// Copyright (c) 2019 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 "lite/backends/opencl/cl_half.h" #include "lite/backends/opencl/cl_include.h" #include "lite/core/kernel.h" #include "lite/core/op_registry.h" #include "lite/kernels/opencl/image_helper.h" #include "lite/operators/op_params.h" #include "lite/utils/replace_stl/stream.h" #include "lite/utils/string.h" namespace paddle { namespace lite { namespace kernels { namespace opencl { class ScaleComputeImage2D : public KernelLite { public: using param_t = operators::ScaleParam; std::string doc() const override { return "Scale using cl::Image2D, kFP16"; } void PrepareForRun() override { auto& context = ctx_->As(); VLOG(1) << "kernel_func_name_:" << kernel_func_name_; context.cl_context()->AddKernel( kernel_func_name_, "image/scale_kernel.cl", build_options_); } void Run() override { const auto& param = *param_.get_mutable(); const auto& in_dims = param.x->dims(); auto* x_img = param.x->data(); const float scale = param.scale; const float bias = param.bias; // LOG(INFO) << "x_image" << x_img; auto out_image_shape = InitImageDimInfoWith(in_dims); #ifndef LITE_SHUTDOWN_LOG VLOG(4) << "out_image_shape = " << out_image_shape["width"] << " " << out_image_shape["height"]; #endif auto* out_img = param.output->mutable_data( out_image_shape["width"], out_image_shape["height"]); // LOG(INFO) << "out_image" << out_img; auto& context = ctx_->As(); CHECK(context.cl_context() != nullptr); STL::stringstream kernel_key; kernel_key << kernel_func_name_ << build_options_; auto kernel = context.cl_context()->GetKernel(kernel_key.str()); auto global_work_size = cl::NDRange{static_cast(out_image_shape["width"]), static_cast(out_image_shape["height"])}; cl_int status; int arg_idx = 0; status = kernel.setArg(arg_idx, *x_img); CL_CHECK_FATAL(status); status = kernel.setArg(++arg_idx, *out_img); CL_CHECK_FATAL(status); status = kernel.setArg(++arg_idx, scale); CL_CHECK_FATAL(status); status = kernel.setArg(++arg_idx, bias); CL_CHECK_FATAL(status); status = context.cl_context()->GetCommandQueue().enqueueNDRangeKernel( kernel, cl::NullRange, global_work_size, cl::NullRange, nullptr, event_.get()); CL_CHECK_FATAL(status); context.cl_wait_list()->emplace(out_img, event_); } private: std::string kernel_func_name_{"scale"}; std::string build_options_{"-DCL_DTYPE_half"}; std::shared_ptr event_{new cl::Event}; }; } // namespace opencl } // namespace kernels } // namespace lite } // namespace paddle REGISTER_LITE_KERNEL(scale, kOpenCL, kFP16, kImageDefault, paddle::lite::kernels::opencl::ScaleComputeImage2D, image2d) .BindInput("X", {LiteType::GetTensorTy(TARGET(kOpenCL), PRECISION(kFP16), DATALAYOUT(kImageDefault))}) .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kOpenCL), PRECISION(kFP16), DATALAYOUT(kImageDefault))}) .Finalize();