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Commit 9afc4a0b authored by coderfeli's avatar coderfeli
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perf ok

parent f8d15f2a
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example/65_gemm_multiply_multiply/CMakeLists.txt
View file @ 9afc4a0b
......@@ -5,3 +5,4 @@ add_example_executable(example_gemm_multiply_multiply_xdl_fp8_bpreshuffle gemm_m
# target_compile_options(example_gemm_multiply_multiply_xdl_fp8_bpreshuffle PRIVATE -save-temps=$PWD -Wno-gnu-line-marker)
add_example_executable(example_gemm_add_add_xdl_fp16 gemm_add_add_xdl_fp16.cpp)
add_example_executable(example_gemm_multiply_multiply_xdl_int8 gemm_multiply_multiply_xdl_int8.cpp)
add_example_executable(example_moe_gemm_fp16 moe_gemm_fp16.cpp)
example/65_gemm_multiply_multiply/gemm_multiply_multiply_xdl_fp8_bpreshuffle.cpp
View file @ 9afc4a0b
......@@ -135,8 +135,8 @@ using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultiD_Xdl_CShu
8, 8,
32, 32,
1, 1,
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,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0,
// CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
// MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
// PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
......@@ -158,8 +158,8 @@ int main(int argc, char* argv[])
// experts = 8
// per expert:
// GEMM shape
ck::index_t N = 4096;
ck::index_t K = 4096;
ck::index_t N = 6144;
ck::index_t K = 8192;
ck::index_t experts = 8;
ck::index_t sorted_tile_num = 8;
ck::index_t sorted_tile_size = 32;
......
example/65_gemm_multiply_multiply/moe_gemm_fp16.cpp 0 → 100644
View file @ 9afc4a0b
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle_v3_b_preshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle_v3.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_moe_gemm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/utility/blkgemmpipe_scheduler.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
// using BF16 = ck::bhalf_t;
// using F16 = ck::f8_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;
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()<F16, float, float, float>(F16& e,
const float& c,
const float& d0,
const float& d1) const
{
// const float x0_f = c * d0 * d1;
const float x0_f = c;
// printf("epi %f\n", c);
e = ck::type_convert<F16>(x0_f);
}
// template <>
// __host__ __device__ constexpr void operator()<BF16, float, float, float>(BF16& e,
// const float& c,
// const float& d0,
// const float& d1) const
// {
// const float x0_f = c;
// // const float x0_f = c * d0 * d1;
// e = ck::type_convert<BF16>(x0_f);
// }
};
void preShuffleBuffer(const F16* src, F16* dst, int N, int K, int NXdl)
{
int KPack = 8;
int NLane = NXdl;
int KLane = 64 / 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];
}
}
}
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;
// using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultiD_Xdl_CShuffle_V3
using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle
// clang-format off
///######| ALayout| BLayout| DsLayout| ELayout| AData| BData| DsData| EData| AccData| CShuffle| 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| Type| Type| DataType| 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|
///######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | S<C, D0, D1>|
///###### RCR
// kernel 1: 256->32x128x128
// < Row, Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 32, 128, 128, 16, 16, 32, 32, 1, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<8, 8, 1>, ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v1, F16>;
// < Row, Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 32, 128, 256, 16, 16, 32, 32, 1, 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, 32, 1, 8>, S<8, 8, 1>, ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v3, F16>;
< Row, Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,
AElementOp, BElementOp, CDEElementOp, GemmSpec, 256,
32, 128, 128,
8, 8,
32, 32,
1, 1,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0,
// CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
// MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
// PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
1, 1, S<1, 32, 1, 8>, S<8, 8, 1>,
ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v1, F16>;
// kernel 2: 128->32x128x128
// < Row, Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 128, 32, 128, 128, 16, 16, 32, 32, 1, 2, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 8>, S<8, 8, 1>, ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v1, F16>;
// clang-format on
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = true;
// tokens = 1
// topk = 1
// experts = 8
// per expert:
// GEMM shape
ck::index_t N = 6144;
ck::index_t K = 8192;
ck::index_t experts = 8;
ck::index_t sorted_tile_num = 8;
ck::index_t sorted_tile_size = 32;
ck::index_t SORTED_SIZE = sorted_tile_num * sorted_tile_size;
ck::index_t tokens = 32;
if(argc == 1)
{
// use default case
}
else if(argc == 6)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
N = std::stoi(argv[4]);
K = std::stoi(argv[5]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf(
"arg4 to 5: N, K\n");
exit(0);
}
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;
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
// const ck::index_t experts = 8;
Tensor<ck::index_t> expert_ids(HostTensorDescriptor({experts}, {1}));
Tensor<ck::index_t> sorted_token_ids(HostTensorDescriptor({SORTED_SIZE}, {1}));
for (int i = 0; i < sorted_tile_num; i++) {
expert_ids.mData[i] = i;
}
int token_per_tile = tokens / sorted_tile_num;
int tokenid = 0;
// sorted_token_ids.mData[0] = 0;
for (int i = 0; i < SORTED_SIZE; i++) {
int tile_off = i % sorted_tile_size;
if(tile_off < token_per_tile)
sorted_token_ids.mData[i] = tokenid++;
else
sorted_token_ids.mData[i] = tokens;
}
Tensor<A0DataType> a0_t_k(HostTensorDescriptor({tokens, K}, {K, 1}));
Tensor<B0DataType> b0_e_n_k(HostTensorDescriptor({experts, N, K}, {N*K, K, 1}));
Tensor<B0DataType> b0_preshuffled(HostTensorDescriptor({experts, N, K}, {N*K, K, 1}));
// Tensor<B0DataType> b0_e_n_k(f_host_tensor_descriptor(K, N * experts, StrideB, B0Layout{}));
// Tensor<B0DataType> b0_preshuffled(
// f_host_tensor_descriptor(K, N, StrideB, B0Layout{})); // use laout only for size
Tensor<D0DataType> d0_t_n(f_host_tensor_descriptor(tokens, N, StrideD, D0Layout{}));
Tensor<D1DataType> d1_t_n(f_host_tensor_descriptor(tokens, N, StrideD, D1Layout{}));
Tensor<B0DataType> e_m_n_host_result(HostTensorDescriptor({SORTED_SIZE, N}, {N, 1}));
Tensor<B0DataType> e_m_n_device_result(HostTensorDescriptor({SORTED_SIZE, N}, {N, 1}));
std::cout << "a0_t_k: " << a0_t_k.mDesc << std::endl;
std::cout << "b0_e_n_k: " << b0_e_n_k.mDesc << std::endl;
std::cout << "d1_t_n: " << d1_t_n.mDesc << std::endl;
std::cout << "d0_t_n: " << d0_t_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_t_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 2});
b0_e_n_k.GenerateTensorValue(GeneratorTensor_2<B0DataType>{0, 2});
d0_t_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-2, 2});
d1_t_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-2, 2});
break;
case 2:
a0_t_k.GenerateTensorValue(GeneratorTensor_1<A0DataType>{});
b0_e_n_k.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
d0_t_n.GenerateTensorValue(GeneratorTensor_1<D0DataType>{});
d1_t_n.GenerateTensorValue(GeneratorTensor_1<D1DataType>{});
break;
default:
a0_t_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
b0_e_n_k.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
d0_t_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
d1_t_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
}
DeviceMem sorted_token_ids_dev(sizeof(ck::index_t) * sorted_token_ids.mDesc.GetElementSpaceSize());
DeviceMem expert_ids_dev(sizeof(ck::index_t) * expert_ids.mDesc.GetElementSpaceSize());
DeviceMem a0_device_buf(sizeof(A0DataType) * a0_t_k.mDesc.GetElementSpaceSize());
DeviceMem b0_device_buf(sizeof(B0DataType) * b0_e_n_k.mDesc.GetElementSpaceSize());
DeviceMem d0_device_buf(sizeof(D0DataType) * d0_t_n.mDesc.GetElementSpaceSize());
DeviceMem d1_device_buf(sizeof(D1DataType) * d1_t_n.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
a0_t_k.savetxt("a.txt");
sorted_token_ids_dev.ToDevice(sorted_token_ids.mData.data());
expert_ids_dev.ToDevice(expert_ids.mData.data());
a0_device_buf.ToDevice(a0_t_k.mData.data());
d0_device_buf.ToDevice(d0_t_n.mData.data());
d1_device_buf.ToDevice(d1_t_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();
constexpr auto I0 = ck::Number<0>{};
// do GEMM
auto device_op = DeviceOpInstance{};
int NPerXdl = device_op.GetPreShuffleParameters();
preShuffleBuffer(b0_e_n_k.mData.data(), b0_preshuffled.mData.data(), N * experts, K, NPerXdl);
b0_device_buf.ToDevice(b0_preshuffled.mData.data());
auto invoker = device_op.MakeInvoker();
auto argument =
device_op.MakeArgument(sorted_token_ids_dev.GetDeviceBuffer(),
expert_ids_dev.GetDeviceBuffer(),
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(),
tokens,
SORTED_SIZE,
N,
K,
StrideA,
StrideB,
std::array<ck::index_t, NumDTensor>{I0, I0},
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");
}
if (time_kernel) {
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * SORTED_SIZE * N * K * experts;
std::size_t num_btype =
sizeof(A0DataType) * SORTED_SIZE * K + sizeof(B0DataType) * K * N * experts + sizeof(EDataType) * SORTED_SIZE * 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, 0 ,0,1});
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
Tensor<CShuffleDataType> c_m_n({SORTED_SIZE, N});
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceMoeGemm<A0DataType,
B0DataType,
CShuffleDataType,
AccDataType,
PassThrough,
PassThrough,
PassThrough>;
auto ref_moe_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_moe_gemm.MakeInvoker();
auto ref_argument = ref_moe_gemm.MakeArgument(
sorted_token_ids, expert_ids, a0_t_k, b0_e_n_k, c_m_n, PassThrough{}, PassThrough{}, PassThrough{});
ref_invoker.Run(ref_argument);
for(int m = 0; m < SORTED_SIZE; ++m)
{
const int t = sorted_token_ids(m);
for(int n = 0; n < N; ++n)
{
cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d0_t_n(t, n), d1_t_n(t, n));
}
}
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
e_m_n_device_result.savetxt("out.txt");
e_m_n_host_result.savetxt("ref.txt");
return ck::utils::check_err(
e_m_n_device_result, e_m_n_host_result, "Error: Incorrect results!", 1e-3, 5e-2)
? 0
: 1;
}
return 0;
}
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