// 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 "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" namespace paddle { namespace lite { namespace kernels { namespace opencl { class ReluComputeImageDefault : public KernelLite { public: using param_t = operators::ActivationParam; std::string doc() const override { return "Relu using cl::Image2D(ImageDefault/RGBA), kFP16"; } void PrepareForRun() override { auto& context = ctx_->As(); context.cl_context()->AddKernel( kernel_func_name_, "image/activation_kernel.cl", build_options_); } void Run() override { auto& param = *param_.get_mutable(); const auto& x_dims = param.X->dims(); auto* x_buf = param.X->data(); auto image_shape = InitImageDimInfoWith(x_dims); auto* out_buf = param.Out->mutable_data( image_shape["width"], image_shape["height"]); const auto& y_dims = param.Out->dims(); // useless: check dim only 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()); int arg_idx = 0; cl_int status = kernel.setArg(arg_idx, *x_buf); CL_CHECK_FATAL(status); status = kernel.setArg(++arg_idx, *out_buf); CL_CHECK_FATAL(status); VLOG(4) << TargetToStr(param.X->target()); VLOG(4) << TargetToStr(param.Out->target()); VLOG(4) << "image_shape(w,h):" << image_shape["width"] << " " << image_shape["height"]; VLOG(4) << "x_dims[" << x_dims.size() << "D]:" << x_dims[0] << " " << x_dims[1] << " " << x_dims[2] << " " << x_dims[3]; VLOG(4) << "y_dims[" << y_dims.size() << "D]:" << y_dims[0] << " " << y_dims[1] << " " << y_dims[2] << " " << y_dims[3]; auto global_work_size = cl::NDRange{static_cast(image_shape["width"]), static_cast(image_shape["height"])}; status = context.cl_context()->GetCommandQueue().enqueueNDRangeKernel( kernel, cl::NullRange, global_work_size, cl::NullRange, nullptr, event_.get()); CL_CHECK_FATAL(status); // TODO(ysh329): io_copy(device->host) jammed if emplace to `cl_wait_list` // context.cl_wait_list()->emplace(out_buf, event_); context.cl_context()->GetCommandQueue().finish(); } private: std::string kernel_func_name_{"relu"}; std::string build_options_{"-DCL_DTYPE_half -DRELU"}; std::shared_ptr event_{new cl::Event}; }; class Relu6ComputeImageDefault : public KernelLite { public: using param_t = operators::ActivationParam; std::string doc() const override { return "Relu6 using cl::Image2D(ImageDefault/RGBA), kFP16"; } void PrepareForRun() override { auto& context = ctx_->As(); context.cl_context()->AddKernel( kernel_func_name_, "image/activation_kernel.cl", build_options_); } void Run() override { auto& param = *param_.get_mutable(); const auto& x_dims = param.X->dims(); auto* x_buf = param.X->data(); auto image_shape = InitImageDimInfoWith(x_dims); auto* out_buf = param.Out->mutable_data( image_shape["width"], image_shape["height"]); const auto& y_dims = param.Out->dims(); // useless: check dim only auto threshold = param.Relu_clipped_coef; 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()); int arg_idx = 0; cl_int status = kernel.setArg(arg_idx, *x_buf); CL_CHECK_FATAL(status); status = kernel.setArg(++arg_idx, *out_buf); CL_CHECK_FATAL(status); status = kernel.setArg(++arg_idx, threshold); CL_CHECK_FATAL(status); VLOG(4) << TargetToStr(param.X->target()); VLOG(4) << TargetToStr(param.Out->target()); VLOG(4) << "image_shape(w,h):" << image_shape["width"] << " " << image_shape["height"]; VLOG(4) << "x_dims[" << x_dims.size() << "D]:" << x_dims[0] << " " << x_dims[1] << " " << x_dims[2] << " " << x_dims[3]; VLOG(4) << "y_dims[" << y_dims.size() << "D]:" << y_dims[0] << " " << y_dims[1] << " " << y_dims[2] << " " << y_dims[3]; VLOG(4) << "threshold:" << threshold; auto global_work_size = cl::NDRange{static_cast(image_shape["width"]), static_cast(image_shape["height"])}; status = context.cl_context()->GetCommandQueue().enqueueNDRangeKernel( kernel, cl::NullRange, global_work_size, cl::NullRange, nullptr, event_.get()); CL_CHECK_FATAL(status); // TODO(ysh329): io_copy(device->host) jammed if emplace to `cl_wait_list` // context.cl_wait_list()->emplace(out_buf, event_); context.cl_context()->GetCommandQueue().finish(); } private: std::string kernel_func_name_{"relu6"}; std::string build_options_{"-DCL_DTYPE_half -DRELU6"}; std::shared_ptr event_{new cl::Event}; }; class SigmoidComputeImageDefault : public KernelLite { public: using param_t = operators::ActivationParam; std::string doc() const override { return "Sigmoid using cl::Image2D(ImageDefault/RGBA), kFP16"; } void PrepareForRun() override { auto& context = ctx_->As(); context.cl_context()->AddKernel( kernel_func_name_, "image/activation_kernel.cl", build_options_); } void Run() override { auto& param = *param_.get_mutable(); const auto& x_dims = param.X->dims(); auto* x_buf = param.X->data(); // use half_t represents half float auto image_shape = InitImageDimInfoWith(x_dims); auto* out_buf = param.Out->mutable_data( // use half_t // represents half float image_shape["width"], image_shape["height"]); const auto& y_dims = param.Out->dims(); // useless: check dim only 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()); int arg_idx = 0; cl_int status = kernel.setArg(arg_idx, *x_buf); CL_CHECK_FATAL(status); status = kernel.setArg(++arg_idx, *out_buf); CL_CHECK_FATAL(status); VLOG(4) << TargetToStr(param.X->target()); VLOG(4) << TargetToStr(param.Out->target()); VLOG(4) << "image_shape(w,h):" << image_shape["width"] << " " << image_shape["height"]; VLOG(4) << "x_dims[" << x_dims.size() << "D]:" << x_dims[0] << " " << x_dims[1] << " " << x_dims[2] << " " << x_dims[3]; VLOG(4) << "y_dims[" << y_dims.size() << "D]:" << y_dims[0] << " " << y_dims[1] << " " << y_dims[2] << " " << y_dims[3]; auto global_work_size = cl::NDRange{static_cast(image_shape["width"]), static_cast(image_shape["height"])}; status = context.cl_context()->GetCommandQueue().enqueueNDRangeKernel( kernel, cl::NullRange, global_work_size, cl::NullRange, nullptr, event_.get()); CL_CHECK_FATAL(status); // TODO(ysh329): io_copy(device->host) jammed if emplace to `cl_wait_list` // context.cl_wait_list()->emplace(out_buf, event_); context.cl_context()->GetCommandQueue().finish(); } private: std::string kernel_func_name_{"sigmoid"}; std::string build_options_{"-DCL_DTYPE_half -DSIGMOID"}; std::shared_ptr event_{new cl::Event}; }; } // namespace opencl } // namespace kernels } // namespace lite } // namespace paddle // Relu REGISTER_LITE_KERNEL(relu, kOpenCL, kFP16, kImageDefault, paddle::lite::kernels::opencl::ReluComputeImageDefault, ImageDefault) .BindInput("X", {LiteType::GetTensorTy(TARGET(kOpenCL), PRECISION(kFP16), DATALAYOUT(kImageDefault))}) .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kOpenCL), PRECISION(kFP16), DATALAYOUT(kImageDefault))}) .Finalize(); // Relu6 REGISTER_LITE_KERNEL(relu6, kOpenCL, kFP16, kImageDefault, paddle::lite::kernels::opencl::Relu6ComputeImageDefault, ImageDefault) .BindInput("X", {LiteType::GetTensorTy(TARGET(kOpenCL), PRECISION(kFP16), DATALAYOUT(kImageDefault))}) .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kOpenCL), PRECISION(kFP16), DATALAYOUT(kImageDefault))}) .Finalize(); // Sigmoid REGISTER_LITE_KERNEL(sigmoid, kOpenCL, kFP16, kImageDefault, paddle::lite::kernels::opencl::SigmoidComputeImageDefault, ImageDefault) .BindInput("X", {LiteType::GetTensorTy(TARGET(kOpenCL), PRECISION(kFP16), DATALAYOUT(kImageDefault))}) .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kOpenCL), PRECISION(kFP16), DATALAYOUT(kImageDefault))}) .Finalize();