mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-07-14 11:07:44 +00:00
add example for moe stage2 fp8 blockscale
This commit is contained in:
mtgu0705
@@ -5,6 +5,7 @@ add_example_executable(example_gemm_add_add_xdl_fp16 gemm_add_add_xdl_fp16.cpp)
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add_example_executable(example_gemm_multiply_multiply_xdl_int8 gemm_multiply_multiply_xdl_int8.cpp)
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add_example_executable(example_moe_gemm1_xdl_fp8 moe_gemm1_xdl_fp8.cpp)
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add_example_executable(example_moe_gemm2_xdl_fp8 moe_gemm2_xdl_fp8.cpp)
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add_example_executable(example_moe_gemm2_xdl_fp8_blockscale moe_gemm2_xdl_fp8_blockscale.cpp)
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list(APPEND gpu_list gfx942)
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set(target 0)
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@@ -0,0 +1,442 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_moe_gemm.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/literals.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_moe_gemm2.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/utility/blkgemmpipe_scheduler.hpp"
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using F16 = ck::half_t;
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// using BF16 = ck::bhalf_t;
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using F8 = ck::f8_t;
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using F32 = float;
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using Row = ck::tensor_layout::gemm::RowMajor;
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using Col = ck::tensor_layout::gemm::ColumnMajor;
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using A0DataType = F8;
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using B0DataType = F8;
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using EDataType = F16;
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using AccDataType = F32;
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using CShuffleDataType = F32;
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using D0DataType = F32;
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using D1DataType = F32;
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using D2DataType = F32;
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using DsDataType = ck::Tuple<D0DataType, D1DataType, D2DataType>;
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using A0Layout = Row;
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using B0Layout = Col;
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using ELayout = Row;
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using D0Layout = Row;
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using D1Layout = Col;
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using D2Layout = ELayout;
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// using DsLayoutGate = ck::Tuple<D0Layout, D1Layout>;
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using DsLayout = ck::Tuple<D0Layout, D1Layout, D2Layout>;
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// d0: ascale, d1: bscale, d2:expert weight
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struct MulABScaleExpertWeight
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{
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template <typename E, typename C, typename D0, typename D1, typename D2>
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__host__ __device__ constexpr void
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operator()(E& e, const C& c, const D0& d0, const D1& d1, const D2& d2) const;
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// for real kernel use
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template <>
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__host__ __device__ constexpr void operator()<EDataType, float, float, float, float>(
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EDataType& e, const float& c, const float& d0, const float& d1, const float& d2) const
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{
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// for real kernel use
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// warning: hack hack hack here!!!! ignore d0 right now as kernel mul d0 * d2 outside.
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// tofix:felix
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(void)d0;
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e = ck::type_convert<EDataType>(c * d1 * d2);
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}
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// for reference cpu
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template <>
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__host__ __device__ constexpr void operator()<float, float, float, float, float>(
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float& e, const float& c, const float& d0, const float& d1, const float& d2) const
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{
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// for reference cpu
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e = ck::type_convert<EDataType>(c * d0 * d1 * d2);
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}
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};
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using CDEElementOp = MulABScaleExpertWeight;
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void preShuffleBuffer(const B0DataType* src, B0DataType* dst, int N, int K, int NXdl)
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{
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int KPack = 16 / sizeof(B0DataType);
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int NLane = NXdl;
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int KLane = 64 / NLane;
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int K0 = K / (KLane * KPack);
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// K -> K0 KLane KPack
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// N -> N0 NLane
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// N, K -> N0 K0 KLane NLane KPack
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int tempk;
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for(int n = 0; n < N; ++n)
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{
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for(int k = 0; k < K; ++k)
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{
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int n0 = n / NLane;
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int n1 = n % NLane;
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int k0 = k / (KLane * KPack);
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tempk = k % (KLane * KPack);
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int k1 = tempk / KPack;
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int k2 = tempk % KPack;
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int outputIndex = n0 * KPack * NLane * KLane * K0 + k0 * KPack * NLane * KLane +
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k1 * KPack * NLane + n1 * KPack + k2;
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dst[outputIndex] = src[n * K + k];
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}
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}
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}
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CDEElementOp = MulABScaleExpertWeight;
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static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
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#if 0
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static constexpr ck::index_t MPerBlock = 128;
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static constexpr ck::index_t BLOCKSIZE = 256;
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static constexpr ck::index_t MXDLPerWave = 2;
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static constexpr ck::index_t NXDLPerWave = 2;
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static constexpr ck::index_t NPerBlock = 128;
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static constexpr ck::index_t MNPerXDL = 32;
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static constexpr ck::index_t KPerBlock = 128 / sizeof(A0DataType);
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static constexpr ck::index_t CShuffleNLane = 32;
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static constexpr ck::index_t CShuffleMLane = BLOCKSIZE / CShuffleNLane;
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static constexpr ck::index_t AK1 = 16 / sizeof(A0DataType);
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static constexpr ck::index_t BK1 = 16 / sizeof(B0DataType);
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static constexpr ck::index_t EVec = 2;
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static constexpr ck::index_t D0Vec = 1;
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static constexpr ck::index_t D1Vec = 1;
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static constexpr ck::index_t D2Vec = 1;
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// clang-format off
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using DeviceOpInstance = ck::tensor_operation::device::DeviceMoeGemm<
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Row, Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,
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AElementOp, BElementOp, CDEElementOp, GemmSpec,
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BLOCKSIZE, MPerBlock, NPerBlock, KPerBlock,
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AK1, BK1,
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MNPerXDL, MNPerXDL,
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MXDLPerWave, NXDLPerWave,
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S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, AK1, AK1, 0,
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S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, AK1, AK1, 0,
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2, 1, S<1, CShuffleMLane, 1, CShuffleNLane>, S<EVec, D0Vec, D1Vec, D2Vec>,
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ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v1, false, false, A0DataType>;
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#else
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static constexpr ck::index_t MPerBlock = 128;
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using DeviceOpInstance = ck::tensor_operation::device::DeviceMoeGemm<
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Row, Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,
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AElementOp, BElementOp, CDEElementOp, GemmSpec,
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256, MPerBlock, 128, 128,
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16, 16,
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32, 32,
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2, 2,
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S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0,
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S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0,
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2, 1, S<1, 8, 1, 32>, S<2, 1, 1, 1>,
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ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v1, false, false, A0DataType>;
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#endif
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// clang-format on
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int main(int argc, char* argv[])
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{
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bool do_verification = true;
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int init_method = 1;
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bool time_kernel = true;
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// tokens = 1
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// topk = 1
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// experts = 8
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// per expert:
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// GEMM shape
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ck::index_t N = 6144;
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ck::index_t K = 4096;
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ck::index_t experts = 8;
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ck::index_t sorted_tile_num = 19;
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ck::index_t valid_tile_num = 16;
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ck::index_t sorted_size = sorted_tile_num * MPerBlock;
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ck::index_t valid_size = valid_tile_num * MPerBlock;
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ck::index_t tokens = 832;
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ck::index_t topk = 2;
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if(argc == 1)
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{
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// use default case
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}
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else if(argc == 3)
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{
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// use default case
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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}
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else if(argc == 7)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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N = std::stoi(argv[4]);
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K = std::stoi(argv[5]);
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tokens = std::stoi(argv[6]);
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}
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else
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{
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printf("arg1: verification (0=no, 1=yes)\n");
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printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
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printf("arg3: time kernel (0=no, 1=yes)\n");
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printf("arg4 to 6: N, K, tokens\n");
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exit(0);
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}
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ck::index_t StrideA = K;
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ck::index_t StrideB = K;
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ck::index_t StrideE = N;
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constexpr ck::index_t NumDTensor = DsDataType::Size();
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constexpr auto StrideDs = std::array<ck::index_t, NumDTensor>{0, 0, 0};
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ck::index_t KBatch = 1;
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Tensor<ck::index_t> expert_ids(HostTensorDescriptor({sorted_tile_num}, {1}));
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Tensor<ck::index_t> sorted_token_ids(HostTensorDescriptor({sorted_size}, {1}));
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Tensor<ck::index_t> max_token_id(HostTensorDescriptor({1}));
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max_token_id.mData = {valid_size, 0, 1, 2, 3, 4, 5, 6, 7, 8};
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// int eids[] = {0, 1, 2, 3, 4, 5, 6, 7, 3, 3, 3}; // {2, 1, 1, 2, 2, 2, 1, 2}
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int eids[] = {0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 3, 3, 3};
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for(int i = 0; i < sorted_tile_num; i++)
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{
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expert_ids.mData[i] = eids[i];
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}
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if(tokens * topk > valid_size)
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{
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printf("err config, tokens * topk > valid_size\n");
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exit(-1);
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}
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int token_per_tile = tokens * topk / valid_tile_num;
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int tokenid = 0;
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// sorted_token_ids.mData[0] = 0;
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for(int i = 0; i < sorted_size; i++)
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{
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int tile_off = i % MPerBlock;
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if(tile_off < token_per_tile && tokenid < tokens * topk)
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{
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sorted_token_ids.mData[i] = (tokenid % tokens) | ((tokenid / tokens) << 24);
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tokenid++;
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}
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else
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{
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sorted_token_ids.mData[i] = tokens;
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}
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}
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expert_ids.savetxt("expert_ids.txt", "int");
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sorted_token_ids.savetxt("sorted_token_ids.txt", "int");
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Tensor<A0DataType> a0_t_k_k(HostTensorDescriptor({tokens, topk, K}, {topk * K, K, 1}));
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Tensor<B0DataType> b0_e_n_k(HostTensorDescriptor({experts, K, N}, {N * K, 1, K}));
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Tensor<B0DataType> b0_preshuffled(HostTensorDescriptor({experts, K, N}, {N * K, 1, K}));
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Tensor<D0DataType> d0_t_n(HostTensorDescriptor({tokens, N}, {StrideDs[0], 0}));
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Tensor<D1DataType> d1_e_n(HostTensorDescriptor({experts, N}, {1, StrideDs[1]}));
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Tensor<D2DataType> d2_e_n(HostTensorDescriptor({sorted_size, N}, {1, 0}));
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Tensor<EDataType> e_t_n_host_result(HostTensorDescriptor({tokens, N}, {N, 1}));
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Tensor<EDataType> e_t_n_device_result(HostTensorDescriptor({tokens, N}, {N, 1}));
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e_t_n_device_result.SetZero();
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std::cout << "a0_t_k_k: " << a0_t_k_k.mDesc << std::endl;
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std::cout << "b0_e_n_k: " << b0_e_n_k.mDesc << std::endl;
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std::cout << "d2_e_n: " << d2_e_n.mDesc << std::endl;
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std::cout << "d1_e_n: " << d1_e_n.mDesc << std::endl;
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std::cout << "d0_t_n: " << d0_t_n.mDesc << std::endl;
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std::cout << "e_t_n: " << e_t_n_host_result.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1:
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a0_t_k_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 2});
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b0_e_n_k.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
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d0_t_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-2, 2});
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d1_e_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-2, 2});
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d2_e_n.GenerateTensorValue(GeneratorTensor_2<D2DataType>{-2, 2});
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break;
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case 2:
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a0_t_k_k.GenerateTensorValue(GeneratorTensor_1<A0DataType>{});
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b0_e_n_k.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
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d0_t_n.GenerateTensorValue(GeneratorTensor_1<D0DataType>{});
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d1_e_n.GenerateTensorValue(GeneratorTensor_1<D1DataType>{});
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d2_e_n.GenerateTensorValue(GeneratorTensor_1<D2DataType>{});
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break;
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default:
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a0_t_k_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
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b0_e_n_k.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
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d0_t_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
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d1_e_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
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d2_e_n.GenerateTensorValue(GeneratorTensor_3<D2DataType>{0.0, 1.0});
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}
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DeviceMem sorted_token_ids_dev(sizeof(ck::index_t) *
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sorted_token_ids.mDesc.GetElementSpaceSize());
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DeviceMem expert_ids_dev(sizeof(ck::index_t) * expert_ids.mDesc.GetElementSpaceSize());
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DeviceMem max_token_id_dev(sizeof(ck::index_t) * max_token_id.mDesc.GetElementSpaceSize());
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DeviceMem a0_device_buf(sizeof(A0DataType) * a0_t_k_k.mDesc.GetElementSpaceSize());
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DeviceMem b0_device_buf(sizeof(B0DataType) * b0_e_n_k.mDesc.GetElementSpaceSize());
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DeviceMem d0_device_buf(sizeof(D0DataType) * d0_t_n.mDesc.GetElementSpaceSize());
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DeviceMem d1_device_buf(sizeof(D1DataType) * d1_e_n.mDesc.GetElementSpaceSize());
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DeviceMem d2_device_buf(sizeof(D2DataType) * d2_e_n.mDesc.GetElementSpaceSize());
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DeviceMem e_device_buf(sizeof(EDataType) * e_t_n_device_result.mDesc.GetElementSpaceSize());
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// a0_t_k_k.savetxt("a.txt");
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// expert_ids.savetxt("expert_ids.txt", "int");
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// sorted_token_ids.savetxt("sorted_token_ids.txt", "int");
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// d0_t_n.savetxt("d0_t_n.txt", "int");
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// d1_e_n.savetxt("d1_e_n.txt", "int");
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// d2_e_n.savetxt("d2_e_n.txt", "int");
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sorted_token_ids_dev.ToDevice(sorted_token_ids.mData.data());
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expert_ids_dev.ToDevice(expert_ids.mData.data());
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max_token_id_dev.ToDevice(max_token_id.mData.data());
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a0_device_buf.ToDevice(a0_t_k_k.mData.data());
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d0_device_buf.ToDevice(d0_t_n.mData.data());
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d1_device_buf.ToDevice(d1_e_n.mData.data());
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d2_device_buf.ToDevice(d2_e_n.mData.data());
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e_device_buf.ToDevice(e_t_n_device_result.mData.data());
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auto a_element_op = AElementOp{};
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auto b_element_op = BElementOp{};
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auto cde_element_op = CDEElementOp{};
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// do GEMM
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auto device_op = DeviceOpInstance{};
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int NPerXdl = device_op.GetPreShuffleParameters();
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preShuffleBuffer(b0_e_n_k.mData.data(), b0_preshuffled.mData.data(), N * experts, K, NPerXdl);
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b0_device_buf.ToDevice(b0_preshuffled.mData.data());
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auto invoker = device_op.MakeInvoker();
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auto argument =
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device_op.MakeArgument(sorted_token_ids_dev.GetDeviceBuffer(),
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expert_ids_dev.GetDeviceBuffer(),
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max_token_id_dev.GetDeviceBuffer(),
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a0_device_buf.GetDeviceBuffer(),
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b0_device_buf.GetDeviceBuffer(),
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std::array<const void*, NumDTensor>{d0_device_buf.GetDeviceBuffer(),
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d1_device_buf.GetDeviceBuffer(),
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d2_device_buf.GetDeviceBuffer()},
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e_device_buf.GetDeviceBuffer(),
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tokens,
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topk,
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sorted_size,
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N,
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K,
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StrideA,
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StrideB,
|
||||
StrideDs,
|
||||
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)
|
||||
{
|
||||
// not result correct here because output buf not setzero
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
|
||||
std::size_t flop = std::size_t(2) * tokens * topk * N * K;
|
||||
std::size_t num_btype = sizeof(A0DataType) * tokens * K * topk +
|
||||
sizeof(B0DataType) * K * N * experts +
|
||||
sizeof(EDataType) * tokens * 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)
|
||||
{
|
||||
// gemm2 use atomic, so need to reinit outputs
|
||||
e_device_buf.ToDevice(e_t_n_device_result.mData.data());
|
||||
invoker.Run(argument, StreamConfig{nullptr, false, 0, 0, 1});
|
||||
|
||||
Tensor<CShuffleDataType> c_t_n({tokens, N});
|
||||
|
||||
using ReferenceGemmInstance =
|
||||
ck::tensor_operation::host::ReferenceMoeGemm2<A0DataType,
|
||||
B0DataType,
|
||||
D0DataType,
|
||||
D1DataType,
|
||||
D2DataType,
|
||||
CShuffleDataType,
|
||||
AccDataType,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
CDEElementOp>;
|
||||
auto ref_moe_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_moe_gemm.MakeInvoker();
|
||||
auto ref_argument = ref_moe_gemm.MakeArgument(sorted_token_ids,
|
||||
expert_ids,
|
||||
max_token_id,
|
||||
MPerBlock,
|
||||
a0_t_k_k,
|
||||
b0_e_n_k,
|
||||
d0_t_n,
|
||||
d1_e_n,
|
||||
d2_e_n,
|
||||
c_t_n,
|
||||
PassThrough{},
|
||||
PassThrough{},
|
||||
cde_element_op);
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
for(int t = 0; t < tokens; ++t)
|
||||
{
|
||||
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
e_t_n_host_result(t, n) = ck::type_convert<EDataType>(c_t_n(t, n));
|
||||
}
|
||||
}
|
||||
|
||||
e_device_buf.FromDevice(e_t_n_device_result.mData.data());
|
||||
// e_t_n_device_result.savetxt("out.txt");
|
||||
// e_t_n_host_result.savetxt("ref.txt");
|
||||
return ck::utils::check_err(
|
||||
e_t_n_device_result, e_t_n_host_result, "Error: Incorrect results!", 1e-3, 5e-2)
|
||||
? 0
|
||||
: 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
Reference in New Issue
Block a user