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c8563f2101
* chore(copyright): update copyright header for test directory * chore(copyright): update copyright header for test directory * chore(copyright): update copyright header for client_example directory * chore(copyright): update copyright header for test directory
332 lines
11 KiB
C++
332 lines
11 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#include <iomanip>
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#include <vector>
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#include <iostream>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/utility/data_type.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm_mx.hpp"
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#include "ck/library/tensor_operation_instance/gpu/gemm_mx.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3_mx.hpp"
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using F16 = ck::half_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 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 CElementOp = PassThrough;
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using ADataType = ck::f8_t;
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using BDataType = ck::f8_t;
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using CDataType = ck::half_t;
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using XDataType = ck::e8m0_bexp_t;
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using XPackedDataType = int32_t;
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template <typename X, typename Y>
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inline constexpr bool is_same_v = ck::is_same<X, Y>::value;
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using ALayout = Row;
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using BLayout = Col;
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using CLayout = Row;
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using AScaleLayout = Row;
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using BScaleLayout = Col;
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template <bool KLast>
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void preShuffleScaleBuffer(ck::e8m0_bexp_t* src, ck::e8m0_bexp_t* dst, int MN, int K)
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{
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int MNXdlPack = 2;
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int KXdlPack = 2;
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int XdlMNThread = 16;
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int XdlKThread = 64 / XdlMNThread;
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int K0 = K / KXdlPack / XdlKThread; // KRepeat
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// The 4 16x128 building blocks will be packed into 1 32x256 for F4
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// The 8 16x16x128 mfma will be packed into 1 32x32x256 for F4
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// unfold the MN32xK(256/32) scale buffer
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// 4 16 2 2
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// To XdlKThread-> XdlMNThread -> KXdlPack -> MNXdlPack
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// Then, MNRepeat->KRepeat
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for(int n = 0; n < MN; ++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 / (XdlMNThread * MNXdlPack); // i MNRepeat
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int tempn = n % (XdlMNThread * MNXdlPack);
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int n1 = tempn % XdlMNThread; // i XdlMNThread
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int n2 = tempn / XdlMNThread; // i MNXdlPack
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int k0 = k / (XdlKThread * KXdlPack); // i KRepeat
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int tempk = k % (XdlKThread * KXdlPack);
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int k1 = tempk % XdlKThread; // i XdlKThread
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int k2 = tempk / XdlKThread; // i KXdlPack
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int outputIndex = n0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread * K0 +
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k0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread +
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k1 * MNXdlPack * KXdlPack * XdlMNThread + n1 * MNXdlPack * KXdlPack +
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k2 * MNXdlPack + n2;
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// src[n * K + k] = ck::type_convert<ck::e8m0_bexp_t>(static_cast<float>(powf(2.0f, n2 +
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// k2 * MNXdlPack)));
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if constexpr(KLast)
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dst[outputIndex] = src[n * K + k];
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else
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dst[outputIndex] = src[k * MN + n];
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}
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}
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}
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struct SimpleDeviceMem
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{
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SimpleDeviceMem() = delete;
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SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
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{
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mem_size_ = mem_size;
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(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
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}
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void* GetDeviceBuffer() { return p_mem_; }
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~SimpleDeviceMem() { (void)hipFree(p_mem_); }
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void* p_mem_;
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std::size_t mem_size_;
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};
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int main(int argc, char* argv[])
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{
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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 = 4096;
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ck::index_t StrideB = 4096;
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ck::index_t StrideC = 4096;
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ck::index_t KBatch = 1;
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/* Require by mx type*/
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constexpr ck::index_t ScaleBlockSize = 32; // scaling block size
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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 == 7)
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{
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M = std::stoi(argv[1]);
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N = std::stoi(argv[2]);
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K = std::stoi(argv[3]);
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StrideA = std::stoi(argv[4]);
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StrideB = std::stoi(argv[5]);
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StrideC = std::stoi(argv[6]);
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}
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else
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{
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printf("arg1 to 6: M, N, K, StrideA, StrideB, StrideC\n");
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exit(0);
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}
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auto f_matrix_space_size =
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[](std::size_t nRow, std::size_t nCol, std::size_t stride, auto layout) {
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using Layout = decltype(layout);
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if constexpr(std::is_same<Layout, Row>::value)
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{
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return (nRow - 1) * stride + nCol;
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}
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else
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{
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return (nCol - 1) * stride + nRow;
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}
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};
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/* Scale stride Calculation */
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auto f_get_default_stride =
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[](ck::index_t row, ck::index_t col, ck::index_t stride, auto layout) {
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if(stride == -1)
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{
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// give a chance if stride is -1, return a default packed stride
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if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
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return static_cast<ck::index_t>(col);
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else
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return static_cast<ck::index_t>(row);
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}
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else
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return static_cast<ck::index_t>(stride);
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};
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if(K % ScaleBlockSize != 0)
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{
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throw std::runtime_error("wrong! K must be multiple of ScaleBlockSize.");
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};
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auto Scale_Padded_M = (M + ScaleBlockSize - 1) / ScaleBlockSize * ScaleBlockSize;
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auto Scale_Stride_AM =
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f_get_default_stride(Scale_Padded_M, K / ScaleBlockSize, -1, AScaleLayout{});
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auto Scale_Stride_BN = f_get_default_stride(K / ScaleBlockSize, N, -1, BScaleLayout{});
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SimpleDeviceMem a_device_buf(sizeof(ADataType) * f_matrix_space_size(M, K, StrideA, ALayout{}));
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SimpleDeviceMem b_device_buf(sizeof(BDataType) * f_matrix_space_size(K, N, StrideB, BLayout{}));
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SimpleDeviceMem c_device_buf(sizeof(CDataType) * f_matrix_space_size(M, N, StrideC, CLayout{}));
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SimpleDeviceMem a_scale_device_buf(
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sizeof(XDataType) *
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f_matrix_space_size(Scale_Padded_M, K / ScaleBlockSize, Scale_Stride_AM, AScaleLayout{}));
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SimpleDeviceMem b_scale_device_buf(
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sizeof(XDataType) *
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f_matrix_space_size(K / ScaleBlockSize, N, Scale_Stride_BN, BScaleLayout{}));
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using DeviceOp =
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ck::tensor_operation::device::DeviceGemmMX<ALayout,
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BLayout,
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CLayout,
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ADataType,
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XPackedDataType,
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BDataType,
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XPackedDataType,
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CDataType,
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ScaleBlockSize,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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const auto a_element_op = AElementOp{};
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const auto b_element_op = BElementOp{};
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const auto c_element_op = CElementOp{};
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std::string best_op_name;
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bool found = false;
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int best_op_id = -1;
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float best_ave_time = 0;
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float best_tflops = 0;
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float best_gb_per_sec = 0;
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// profile device operation instances
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std::cout << "Run all instances and do timing" << std::endl;
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for(int i = 0; i < op_ptrs.size(); ++i)
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{
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auto& op_ptr = op_ptrs[i];
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auto argument_ptr = op_ptr->MakeArgumentPointer(
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static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
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static_cast<XPackedDataType*>(a_scale_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
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static_cast<XPackedDataType*>(b_scale_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_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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Scale_Stride_AM,
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StrideB,
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Scale_Stride_BN,
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StrideC,
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KBatch,
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a_element_op,
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b_element_op,
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c_element_op);
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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std::string op_name = op_ptr->GetTypeString();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
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std::size_t flop =
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std::size_t(2) * M * N * K + std::size_t(2) * M * N * K / ScaleBlockSize;
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std::size_t num_btype = sizeof(ADataType) * M * K / ck::packed_size_v<ADataType> +
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sizeof(BDataType) * K * N / ck::packed_size_v<BDataType> +
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sizeof(CDataType) * M * N +
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sizeof(XDataType) * M * K / ScaleBlockSize +
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sizeof(XDataType) * N * K / ScaleBlockSize;
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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: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
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<< gb_per_sec << " GB/s, " << op_name << std::endl;
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if(tflops > best_tflops)
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{
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found = true;
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best_op_id = i;
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best_op_name = op_name;
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best_tflops = tflops;
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best_ave_time = ave_time;
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best_gb_per_sec = gb_per_sec;
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}
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}
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else
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{
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std::cout << op_name << " does not support this problem" << std::endl;
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}
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}
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std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
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<< best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
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// run the best intance
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if(found)
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{
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auto& op_ptr = op_ptrs[best_op_id];
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std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
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<< std::endl;
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auto argument_ptr = op_ptr->MakeArgumentPointer(
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static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
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static_cast<XPackedDataType*>(a_scale_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
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static_cast<XPackedDataType*>(b_scale_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_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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Scale_Stride_AM,
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StrideB,
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Scale_Stride_BN,
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StrideC,
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KBatch,
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a_element_op,
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b_element_op,
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c_element_op);
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
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}
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std::cout << "Done" << std::endl;
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}
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return 0;
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}
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