diff --git a/example/65_gemm_multiply_multiply/CMakeLists.txt b/example/65_gemm_multiply_multiply/CMakeLists.txt index 95fd8bace8..3f93733b48 100644 --- a/example/65_gemm_multiply_multiply/CMakeLists.txt +++ b/example/65_gemm_multiply_multiply/CMakeLists.txt @@ -5,6 +5,7 @@ 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_gemm1_xdl_fp8 moe_gemm1_xdl_fp8.cpp) add_example_executable(example_moe_gemm2_xdl_fp8 moe_gemm2_xdl_fp8.cpp) +add_example_executable(example_moe_gemm2_xdl_fp8_blockscale moe_gemm2_xdl_fp8_blockscale.cpp) list(APPEND gpu_list gfx942) set(target 0) diff --git a/example/65_gemm_multiply_multiply/moe_gemm2_xdl_fp8_blockscale.cpp b/example/65_gemm_multiply_multiply/moe_gemm2_xdl_fp8_blockscale.cpp new file mode 100644 index 0000000000..fef05328e0 --- /dev/null +++ b/example/65_gemm_multiply_multiply/moe_gemm2_xdl_fp8_blockscale.cpp @@ -0,0 +1,442 @@ +// SPDX-License-Identifier: MIT +// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved. + +#include +#include +#include +#include + +#include "ck/ck.hpp" +#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp" +#include "ck/tensor_operation/gpu/device/impl/device_moe_gemm.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_gemm2.hpp" +#include "ck/library/utility/check_err.hpp" + +#include "ck/utility/blkgemmpipe_scheduler.hpp" + +template +using S = ck::Sequence; + +using F16 = ck::half_t; +// using BF16 = ck::bhalf_t; +using F8 = ck::f8_t; +using F32 = float; + +using Row = ck::tensor_layout::gemm::RowMajor; +using Col = ck::tensor_layout::gemm::ColumnMajor; + +using A0DataType = F8; +using B0DataType = F8; +using EDataType = F16; +using AccDataType = F32; +using CShuffleDataType = F32; +using D0DataType = F32; +using D1DataType = F32; +using D2DataType = F32; +using DsDataType = ck::Tuple; + +using A0Layout = Row; +using B0Layout = Col; +using ELayout = Row; +using D0Layout = Row; +using D1Layout = Col; +using D2Layout = ELayout; +// using DsLayoutGate = ck::Tuple; +using DsLayout = ck::Tuple; + +// d0: ascale, d1: bscale, d2:expert weight +struct MulABScaleExpertWeight +{ + template + __host__ __device__ constexpr void + operator()(E& e, const C& c, const D0& d0, const D1& d1, const D2& d2) const; + // for real kernel use + template <> + __host__ __device__ constexpr void operator()( + EDataType& e, const float& c, const float& d0, const float& d1, const float& d2) const + { + // for real kernel use + // warning: hack hack hack here!!!! ignore d0 right now as kernel mul d0 * d2 outside. + // tofix:felix + (void)d0; + e = ck::type_convert(c * d1 * d2); + } + // for reference cpu + template <> + __host__ __device__ constexpr void operator()( + float& e, const float& c, const float& d0, const float& d1, const float& d2) const + { + // for reference cpu + e = ck::type_convert(c * d0 * d1 * d2); + } +}; + +using CDEElementOp = MulABScaleExpertWeight; + +void preShuffleBuffer(const B0DataType* src, B0DataType* dst, int N, int K, int NXdl) +{ + int KPack = 16 / sizeof(B0DataType); + 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 = MulABScaleExpertWeight; + +static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default; + +#if 0 +static constexpr ck::index_t MPerBlock = 128; +static constexpr ck::index_t BLOCKSIZE = 256; +static constexpr ck::index_t MXDLPerWave = 2; +static constexpr ck::index_t NXDLPerWave = 2; +static constexpr ck::index_t NPerBlock = 128; +static constexpr ck::index_t MNPerXDL = 32; +static constexpr ck::index_t KPerBlock = 128 / sizeof(A0DataType); + +static constexpr ck::index_t CShuffleNLane = 32; +static constexpr ck::index_t CShuffleMLane = BLOCKSIZE / CShuffleNLane; +static constexpr ck::index_t AK1 = 16 / sizeof(A0DataType); +static constexpr ck::index_t BK1 = 16 / sizeof(B0DataType); +static constexpr ck::index_t EVec = 2; +static constexpr ck::index_t D0Vec = 1; +static constexpr ck::index_t D1Vec = 1; +static constexpr ck::index_t D2Vec = 1; + +// clang-format off + +using DeviceOpInstance = ck::tensor_operation::device::DeviceMoeGemm< + Row, Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType, + AElementOp, BElementOp, CDEElementOp, GemmSpec, + BLOCKSIZE, MPerBlock, NPerBlock, KPerBlock, + AK1, BK1, + MNPerXDL, MNPerXDL, + MXDLPerWave, NXDLPerWave, + S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, AK1, AK1, 0, + S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, AK1, AK1, 0, + 2, 1, S<1, CShuffleMLane, 1, CShuffleNLane>, S, + ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v1, false, false, A0DataType>; +#else +static constexpr ck::index_t MPerBlock = 128; +using DeviceOpInstance = ck::tensor_operation::device::DeviceMoeGemm< + Row, Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType, + AElementOp, BElementOp, CDEElementOp, GemmSpec, + 256, MPerBlock, 128, 128, + 16, 16, + 32, 32, + 2, 2, + 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, + 2, 1, S<1, 8, 1, 32>, S<2, 1, 1, 1>, + ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v1, false, false, A0DataType>; +#endif +// 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 = 4096; + ck::index_t experts = 8; + ck::index_t sorted_tile_num = 19; + ck::index_t valid_tile_num = 16; + ck::index_t sorted_size = sorted_tile_num * MPerBlock; + ck::index_t valid_size = valid_tile_num * MPerBlock; + ck::index_t tokens = 832; + ck::index_t topk = 2; + + if(argc == 1) + { + // use default case + } + else if(argc == 3) + { + // use default case + do_verification = std::stoi(argv[1]); + init_method = std::stoi(argv[2]); + time_kernel = std::stoi(argv[3]); + } + else if(argc == 7) + { + 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]); + tokens = std::stoi(argv[6]); + } + 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 6: N, K, tokens\n"); + exit(0); + } + + ck::index_t StrideA = K; + ck::index_t StrideB = K; + ck::index_t StrideE = N; + constexpr ck::index_t NumDTensor = DsDataType::Size(); + constexpr auto StrideDs = std::array{0, 0, 0}; + + ck::index_t KBatch = 1; + + Tensor expert_ids(HostTensorDescriptor({sorted_tile_num}, {1})); + Tensor sorted_token_ids(HostTensorDescriptor({sorted_size}, {1})); + Tensor max_token_id(HostTensorDescriptor({1})); + + max_token_id.mData = {valid_size, 0, 1, 2, 3, 4, 5, 6, 7, 8}; + // int eids[] = {0, 1, 2, 3, 4, 5, 6, 7, 3, 3, 3}; // {2, 1, 1, 2, 2, 2, 1, 2} + int eids[] = {0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 3, 3, 3}; + for(int i = 0; i < sorted_tile_num; i++) + { + expert_ids.mData[i] = eids[i]; + } + if(tokens * topk > valid_size) + { + printf("err config, tokens * topk > valid_size\n"); + exit(-1); + } + int token_per_tile = tokens * topk / valid_tile_num; + int tokenid = 0; + // sorted_token_ids.mData[0] = 0; + for(int i = 0; i < sorted_size; i++) + { + int tile_off = i % MPerBlock; + if(tile_off < token_per_tile && tokenid < tokens * topk) + { + sorted_token_ids.mData[i] = (tokenid % tokens) | ((tokenid / tokens) << 24); + tokenid++; + } + else + { + sorted_token_ids.mData[i] = tokens; + } + } + expert_ids.savetxt("expert_ids.txt", "int"); + sorted_token_ids.savetxt("sorted_token_ids.txt", "int"); + Tensor a0_t_k_k(HostTensorDescriptor({tokens, topk, K}, {topk * K, K, 1})); + Tensor b0_e_n_k(HostTensorDescriptor({experts, K, N}, {N * K, 1, K})); + Tensor b0_preshuffled(HostTensorDescriptor({experts, K, N}, {N * K, 1, K})); + Tensor d0_t_n(HostTensorDescriptor({tokens, N}, {StrideDs[0], 0})); + Tensor d1_e_n(HostTensorDescriptor({experts, N}, {1, StrideDs[1]})); + Tensor d2_e_n(HostTensorDescriptor({sorted_size, N}, {1, 0})); + Tensor e_t_n_host_result(HostTensorDescriptor({tokens, N}, {N, 1})); + Tensor e_t_n_device_result(HostTensorDescriptor({tokens, N}, {N, 1})); + e_t_n_device_result.SetZero(); + std::cout << "a0_t_k_k: " << a0_t_k_k.mDesc << std::endl; + std::cout << "b0_e_n_k: " << b0_e_n_k.mDesc << std::endl; + std::cout << "d2_e_n: " << d2_e_n.mDesc << std::endl; + std::cout << "d1_e_n: " << d1_e_n.mDesc << std::endl; + std::cout << "d0_t_n: " << d0_t_n.mDesc << std::endl; + std::cout << "e_t_n: " << e_t_n_host_result.mDesc << std::endl; + + switch(init_method) + { + case 0: break; + case 1: + a0_t_k_k.GenerateTensorValue(GeneratorTensor_2{-2, 2}); + b0_e_n_k.GenerateTensorValue(GeneratorTensor_2{-2, 2}); + d0_t_n.GenerateTensorValue(GeneratorTensor_2{-2, 2}); + d1_e_n.GenerateTensorValue(GeneratorTensor_2{-2, 2}); + d2_e_n.GenerateTensorValue(GeneratorTensor_2{-2, 2}); + break; + case 2: + a0_t_k_k.GenerateTensorValue(GeneratorTensor_1{}); + b0_e_n_k.GenerateTensorValue(GeneratorTensor_1{}); + d0_t_n.GenerateTensorValue(GeneratorTensor_1{}); + d1_e_n.GenerateTensorValue(GeneratorTensor_1{}); + d2_e_n.GenerateTensorValue(GeneratorTensor_1{}); + break; + default: + a0_t_k_k.GenerateTensorValue(GeneratorTensor_3{0.0, 1.0}); + b0_e_n_k.GenerateTensorValue(GeneratorTensor_3{-0.5, 0.5}); + d0_t_n.GenerateTensorValue(GeneratorTensor_3{0.0, 1.0}); + d1_e_n.GenerateTensorValue(GeneratorTensor_3{0.0, 1.0}); + d2_e_n.GenerateTensorValue(GeneratorTensor_3{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 max_token_id_dev(sizeof(ck::index_t) * max_token_id.mDesc.GetElementSpaceSize()); + DeviceMem a0_device_buf(sizeof(A0DataType) * a0_t_k_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_e_n.mDesc.GetElementSpaceSize()); + DeviceMem d2_device_buf(sizeof(D2DataType) * d2_e_n.mDesc.GetElementSpaceSize()); + DeviceMem e_device_buf(sizeof(EDataType) * e_t_n_device_result.mDesc.GetElementSpaceSize()); + // a0_t_k_k.savetxt("a.txt"); + // expert_ids.savetxt("expert_ids.txt", "int"); + // sorted_token_ids.savetxt("sorted_token_ids.txt", "int"); + // d0_t_n.savetxt("d0_t_n.txt", "int"); + // d1_e_n.savetxt("d1_e_n.txt", "int"); + // d2_e_n.savetxt("d2_e_n.txt", "int"); + sorted_token_ids_dev.ToDevice(sorted_token_ids.mData.data()); + expert_ids_dev.ToDevice(expert_ids.mData.data()); + max_token_id_dev.ToDevice(max_token_id.mData.data()); + a0_device_buf.ToDevice(a0_t_k_k.mData.data()); + d0_device_buf.ToDevice(d0_t_n.mData.data()); + d1_device_buf.ToDevice(d1_e_n.mData.data()); + d2_device_buf.ToDevice(d2_e_n.mData.data()); + e_device_buf.ToDevice(e_t_n_device_result.mData.data()); + + auto a_element_op = AElementOp{}; + auto b_element_op = BElementOp{}; + auto cde_element_op = CDEElementOp{}; + + // 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(), + max_token_id_dev.GetDeviceBuffer(), + a0_device_buf.GetDeviceBuffer(), + b0_device_buf.GetDeviceBuffer(), + std::array{d0_device_buf.GetDeviceBuffer(), + d1_device_buf.GetDeviceBuffer(), + d2_device_buf.GetDeviceBuffer()}, + e_device_buf.GetDeviceBuffer(), + tokens, + topk, + sorted_size, + N, + K, + StrideA, + 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(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 c_t_n({tokens, N}); + + using ReferenceGemmInstance = + ck::tensor_operation::host::ReferenceMoeGemm2; + 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(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; +}