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Wmma support for gemm_multiply_multiply_wp (#3278)

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* Initial implementation with splitK support

* Add gfx11 support

* Fix compilation error

* Add instances

* Add irregular instances

* Fix GetBuffer arguments

* Minor changes

* Address review comments

* Fix compilation errors

* Fix copyright header
This commit is contained in:
Enrico Degregori
2025-12-03 16:38:23 +01:00
committed by GitHub
parent f29b67cf9b
commit 161835533b
30 changed files with 2482 additions and 86 deletions
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2
example/65_gemm_multiply_multiply/CMakeLists.txt
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@@ -75,3 +75,5 @@ example_compile_options(example_moe_gemm2_xdl_fp8_blockscale PRIVATE ${BLOCKSCAL
example_compile_options(example_moe_gemm1_xdl_fp8_blockscale PRIVATE ${BLOCKSCALE_GEMM_OPTIONS})
add_example_executable(example_gemm_add_add_wmma_fp16 gemm_add_add_wmma_fp16.cpp)
add_example_executable(example_gemm_multiply_multiply_wmma_fp16_bpreshuffle gemm_multiply_multiply_wmma_fp16_bpreshuffle.cpp)
add_example_executable(example_gemm_multiply_multiply_wmma_fp8_bpreshuffle gemm_multiply_multiply_wmma_fp8_bpreshuffle.cpp)

82
example/65_gemm_multiply_multiply/common.hpp Normal file
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@@ -0,0 +1,82 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
#include "ck/ck.hpp"
struct MultiplyMultiply
{
template <typename E, typename C, typename D0, typename D1>
__host__ __device__ constexpr void
operator()(E& e, const C& c, const D0& d0, const D1& d1) const;
template <>
__host__ __device__ constexpr void operator()<ck::half_t, float, float, float>(
ck::half_t& e, const float& c, const float& d0, const float& d1) const
{
const float x0_f = c * d0 * d1;
e = ck::type_convert<ck::half_t>(x0_f);
}
template <>
__host__ __device__ constexpr void operator()<ck::bhalf_t, float, float, float>(
ck::bhalf_t& e, const float& c, const float& d0, const float& d1) const
{
const float x0_f = c * d0 * d1;
e = ck::type_convert<ck::bhalf_t>(x0_f);
}
template <>
__host__ __device__ constexpr void operator()<ck::half_t, int, float, float>(
ck::half_t& e, const int& c, const float& d0, const float& d1) const
{
const float x0_f =
ck::type_convert<float>(c) * ck::type_convert<float>(d0) * ck::type_convert<float>(d1);
e = ck::type_convert<ck::half_t>(x0_f);
}
template <>
__host__ __device__ constexpr void operator()<ck::bhalf_t, int, float, float>(
ck::bhalf_t& e, const int& c, const float& d0, const float& d1) const
{
const float x0_f =
ck::type_convert<float>(c) * ck::type_convert<float>(d0) * ck::type_convert<float>(d1);
e = ck::type_convert<ck::bhalf_t>(x0_f);
}
};
template <int KPack, typename T>
void preShuffleBuffer(const T* src, T* dst, int N, int K, int NWmma)
{
int NLane = NWmma;
int KLane = ck::get_warp_size() / NLane;
int K0 = K / (KLane * KPack);
// K -> K0 KLane KPack
// N -> N0 NLane
// N, K -> N0 K0 KLane NLane KPack
int tempk;
for(int n = 0; n < N; ++n)
{
for(int k = 0; k < K; ++k)
{
int n0 = n / NLane;
int n1 = n % NLane;
int k0 = k / (KLane * KPack);
tempk = k % (KLane * KPack);
int k1 = tempk / KPack;
int k2 = tempk % KPack;
int outputIndex = n0 * KPack * NLane * KLane * K0 + k0 * KPack * NLane * KLane +
k1 * KPack * NLane + n1 * KPack + k2;
dst[outputIndex] = src[n * K + k];
}
}
}

82
example/65_gemm_multiply_multiply/gemm_multiply_multiply_wmma_fp16_bpreshuffle.cpp Normal file
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@@ -0,0 +1,82 @@
// 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/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_wmma_cshuffle_v3_b_preshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.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/library/utility/check_err.hpp"
#include "ck/utility/blkgemmpipe_scheduler.hpp"
#include "common.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using A0DataType = F16;
using B0DataType = F16;
using AccDataType = F32;
using CShuffleDataType = F32;
using D0DataType = F32;
using D1DataType = F32;
using DsDataType = ck::Tuple<D0DataType, D1DataType>;
using EDataType = F16;
using A0Layout = Row;
using B0Layout = Col;
using D0Layout = Row;
using D1Layout = Col;
using DsLayout = ck::Tuple<D0Layout, D1Layout>;
using ELayout = Row;
static constexpr int KPack = 8;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = MultiplyMultiply;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
// clang-format off
using DeviceOpInstance =
ck::tensor_operation::device::DeviceGemmMultiD_Wmma_CShuffle_V3_BPreshuffle<
Row, Col, DsLayout, ELayout,
A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,
AElementOp, BElementOp, CDEElementOp, GemmSpec,
128,
32, 128, 128,
8, 8,
16, 16,
2, 2,
S<16, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0,
S<16, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0,
1, 1, S<1, 16, 1, 8>, S<4, 4, 1>,
ck::BlockGemmPipelineScheduler::Intrawave,
ck::BlockGemmPipelineVersion::v1,
F16>;
// clang-format on
#include "run_gemm_multiply_multiply_wp_example.inc"
int main(int argc, char* argv[]) { return run_gemm_example(argc, argv); }

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example/65_gemm_multiply_multiply/gemm_multiply_multiply_wmma_fp8_bpreshuffle.cpp Normal file
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@@ -0,0 +1,94 @@
// 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/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_wmma_cshuffle_v3_b_preshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.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/library/utility/check_err.hpp"
#include "ck/utility/blkgemmpipe_scheduler.hpp"
#include "common.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F8 = ck::f8_t;
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using A0DataType = F8;
using B0DataType = F8;
static constexpr int KPack = 16;
using ComputeType = F8;
using AccDataType = F32;
using CShuffleDataType = F32;
using D0DataType = F32;
using D1DataType = F32;
using DsDataType = ck::Tuple<D0DataType, D1DataType>;
using EDataType = F16;
using A0Layout = Row;
using B0Layout = Col;
using D0Layout = Row;
using D1Layout = Col;
using DsLayout = ck::Tuple<D0Layout, D1Layout>;
using ELayout = Row;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = MultiplyMultiply;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
// clang-format off
using DeviceOpInstance =
ck::tensor_operation::device::DeviceGemmMultiD_Wmma_CShuffle_V3_BPreshuffle<
Row, Col, DsLayout, ELayout,
A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,
AElementOp, BElementOp, CDEElementOp, GemmSpec,
256,
32, 128, 256,
16, 16,
16, 16,
2, 1,
S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0,
S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0,
1, 1, S<1, 16, 1, 16>, S<8, 8, 1>,
ck::BlockGemmPipelineScheduler::Intrawave,
ck::BlockGemmPipelineVersion::v1,
ComputeType>;
// clang-format on
#include "run_gemm_multiply_multiply_wp_example.inc"
int main(int argc, char* argv[])
{
// disable on gfx11 (fp8 not supported)
if(ck::is_gfx11_supported())
{
return 0;
}
return run_gemm_example(argc, argv);
}

246
example/65_gemm_multiply_multiply/run_gemm_multiply_multiply_wp_example.inc Normal file
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@@ -0,0 +1,246 @@
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#pragma once
int run_gemm_example(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 3840;
ck::index_t N = 4096;
ck::index_t K = 4096;
ck::index_t StrideA = K;
ck::index_t StrideB = K;
ck::index_t StrideD = 0;
ck::index_t StrideE = N;
ck::index_t KBatch = 1;
if(argc == 1)
{
// use default case
}
else if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 12)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
StrideA = std::stoi(argv[7]);
StrideB = std::stoi(argv[8]);
StrideD = std::stoi(argv[9]);
StrideE = std::stoi(argv[10]);
KBatch = std::stoi(argv[11]);
}
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 to 11: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE, KBatch\n");
exit(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 ck::HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return ck::HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
ck::Tensor<A0DataType> a0_m_k(f_host_tensor_descriptor(M, K, StrideA, A0Layout{}));
ck::Tensor<B0DataType> b0_k_n(f_host_tensor_descriptor(K, N, StrideB, B0Layout{}));
ck::Tensor<B0DataType> b0_preshuffled(
f_host_tensor_descriptor(K, N, StrideB, B0Layout{})); // use laout only for size
ck::Tensor<D0DataType> d0_m_n(f_host_tensor_descriptor(M, N, StrideD, D0Layout{}));
ck::Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor(M, N, StrideD, D1Layout{}));
ck::Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
ck::Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
// Update strides based on tensor properties if they are <= 0
auto get_stride = [](auto& tensor, auto layout, ck::index_t current_stride) -> ck::index_t {
if(current_stride <= 0)
{
if constexpr(std::is_same_v<decltype(layout), Row>)
{
return tensor.GetStrides()[0];
}
else
{
return tensor.GetStrides()[1];
}
}
return current_stride;
};
StrideA = get_stride(a0_m_k, A0Layout{}, StrideA);
StrideB = get_stride(b0_k_n, B0Layout{}, StrideB);
ck::index_t StrideD0 = get_stride(d0_m_n, D0Layout{}, StrideD);
ck::index_t StrideD1 = get_stride(d1_m_n, D1Layout{}, StrideD);
StrideE = get_stride(e_m_n_host_result, ELayout{}, StrideE);
std::cout << "a0_m_k: " << a0_m_k.mDesc << std::endl;
std::cout << "b0_k_n: " << b0_k_n.mDesc << std::endl;
std::cout << "d1_m_n: " << d1_m_n.mDesc << std::endl;
std::cout << "d0_m_n: " << d0_m_n.mDesc << std::endl;
std::cout << "e_m_n : " << e_m_n_host_result.mDesc << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a0_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 2});
b0_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{0, 2});
d0_m_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-2, 2});
d1_m_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-2, 2});
break;
case 2:
a0_m_k.GenerateTensorValue(GeneratorTensor_1<A0DataType>{});
b0_k_n.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
d0_m_n.GenerateTensorValue(GeneratorTensor_1<D0DataType>{});
d1_m_n.GenerateTensorValue(GeneratorTensor_1<D1DataType>{});
break;
default:
a0_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
b0_k_n.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
d0_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
}
ck::DeviceMem a0_device_buf(sizeof(A0DataType) * a0_m_k.mDesc.GetElementSpaceSize());
ck::DeviceMem b0_device_buf(sizeof(B0DataType) * b0_k_n.mDesc.GetElementSpaceSize());
ck::DeviceMem d0_device_buf(sizeof(D0DataType) * d0_m_n.mDesc.GetElementSpaceSize());
ck::DeviceMem d1_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpaceSize());
ck::DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
a0_device_buf.ToDevice(a0_m_k.mData.data());
d0_device_buf.ToDevice(d0_m_n.mData.data());
d1_device_buf.ToDevice(d1_m_n.mData.data());
e_device_buf.ToDevice(e_m_n_device_result.mData.data());
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
constexpr ck::index_t NumDTensor = DsDataType::Size();
// do GEMM
auto device_op = DeviceOpInstance{};
int NPerWmma = device_op.GetPreShuffleParameters();
preShuffleBuffer<KPack>(b0_k_n.mData.data(), b0_preshuffled.mData.data(), N, K, NPerWmma);
b0_device_buf.ToDevice(b0_preshuffled.mData.data());
auto invoker = device_op.MakeInvoker();
auto argument =
device_op.MakeArgument(a0_device_buf.GetDeviceBuffer(),
b0_device_buf.GetDeviceBuffer(),
std::array<const void*, NumDTensor>{d0_device_buf.GetDeviceBuffer(),
d1_device_buf.GetDeviceBuffer()},
e_device_buf.GetDeviceBuffer(),
M,
N,
K,
StrideA,
StrideB,
std::array<ck::index_t, NumDTensor>{StrideD0, StrideD1},
StrideE,
KBatch,
a_element_op,
b_element_op,
cde_element_op);
if(!device_op.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_gemm with the specified compilation parameters does "
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel, 0, 50, 50, false, 1});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype = sizeof(A0DataType) * M * K + sizeof(B0DataType) * K * N +
sizeof(D0DataType) * M * N + sizeof(D1DataType) * M * N +
sizeof(EDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
<< std::endl;
if(do_verification)
{
invoker.Run(argument, StreamConfig{nullptr, false});
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
ck::Tensor<CShuffleDataType> c_m_n({M, N});
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<A0DataType,
B0DataType,
CShuffleDataType,
AccDataType,
PassThrough,
PassThrough,
PassThrough>;
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(
a0_m_k, b0_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)
{
cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d0_m_n(m, n), d1_m_n(m, n));
}
}
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
if(ck::utils::check_err(
e_m_n_device_result, e_m_n_host_result, "Error: Incorrect results!", 1e-3, 5e-2))
{
std::cout << "Example PASS\n";
return 0;
}
else
{
std::cout << "Example FAIL\n";
return 1;
}
}
return 0;
}