|  | #ifndef EIGEN_TEST_GPU_COMMON_H | 
|  | #define EIGEN_TEST_GPU_COMMON_H | 
|  |  | 
|  | #ifdef EIGEN_USE_HIP | 
|  | #include <hip/hip_runtime.h> | 
|  | #include <hip/hip_runtime_api.h> | 
|  | #else | 
|  | #include <cuda.h> | 
|  | #include <cuda_runtime.h> | 
|  | #include <cuda_runtime_api.h> | 
|  | #endif | 
|  |  | 
|  | #include <iostream> | 
|  |  | 
|  | #define EIGEN_USE_GPU | 
|  | #include <unsupported/Eigen/CXX11/src/Tensor/TensorGpuHipCudaDefines.h> | 
|  |  | 
|  | #if !defined(__CUDACC__) && !defined(__HIPCC__) | 
|  | dim3 threadIdx, blockDim, blockIdx; | 
|  | #endif | 
|  |  | 
|  | template<typename Kernel, typename Input, typename Output> | 
|  | void run_on_cpu(const Kernel& ker, int n, const Input& in, Output& out) | 
|  | { | 
|  | for(int i=0; i<n; i++) | 
|  | ker(i, in.data(), out.data()); | 
|  | } | 
|  |  | 
|  |  | 
|  | template<typename Kernel, typename Input, typename Output> | 
|  | __global__ | 
|  | EIGEN_HIP_LAUNCH_BOUNDS_1024 | 
|  | void run_on_gpu_meta_kernel(const Kernel ker, int n, const Input* in, Output* out) | 
|  | { | 
|  | int i = threadIdx.x + blockIdx.x*blockDim.x; | 
|  | if(i<n) { | 
|  | ker(i, in, out); | 
|  | } | 
|  | } | 
|  |  | 
|  |  | 
|  | template<typename Kernel, typename Input, typename Output> | 
|  | void run_on_gpu(const Kernel& ker, int n, const Input& in, Output& out) | 
|  | { | 
|  | typename Input::Scalar*  d_in; | 
|  | typename Output::Scalar* d_out; | 
|  | std::ptrdiff_t in_bytes  = in.size()  * sizeof(typename Input::Scalar); | 
|  | std::ptrdiff_t out_bytes = out.size() * sizeof(typename Output::Scalar); | 
|  |  | 
|  | gpuMalloc((void**)(&d_in),  in_bytes); | 
|  | gpuMalloc((void**)(&d_out), out_bytes); | 
|  |  | 
|  | gpuMemcpy(d_in,  in.data(),  in_bytes,  gpuMemcpyHostToDevice); | 
|  | gpuMemcpy(d_out, out.data(), out_bytes, gpuMemcpyHostToDevice); | 
|  |  | 
|  | // Simple and non-optimal 1D mapping assuming n is not too large | 
|  | // That's only for unit testing! | 
|  | dim3 Blocks(128); | 
|  | dim3 Grids( (n+int(Blocks.x)-1)/int(Blocks.x) ); | 
|  |  | 
|  | gpuDeviceSynchronize(); | 
|  |  | 
|  | #ifdef EIGEN_USE_HIP | 
|  | hipLaunchKernelGGL(HIP_KERNEL_NAME(run_on_gpu_meta_kernel<Kernel, | 
|  | typename std::decay<decltype(*d_in)>::type, | 
|  | typename std::decay<decltype(*d_out)>::type>), | 
|  | dim3(Grids), dim3(Blocks), 0, 0, ker, n, d_in, d_out); | 
|  | #else | 
|  | run_on_gpu_meta_kernel<<<Grids,Blocks>>>(ker, n, d_in, d_out); | 
|  | #endif | 
|  |  | 
|  | gpuDeviceSynchronize(); | 
|  |  | 
|  | // check inputs have not been modified | 
|  | gpuMemcpy(const_cast<typename Input::Scalar*>(in.data()),  d_in,  in_bytes,  gpuMemcpyDeviceToHost); | 
|  | gpuMemcpy(out.data(), d_out, out_bytes, gpuMemcpyDeviceToHost); | 
|  |  | 
|  | gpuFree(d_in); | 
|  | gpuFree(d_out); | 
|  | } | 
|  |  | 
|  |  | 
|  | template<typename Kernel, typename Input, typename Output> | 
|  | void run_and_compare_to_gpu(const Kernel& ker, int n, const Input& in, Output& out) | 
|  | { | 
|  | Input  in_ref,  in_gpu; | 
|  | Output out_ref, out_gpu; | 
|  | #if !defined(__CUDA_ARCH__) && !defined(__HIP_DEVICE_COMPILE__) | 
|  | in_ref = in_gpu = in; | 
|  | out_ref = out_gpu = out; | 
|  | #else | 
|  | EIGEN_UNUSED_VARIABLE(in); | 
|  | EIGEN_UNUSED_VARIABLE(out); | 
|  | #endif | 
|  | run_on_cpu (ker, n, in_ref,  out_ref); | 
|  | run_on_gpu(ker, n, in_gpu, out_gpu); | 
|  | #if !defined(__CUDA_ARCH__) && !defined(__HIP_DEVICE_COMPILE__) | 
|  | VERIFY_IS_APPROX(in_ref, in_gpu); | 
|  | VERIFY_IS_APPROX(out_ref, out_gpu); | 
|  | #endif | 
|  | } | 
|  |  | 
|  | struct compile_time_device_info { | 
|  | EIGEN_DEVICE_FUNC | 
|  | void operator()(int /*i*/, const int* /*in*/, int* info) const | 
|  | { | 
|  | #if defined(__CUDA_ARCH__) | 
|  | info[0] = int(__CUDA_ARCH__ +0); | 
|  | #endif | 
|  | #if defined(EIGEN_HIP_DEVICE_COMPILE) | 
|  | info[1] = int(EIGEN_HIP_DEVICE_COMPILE +0); | 
|  | #endif | 
|  | } | 
|  | }; | 
|  |  | 
|  | void ei_test_init_gpu() | 
|  | { | 
|  | int device = 0; | 
|  | gpuDeviceProp_t deviceProp; | 
|  | gpuGetDeviceProperties(&deviceProp, device); | 
|  |  | 
|  | ArrayXi dummy(1), info(10); | 
|  | info = -1; | 
|  | run_on_gpu(compile_time_device_info(),10,dummy,info); | 
|  |  | 
|  |  | 
|  | std::cout << "GPU compile-time info:\n"; | 
|  |  | 
|  | #ifdef EIGEN_CUDACC | 
|  | std::cout << "  EIGEN_CUDACC:                 " << int(EIGEN_CUDACC) << "\n"; | 
|  | #endif | 
|  |  | 
|  | #ifdef EIGEN_CUDA_SDK_VER | 
|  | std::cout << "  EIGEN_CUDA_SDK_VER:             " << int(EIGEN_CUDA_SDK_VER) << "\n"; | 
|  | #endif | 
|  |  | 
|  | #ifdef EIGEN_COMP_NVCC | 
|  | std::cout << "  EIGEN_COMP_NVCC:             " << int(EIGEN_COMP_NVCC) << "\n"; | 
|  | #endif | 
|  |  | 
|  | #ifdef EIGEN_HIPCC | 
|  | std::cout << "  EIGEN_HIPCC:                 " << int(EIGEN_HIPCC) << "\n"; | 
|  | #endif | 
|  |  | 
|  | std::cout << "  EIGEN_CUDA_ARCH:             " << info[0] << "\n"; | 
|  | std::cout << "  EIGEN_HIP_DEVICE_COMPILE:    " << info[1] << "\n"; | 
|  |  | 
|  | std::cout << "GPU device info:\n"; | 
|  | std::cout << "  name:                        " << deviceProp.name << "\n"; | 
|  | std::cout << "  capability:                  " << deviceProp.major << "." << deviceProp.minor << "\n"; | 
|  | std::cout << "  multiProcessorCount:         " << deviceProp.multiProcessorCount << "\n"; | 
|  | std::cout << "  maxThreadsPerMultiProcessor: " << deviceProp.maxThreadsPerMultiProcessor << "\n"; | 
|  | std::cout << "  warpSize:                    " << deviceProp.warpSize << "\n"; | 
|  | std::cout << "  regsPerBlock:                " << deviceProp.regsPerBlock << "\n"; | 
|  | std::cout << "  concurrentKernels:           " << deviceProp.concurrentKernels << "\n"; | 
|  | std::cout << "  clockRate:                   " << deviceProp.clockRate << "\n"; | 
|  | std::cout << "  canMapHostMemory:            " << deviceProp.canMapHostMemory << "\n"; | 
|  | std::cout << "  computeMode:                 " << deviceProp.computeMode << "\n"; | 
|  | } | 
|  |  | 
|  | #endif // EIGEN_TEST_GPU_COMMON_H |