diff options
Diffstat (limited to 'unsupported/test/cxx11_tensor_reduction_sycl.cpp')
-rw-r--r-- | unsupported/test/cxx11_tensor_reduction_sycl.cpp | 147 |
1 files changed, 69 insertions, 78 deletions
diff --git a/unsupported/test/cxx11_tensor_reduction_sycl.cpp b/unsupported/test/cxx11_tensor_reduction_sycl.cpp index bd09744a6..a9ef82907 100644 --- a/unsupported/test/cxx11_tensor_reduction_sycl.cpp +++ b/unsupported/test/cxx11_tensor_reduction_sycl.cpp @@ -22,126 +22,117 @@ -static void test_full_reductions_sycl() { - - - cl::sycl::gpu_selector s; - cl::sycl::queue q(s, [=](cl::sycl::exception_list l) { - for (const auto& e : l) { - try { - std::rethrow_exception(e); - } catch (cl::sycl::exception e) { - std::cout << e.what() << std::endl; - } - } - }); - Eigen::SyclDevice sycl_device(q); +static void test_full_reductions_sycl(const Eigen::SyclDevice& sycl_device) { const int num_rows = 452; const int num_cols = 765; array<int, 2> tensorRange = {{num_rows, num_cols}}; Tensor<float, 2> in(tensorRange); + Tensor<float, 0> full_redux; + Tensor<float, 0> full_redux_gpu; + in.setRandom(); - Tensor<float, 0> full_redux; - Tensor<float, 0> full_redux_g; full_redux = in.sum(); - float* out_data = (float*)sycl_device.allocate(sizeof(float)); - TensorMap<Tensor<float, 2> > in_gpu(in.data(), tensorRange); - TensorMap<Tensor<float, 0> > full_redux_gpu(out_data); - full_redux_gpu.device(sycl_device) = in_gpu.sum(); - sycl_device.deallocate(out_data); - // Check that the CPU and GPU reductions return the same result. - VERIFY_IS_APPROX(full_redux_gpu(), full_redux()); -} + float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); + float* gpu_out_data =(float*)sycl_device.allocate(sizeof(float)); + TensorMap<Tensor<float, 2> > in_gpu(gpu_in_data, tensorRange); + TensorMap<Tensor<float, 0> > out_gpu(gpu_out_data); -static void test_first_dim_reductions_sycl() { + sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); + out_gpu.device(sycl_device) = in_gpu.sum(); + sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(float)); + // Check that the CPU and GPU reductions return the same result. + VERIFY_IS_APPROX(full_redux_gpu(), full_redux()); + sycl_device.deallocate(gpu_in_data); + sycl_device.deallocate(gpu_out_data); +} - cl::sycl::gpu_selector s; - cl::sycl::queue q(s, [=](cl::sycl::exception_list l) { - for (const auto& e : l) { - try { - std::rethrow_exception(e); - } catch (cl::sycl::exception e) { - std::cout << e.what() << std::endl; - } - } - }); - Eigen::SyclDevice sycl_device(q); +static void test_first_dim_reductions_sycl(const Eigen::SyclDevice& sycl_device) { int dim_x = 145; int dim_y = 1; int dim_z = 67; array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}}; - - Tensor<float, 3> in(tensorRange); - in.setRandom(); Eigen::array<int, 1> red_axis; red_axis[0] = 0; - Tensor<float, 2> redux = in.sum(red_axis); array<int, 2> reduced_tensorRange = {{dim_y, dim_z}}; - Tensor<float, 2> redux_g(reduced_tensorRange); - TensorMap<Tensor<float, 3> > in_gpu(in.data(), tensorRange); - float* out_data = (float*)sycl_device.allocate(dim_y*dim_z*sizeof(float)); - TensorMap<Tensor<float, 2> > redux_gpu(out_data, dim_y, dim_z ); - redux_gpu.device(sycl_device) = in_gpu.sum(red_axis); - sycl_device.deallocate(out_data); - // Check that the CPU and GPU reductions return the same result. - for(int j=0; j<dim_y; j++ ) - for(int k=0; k<dim_z; k++ ) - VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k)); -} + Tensor<float, 3> in(tensorRange); + Tensor<float, 2> redux(reduced_tensorRange); + Tensor<float, 2> redux_gpu(reduced_tensorRange); + + in.setRandom(); + redux= in.sum(red_axis); -static void test_last_dim_reductions_sycl() { + float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); + float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float))); + TensorMap<Tensor<float, 3> > in_gpu(gpu_in_data, tensorRange); + TensorMap<Tensor<float, 2> > out_gpu(gpu_out_data, reduced_tensorRange); - cl::sycl::gpu_selector s; - cl::sycl::queue q(s, [=](cl::sycl::exception_list l) { - for (const auto& e : l) { - try { - std::rethrow_exception(e); - } catch (cl::sycl::exception e) { - std::cout << e.what() << std::endl; - } - } - }); - Eigen::SyclDevice sycl_device(q); + sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); + out_gpu.device(sycl_device) = in_gpu.sum(red_axis); + sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float)); + + // Check that the CPU and GPU reductions return the same result. + for(int j=0; j<reduced_tensorRange[0]; j++ ) + for(int k=0; k<reduced_tensorRange[1]; k++ ) + VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k)); + + sycl_device.deallocate(gpu_in_data); + sycl_device.deallocate(gpu_out_data); +} + +static void test_last_dim_reductions_sycl(const Eigen::SyclDevice &sycl_device) { int dim_x = 567; int dim_y = 1; int dim_z = 47; array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}}; - - Tensor<float, 3> in(tensorRange); - in.setRandom(); Eigen::array<int, 1> red_axis; red_axis[0] = 2; - Tensor<float, 2> redux = in.sum(red_axis); array<int, 2> reduced_tensorRange = {{dim_x, dim_y}}; - Tensor<float, 2> redux_g(reduced_tensorRange); - TensorMap<Tensor<float, 3> > in_gpu(in.data(), tensorRange); - float* out_data = (float*)sycl_device.allocate(dim_x*dim_y*sizeof(float)); - TensorMap<Tensor<float, 2> > redux_gpu(out_data, dim_x, dim_y ); - redux_gpu.device(sycl_device) = in_gpu.sum(red_axis); - sycl_device.deallocate(out_data); + Tensor<float, 3> in(tensorRange); + Tensor<float, 2> redux(reduced_tensorRange); + Tensor<float, 2> redux_gpu(reduced_tensorRange); + + in.setRandom(); + + redux= in.sum(red_axis); + + float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float))); + float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float))); + + TensorMap<Tensor<float, 3> > in_gpu(gpu_in_data, tensorRange); + TensorMap<Tensor<float, 2> > out_gpu(gpu_out_data, reduced_tensorRange); + + sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float)); + out_gpu.device(sycl_device) = in_gpu.sum(red_axis); + sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float)); // Check that the CPU and GPU reductions return the same result. - for(int j=0; j<dim_x; j++ ) - for(int k=0; k<dim_y; k++ ) + for(int j=0; j<reduced_tensorRange[0]; j++ ) + for(int k=0; k<reduced_tensorRange[1]; k++ ) VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k)); + + sycl_device.deallocate(gpu_in_data); + sycl_device.deallocate(gpu_out_data); + } void test_cxx11_tensor_reduction_sycl() { - CALL_SUBTEST((test_full_reductions_sycl())); - CALL_SUBTEST((test_first_dim_reductions_sycl())); - CALL_SUBTEST((test_last_dim_reductions_sycl())); + cl::sycl::gpu_selector s; + Eigen::SyclDevice sycl_device(s); + CALL_SUBTEST((test_full_reductions_sycl(sycl_device))); + CALL_SUBTEST((test_first_dim_reductions_sycl(sycl_device))); + CALL_SUBTEST((test_last_dim_reductions_sycl(sycl_device))); } |