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https://github.com/ROCm/composable_kernel.git
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500fa99512
* convnd_fwd fp16 example * update example * update example * update instance * updating refernce conv * update reference conv * update conv fwd profiler * update conv 1d and 3d instance * update include path * clean * update profiler for conv bwd data and weight * update conv bwd weight * clean * update conv example * update profiler for conv bwd weight * update ckprofiler for conv bwd data * fix reference conv bwd data bug; update conv bwd data test * update examples * fix initialization issue * update test for conv fwd * clean * clean * remove test case too sensitive to error threshhold * fix test * clean * fix build * adding conv multiple d * adding conv multiple D * add matrix padder * add gemm padding to convnd * adding group conv * update gemm multi-d * refactor * refactor * refactor * clean * clean * refactor * refactor * reorg * add ds * add bias * clean * add G * adding group * adding group * adding group * update Tensor * clean * update example * update DeviceGemmMultipleD_Xdl_CShuffle * update conv bwd-data and bwd-weight * upate contraction example * update gemm and batch gemm with e permute * fix example build * instance for grouped conv1d * update example * adding group conv instance * update gemm bilinear instance * update gemm+add+add+fastgelu instance * update profiler * update profiler * update test * update test and client example * clean * add grouped conv into profiler * update profiler * clean * add test grouped conv, update all conv test to gtest * update test
244 lines
8.9 KiB
C++
244 lines
8.9 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/library/utility/check_err.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/reference_tensor_operation/cpu/reference_gemm.hpp"
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namespace ck {
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namespace gemm_util {
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struct GemmParams
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{
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GemmParams()
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: M(1024), N(1024), K(1024), StrideA(1024), StrideB(1024), StrideC(1024), alpha(1), beta(0)
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{
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}
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ck::index_t M;
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ck::index_t N;
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ck::index_t K;
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ck::index_t StrideA;
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ck::index_t StrideB;
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ck::index_t StrideC;
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float alpha;
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float beta;
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};
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template <typename GemmInstance,
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typename ADataType,
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typename BDataType,
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typename CDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation>
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void RunHostGEMM(const Tensor<ADataType>& A,
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const Tensor<BDataType>& B,
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Tensor<CDataType>& C,
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AElementwiseOperation a_element_op,
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BElementwiseOperation b_element_op,
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CElementwiseOperation c_element_op)
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{
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auto ref_gemm = GemmInstance{};
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auto ref_invoker = ref_gemm.MakeInvoker();
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auto ref_argument = ref_gemm.MakeArgument(A, B, C, a_element_op, b_element_op, c_element_op);
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ref_invoker.Run(ref_argument);
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}
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template <typename DeviceGemmPtr_,
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typename ADataType,
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typename BDataType,
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typename CDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation>
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bool RunDeviceGEMM(DeviceGemmPtr_& gemmPtr,
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const ck::gemm_util::GemmParams& params,
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const Tensor<ADataType>& A,
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const Tensor<BDataType>& B,
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Tensor<CDataType>& C,
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AElementwiseOperation a_element_op,
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BElementwiseOperation b_element_op,
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CElementwiseOperation c_element_op)
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{
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DeviceMem a_m_k_device_buf(sizeof(ADataType) * A.mDesc.GetElementSpaceSize());
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DeviceMem b_k_n_device_buf(sizeof(BDataType) * B.mDesc.GetElementSpaceSize());
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DeviceMem c_m_n_device_buf(sizeof(CDataType) * C.mDesc.GetElementSpaceSize());
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auto invoker_ptr = gemmPtr->MakeInvokerPointer();
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auto argument_ptr =
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gemmPtr->MakeArgumentPointer(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
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params.M,
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params.N,
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params.K,
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params.StrideA,
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params.StrideB,
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params.StrideC,
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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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if(gemmPtr->IsSupportedArgument(argument_ptr.get()))
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{
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a_m_k_device_buf.ToDevice(A.mData.data());
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b_k_n_device_buf.ToDevice(B.mData.data());
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invoker_ptr->Run(argument_ptr.get());
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c_m_n_device_buf.FromDevice(C.mData.data());
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return true;
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}
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else
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{
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std::cout << "device_gemm with the specified compilation parameters does "
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"not support this GEMM problem"
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<< std::endl;
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return false;
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}
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}
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template <typename DeviceGemmPtr_,
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typename ADataType,
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typename BDataType,
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typename CDataType,
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typename AccDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation>
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struct TestGemm
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{
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auto PrepareGemmTensor(const ck::gemm_util::GemmParams& params)
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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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if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({stride, 1}));
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}
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else
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({1, stride}));
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}
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};
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Tensor<ADataType> a_m_k(
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f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{}));
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Tensor<BDataType> b_k_n(
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f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{}));
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Tensor<CDataType> c_m_n_host_result(
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f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_device_result(
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f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
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auto f_generate_tensor_value = [](auto& tensor, auto type) {
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using dataType = decltype(type);
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tensor.GenerateTensorValue(GeneratorTensor_2<dataType>{-5, 5});
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};
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f_generate_tensor_value(a_m_k, ADataType{});
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f_generate_tensor_value(b_k_n, BDataType{});
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return std::make_tuple(a_m_k, b_k_n, c_m_n_host_result, c_m_n_device_result);
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}
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auto operator()(const DeviceGemmPtr_& gemmPtr)
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{
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std::cout << "ALayout = " << ALayout{}.name << ", BLayout = " << BLayout{}.name
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<< ", CLayout = " << CLayout{}.name << std::endl;
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std::cout << gemmPtr->GetTypeString() << std::endl;
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// Arrange
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ck::gemm_util::GemmParams params;
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params.M = 1024;
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params.N = 1024;
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params.K = 1024;
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params.StrideA = 1024;
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params.StrideB = 1024;
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params.StrideC = 1024;
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auto host_tensors = PrepareGemmTensor(params);
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const Tensor<ADataType>& a = std::get<0>(host_tensors);
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const Tensor<BDataType>& b = std::get<1>(host_tensors);
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Tensor<CDataType>& c_host = std::get<2>(host_tensors);
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Tensor<CDataType>& c_device = std::get<3>(host_tensors);
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auto a_element_op = AElementwiseOperation{};
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auto b_element_op = BElementwiseOperation{};
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auto c_element_op = CElementwiseOperation{};
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using ReferenceGemmInstance =
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ck::tensor_operation::host::ReferenceGemm<ADataType,
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BDataType,
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CDataType,
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AccDataType,
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AElementwiseOperation,
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BElementwiseOperation,
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CElementwiseOperation>;
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ck::gemm_util::RunHostGEMM<ReferenceGemmInstance>(
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a, b, c_host, a_element_op, b_element_op, c_element_op);
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// Act
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bool is_supported = ck::gemm_util::RunDeviceGEMM(
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gemmPtr, params, a, b, c_device, a_element_op, b_element_op, c_element_op);
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if(is_supported)
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{
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// Assert
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bool res = false;
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if(std::is_same<CDataType, float>::value)
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{
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res = ck::utils::check_err(c_device.mData, c_host.mData);
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std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
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}
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else if(std::is_same<CDataType, ck::half_t>::value)
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{
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res = ck::utils::check_err(c_device.mData, c_host.mData);
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std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
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}
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else if(std::is_same<CDataType, ck::bhalf_t>::value)
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{
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res = ck::utils::check_err(c_device.mData, c_host.mData);
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std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
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}
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else if(std::is_same<CDataType, int8_t>::value)
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{
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res = ck::utils::check_err(c_device.mData, c_host.mData);
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std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
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}
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else if(std::is_same<CDataType, double>::value)
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{
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res = ck::utils::check_err(c_device.mData, c_host.mData);
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std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
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}
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return res;
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}
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else
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{
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return true;
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}
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}
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};
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} // namespace gemm_util
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} // namespace ck
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