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@@ -3,3 +3,4 @@ add_example_executable(example_gemm_multiply_multiply_xdl_fp8_ab_scale gemm_mult
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add_example_executable(example_gemm_multiply_multiply_xdl_fp8_bpreshuffle gemm_multiply_multiply_xdl_fp8_bpreshuffle.cpp)
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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_gemm_multiply_multiply_xdl_fp8_bpreshuffle_padding gemm_multiply_multiply_xdl_fp8_bpreshuffle_padding.cpp)
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@@ -0,0 +1,397 @@
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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_gemm_multiple_d_xdl_cshuffle_v3_b_preshuffle.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle_v3.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_gemm.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 FP8 = 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 = FP8;
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using B0DataType = FP8;
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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 DsDataType = ck::Tuple<D0DataType, D1DataType>;
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using EDataType = F16;
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using A0Layout = Row;
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using B0Layout = Col;
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using D0Layout = Row;
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using D1Layout = Col;
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using DsLayout = ck::Tuple<D0Layout, D1Layout>;
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using ELayout = Row;
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struct MultiplyMultiply
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{
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template <typename E, typename C, typename D0, typename D1>
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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;
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template <>
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__host__ __device__ constexpr void operator()<F16, float, float, float>(F16& e,
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const float& c,
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const float& d0,
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const float& d1) const
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{
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const float x0_f = c * d0 * d1;
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e = ck::type_convert<F16>(x0_f);
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}
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template <>
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__host__ __device__ constexpr void operator()<BF16, float, float, float>(BF16& e,
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const float& c,
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const float& d0,
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const float& d1) const
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{
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const float x0_f = c * d0 * d1;
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e = ck::type_convert<BF16>(x0_f);
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}
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template <>
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__host__ __device__ constexpr void operator()<ck::half_t, int, float, float>(
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ck::half_t& e, const int& c, const float& d0, const float& d1) const
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{
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const float x0_f =
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ck::type_convert<float>(c) * ck::type_convert<float>(d0) * ck::type_convert<float>(d1);
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e = ck::type_convert<ck::half_t>(x0_f);
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}
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template <>
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__host__ __device__ constexpr void operator()<ck::bhalf_t, int, float, float>(
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ck::bhalf_t& e, const int& c, const float& d0, const float& d1) const
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{
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const float x0_f =
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ck::type_convert<float>(c) * ck::type_convert<float>(d0) * ck::type_convert<float>(d1);
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e = ck::type_convert<ck::bhalf_t>(x0_f);
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}
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};
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void preShuffleBuffer(const FP8* src, FP8* dst, int N, int K, int NXdl)
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{
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int KPack = 16;
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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 = MultiplyMultiply;
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static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::KPadding;
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using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle
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// clang-format off
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< Row, Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,
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AElementOp, BElementOp, CDEElementOp, GemmSpec, 256,
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256, 256, 128,
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16, 16,
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16, 16,
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8, 8,
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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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1, 2, S<1, 32, 1, 8>, S<8, 8, 1>,
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ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v3, FP8>;
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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 = false;
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// GEMM shape
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ck::index_t M = 3840;
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ck::index_t N = 4096;
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ck::index_t K = 4096;
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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 StrideD = 0;
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ck::index_t StrideE = N;
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ck::index_t KBatch = 1;
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ck::index_t Warmup = 50;
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ck::index_t Repeat = 50;
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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 == 4)
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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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}
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else if(argc == 12)
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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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M = std::stoi(argv[4]);
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N = std::stoi(argv[5]);
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K = std::stoi(argv[6]);
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StrideA = std::stoi(argv[7]);
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StrideB = std::stoi(argv[8]);
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StrideD = std::stoi(argv[9]);
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StrideE = std::stoi(argv[10]);
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KBatch = std::stoi(argv[11]);
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}
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else if(argc == 14)
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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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M = std::stoi(argv[4]);
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N = std::stoi(argv[5]);
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K = std::stoi(argv[6]);
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StrideA = std::stoi(argv[7]);
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StrideB = std::stoi(argv[8]);
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StrideD = std::stoi(argv[9]);
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StrideE = std::stoi(argv[10]);
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KBatch = std::stoi(argv[11]);
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Warmup = std::stoi(argv[12]);
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Repeat = std::stoi(argv[13]);
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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(
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"arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE, KBatch\n");
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printf("arg10 to 11: Warmup, Repeat\n");
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exit(0);
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}
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auto f_host_tensor_descriptor =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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using namespace ck::literals;
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if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor({row, col}, {stride, 1_uz});
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}
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else
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{
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return HostTensorDescriptor({row, col}, {1_uz, stride});
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}
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};
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auto Knew = (K + 64 - 1) / 64;
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auto StrideBnew = Knew;
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Tensor<A0DataType> a0_m_k(f_host_tensor_descriptor(M, K, StrideA, A0Layout{}));
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Tensor<B0DataType> b0_k_n(f_host_tensor_descriptor(K, N, StrideB, B0Layout{}));
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Tensor<B0DataType> b0_preshuffled(
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f_host_tensor_descriptor(K, N, StrideB, B0Layout{})); // use laout only for size
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Tensor<D0DataType> d0_m_n(f_host_tensor_descriptor(M, N, StrideD, D0Layout{}));
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Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor(M, N, StrideD, D1Layout{}));
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Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
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Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
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std::cout << "a0_m_k: " << a0_m_k.mDesc << std::endl;
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std::cout << "b0_k_n: " << b0_k_n.mDesc << std::endl;
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std::cout << "d1_m_n: " << d1_m_n.mDesc << std::endl;
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std::cout << "d0_m_n: " << d0_m_n.mDesc << std::endl;
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std::cout << "e_m_n: " << e_m_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_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 2});
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b0_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{0, 2});
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d0_m_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-2, 2});
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d1_m_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-2, 2});
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break;
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case 2:
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a0_m_k.GenerateTensorValue(GeneratorTensor_1<A0DataType>{});
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b0_k_n.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
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d0_m_n.GenerateTensorValue(GeneratorTensor_1<D0DataType>{});
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d1_m_n.GenerateTensorValue(GeneratorTensor_1<D1DataType>{});
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break;
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default:
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a0_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
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b0_k_n.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
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d0_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
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d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
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}
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DeviceMem a0_device_buf(sizeof(A0DataType) * a0_m_k.mDesc.GetElementSpaceSize());
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DeviceMem b0_device_buf(sizeof(B0DataType) * b0_k_n.mDesc.GetElementSpaceSize());
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DeviceMem d0_device_buf(sizeof(D0DataType) * d0_m_n.mDesc.GetElementSpaceSize());
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DeviceMem d1_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpaceSize());
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DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
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a0_device_buf.ToDevice(a0_m_k.mData.data());
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d0_device_buf.ToDevice(d0_m_n.mData.data());
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d1_device_buf.ToDevice(d1_m_n.mData.data());
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e_device_buf.ToDevice(e_m_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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constexpr ck::index_t NumDTensor = DsDataType::Size();
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constexpr auto I0 = ck::Number<0>{};
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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_k_n.mData.data(), b0_preshuffled.mData.data(), N, 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(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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e_device_buf.GetDeviceBuffer(),
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M,
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N,
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K,
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StrideA,
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StrideB,
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std::array<ck::index_t, NumDTensor>{I0, I0},
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StrideE,
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KBatch,
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a_element_op,
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b_element_op,
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cde_element_op);
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if(!device_op.IsSupportedArgument(argument))
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{
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throw std::runtime_error(
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"wrong! device_gemm with the specified compilation parameters does "
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"not support this GEMM problem");
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}
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size_t total_size =
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(M * K * sizeof(A0DataType) + N * K * sizeof(B0DataType) + M * sizeof(D0DataType) +
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N * sizeof(D1DataType) + M * N * sizeof(EDataType));
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int rotate_buf_num =
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ck::math::min(size_t(Repeat), ck::math::integer_divide_ceil(512 * 1024 * 1024, total_size));
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float ave_time = invoker.Run(
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argument, StreamConfig{nullptr, time_kernel, 0, Warmup, Repeat, true, rotate_buf_num});
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_btype =
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sizeof(A0DataType) * M * K + sizeof(B0DataType) * K * N + sizeof(EDataType) * M * N;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
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<< std::endl;
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if(do_verification)
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{
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invoker.Run(argument, StreamConfig{nullptr, false});
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e_device_buf.FromDevice(e_m_n_device_result.mData.data());
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Tensor<CShuffleDataType> c_m_n({M, N});
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<A0DataType,
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B0DataType,
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CShuffleDataType,
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AccDataType,
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PassThrough,
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PassThrough,
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PassThrough>;
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auto ref_gemm = ReferenceGemmInstance{};
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auto ref_invoker = ref_gemm.MakeInvoker();
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auto ref_argument = ref_gemm.MakeArgument(
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a0_m_k, b0_k_n, c_m_n, PassThrough{}, PassThrough{}, PassThrough{});
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ref_invoker.Run(ref_argument);
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for(int m = 0; m < M; ++m)
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{
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for(int n = 0; n < N; ++n)
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{
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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));
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}
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}
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e_device_buf.FromDevice(e_m_n_device_result.mData.data());
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return ck::utils::check_err(
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e_m_n_device_result, e_m_n_host_result, "Error: Incorrect results!", 1e-3, 5e-2)
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? 0
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: 1;
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}
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return 0;
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}
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@@ -526,7 +526,7 @@ struct DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle
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return false;
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}
|
||||
|
||||
if(arg.N % NPerBlock != 0 || arg.K % KPerBlock != 0)
|
||||
if(arg.N % NPerBlock != 0 || (arg.K % KPerBlock != 0 && GemmSpec != GemmSpecialization::KPadding))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
@@ -351,10 +351,32 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
|
||||
|
||||
__host__ __device__ static auto MakeBGridDescriptor_Preshuffled(index_t N0, index_t K0)
|
||||
{
|
||||
constexpr index_t NkSwizzleNumber = Number<warpSize * KPack>{};
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(N0 / NWave, NWave, K0, NkSwizzleNumber),
|
||||
make_tuple(NWave * K0 * NkSwizzleNumber, K0 * NkSwizzleNumber, NkSwizzleNumber, I1));
|
||||
// using GemmSpecialization = tensor_operation::device::GemmSpecialization;
|
||||
//if K padding
|
||||
// if constexpr(GemmSpec == GemmSpecialization::KPadding ||
|
||||
// GemmSpec == GemmSpecialization::NKPadding)
|
||||
{
|
||||
// origin: [N0,K0,KLane,NLane,KPack]
|
||||
// const auto b_grid_desc_raw = make_naive_tensor_descriptor(
|
||||
// make_tuple(N0 / NWave, NWave, K0, NkSwizzleNumber),
|
||||
// make_tuple(
|
||||
// NWave * K0 * NkSwizzleNumber, K0 * NkSwizzleNumber, NkSwizzleNumber, I1));
|
||||
// const auto b_grid_desc_n_k =
|
||||
// transform_tensor_descriptor(b_grid_desc_nraw_kraw,
|
||||
// make_tuple(make_pass_through_transform(N),
|
||||
// make_right_pad_transform(K, KPad - K)),
|
||||
// make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
// make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
// ignore = b_grid_desc_n_k;
|
||||
}
|
||||
// else
|
||||
{
|
||||
constexpr index_t NkSwizzleNumber = Number<warpSize * KPack>{};
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(N0 / NWave, NWave, K0, NkSwizzleNumber),
|
||||
make_tuple(
|
||||
NWave * K0 * NkSwizzleNumber, K0 * NkSwizzleNumber, NkSwizzleNumber, I1));
|
||||
}
|
||||
}
|
||||
|
||||
__host__ __device__ static auto MakeBGridDescriptor_BK0_N_BK1(
|
||||
|
||||
Reference in New Issue
Block a user