Reorganize files, Part 1 (#119)

* delete obselete files

* move files

* build

* update cmake

* update cmake

* fix build

* reorg examples

* update cmake for example and test

test/gemm/gemm_fp32.cpp

@@ -0,0 +1,138 @@
+#include <algorithm>
+#include <cstdlib>
+#include <half.hpp>
+#include <iostream>
+#include <numeric>
+#include <tuple>
+#include <vector>
+
+#include "gemm_util.hpp"
+#include "config.hpp"
+#include "print.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "host_gemm.hpp"
+#include "device_tensor.hpp"
+#include "device_gemm_xdl.hpp"
+#include "device_gemm_xdl_c_shuffle.hpp"
+#include "element_wise_operation.hpp"
+#include "reference_gemm.hpp"
+#include "gemm_specialization.hpp"
+#include "test_util.hpp"
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using DeviceGemmPtr_ =
+    ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
+                                                ck::tensor_operation::element_wise::PassThrough,
+                                                ck::tensor_operation::element_wise::PassThrough>;
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace device_gemm_instance {
+void add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(std::vector<DeviceGemmPtr_>&);
+}
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck
+
+namespace {
+
+using ADataType   = float;
+using BDataType   = float;
+using CDataType   = float;
+using AccDataType = float;
+
+using ALayout = ck::tensor_layout::gemm::RowMajor;
+using BLayout = ck::tensor_layout::gemm::ColumnMajor;
+using CLayout = ck::tensor_layout::gemm::RowMajor;
+
+auto PrepareGemmTensor(const ck::gemm_util::GemmParams& params)
+{
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({stride, 1}));
+            }
+            else
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({1, stride}));
+            }
+        };
+
+    Tensor<ADataType> a_m_k(
+        f_host_tensor_descriptor(params.M, params.K, params.StrideA, ALayout{}));
+    Tensor<BDataType> b_k_n(
+        f_host_tensor_descriptor(params.K, params.N, params.StrideB, BLayout{}));
+    Tensor<CDataType> c_m_n_host_result(
+        f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
+    Tensor<CDataType> c_m_n_device_result(
+        f_host_tensor_descriptor(params.M, params.N, params.StrideC, CLayout{}));
+
+    a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
+    b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+
+    return std::make_tuple(a_m_k, b_k_n, c_m_n_host_result, c_m_n_device_result);
+}
+
+bool TestGemm(DeviceGemmPtr_& gemmPtr)
+{
+    // Arrange
+    ck::gemm_util::GemmParams params;
+    params.M       = 1024;
+    params.N       = 1024;
+    params.K       = 1024;
+    params.StrideA = 1024;
+    params.StrideB = 1024;
+    params.StrideC = 1024;
+
+    auto host_tensors           = PrepareGemmTensor(params);
+    const Tensor<ADataType>& a  = std::get<0>(host_tensors);
+    const Tensor<BDataType>& b  = std::get<1>(host_tensors);
+    Tensor<CDataType>& c_host   = std::get<2>(host_tensors);
+    Tensor<CDataType>& c_device = std::get<3>(host_tensors);
+
+    auto a_element_op = PassThrough{};
+    auto b_element_op = PassThrough{};
+    auto c_element_op = PassThrough{};
+
+    using ReferenceGemmInstance = ck::tensor_operation::host::
+        ReferenceGemm<ADataType, BDataType, CDataType, PassThrough, PassThrough, PassThrough>;
+    ck::gemm_util::RunHostGEMM<ReferenceGemmInstance>(
+        a, b, c_host, a_element_op, b_element_op, c_element_op);
+
+    // Act
+    ck::gemm_util::RunDeviceGEMM(
+        gemmPtr, params, a, b, c_device, a_element_op, b_element_op, c_element_op);
+
+    // Assert
+    bool res = test_util::check_err(
+        c_device.mData, c_host.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
+
+    std::cout << (res ? "SUCCESS" : "FAILURE") << std::endl;
+
+    return res;
+}
+
+} // anonymous namespace
+
+int main()
+{
+    std::vector<DeviceGemmPtr_> gemmPtrs;
+    ck::tensor_operation::device::device_gemm_instance::
+        add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(gemmPtrs);
+
+    bool res = true;
+
+    for(auto& gemmPtr : gemmPtrs)
+    {
+        res &= TestGemm(gemmPtr);
+    }
+
+    std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
+}