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https://github.com/ROCm/composable_kernel.git
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* 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
[ROCm/composable_kernel commit: 500fa99512]
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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