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https://github.com/ROCm/composable_kernel.git
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Reference CGEMM + test stub
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myamlak
1
example/19_cgemm/CMakeLists.txt
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1
example/19_cgemm/CMakeLists.txt
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add_example_executable(example_cgemm_xdl_bf16 cgemm_xdl_bf16.cpp)
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23
example/19_cgemm/cgemm_xdl_bf16.cpp
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example/19_cgemm/cgemm_xdl_bf16.cpp
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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 <stdlib.h>
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#include <half.hpp>
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#include "check_err.hpp"
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#include "config.hpp"
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#include "device.hpp"
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#include "host_tensor.hpp"
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#include "host_tensor_generator.hpp"
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#include "device_tensor.hpp"
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#include "device_gemm_xdl_cshuffle.hpp"
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#include "element_wise_operation.hpp"
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#include "reference_cgemm.hpp"
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#include "gemm_specialization.hpp"
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// stub only
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int main()
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{
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return 0;
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}
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@@ -38,7 +38,8 @@ add_subdirectory(11_conv2d_bwd_weight)
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add_subdirectory(12_reduce)
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add_subdirectory(13_pool2d_fwd)
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add_subdirectory(14_gemm_xdl_requant_relu_requant)
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add_subdirectory(17_convnd_bwd_data_xdl)
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add_subdirectory(15_grouped_gemm)
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add_subdirectory(16_gemm_reduce)
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add_subdirectory(17_convnd_bwd_data_xdl)
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add_subdirectory(18_batched_gemm_reduce)
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add_subdirectory(19_cgemm)
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@@ -0,0 +1,197 @@
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#pragma once
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#include <iostream>
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#include <sstream>
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#include "device_base.hpp"
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#include "host_tensor.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace host {
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template <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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struct ReferenceCGemm : public device::BaseOperator
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{
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// Argument
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struct Argument : public device::BaseArgument
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{
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Argument(const Tensor<ADataType>& a_m_k_real,
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const Tensor<ADataType>& a_m_k_imag,
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const Tensor<BDataType>& b_k_n_real,
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const Tensor<BDataType>& b_k_n_imag,
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Tensor<CDataType>& c_m_n_real,
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Tensor<CDataType>& c_m_n_imag,
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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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: a_m_k_real_{a_m_k_real},
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a_m_k_imag_{a_m_k_imag},
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b_k_n_real_{b_k_n_real},
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b_k_n_imag_{b_k_n_imag},
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c_m_n_real_{c_m_n_real},
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c_m_n_imag_{c_m_n_imag},
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a_element_op_{a_element_op},
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b_element_op_{b_element_op},
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c_element_op_{c_element_op}
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{
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}
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const Tensor<ADataType>& a_m_k_real_;
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const Tensor<ADataType>& a_m_k_imag_;
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const Tensor<BDataType>& b_k_n_real_;
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const Tensor<BDataType>& b_k_n_imag_;
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Tensor<CDataType>& c_m_n_real_;
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Tensor<CDataType>& c_m_n_imag_;
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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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// Invoker
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struct Invoker : public device::BaseInvoker
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{
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using Argument = ReferenceCGemm::Argument;
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float Run(const Argument& arg)
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{
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auto f_mk_kn_mn_real = [&](auto m, auto n) {
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const int K = arg.a_m_k_real_.mDesc.GetLengths()[1];
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if(K != arg.a_m_k_imag_.mDesc.GetLengths()[1])
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{
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throw std::runtime_error(
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"wrong! Incompatible real and imag sizes in CGEMM");
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}
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float v_acc = 0;
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for(int k = 0; k < K; ++k)
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{
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float v_a_real;
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float v_b_real;
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float v_a_imag;
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float v_b_imag;
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arg.a_element_op_(v_a_real, static_cast<const float>(arg.a_m_k_real_(m, k)));
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arg.a_element_op_(v_a_imag, static_cast<const float>(arg.a_m_k_imag_(m, k)));
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arg.b_element_op_(v_b_real, static_cast<const float>(arg.b_k_n_real_(k, n)));
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arg.b_element_op_(v_b_imag, static_cast<const float>(arg.b_k_n_imag_(k, n)));
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v_acc += v_a_real * v_b_real - v_a_imag * v_b_imag;
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}
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float v_c_real;
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arg.c_element_op_(v_c_real, v_acc);
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arg.c_m_n_real_(m, n) = v_c_real;
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};
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auto f_mk_kn_mn_imag = [&](auto m, auto n) {
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const int K = arg.a_m_k_real_.mDesc.GetLengths()[1];
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if(K != arg.a_m_k_imag_.mDesc.GetLengths()[1])
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{
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throw std::runtime_error(
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"wrong! Incompatible real and imag sizes in CGEMM");
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}
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float v_acc = 0;
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for(int k = 0; k < K; ++k)
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{
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float v_a_real;
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float v_b_real;
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float v_a_imag;
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float v_b_imag;
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arg.a_element_op_(v_a_real, static_cast<const float>(arg.a_m_k_real_(m, k)));
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arg.a_element_op_(v_a_imag, static_cast<const float>(arg.a_m_k_imag_(m, k)));
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arg.b_element_op_(v_b_real, static_cast<const float>(arg.b_k_n_real_(k, n)));
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arg.b_element_op_(v_b_imag, static_cast<const float>(arg.b_k_n_imag_(k, n)));
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v_acc += v_a_real * v_b_imag - v_a_imag * v_b_real;
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}
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float v_c_imag;
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arg.c_element_op_(v_c_imag, v_acc);
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arg.c_m_n_imag_(m, n) = v_c_imag;
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};
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make_ParallelTensorFunctor(f_mk_kn_mn_real,
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arg.c_m_n_real_.mDesc.GetLengths()[0],
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arg.c_m_n_real_.mDesc.GetLengths()[1])(
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std::thread::hardware_concurrency());
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make_ParallelTensorFunctor(f_mk_kn_mn_imag,
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arg.c_m_n_imag_.mDesc.GetLengths()[0],
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arg.c_m_n_imag_.mDesc.GetLengths()[1])(
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std::thread::hardware_concurrency());
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return 0;
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}
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float Run(const device::BaseArgument* p_arg, int) override
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{
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return Run(*dynamic_cast<const Argument*>(p_arg));
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}
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};
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static constexpr bool IsValidCompilationParameter()
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{
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// TODO: properly implement this check
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return true;
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}
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bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
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static auto MakeArgument(const Tensor<ADataType>& a_m_k_real,
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const Tensor<ADataType>& a_m_k_imag,
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const Tensor<BDataType>& b_k_n_real,
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const Tensor<BDataType>& b_k_n_imag,
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Tensor<CDataType>& c_m_n_real,
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Tensor<CDataType>& c_m_n_imag,
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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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return Argument{a_m_k_real,
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a_m_k_imag,
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b_k_n_real,
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b_k_n_imag,
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c_m_n_real,
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c_m_n_imag,
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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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}
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static auto MakeInvoker() { return Invoker{}; }
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virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
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{
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return std::make_unique<Invoker>(Invoker{});
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}
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std::string GetTypeString() const override
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{
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auto str = std::stringstream();
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// clang-format off
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str << "ReferenceCGemm"
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<< std::endl;
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// clang-format on
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return str.str();
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}
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};
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} // namespace host
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} // namespace tensor_operation
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} // namespace ck
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