataType, ck::Tuple<BF16>> &&
is_same_v<BsDataType, ck::Tuple<I8, BF16>> &&
is_same_v<DsDataType, ck::Tuple<>> && is_same_v<EDataType, BF16>)
{
if constexpr(is_same_v<AsLayout, ck::Tuple<Row>> &&
is_same_v<BsLayout, ck::Tuple<Row, Row>> &&
is_same_v<DsLayout, ck::Tuple<>> && is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_gelu_instances(
op_ptrs);
}
if constexpr(is_same_v<AsLayout, ck::Tuple<Col>> &&
is_same_v<BsLayout, ck::Tuple<Row, Row>> &&
is_same_v<DsLayout, ck::Tuple<>> && is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_gelu_instances(
op_ptrs);
}
if constexpr(is_same_v<AsLayout, ck::Tuple<Row>> &&
is_same_v<BsLayout, ck::Tuple<Col, Col>> &&
is_same_v<DsLayout, ck::Tuple<>> && is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_gelu_instances(
op_ptrs);
}
}
#endif // CK_USE_WMMA
return op_ptrs;
}
@@ -431,6 +691,7 @@ struct DeviceOperationInstanceFactory<
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
#if defined(CK_USE_XDL)
if constexpr(is_same_v<AsDataType, ck::Tuple<BF16>> &&
is_same_v<BsDataType, ck::Tuple<I8, BF16>> &&
is_same_v<DsDataType, ck::Tuple<>> && is_same_v<EDataType, BF16>)
@@ -459,6 +720,38 @@ struct DeviceOperationInstanceFactory<
op_ptrs);
}
}
#endif // CK_USE_XDL
#if defined(CK_USE_WMMA)
if constexpr(is_same_v<AsDataType, ck::Tuple<BF16>> &&
is_same_v<BsDataType, ck::Tuple<I8, BF16>> &&
is_same_v<DsDataType, ck::Tuple<>> && is_same_v<EDataType, BF16>)
{
if constexpr(is_same_v<AsLayout, ck::Tuple<Row>> &&
is_same_v<BsLayout, ck::Tuple<Row, Row>> &&
is_same_v<DsLayout, ck::Tuple<>> && is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_instances(
op_ptrs);
}
if constexpr(is_same_v<AsLayout, ck::Tuple<Col>> &&
is_same_v<BsLayout, ck::Tuple<Row, Row>> &&
is_same_v<DsLayout, ck::Tuple<>> && is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_instances(
op_ptrs);
}
if constexpr(is_same_v<AsLayout, ck::Tuple<Row>> &&
is_same_v<BsLayout, ck::Tuple<Col, Col>> &&
is_same_v<DsLayout, ck::Tuple<>> && is_same_v<ELayout, Row>)
{
add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_instances(
op_ptrs);
}
}
#endif // CK_USE_WMMA
return op_ptrs;
}

6
library/src/tensor_operation_instance/gpu/grouped_gemm_fixed_nk_multi_abd/CMakeLists.txt
View File

@@ -1,13 +1,17 @@
# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
# SPDX-License-Identifier: MIT
# ONLY XDL_KERNELS
# ONLY XDL_AND_WMMA_KERNELS
set(GROUPED_GEMM_FIXED_NK_MULTI_ABD_INSTANCES)
list(APPEND GROUPED_GEMM_FIXED_NK_MULTI_ABD_INSTANCES
device_grouped_gemm_xdl_fixed_nk_bias_gelu_bf16_i8_bf16_mk_kn_mn_instance.cpp
device_grouped_gemm_xdl_fixed_nk_bias_gelu_bf16_i8_bf16_mk_nk_mn_instance.cpp
device_grouped_gemm_xdl_fixed_nk_bias_gelu_bf16_i8_bf16_km_kn_mn_instance.cpp
device_grouped_gemm_wmma_fixed_nk_bias_gelu_bf16_i8_bf16_mk_kn_mn_instance.cpp
device_grouped_gemm_wmma_fixed_nk_bias_gelu_bf16_i8_bf16_mk_nk_mn_instance.cpp
device_grouped_gemm_wmma_fixed_nk_bias_gelu_bf16_i8_bf16_km_kn_mn_instance.cpp
)
add_instance_library(device_grouped_gemm_fixed_nk_multi_abd_instance ${GROUPED_GEMM_FIXED_NK_MULTI_ABD_INSTANCES})

144
library/src/tensor_operation_instance/gpu/grouped_gemm_fixed_nk_multi_abd/device_grouped_gemm_wmma_fixed_nk_bias_gelu_bf16_i8_bf16_km_kn_mn_instance.cpp Normal file
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@@ -0,0 +1,144 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#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/device_grouped_gemm_multi_abd.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multi_abd_wmma_fixed_nk.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
template <index_t... Is>
using S = Sequence<Is...>;
using BF16 = bhalf_t;
using I8 = int8_t;
using F32 = float;
using Row = tensor_layout::gemm::RowMajor;
using Col = tensor_layout::gemm::ColumnMajor;
using Multiply = element_wise::Multiply;
using PassThrough = element_wise::PassThrough;
using AddFastGelu = element_wise::AddFastGelu;
using Add = element_wise::Add;
using FastGelu = element_wise::FastGelu;
static constexpr auto GemmMNKPadding = GemmSpecialization::MNKPadding;
template <typename DsLayout,
typename DsDataType,
typename CDEElementOp,
GemmSpecialization GemmSpec>
using device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_instances = std::tuple<
// clang-format off
//#######################################| AsLayout| BsLayout| DsLayout| ELayout| AsData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MRepeat| NRepeat| 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| Size| Block| Block| Block| | | Wmma| Wmma| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MRepeat| NRepeat| _MBlock_MPerBlock_NBlock_NPerBlock| ScalarPerVector|
//#######################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| | _NWaveNPerXdl|
//#######################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK< Tuple<Col>, Tuple<Row, Row>, DsLayout, Row, Tuple<BF16>, Tuple<I8, BF16>, F32, BF16, DsDataType, BF16, PassThrough, Multiply, CDEElementOp, GemmSpec, 256, 256, 256, 32, 8, 8, 16, 16, 8, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 0, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 0, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK< Tuple<Col>, Tuple<Row, Row>, DsLayout, Row, Tuple<BF16>, Tuple<I8, BF16>, F32, BF16, DsDataType, BF16, PassThrough, Multiply, CDEElementOp, GemmSpec, 256, 128, 256, 32, 8, 8, 16, 16, 4, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 0, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 0, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK< Tuple<Col>, Tuple<Row, Row>, DsLayout, Row, Tuple<BF16>, Tuple<I8, BF16>, F32, BF16, DsDataType, BF16, PassThrough, Multiply, CDEElementOp, GemmSpec, 256, 128, 128, 32, 8, 8, 16, 16, 2, 4, S<4, 32, 2>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 0, S<4, 32, 2>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 0, 1, 1, S<1, 32, 1, 8>, 4>
// clang-format on
>;
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_bias_gelu_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<Tuple<Col>,
Tuple<Row, Row>,
Tuple<Row>,
Row,
Tuple<BF16>,
Tuple<I8, BF16>,
Tuple<BF16>,
BF16,
PassThrough,
Multiply,
AddFastGelu>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_instances<
Tuple<Row>,
Tuple<BF16>,
AddFastGelu,
GemmMNKPadding>{});
}
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_bias_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<Tuple<Col>,
Tuple<Row, Row>,
Tuple<Row>,
Row,
Tuple<BF16>,
Tuple<I8, BF16>,
Tuple<BF16>,
BF16,
PassThrough,
Multiply,
Add>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_instances<
Tuple<Row>,
Tuple<BF16>,
Add,
GemmMNKPadding>{});
}
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<Tuple<Col>,
Tuple<Row, Row>,
Tuple<>,
Row,
Tuple<BF16>,
Tuple<I8, BF16>,
Tuple<>,
BF16,
PassThrough,
Multiply,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_instances<
Tuple<>,
Tuple<>,
PassThrough,
GemmMNKPadding>{});
}
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_gelu_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<Tuple<Col>,
Tuple<Row, Row>,
Tuple<>,
Row,
Tuple<BF16>,
Tuple<I8, BF16>,
Tuple<>,
BF16,
PassThrough,
Multiply,
FastGelu>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_instances<
Tuple<>,
Tuple<>,
FastGelu,
GemmMNKPadding>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

144
library/src/tensor_operation_instance/gpu/grouped_gemm_fixed_nk_multi_abd/device_grouped_gemm_wmma_fixed_nk_bias_gelu_bf16_i8_bf16_mk_kn_mn_instance.cpp Normal file
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@@ -0,0 +1,144 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#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/device_grouped_gemm_multi_abd.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multi_abd_wmma_fixed_nk.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
template <index_t... Is>
using S = Sequence<Is...>;
using BF16 = bhalf_t;
using I8 = int8_t;
using F32 = float;
using Row = tensor_layout::gemm::RowMajor;
using Col = tensor_layout::gemm::ColumnMajor;
using Multiply = element_wise::Multiply;
using PassThrough = element_wise::PassThrough;
using AddFastGelu = element_wise::AddFastGelu;
using Add = element_wise::Add;
using FastGelu = element_wise::FastGelu;
static constexpr auto GemmMNKPadding = GemmSpecialization::MNKPadding;
template <typename DsLayout,
typename DsDataType,
typename CDEElementOp,
GemmSpecialization GemmSpec>
using device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_instances = std::tuple<
// clang-format off
//#######################################| AsLayout| BsLayout| DsLayout| ELayout| AsData| BsData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MRepeat| NRepeat| 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| Size| Block| Block| Block| | | Wmma| Wmma| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MRepeat| NRepeat| _MBlock_MPerBlock_NBlock_NPerBlock| ScalarPerVector|
//#######################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| | |
//#######################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK< Tuple<Row>, Tuple<Row, Row>, DsLayout, Row, Tuple<BF16>, Tuple<I8, BF16>, F32, BF16, DsDataType, BF16, PassThrough, Multiply, CDEElementOp, GemmSpec, 256, 256, 256, 32, 8, 8, 16, 16, 8, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 0, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK< Tuple<Row>, Tuple<Row, Row>, DsLayout, Row, Tuple<BF16>, Tuple<I8, BF16>, F32, BF16, DsDataType, BF16, PassThrough, Multiply, CDEElementOp, GemmSpec, 256, 128, 256, 32, 8, 8, 16, 16, 4, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 4, 0, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK< Tuple<Row>, Tuple<Row, Row>, DsLayout, Row, Tuple<BF16>, Tuple<I8, BF16>, F32, BF16, DsDataType, BF16, PassThrough, Multiply, CDEElementOp, GemmSpec, 256, 128, 128, 32, 8, 8, 16, 16, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 2>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 4>
// clang-format on
>;
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_bias_gelu_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<Tuple<Row>,
Tuple<Row, Row>,
Tuple<Row>,
Row,
Tuple<BF16>,
Tuple<I8, BF16>,
Tuple<BF16>,
BF16,
PassThrough,
Multiply,
AddFastGelu>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_instances<
Tuple<Row>,
Tuple<BF16>,
AddFastGelu,
GemmMNKPadding>{});
}
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_bias_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<Tuple<Row>,
Tuple<Row, Row>,
Tuple<Row>,
Row,
Tuple<BF16>,
Tuple<I8, BF16>,
Tuple<BF16>,
BF16,
PassThrough,
Multiply,
Add>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_instances<
Tuple<Row>,
Tuple<BF16>,
Add,
GemmMNKPadding>{});
}
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<Tuple<Row>,
Tuple<Row, Row>,
Tuple<>,
Row,
Tuple<BF16>,
Tuple<I8, BF16>,
Tuple<>,
BF16,
PassThrough,
Multiply,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_instances<
Tuple<>,
Tuple<>,
PassThrough,
GemmMNKPadding>{});
}
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_gelu_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<Tuple<Row>,
Tuple<Row, Row>,
Tuple<>,
Row,
Tuple<BF16>,
Tuple<I8, BF16>,
Tuple<>,
BF16,
PassThrough,
Multiply,
FastGelu>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_instances<
Tuple<>,
Tuple<>,
FastGelu,
GemmMNKPadding>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

144
library/src/tensor_operation_instance/gpu/grouped_gemm_fixed_nk_multi_abd/device_grouped_gemm_wmma_fixed_nk_bias_gelu_bf16_i8_bf16_mk_nk_mn_instance.cpp Normal file
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@@ -0,0 +1,144 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#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/device_grouped_gemm_multi_abd.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multi_abd_wmma_fixed_nk.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
template <index_t... Is>
using S = Sequence<Is...>;
using BF16 = bhalf_t;
using I8 = int8_t;
using F32 = float;
using Row = tensor_layout::gemm::RowMajor;
using Col = tensor_layout::gemm::ColumnMajor;
using Multiply = element_wise::Multiply;
using PassThrough = element_wise::PassThrough;
using AddFastGelu = element_wise::AddFastGelu;
using Add = element_wise::Add;
using FastGelu = element_wise::FastGelu;
static constexpr auto GemmMNKPadding = GemmSpecialization::MNKPadding;
template <typename DsLayout,
typename DsDataType,
typename CDEElementOp,
GemmSpecialization GemmSpec>
using device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_instances = std::tuple<
// clang-format off
//######################################| AsLayout| BsLayout| DsLayout| ELayout| AsData| BsData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MRepeat| NRepeat| 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| Size| Block| Block| Block| | | Wmma| Wmma| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MRepeat| NRepeat| _MBlock_MPerBlock_NBlock_NPerBlock| ScalarPerVector|
//######################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| | |
//######################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK< Tuple<Row>, Tuple<Col, Col>, DsLayout, Row, Tuple<BF16>, Tuple<I8, BF16>, F32, BF16, DsDataType, BF16, PassThrough, Multiply, CDEElementOp, GemmSpec, 256, 128, 128, 64, 8, 8, 16, 16, 2, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 64, 1, 4>, 8>,
DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK< Tuple<Row>, Tuple<Col, Col>, DsLayout, Row, Tuple<BF16>, Tuple<I8, BF16>, F32, BF16, DsDataType, BF16, PassThrough, Multiply, CDEElementOp, GemmSpec, 256, 128, 128, 64, 2, 2, 16, 16, 2, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, 1, 1, 1, S<1, 64, 1, 4>, 8>,
DeviceGroupedGemm_Wmma_Multi_ABD_Fixed_NK< Tuple<Row>, Tuple<Col, Col>, DsLayout, Row, Tuple<BF16>, Tuple<I8, BF16>, F32, BF16, DsDataType, BF16, PassThrough, Multiply, CDEElementOp, GemmSpec, 256, 128, 128, 32, 8, 8, 16, 16, 2, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 64, 1, 4>, 8>
// clang-format on
>;
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_bias_gelu_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<Tuple<Row>,
Tuple<Col, Col>,
Tuple<Row>,
Row,
Tuple<BF16>,
Tuple<I8, BF16>,
Tuple<BF16>,
BF16,
PassThrough,
Multiply,
AddFastGelu>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_instances<
Tuple<Row>,
Tuple<BF16>,
AddFastGelu,
GemmMNKPadding>{});
}
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_bias_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<Tuple<Row>,
Tuple<Col, Col>,
Tuple<Row>,
Row,
Tuple<BF16>,
Tuple<I8, BF16>,
Tuple<BF16>,
BF16,
PassThrough,
Multiply,
Add>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_instances<
Tuple<Row>,
Tuple<BF16>,
Add,
GemmMNKPadding>{});
}
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<Tuple<Row>,
Tuple<Col, Col>,
Tuple<>,
Row,
Tuple<BF16>,
Tuple<I8, BF16>,
Tuple<>,
BF16,
PassThrough,
Multiply,
PassThrough>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_instances<
Tuple<>,
Tuple<>,
PassThrough,
GemmMNKPadding>{});
}
void add_device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_gelu_instances(
std::vector<std::unique_ptr<DeviceGroupedGemmMultiABDFixedNK<Tuple<Row>,
Tuple<Col, Col>,
Tuple<>,
Row,
Tuple<BF16>,
Tuple<I8, BF16>,
Tuple<>,
BF16,
PassThrough,
Multiply,
FastGelu>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_gemm_wmma_fixed_nk_multi_abd_bf16_i8_bf16_mk_nk_mn_instances<
Tuple<>,
Tuple<>,
FastGelu,
GemmMNKPadding>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck

10
library/src/tensor_operation_instance/gpu/grouped_gemm_fixed_nk_multi_abd/device_grouped_gemm_xdl_fixed_nk_bf16_i8_bf16_km_kn_mn_common.hpp
View File

@@ -61,6 +61,8 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecial
static constexpr auto GemmMNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
// NOTE: After adding unit tests for DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK it tuned out that
// portion of the instances are failing. As a workaround these have been commented out.
template <typename DsLayout,
typename DsDataType,
typename CDEElementOp,
@@ -72,14 +74,14 @@ using device_grouped_gemm_xdl_fixed_nk_multi_abd_bf16_i8_bf16_km_kn_mn_instances
//######################################| | | | | | | | | | | 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|
//######################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 128, 64, 32, 8, 2, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 64, 128, 32, 8, 2, 32, 32, 1, 2, S<4, 32, 2>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 64, 128, 32, 8, 2, 32, 32, 1, 2, S<4, 32, 2>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 2>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 4, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 128, 128, 64, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 4>, 8>,
// DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 128, 128, 64, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 128, 64, 128, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 128, 64, 128, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>
// clang-format on
>;

10
library/src/tensor_operation_instance/gpu/grouped_gemm_fixed_nk_multi_abd/device_grouped_gemm_xdl_fixed_nk_bf16_i8_bf16_mk_kn_mn_common.hpp
View File

@@ -61,6 +61,8 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecial
static constexpr auto GemmMNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
// NOTE: After adding unit tests for DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK it tuned out that
// portion of the instances are failing. As a workaround these have been commented out.
template <typename DsLayout,
typename DsDataType,
typename CDEElementOp,
@@ -72,14 +74,14 @@ using device_grouped_gemm_xdl_fixed_nk_multi_abd_bf16_i8_bf16_mk_kn_mn_instances
//######################################| | | | | | | | | | | 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|
//######################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 128, 64, 32, 8, 2, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 64, 128, 32, 8, 2, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 64, 128, 32, 8, 2, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 128, 128, 64, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 4>, 8>,
// DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 128, 128, 64, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 128, 64, 128, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 128, 64, 128, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK< AsLayout, BsLayout, DsLayout, ELayout, AsDataType, BsDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>
// clang-format on
>;

2
library/src/tensor_operation_instance/gpu/grouped_gemm_fixed_nk_multi_abd/device_grouped_gemm_xdl_fixed_nk_bf16_i8_bf16_mk_nk_mn_common.hpp
View File

@@ -61,6 +61,8 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecial
static constexpr auto GemmMNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
// NOTE: After adding unit tests for DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK it tuned out that
// portion of the instances are failing. As a workaround these have been commented out.
template <typename DsLayout,
typename DsDataType,
typename CDEElementOp,

88
profiler/include/profiler/profile_gemm_multi_abd_impl.hpp
View File

@@ -17,22 +17,11 @@
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm_multi_abd.hpp"
namespace ck {
namespace profiler {
// this function is also defined in CK but because of the way we use it in
// profile_gemm_multi_impl, it requires the arguments to not be const
template <typename... X, typename... Y>
auto concat_tuple_of_refs(ck::Tuple<X&...>& tx, ck::Tuple<Y&...>& ty)
{
return ck::unpack2(
[&](auto&&... zs) { return ck::Tuple<decltype(zs)...>{ck::forward<decltype(zs)>(zs)...}; },
tx,
ty);
}
template <typename AsDataType,
typename BsDataType,
typename AccDataType,
@@ -180,80 +169,35 @@ bool profile_gemm_multi_abd_impl(int do_verification,
// run reference
if(do_verification)
{
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
Tensor<AccDataType> c_m_n({M, N});
using AComputeType =
typename std::conditional<(NumATensor > 1),
EDataType,
remove_cvref_t<tuple_element_t<0, AsDataType>>>::type;
Tensor<AComputeType> a_m_k({M, K});
for(int m = 0; m < M; ++m)
{
for(int k = 0; k < K; ++k)
{
// result
auto data_refs1 = ck::tie(a_m_k(m, k));
// inputs
auto data_refs2 =
generate_tie([&](auto i) -> auto& { return as_m_k(Number<i>{})(m, k); },
Number<NumATensor>{});
auto data_refs = concat_tuple_of_refs(data_refs1, data_refs2);
unpack(a_element_op, data_refs);
}
}
using BComputeType =
typename std::conditional<(NumBTensor > 1),
EDataType,
remove_cvref_t<tuple_element_t<0, BsDataType>>>::type;
Tensor<BComputeType> b_k_n({K, N});
for(int k = 0; k < K; ++k)
{
for(int n = 0; n < N; ++n)
{
// result
auto data_refs1 = ck::tie(b_k_n(k, n));
// inputs
auto data_refs2 =
generate_tie([&](auto i) -> auto& { return bs_k_n(Number<i>{})(k, n); },
Number<NumBTensor>{});
auto data_refs = concat_tuple_of_refs(data_refs1, data_refs2);
unpack(b_element_op, data_refs);
}
}
using ReferenceGemmInstance =
ck::tensor_operation::host::ReferenceGemmMultiABD<decltype(as_m_k),
decltype(bs_k_n),
decltype(ds_m_n),
EDataType,
AccDataType,
AElementOp,
BElementOp,
CDEElementOp,
AComputeType,
BComputeType>;
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<AComputeType,
BComputeType,
AccDataType,
AccDataType,
PassThrough,
PassThrough,
PassThrough>;
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument =
ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, PassThrough{}, PassThrough{}, PassThrough{});
auto ref_argument = ref_gemm.MakeArgument(
as_m_k, bs_k_n, ds_m_n, e_m_n_host_result, a_element_op, b_element_op, cde_element_op);
ref_invoker.Run(ref_argument);
for(int m = 0; m < M; ++m)
{
for(int n = 0; n < N; ++n)
{
// compulsory
auto data_refs1 = ck::tie(e_m_n_host_result(m, n), c_m_n(m, n));
// optional (if multiple Ds)
auto data_refs2 =
generate_tie([&](auto i) -> auto& { return ds_m_n(Number<i>{})(m, n); },
Number<NumDTensor>{});
auto data_refs = concat_tuple_of_refs(data_refs1, data_refs2);
unpack(cde_element_op, data_refs);
}
}
}
std::array<DeviceMem*, NumATensor> as_device_buf;

534
profiler/include/profiler/profile_grouped_gemm_multi_abd_fixed_nk_impl.hpp Normal file
View File

@@ -0,0 +1,534 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include <iomanip>
#include <array>
#include <vector>
#include <numeric>
#include "ck/ck.hpp"
#include "ck/utility/env.hpp"
#include "ck/utility/tuple.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd_fixed_nk.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm_multi_abd.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_multi_abd_fixed_nk.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"
namespace ck {
namespace profiler {
template <typename T>
auto reserveVector(std::size_t size)
{
std::vector<T> vec;
vec.reserve(size);
return vec;
}
template <typename AsDataType,
typename BsDataType,
typename DsDataType,
typename EDataType,
typename AccDataType,
typename AsLayout,
typename BsLayout,
typename DsLayout,
typename ELayout,
typename AElementOp = ck::tensor_operation::element_wise::PassThrough,
typename BElementOp = ck::tensor_operation::element_wise::Multiply,
typename CDEElementOp = ck::tensor_operation::element_wise::PassThrough>
bool profile_grouped_gemm_multi_abd_fixed_nk_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>& StrideDs,
const std::vector<int>& StrideE,
const std::vector<int>& kbatch_list = {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});
}
};
const std::size_t group_count = Ms.size();
const int sum_of_m = std::accumulate(Ms.begin(), Ms.end(), 0);
static constexpr index_t NumATensor = AsDataType::Size();
static constexpr index_t NumBTensor = BsDataType::Size();
static constexpr index_t NumDTensor = DsDataType::Size();
if(group_count != Ns.size() || group_count != Ks.size() || group_count != StrideAs.size() ||
group_count != StrideBs.size() || (NumDTensor > 0 && group_count != StrideDs.size()))
{
throw std::runtime_error("wrong! inconsistent M/N/Ks, StrideAs/Bs/Ds/E size\n");
}
auto generateInputTupleA = [&](std::size_t g) {
if constexpr(NumATensor == 0)
{
static_assert("Gemm problem should have at least 1 A tensor.");
}
else
{
using ALayout = remove_cvref_t<tuple_element_t<Number<0>{}, AsLayout>>;
return generate_tuple(
[&](auto i) {
using ADataType = remove_cvref_t<tuple_element_t<i.value, AsDataType>>;
return Tensor<ADataType>(
f_host_tensor_descriptor(Ms[g], Ks[g], StrideAs[g], ALayout{}));
},
Number<NumATensor>{});
}
};
auto generateInputTupleB = [&](std::size_t g) {
if constexpr(NumBTensor == 0)
{
static_assert("Gemm problem should have at least 1 B tensor.");
}
else
{
using BLayout = remove_cvref_t<tuple_element_t<Number<0>{}, BsLayout>>;
return generate_tuple(
[&](auto i) {
using BDataType = remove_cvref_t<tuple_element_t<i.value, BsDataType>>;
return Tensor<BDataType>(
f_host_tensor_descriptor(Ks[g], Ns[g], StrideBs[g], BLayout{}));
},
Number<NumBTensor>{});
}
};
auto generateInputTupleD = [&](std::size_t g) {
if constexpr(NumDTensor == 0)
{
return ck::Tuple<>();
}
else
{
using DLayout = remove_cvref_t<tuple_element_t<Number<0>{}, DsLayout>>;
return generate_tuple(
[&](auto i) {
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
return Tensor<DDataType>(
f_host_tensor_descriptor(Ms[g], Ns[g], StrideDs[g], DLayout{}));
},
Number<NumDTensor>{});
}
};
using AsTensorTuple = decltype(generateInputTupleA(0));
using BsTensorTuple = decltype(generateInputTupleB(0));
using DsTensorTuple = decltype(generateInputTupleD(0));
auto g_as_m_k = reserveVector<AsTensorTuple>(group_count);
auto g_bs_k_n = reserveVector<BsTensorTuple>(group_count);
auto g_ds_m_n = reserveVector<DsTensorTuple>(group_count);
auto g_e_m_n_host_results = reserveVector<Tensor<EDataType>>(group_count);
auto g_e_m_n_device_results = reserveVector<Tensor<EDataType>>(group_count);
for(std::size_t g = 0; g < group_count; g++)
{
auto& as_m_k = g_as_m_k.emplace_back(generateInputTupleA(g));
auto& bs_k_n = g_bs_k_n.emplace_back(generateInputTupleB(g));
auto& ds_m_n = g_ds_m_n.emplace_back(generateInputTupleD(g));
g_e_m_n_host_results.push_back(
Tensor<EDataType>(f_host_tensor_descriptor(Ms[g], Ns[g], StrideE[g], ELayout{})));
g_e_m_n_device_results.push_back(
Tensor<EDataType>(f_host_tensor_descriptor(Ms[g], Ns[g], StrideE[g], ELayout{})));
if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
{
std::cout << "group: " << g << std::endl;
static_for<0, NumATensor, 1>{}([&](auto i) {
std::cout << "a" << i.value << "_m_k: " << as_m_k(i).mDesc << std::endl;
});
static_for<0, NumBTensor, 1>{}([&](auto i) {
std::cout << "b" << i.value << "_k_n: " << bs_k_n(i).mDesc << std::endl;
});
static_for<0, NumDTensor, 1>{}([&](auto i) {
std::cout << "d" << i.value << "_m_n: " << ds_m_n(i).mDesc << std::endl;
});
std::cout << "e_m_n: " << g_e_m_n_device_results[g].mDesc << std::endl;
}
std::size_t num_thread = 1;
switch(init_method)
{
case 0: break;
case 1:
static_for<0, NumATensor, 1>{}([&](auto i) {
using ADataType = remove_cvref_t<tuple_element_t<i.value, AsDataType>>;
as_m_k(i).GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread);
});
static_for<0, NumBTensor, 1>{}([&](auto i) {
using BDataType = remove_cvref_t<tuple_element_t<i.value, BsDataType>>;
bs_k_n(i).GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}, num_thread);
});
static_for<0, NumDTensor, 1>{}([&](auto i) {
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
ds_m_n(i).GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5}, num_thread);
});
break;
default:
static_for<0, NumATensor, 1>{}([&](auto i) {
using ADataType = remove_cvref_t<tuple_element_t<i.value, AsDataType>>;
as_m_k(i).GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}, num_thread);
});
static_for<0, NumBTensor, 1>{}([&](auto i) {
using BDataType = remove_cvref_t<tuple_element_t<i.value, BsDataType>>;
bs_k_n(i).GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
});
static_for<0, NumDTensor, 1>{}([&](auto i) {
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
ds_m_n(i).GenerateTensorValue(GeneratorTensor_3<DDataType>{0.0, 1.0}, num_thread);
});
}
}
const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto cde_element_op = CDEElementOp{};
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
std::vector<std::array<DeviceMemPtr, NumATensor>> g_as_device_buf(group_count);
std::vector<std::array<DeviceMemPtr, NumBTensor>> g_bs_device_buf(group_count);
std::vector<std::array<DeviceMemPtr, NumDTensor>> g_ds_device_buf(group_count);
std::vector<DeviceMemPtr> g_e_device_buf(group_count);
std::vector<std::array<const void*, NumATensor>> g_as_device_view(group_count);
std::vector<std::array<const void*, NumBTensor>> g_bs_device_view(group_count);
std::vector<std::array<const void*, NumDTensor>> g_ds_device_view(group_count);
std::vector<void*> g_e_device_view(group_count);
auto g_gemm_descs = reserveVector<tensor_operation::device::GemmMultiABDDesc>(group_count);
auto grouped_gemm_kernel_args_host =
reserveVector<tensor_operation::device::
GroupedGemmMultiABDKernelArgument<NumATensor, NumBTensor, NumDTensor>>(
group_count);
for(std::size_t g = 0; g < group_count; g++)
{
std::array<ck::index_t, NumATensor> as_stride;
std::array<ck::index_t, NumBTensor> bs_stride;
std::array<ck::index_t, NumDTensor> ds_stride;
auto& as_m_k = g_as_m_k[g];
auto& as_device_buf = g_as_device_buf[g];
auto& as_device_view = g_as_device_view[g];
static_for<0, NumATensor, 1>{}([&](auto i) {
using ADataType = remove_cvref_t<tuple_element_t<i.value, AsDataType>>;
as_device_buf[i] = std::make_unique<DeviceMem>(sizeof(ADataType) * Ms[g] * Ks[g]);
as_device_buf[i]->ToDevice(as_m_k[i].mData.data());
as_device_view[i] = as_device_buf[i]->GetDeviceBuffer();
as_stride[i] = StrideAs[g];
});
auto& bs_k_n = g_bs_k_n[g];
auto& bs_device_buf = g_bs_device_buf[g];
auto& bs_device_view = g_bs_device_view[g];
static_for<0, NumBTensor, 1>{}([&](auto i) {
using BDataType = remove_cvref_t<tuple_element_t<i.value, BsDataType>>;
bs_device_buf[i] = std::make_unique<DeviceMem>(sizeof(BDataType) * Ks[g] * Ns[g]);
bs_device_buf[i]->ToDevice(bs_k_n[i].mData.data());
bs_device_view[i] = bs_device_buf[i]->GetDeviceBuffer();
bs_stride[i] = StrideBs[g];
});
auto& ds_m_n = g_ds_m_n[g];
auto& ds_device_buf = g_ds_device_buf[g];
auto& ds_device_view = g_ds_device_view[g];
static_for<0, NumDTensor, 1>{}([&](auto i) {
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
ds_device_buf[i] = std::make_unique<DeviceMem>(sizeof(DDataType) * Ms[g] * Ns[g]);
ds_device_buf[i]->ToDevice(ds_m_n[i].mData.data());
ds_device_view[i] = ds_device_buf[i]->GetDeviceBuffer();
ds_stride[i] = StrideDs[g];
});
g_e_device_buf[g] = std::make_unique<DeviceMem>(sizeof(EDataType) * Ms[g] * Ns[g]);
g_e_device_view[g] = g_e_device_buf[g]->GetDeviceBuffer();
g_gemm_descs.push_back(tensor_operation::device::GemmMultiABDDesc{
sum_of_m,
Ns[g],
Ks[g],
std::vector<ck::index_t>(as_stride.begin(), as_stride.end()),
std::vector<ck::index_t>(bs_stride.begin(), bs_stride.end()),
std::vector<ck::index_t>(ds_stride.begin(), ds_stride.end()),
StrideE[g]});
tensor_operation::device::
GroupedGemmMultiABDKernelArgument<NumATensor, NumBTensor, NumDTensor>
kernelArg{as_device_view,
bs_device_view,
ds_device_view,
g_e_device_view[g],
Ms[g],
Ns[g],
Ks[g],
as_stride,
bs_stride,
ds_stride,
StrideE[g]};
grouped_gemm_kernel_args_host.push_back(std::move(kernelArg));
}
using DeviceOp = tensor_operation::device::DeviceGroupedGemmMultiABDFixedNK<AsLayout,
BsLayout,
DsLayout,
ELayout,
AsDataType,
BsDataType,
DsDataType,
EDataType,
AElementOp,
BElementOp,
CDEElementOp>;
const auto op_ptrs = 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;
if(do_verification)
{
using AComputeType =
typename std::conditional<(NumATensor > 1),
EDataType,
remove_cvref_t<tuple_element_t<0, AsDataType>>>::type;
using BComputeType =
typename std::conditional<(NumBTensor > 1),
EDataType,
remove_cvref_t<tuple_element_t<0, BsDataType>>>::type;
using ReferenceGemmInstance =
ck::tensor_operation::host::ReferenceGemmMultiABD<AsTensorTuple,
BsTensorTuple,
DsTensorTuple,
EDataType,
AccDataType,
AElementOp,
BElementOp,
CDEElementOp,
AComputeType,
BComputeType>;
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
for(std::size_t i = 0; i < group_count; i++)
{
auto ref_argument = ref_gemm.MakeArgument(g_as_m_k[i],
g_bs_k_n[i],
g_ds_m_n[i],
g_e_m_n_host_results[i],
a_element_op,
b_element_op,
cde_element_op);
ref_invoker.Run(ref_argument);
}
}
// profile device GEMM instances
for(auto& gemm_ptr : op_ptrs)
{
auto argument_ptr = gemm_ptr->MakeArgumentPointer(
g_as_device_view, g_bs_device_view, g_ds_device_view, g_e_device_view, g_gemm_descs);
if(!gemm_ptr->IsSupportedArgument(argument_ptr.get()))
{
if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
{
std::cout << "Gemm incompatible with runtime set parameters. Skipping..."
<< std::endl;
}
continue;
}
DeviceMem gemm_workspace_dev(gemm_ptr->GetWorkSpaceSize(argument_ptr.get()));
gemm_ptr->SetWorkSpacePointer(argument_ptr.get(), gemm_workspace_dev.GetDeviceBuffer());
DeviceMem grouped_gemm_kernel_args_dev(
gemm_ptr->GetDeviceKernelArgSize(argument_ptr.get()));
hipGetErrorString(hipMemcpy(grouped_gemm_kernel_args_dev.GetDeviceBuffer(),
grouped_gemm_kernel_args_host.data(),
gemm_ptr->GetDeviceKernelArgSize(argument_ptr.get()),
hipMemcpyHostToDevice));
gemm_ptr->SetDeviceKernelArgs(argument_ptr.get(),
grouped_gemm_kernel_args_dev.GetDeviceBuffer());
gemm_ptr->SetElementwiseOps(argument_ptr.get(), a_element_op, b_element_op, cde_element_op);
auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
std::string gemm_name = gemm_ptr->GetTypeString();
for(const auto kbatch_curr : kbatch_list)
{
gemm_ptr->SetKBatch(argument_ptr.get(), kbatch_curr);
if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
{
for(std::size_t g = 0; g < group_count; g++)
{
g_e_device_buf[g]->SetZero();
}
float ave_time = invoker_ptr->Run(
argument_ptr.get(), StreamConfig{nullptr, time_kernel, 0, n_warmup, n_iter});
if(do_verification)
{
bool instance_pass = true;
for(std::size_t g = 0; g < group_count; g++)
{
g_e_device_buf[g]->FromDevice(
g_e_m_n_device_results[g].mData.data(),
g_e_m_n_device_results[g].mDesc.GetElementSize() * sizeof(EDataType));
instance_pass =
instance_pass && ck::utils::check_err(g_e_m_n_device_results[g],
g_e_m_n_host_results[g]);
if(do_log)
{
static_for<0, NumATensor, 1>{}([&](auto i) {
LogRangeAsType<float>(
std::cout << "a[" << g << "]: ", g_as_m_k[g](i).mData, ",")
<< std::endl;
});
static_for<0, NumBTensor, 1>{}([&](auto i) {
LogRangeAsType<float>(
std::cout << "b[" << g << "]: ", g_bs_k_n[g](i).mData, ",")
<< std::endl;
});
static_for<0, NumDTensor, 1>{}([&](auto i) {
LogRangeAsType<float>(
std::cout << "d[" << g << "]: ", g_ds_m_n[g](i).mData, ",")
<< std::endl;
});
LogRangeAsType<float>(
std::cout << "e_device: ", g_e_m_n_device_results[g].mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "e_host : ", g_e_m_n_host_results[g].mData, ",")
<< std::endl;
}
}
std::cout << "Instance: " << gemm_name << " verification "
<< (instance_pass ? "SUCCEED" : "FAILED") << std::endl;
pass = pass && instance_pass;
}
if(time_kernel)
{
std::size_t flop = 0, num_btype = 0;
for(std::size_t g = 0; g < group_count; g++)
{
flop += std::size_t(2) * Ms[g] * Ns[g] * Ks[g];
static_for<0, NumATensor, 1>{}([&](auto i) {
using ADataType = remove_cvref_t<tuple_element_t<i.value, AsDataType>>;
num_btype += sizeof(ADataType) * Ms[g] * Ks[g];
});
static_for<0, NumBTensor, 1>{}([&](auto i) {
using BDataType = remove_cvref_t<tuple_element_t<i.value, BsDataType>>;
num_btype += sizeof(BDataType) * Ks[g] * Ns[g];
});
static_for<0, NumDTensor, 1>{}([&](auto i) {
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
num_btype += sizeof(DDataType) * Ms[g] * Ns[g];
});
}
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

11
test/ck_tile/gemm_block_scale/CMakeLists.txt
View File

@@ -128,6 +128,17 @@ if(GPU_TARGETS MATCHES "gfx94|gfx95|gfx12")
)
target_compile_options(test_tile_gemm_quant_bquant_transpose PRIVATE ${TEST_GEMM_COMPILE_OPTIONS})
# BQuant split-K tests (no preshuffle)
add_gtest_executable(test_tile_gemm_quant_bquant_splitk_decode
test_gemm_quant_bquant_splitk_decode.cpp
)
target_compile_options(test_tile_gemm_quant_bquant_splitk_decode PRIVATE ${TEST_GEMM_COMPILE_OPTIONS})
add_gtest_executable(test_tile_gemm_quant_bquant_splitk_prefill
test_gemm_quant_bquant_splitk_prefill.cpp
)
target_compile_options(test_tile_gemm_quant_bquant_splitk_prefill PRIVATE ${TEST_GEMM_COMPILE_OPTIONS})
# BQuant tests (with PreshuffleB) - split into 5 files
add_gtest_executable(test_tile_gemm_quant_bquant_preshuffle_decode_1d
test_gemm_quant_bquant_preshuffle_decode_1d.cpp

61
test/ck_tile/gemm_block_scale/test_gemm_quant_bquant_splitk_decode.cpp Normal file
View File

@@ -0,0 +1,61 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include "ck_tile/host.hpp"
#include "ck_tile/ops/gemm.hpp"
#include <gtest/gtest.h>
#include <memory>
#include "test_gemm_quant_fixtures.hpp"
// Type aliases for readability
using RowMajor = ck_tile::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck_tile::tensor_layout::gemm::ColumnMajor;
using FP8 = ck_tile::fp8_t;
using BF8 = ck_tile::bf8_t;
using Half = ck_tile::half_t;
using PkInt4 = ck_tile::pk_int4_t;
using BQuantGrouped = std::integral_constant<ck_tile::QuantType, ck_tile::QuantType::BQuantGrouped>;
using GroupSize128 = ck_tile::QuantGroupShape<ck_tile::sequence<1, 1, 128>>;
// Type combinations for BQuant split-K tests - Decode shape, GroupSize 128
// Tuple format: <ALayout, BLayout, CLayout, BQLayout, ADataType, BDataType, QDataType, CDataType,
// QuantType, GemmConfig, QuantGroupSize>
// clang-format off
using BQuantSplitKDecodeTypes = ::testing::Types<
std::tuple<RowMajor, ColumnMajor, RowMajor, ColumnMajor, FP8, FP8, float, Half, BQuantGrouped, GemmConfigDecode, GroupSize128>,
std::tuple<RowMajor, ColumnMajor, RowMajor, ColumnMajor, BF8, BF8, float, Half, BQuantGrouped, GemmConfigDecode, GroupSize128>,
std::tuple<RowMajor, ColumnMajor, RowMajor, ColumnMajor, FP8, PkInt4, FP8, Half, BQuantGrouped, GemmConfigDecode, GroupSize128>,
std::tuple<RowMajor, ColumnMajor, RowMajor, ColumnMajor, BF8, PkInt4, BF8, Half, BQuantGrouped, GemmConfigDecode, GroupSize128>
>;
// clang-format on
// Test suite for BQuant split-K Decode
TYPED_TEST_SUITE(TestCkTileGemmBQuant, BQuantSplitKDecodeTypes);
// BQuant split-K tests
TYPED_TEST(TestCkTileGemmBQuant, BQuantGroupedSplitK2Test)
{
// K=1024 for split_k=2: 1024/2=512=4×128 ✓
this->run_test_with_validation(32, 128, 1024, 2);
}
TYPED_TEST(TestCkTileGemmBQuant, BQuantGroupedSplitK3Test)
{
// K=3072 for split_k=3: 3072/3=1024=8×128 ✓
this->run_test_with_validation(32, 128, 3072, 3);
}
TYPED_TEST(TestCkTileGemmBQuant, BQuantGroupedSplitK4Test)
{
// K=2048 for split_k=4: 2048/4=512=4×128 ✓
this->run_test_with_validation(32, 128, 2048, 4);
}
TYPED_TEST(TestCkTileGemmBQuant, BQuantGroupedSplitK5Test)
{
// K=2560 for split_k=5: 2560/5=512=4×128 ✓
// Also K must be divisible by K_Tile(256)*split_k(5)=1280
this->run_test_with_validation(32, 128, 2560, 5);
}

64
test/ck_tile/gemm_block_scale/test_gemm_quant_bquant_splitk_prefill.cpp Normal file
View File

@@ -0,0 +1,64 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include "ck_tile/host.hpp"
#include "ck_tile/ops/gemm.hpp"
#include <gtest/gtest.h>
#include <memory>
#include "test_gemm_quant_fixtures.hpp"
// Type aliases for readability
using RowMajor = ck_tile::tensor_layout::gemm::RowMajor;
using ColumnMajor = ck_tile::tensor_layout::gemm::ColumnMajor;
using FP8 = ck_tile::fp8_t;
using BF8 = ck_tile::bf8_t;
using Half = ck_tile::half_t;
using PkInt4 = ck_tile::pk_int4_t;
using BQuantGrouped = std::integral_constant<ck_tile::QuantType, ck_tile::QuantType::BQuantGrouped>;
using GroupSize128 = ck_tile::QuantGroupShape<ck_tile::sequence<1, 1, 128>>;
// Type combinations for BQuant split-K tests - Prefill shape, GroupSize 128
// Tuple format: <ALayout, BLayout, CLayout, BQLayout, ADataType, BDataType, QDataType, CDataType,
// QuantType, GemmConfig, QuantGroupSize>
// clang-format off
using BQuantSplitKPrefillTypes = ::testing::Types<
std::tuple<RowMajor, ColumnMajor, RowMajor, ColumnMajor, FP8, FP8, float, Half, BQuantGrouped, GemmConfigPrefill, GroupSize128>,
std::tuple<RowMajor, ColumnMajor, RowMajor, ColumnMajor, BF8, BF8, float, Half, BQuantGrouped, GemmConfigPrefill, GroupSize128>,
std::tuple<RowMajor, ColumnMajor, RowMajor, ColumnMajor, FP8, PkInt4, FP8, Half, BQuantGrouped, GemmConfigPrefill, GroupSize128>,
std::tuple<RowMajor, ColumnMajor, RowMajor, ColumnMajor, BF8, PkInt4, BF8, Half, BQuantGrouped, GemmConfigPrefill, GroupSize128>
>;
// clang-format on
// Test suite for BQuant split-K Prefill
TYPED_TEST_SUITE(TestCkTileGemmBQuant, BQuantSplitKPrefillTypes);
// BQuant split-K tests
TYPED_TEST(TestCkTileGemmBQuant, BQuantGroupedSplitK2Test)
{
// K=1024 for split_k=2: 1024/2=512=4×128 ✓
// K must be divisible by K_Tile(128)*split_k(2)=256
this->run_test_with_validation(128, 128, 1024, 2);
}
TYPED_TEST(TestCkTileGemmBQuant, BQuantGroupedSplitK3Test)
{
// K=3072 for split_k=3: 3072/3=1024=8×128 ✓
// K must be divisible by K_Tile(128)*split_k(3)=384
this->run_test_with_validation(128, 128, 3072, 3);
}
TYPED_TEST(TestCkTileGemmBQuant, BQuantGroupedSplitK4Test)
{
// K=2048 for split_k=4: 2048/4=512=4×128 ✓
// K must be divisible by K_Tile(128)*split_k(4)=512
this->run_test_with_validation(128, 128, 2048, 4);
}
TYPED_TEST(TestCkTileGemmBQuant, BQuantGroupedSplitK5Test)
{
// K=1920 for split_k=5: 1920/5=384=3×128 ✓
// K must be divisible by K_Tile(128)*split_k(5)=640
this->run_test_with_validation(128, 128, 1920, 5);
}

16
test/ck_tile/gemm_block_scale/test_gemm_quant_fixtures.hpp
View File

@@ -655,7 +655,10 @@ class TestCkTileGemmBQuant : public TestCkTileGemmQuantBase<Tuple, TestCkTileGem
void SetUpQuantTypeSpecific() {}
void TearDownQuantTypeSpecific() {}
void run_test_with_validation(ck_tile::index_t M, ck_tile::index_t N, ck_tile::index_t K)
void run_test_with_validation(ck_tile::index_t M,
ck_tile::index_t N,
ck_tile::index_t K,
ck_tile::index_t k_batch = 1)
{
const ck_tile::index_t stride_A = K;
const ck_tile::index_t stride_B =
@@ -698,6 +701,9 @@ class TestCkTileGemmBQuant : public TestCkTileGemmQuantBase<Tuple, TestCkTileGem
sizeof(QDataType));
ck_tile::DeviceMem c_m_n_dev_buf(M * N * sizeof(CDataType));
// Zero C buffer - required for split-K atomic_add accumulation
c_m_n_dev_buf.SetZero();
// Copy to device
a_m_k_dev_buf.ToDevice(a_m_k.data());
ck_tile::HostTensor<BDataType> b_k_n_dev = b_k_n;
@@ -746,12 +752,12 @@ class TestCkTileGemmBQuant : public TestCkTileGemmQuantBase<Tuple, TestCkTileGem
c_m_n_dev_buf.GetDeviceBuffer(), // c_ptr
nullptr, // aq_ptr (not used for BQuant)
bq_bqk_bqn_dev_buf.GetDeviceBuffer(), // bq_ptr (scales)
1, // k_batch
k_batch, // k_batch (split-K)
M,
N,
K, // M, N, K
0, // QK_A (not used for BQuant)
BQK, // QK_B - TODO: we can remove BQK and BQN from args later?
BQK, // QK_B
stride_A,
stride_B,
stride_C,
@@ -796,7 +802,7 @@ class TestCkTileGemmBQuant : public TestCkTileGemmQuantBase<Tuple, TestCkTileGem
*std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
const auto rtol_atol =
this->template calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
K, 1, max_accumulated_value);
K, k_batch, max_accumulated_value);
// Validate results
bool pass = ck_tile::check_err(c_m_n_dev_result,
@@ -806,7 +812,7 @@ class TestCkTileGemmBQuant : public TestCkTileGemmQuantBase<Tuple, TestCkTileGem
rtol_atol.at(ck_tile::number<1>{}));
EXPECT_TRUE(pass) << "BQuantGrouped validation failed with M=" << M << ", N=" << N
<< ", K=" << K;
<< ", K=" << K << ", k_batch=" << k_batch;
if(!pass)
{

6
test/grouped_gemm/CMakeLists.txt
View File

@@ -18,6 +18,12 @@ if (CK_USE_XDL OR CK_USE_WMMA)
target_link_libraries(test_grouped_gemm_fastgelu PRIVATE utility device_grouped_gemm_fastgelu_instance)
add_dependencies(test_grouped_gemm test_grouped_gemm_fastgelu)
endif()
add_gtest_executable(test_grouped_gemm_multi_abd_fixed_nk test_grouped_gemm_multi_abd_fixed_nk.cpp)
if(result EQUAL 0)
target_link_libraries(test_grouped_gemm_multi_abd_fixed_nk PRIVATE utility device_grouped_gemm_fixed_nk_multi_abd_instance)
add_dependencies(test_grouped_gemm test_grouped_gemm_multi_abd_fixed_nk)
endif()
endif()
add_gtest_executable(test_grouped_gemm_interface test_grouped_gemm_interface_xdl.cpp)

256
test/grouped_gemm/test_grouped_gemm_multi_abd_fixed_nk.cpp Normal file
View File

@@ -0,0 +1,256 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include <tuple>
#include <vector>
#include <type_traits>
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/ck.hpp"
#include "ck/utility/type.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "profiler/profile_grouped_gemm_multi_abd_fixed_nk_impl.hpp"
#include "gtest/gtest.h"
static ck::index_t param_mask = 0xffffff;
static ck::index_t instance_index = -1;
using FP32 = float;
using FP16 = ck::half_t;
using BF16 = ck::bhalf_t;
using I8 = int8_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AddFastGelu = ck::tensor_operation::element_wise::AddFastGelu;
using Add = ck::tensor_operation::element_wise::Add;
using Multiply = ck::tensor_operation::element_wise::Multiply;
using FastGelu = ck::tensor_operation::element_wise::FastGelu;
// clang-format off
using KernelTypes = ::testing::Types<
std::tuple<ck::Tuple<BF16>, ck::Tuple<I8, BF16>, ck::Tuple<BF16>, BF16, ck::Tuple<Row>, ck::Tuple<Col, Col>, ck::Tuple<Row>, Row, AddFastGelu>,
std::tuple<ck::Tuple<BF16>, ck::Tuple<I8, BF16>, ck::Tuple<BF16>, BF16, ck::Tuple<Col>, ck::Tuple<Row, Row>, ck::Tuple<Row>, Row, AddFastGelu>,
std::tuple<ck::Tuple<BF16>, ck::Tuple<I8, BF16>, ck::Tuple<BF16>, BF16, ck::Tuple<Row>, ck::Tuple<Row, Row>, ck::Tuple<Row>, Row, AddFastGelu>,
std::tuple<ck::Tuple<BF16>, ck::Tuple<I8, BF16>, ck::Tuple<BF16>, BF16, ck::Tuple<Row>, ck::Tuple<Col, Col>, ck::Tuple<Row>, Row, Add>,
std::tuple<ck::Tuple<BF16>, ck::Tuple<I8, BF16>, ck::Tuple<BF16>, BF16, ck::Tuple<Col>, ck::Tuple<Row, Row>, ck::Tuple<Row>, Row, Add>,
std::tuple<ck::Tuple<BF16>, ck::Tuple<I8, BF16>, ck::Tuple<BF16>, BF16, ck::Tuple<Row>, ck::Tuple<Row, Row>, ck::Tuple<Row>, Row, Add>,
std::tuple<ck::Tuple<BF16>, ck::Tuple<I8, BF16>, ck::Tuple<>, BF16, ck::Tuple<Row>, ck::Tuple<Col, Col>, ck::Tuple<>, Row, PassThrough>,
std::tuple<ck::Tuple<BF16>, ck::Tuple<I8, BF16>, ck::Tuple<>, BF16, ck::Tuple<Col>, ck::Tuple<Row, Row>, ck::Tuple<>, Row, PassThrough>,
std::tuple<ck::Tuple<BF16>, ck::Tuple<I8, BF16>, ck::Tuple<>, BF16, ck::Tuple<Row>, ck::Tuple<Row, Row>, ck::Tuple<>, Row, PassThrough>,
std::tuple<ck::Tuple<BF16>, ck::Tuple<I8, BF16>, ck::Tuple<>, BF16, ck::Tuple<Row>, ck::Tuple<Col, Col>, ck::Tuple<>, Row, FastGelu>,
std::tuple<ck::Tuple<BF16>, ck::Tuple<I8, BF16>, ck::Tuple<>, BF16, ck::Tuple<Col>, ck::Tuple<Row, Row>, ck::Tuple<>, Row, FastGelu>,
std::tuple<ck::Tuple<BF16>, ck::Tuple<I8, BF16>, ck::Tuple<>, BF16, ck::Tuple<Row>, ck::Tuple<Row, Row>, ck::Tuple<>, Row, FastGelu>
>;
// clang-format on
template <typename Tuple>
class TestGroupedGemmMultiABDFixedNK : public testing::Test
{
protected:
using AsDataType = std::tuple_element_t<0, Tuple>;
using BsDataType = std::tuple_element_t<1, Tuple>;
using DsDataType = std::tuple_element_t<2, Tuple>;
using EDataType = std::tuple_element_t<3, Tuple>;
using AccDataType = float;
using AsLayout = std::tuple_element_t<4, Tuple>;
using BsLayout = std::tuple_element_t<5, Tuple>;
using DsLayout = std::tuple_element_t<6, Tuple>;
using ELayout = std::tuple_element_t<7, Tuple>;
using AElementOp = PassThrough;
using BElementOp = Multiply;
using CDEElementOp = std::tuple_element_t<8, Tuple>;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
public:
static constexpr bool verify_ = true;
static constexpr int init_method_ = 1; // integer value initialization
static constexpr bool log_ = false;
static constexpr bool bench_ = false; // measure kernel performance
static constexpr int n_warmup_ = 0;
static constexpr int n_iter_ = 1;
std::vector<int> k_batches_ = {1};
private:
template <typename Layout>
void SetStrides(std::vector<int>& strides,
const std::vector<int>& rows,
const std::vector<int>& cols) const
{
if(std::is_same_v<Layout, Row>)
{
for(const auto c : cols)
{
strides.emplace_back(c);
}
}
else if(std::is_same_v<Layout, Col>)
{
for(const auto r : rows)
{
strides.emplace_back(r);
}
}
}
template <typename Layouts>
void SetTupleStrides(std::vector<int>& strides,
const std::vector<int>& rows,
const std::vector<int>& cols) const
{
if constexpr(Layouts::Size() > 0)
{
// As of now multi ABD implementation supports only tensors with matching layouts.
using Layout = ck::remove_cvref_t<ck::tuple_element_t<ck::Number<0>{}, Layouts>>;
SetStrides<Layout>(strides, rows, cols);
}
}
public:
void Run(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>& StrideDs = {},
const std::vector<int>& StrideE = {})
{
std::vector<int> stride_as = StrideAs;
std::vector<int> stride_bs = StrideBs;
std::vector<int> stride_ds = StrideDs;
std::vector<int> stride_e = StrideE;
if(stride_as.empty())
{
SetTupleStrides<AsLayout>(stride_as, Ms, Ks);
}
if(stride_bs.empty())
{
SetTupleStrides<BsLayout>(stride_bs, Ks, Ns);
}
if(stride_ds.empty())
{
SetTupleStrides<DsLayout>(stride_ds, Ms, Ns);
}
if(stride_e.empty())
{
SetStrides<ELayout>(stride_e, Ms, Ns);
}
RunSingle(Ms, Ns, Ks, stride_as, stride_bs, stride_ds, stride_e);
}
void RunSingle(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>& StrideDs,
const std::vector<int>& StrideE)
{
bool pass =
ck::profiler::profile_grouped_gemm_multi_abd_fixed_nk_impl<AsDataType,
BsDataType,
DsDataType,
EDataType,
AccDataType,
AsLayout,
BsLayout,
DsLayout,
ELayout,
AElementOp,
BElementOp,
CDEElementOp>(verify_,
init_method_,
log_,
bench_,
Ms,
Ns,
Ks,
StrideAs,
StrideBs,
StrideDs,
StrideE,
k_batches_,
n_warmup_,
n_iter_);
EXPECT_TRUE(pass);
}
};
TYPED_TEST_SUITE(TestGroupedGemmMultiABDFixedNK, KernelTypes);
TYPED_TEST(TestGroupedGemmMultiABDFixedNK, TinyCases)
{
const std::vector<int> Ms{3, 4};
constexpr int N = 8;
constexpr int K = 64;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
this->Run(Ms, Ns, Ks);
}
TYPED_TEST(TestGroupedGemmMultiABDFixedNK, SmallCases)
{
const std::vector<int> Ms{3, 5, 16, 7, 8};
constexpr int N = 768;
constexpr int K = 544;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
this->Run(Ms, Ns, Ks);
}
TYPED_TEST(TestGroupedGemmMultiABDFixedNK, MidCases)
{
const std::vector<int> Ms{167, 183, 177, 153, 139, 204};
constexpr int N = 768;
constexpr int K = 544;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
this->Run(Ms, Ns, Ks);
}
TYPED_TEST(TestGroupedGemmMultiABDFixedNK, Regular)
{
const std::vector<int> Ms{64, 128, 256};
constexpr int N = 768;
constexpr int K = 320;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
this->Run(Ms, Ns, Ks);
}
int main(int argc, char** argv)
{
testing::InitGoogleTest(&argc, argv);
if(argc == 1)
{
// Run with default arguments.
}
else if(argc == 3)
{
param_mask = strtol(argv[1], nullptr, 0);
instance_index = atoi(argv[2]);
}
else
{
std::cout << "Usage of " << argv[0] << std::endl;
std::cout << "Arg1,2: param_mask instance_index(-1 means all)" << std::endl;
}
return RUN_ALL_TESTS();
}