/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ #include #include #include #include #include #include "lite/backends/opencl/cl_caller.h" #include "lite/backends/opencl/cl_context.h" #include "lite/backends/opencl/cl_image.h" #include "lite/backends/opencl/cl_runtime.h" #include "lite/backends/opencl/target_wrapper.h" #include "lite/core/tensor.h" #include "lite/utils/cp_logging.h" namespace paddle { namespace lite { TEST(cl_test, runtime_test) { auto *runtime = CLRuntime::Global(); CHECK(runtime->IsInitSuccess()); runtime->platform(); runtime->device(); runtime->command_queue(); auto &context = runtime->context(); auto program = runtime->CreateProgram(context, "buffer/elementwise_add_kernel.cl"); auto event = runtime->CreateEvent(context); const std::string build_option("-DCL_DTYPE_float"); CHECK(runtime->BuildProgram(program.get(), build_option)); } TEST(cl_test, context_test) { auto *runtime = CLRuntime::Global(); CHECK(runtime->IsInitSuccess()); CLContext context; context.AddKernel("pool_max", "image/pool_kernel.cl", "-DCL_DTYPE_float"); context.AddKernel( "elementwise_add", "image/elementwise_add_kernel.cl", "-DCL_DTYPE_float"); context.AddKernel( "elementwise_add", "image/elementwise_add_kernel.cl", "-DCL_DTYPE_float"); } TEST(cl_test, kernel_test) { auto *runtime = CLRuntime::Global(); CHECK(runtime->IsInitSuccess()); std::unique_ptr context(new CLContext); context->AddKernel( "elementwise_add", "image/elementwise_add_kernel.cl", "-DCL_DTYPE_float"); context->AddKernel("pool_max", "image/pool_kernel.cl", "-DCL_DTYPE_float"); context->AddKernel( "elementwise_add", "image/elementwise_add_kernel.cl", "-DCL_DTYPE_float"); auto kernel = context->GetKernel(2); std::unique_ptr in_data(new float[4 * 3 * 256 * 512]); for (int i = 0; i < 4 * 3 * 256 * 512; i++) { in_data[i] = 1.f; } const DDim in_dim = DDim(std::vector{4, 3, 256, 512}); CLImage in_image; in_image.set_tensor_data(in_data.get(), in_dim); in_image.InitNormalCLImage(context->GetContext()); LOG(INFO) << in_image; std::unique_ptr bias_data(new float[4 * 3 * 256 * 512]); for (int i = 0; i < 4 * 3 * 256 * 512; i++) { bias_data[i] = 2.f; } const DDim bias_dim = DDim(std::vector{4, 3, 256, 512}); CLImage bias_image; bias_image.set_tensor_data(bias_data.get(), bias_dim); bias_image.InitNormalCLImage(context->GetContext()); LOG(INFO) << bias_image; CLImage out_image; const DDim out_dim = DDim(std::vector{4, 3, 256, 512}); out_image.InitEmptyImage(context->GetContext(), out_dim); LOG(INFO) << out_image; cl_int status; status = kernel.setArg(0, *in_image.cl_image()); CL_CHECK_FATAL(status); status = kernel.setArg(1, *bias_image.cl_image()); CL_CHECK_FATAL(status); status = kernel.setArg(2, *out_image.cl_image()); CL_CHECK_FATAL(status); size_t width = in_image.ImageWidth(); size_t height = in_image.ImageHeight(); auto global_work_size = cl::NDRange{width, height}; cl::Event event; status = context->GetCommandQueue().enqueueNDRangeKernel( kernel, cl::NullRange, global_work_size, cl::NullRange, nullptr, &event); CL_CHECK_FATAL(status); status = context->GetCommandQueue().finish(); CL_CHECK_FATAL(status); double start_nanos = event.getProfilingInfo(); double stop_nanos = event.getProfilingInfo(); double elapsed_micros = (stop_nanos - start_nanos) / 1000.0; LOG(INFO) << "Kernel Run Cost Time: " << elapsed_micros << " us."; LOG(INFO) << out_image; } TEST(cl_test, target_wrapper_buffer_test) { bool inited = InitOpenCLRuntime(); CHECK(inited) << "Fail to initialize OpenCL runtime."; std::unique_ptr context(new CLContext); std::string kernel_name = "elementwise_add"; std::string build_options = "-DCL_DTYPE_float"; context->AddKernel( kernel_name, "buffer/elementwise_add_kernel.cl", build_options); std::vector h_a; std::vector h_b; std::vector h_out; std::vector h_ref; for (int i = 0; i < 10; i++) { h_a.push_back(3.14f * i); h_b.push_back(6.28f * i); h_out.push_back(0); h_ref.push_back((3.14f + 6.28f) * i); } auto *d_a = static_cast( TargetWrapperCL::Malloc(sizeof(float) * h_a.size())); auto *d_b = static_cast( TargetWrapperCL::Malloc(sizeof(float) * h_b.size())); auto *d_out = static_cast(TargetWrapperCL::Malloc(sizeof(float) * 10)); auto *d_copy = static_cast(TargetWrapperCL::Malloc(sizeof(float) * 10)); TargetWrapperCL::MemcpySync( d_a, h_a.data(), sizeof(float) * h_a.size(), IoDirection::HtoD); TargetWrapperCL::MemcpySync( d_b, h_b.data(), sizeof(float) * h_b.size(), IoDirection::HtoD); // x + y: x[n=1, c=10, h=1, w=1], y[c=10] auto kernel = context->GetKernel(kernel_name + build_options); cl_int status = kernel.setArg(0, *d_a); CL_CHECK_FATAL(status); status = kernel.setArg(1, *d_b); CL_CHECK_FATAL(status); status = kernel.setArg(2, *d_out); CL_CHECK_FATAL(status); status = kernel.setArg(3, 1); CL_CHECK_FATAL(status); status = kernel.setArg(4, 10); CL_CHECK_FATAL(status); status = kernel.setArg(5, 1); CL_CHECK_FATAL(status); auto global_work_size = cl::NDRange{10, 1}; status = context->GetCommandQueue().enqueueNDRangeKernel( kernel, cl::NullRange, global_work_size, cl::NullRange, nullptr, nullptr); CL_CHECK_FATAL(status); status = context->GetCommandQueue().finish(); CL_CHECK_FATAL(status); TargetWrapperCL::MemcpySync( h_out.data(), d_out, sizeof(float) * 10, IoDirection::DtoH); for (int i = 0; i < 10; i++) { std::cout << h_out[i] << " "; } std::cout << std::endl; for (int i = 0; i < 10; i++) { EXPECT_NEAR(h_out[i], h_ref[i], 1e-5); } TargetWrapperCL::MemcpySync( d_copy, d_out, sizeof(float) * 10, IoDirection::DtoD); std::fill(h_out.begin(), h_out.end(), 0); for (int i = 0; i < 10; i++) { EXPECT_NEAR(h_out[i], 0, 1e-5); } TargetWrapperCL::MemcpySync( h_out.data(), d_copy, sizeof(float) * 10, IoDirection::DtoH); for (int i = 0; i < 10; i++) { EXPECT_NEAR(h_out[i], h_ref[i], 1e-5); } auto *mapped_ptr = static_cast(TargetWrapperCL::Map(d_copy, 0, sizeof(float) * 10)); for (int i = 0; i < 10; i++) { EXPECT_NEAR(mapped_ptr[i], h_ref[i], 1e-5); } TargetWrapperCL::Unmap(d_copy, mapped_ptr); TargetWrapperCL::Free(d_copy); TargetWrapperCL::Free(d_out); TargetWrapperCL::Free(d_b); TargetWrapperCL::Free(d_a); } TEST(cl_test, target_wrapper_image_test) { const size_t cl_image2d_width = 28; const size_t cl_image2d_height = 32; const size_t cl_image2d_elem_size = cl_image2d_width * cl_image2d_height * 4; // 4 for RGBA channels const size_t cl_image2d_row_pitch{0}; const size_t cl_image2d_slice_pitch{0}; auto *d_image = static_cast( TargetWrapperCL::MallocImage(cl_image2d_width, cl_image2d_height)); // Map/Unmap test auto *h_image = static_cast(TargetWrapperCL::MapImage(d_image, cl_image2d_width, cl_image2d_height, cl_image2d_row_pitch, cl_image2d_slice_pitch)); CHECK_EQ(cl_image2d_slice_pitch, 0); LOG(INFO) << "cl_image2d_row_pitch = " << cl_image2d_row_pitch << ", cl_image2d_slice_pitch " << cl_image2d_slice_pitch; for (int i = 0; i < cl_image2d_elem_size; i++) { h_image[i] = 3.14f * i; } TargetWrapperCL::Unmap(d_image, h_image); auto *h_ptr = static_cast(TargetWrapperCL::MapImage(d_image, cl_image2d_width, cl_image2d_height, cl_image2d_row_pitch, cl_image2d_slice_pitch)); for (int i = 0; i < cl_image2d_elem_size; i++) { EXPECT_NEAR(h_ptr[i], 3.14f * i, 1e-6); } TargetWrapperCL::Unmap(d_image, h_ptr); // Imagecpy test std::vector h_image_cpy(cl_image2d_elem_size); for (int i = 0; i < cl_image2d_elem_size; i++) { h_image_cpy[i] = 3.14f; } TargetWrapperCL::ImgcpySync(d_image, h_image_cpy.data(), cl_image2d_width, cl_image2d_height, cl_image2d_row_pitch, cl_image2d_slice_pitch, IoDirection::HtoD); auto *d_image_cpy = static_cast( TargetWrapperCL::MallocImage(cl_image2d_width, cl_image2d_height)); // device to device TargetWrapperCL::ImgcpySync(d_image_cpy, d_image, cl_image2d_width, cl_image2d_height, cl_image2d_row_pitch, cl_image2d_slice_pitch, IoDirection::DtoD); std::fill(h_image_cpy.begin(), h_image_cpy.end(), 0); // host to device TargetWrapperCL::ImgcpySync(h_image_cpy.data(), d_image_cpy, cl_image2d_width, cl_image2d_height, cl_image2d_row_pitch, cl_image2d_slice_pitch, IoDirection::DtoH); for (int i = 0; i < cl_image2d_elem_size; i++) { EXPECT_NEAR(h_image_cpy[i], 3.14f, 1e-6); } TargetWrapperCL::FreeImage(d_image_cpy); TargetWrapperCL::FreeImage(d_image); } } // namespace lite } // namespace paddle