diff options
Diffstat (limited to 'src/dynamic_fusion/runtime/gpu')
| -rw-r--r-- | src/dynamic_fusion/runtime/gpu/cl/ClKernelRuntime.cpp | 200 | ||||
| -rw-r--r-- | src/dynamic_fusion/runtime/gpu/cl/ClKernelRuntime.h | 76 | ||||
| -rw-r--r-- | src/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.cpp | 351 |
3 files changed, 627 insertions, 0 deletions
diff --git a/src/dynamic_fusion/runtime/gpu/cl/ClKernelRuntime.cpp b/src/dynamic_fusion/runtime/gpu/cl/ClKernelRuntime.cpp new file mode 100644 index 0000000000..93fbdfed63 --- /dev/null +++ b/src/dynamic_fusion/runtime/gpu/cl/ClKernelRuntime.cpp @@ -0,0 +1,200 @@ +/* + * Copyright (c) 2022 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "ClKernelRuntime.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "src/core/CL/CLUtils.h" +#include "src/dynamic_fusion/sketch/gpu/GpuKernelSourceCode.h" +#include "src/gpu/cl/ClKernelLibrary.h" + +#include "support/Cast.h" +namespace arm_compute +{ +namespace experimental +{ +namespace dynamic_fusion +{ +using namespace arm_compute::opencl; + +void ClKernelRuntime::configure(const ClCompileContext &compile_ctx, const GpuKernelSourceCode &code) +{ + // Create kernel from kernel source string + opencl::ClKernelLibrary &klib = opencl::ClKernelLibrary::get(); + _kernel = static_cast<cl::Kernel>(compile_ctx.create_kernel(code.name(), + "" /* Program name: Used to as part of a unique string for built kernel cache. Not needed */, + code.code(), + klib.kernel_path() /* Kernel path: Used in cases of embedded kernels */, + code.build_options().options(), + false /* Is source binary */)); + + // Configure execution window + IClKernel::configure_internal(code.window()); + + // Set config id for lws tuning + _config_id = code.config_id(); + + // Set kernel arguments + _arguments = code.arguments(); +} + +inline void ClKernelRuntime::add_tensor_argument(unsigned int &idx, const GpuKernelArgumentInfo &arg, const ICLTensor *tensor, const Window &arg_slice, std::vector<cl::Image2D> &cl_images) +{ + switch(arg.type) + { + case GpuKernelArgumentInfo::Type::Scalar: + { + ARM_COMPUTE_ERROR("Unsupported yet"); + break; + } + + case GpuKernelArgumentInfo::Type::Vector: + { + add_1D_tensor_argument(idx, tensor, arg_slice); + break; + } + + case GpuKernelArgumentInfo::Type::Image: + { + add_2D_tensor_argument(idx, tensor, arg_slice); + break; + } + case GpuKernelArgumentInfo::Type::Image_Reinterpret_As_3D: + { + add_2D_tensor_argument(idx, tensor, arg_slice); + const unsigned int total_cross_plane_pad = tensor->info()->padding().top + tensor->info()->padding().bottom; + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad)); + break; + } + case GpuKernelArgumentInfo::Type::Image_Export_To_ClImage2D: + { + const TensorShape shape2d(tensor->info()->dimension(0) / 4, tensor->info()->dimension(1) * tensor->info()->dimension(2) * tensor->info()->dimension(3)); + const size_t image_row_pitch = tensor->info()->strides_in_bytes()[1]; + cl::Image2D tensor_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), tensor->cl_buffer(), shape2d, tensor->info()->data_type(), image_row_pitch); + cl_images.push_back(tensor_image2d); + _kernel.setArg(idx++, tensor_image2d); + break; + } + + case GpuKernelArgumentInfo::Type::Image_3D: + { + add_2D_tensor_argument(idx, tensor, arg_slice); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(tensor->info()->strides_in_bytes()[2])); + break; + } + case GpuKernelArgumentInfo::Type::Image_3D_Export_To_ClImage2D: + { + const TensorShape shape2d(tensor->info()->dimension(0) / 4, tensor->info()->dimension(1) * tensor->info()->dimension(2) * tensor->info()->dimension(3)); + const size_t image_row_pitch = tensor->info()->strides_in_bytes()[1]; + cl::Image2D tensor_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), tensor->cl_buffer(), shape2d, tensor->info()->data_type(), image_row_pitch); + cl_images.push_back(tensor_image2d); + _kernel.setArg(idx++, tensor_image2d); + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(tensor->info()->strides_in_bytes()[2])); + break; + } + + case GpuKernelArgumentInfo::Type::Tensor_3D: + { + add_3D_tensor_argument(idx, tensor, arg_slice); + break; + } + + case GpuKernelArgumentInfo::Type::Tensor_4D: + { + add_4D_tensor_argument(idx, tensor, arg_slice); + break; + } + case GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer: + { + add_4d_tensor_nhwc_argument(idx, tensor); + break; + } + case GpuKernelArgumentInfo::Type::Tensor_4D_t_Image: + { + const size_t image_w = tensor->info()->dimension(0) / 4; + const size_t image_h = tensor->info()->tensor_shape().total_size_upper(1); + const size_t image_stride_y = tensor->info()->strides_in_bytes()[1]; + + cl::Image2D tensor_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), tensor->cl_buffer(), + TensorShape(image_w, image_h), tensor->info()->data_type(), image_stride_y); + cl_images.push_back(tensor_image2d); + + _kernel.setArg(idx++, tensor_image2d); + add_4d_tensor_nhwc_argument(idx, tensor); + break; + } + default: + { + ARM_COMPUTE_ERROR("Unsupported"); + } + } +} + +void ClKernelRuntime::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + Window slice = window.first_slice_window_3D(); + // Don't slice matrix along the z dimension if matrix has just 2 dimensions and matrix A more than 2 + // This scenario can happen when the matrix multiplication is used to perform a convolution operation + Window slice_fixed_z = slice; + slice_fixed_z.set(Window::DimX, Window::Dimension(0, 1, 1)); + slice_fixed_z.set(Window::DimY, Window::Dimension(0, 1, 1)); + + /// NOTE: Parameters extracted from old kernels. So far they seem to be constant + /// but we may need to make them into another configuration passed from GpuWorkloadSourceCode if needed in the future + constexpr bool slide_along_dimz = true; + constexpr bool skip_sliding_window = false; + constexpr bool use_dummy_work_items = false; + + unsigned int idx = 0; + do + { + // Set kernel arguments + Window arg_slice = slice; + // CLImages created from tensor arguments. Need to be retained until enqueue + std::vector<cl::Image2D> cl_images; + for(auto id_arg : _arguments) + { + const auto arg = id_arg.second; + auto tensor = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(id_arg.first)); + ARM_COMPUTE_ERROR_ON_NULLPTR(tensor); + ARM_COMPUTE_ERROR_ON_NULLPTR(tensor->info()); + if(!slide_along_dimz) + { + // The stride_z for matrix must be zero if we do not slice + ARM_COMPUTE_ERROR_ON(tensor->info()->strides_in_bytes()[3] != 0); + arg_slice = slice_fixed_z; + } + add_tensor_argument(idx, *arg.kernel_argument_info(), tensor, arg_slice, cl_images); + } + + // Dispatch kernel + enqueue(queue, *this, slice, lws_hint(), use_dummy_work_items); + } + while(skip_sliding_window && window.slide_window_slice_3D(slice)); +} + +} // namespace dynamic_fusion +} // namespace experimental +} // namespace arm_compute diff --git a/src/dynamic_fusion/runtime/gpu/cl/ClKernelRuntime.h b/src/dynamic_fusion/runtime/gpu/cl/ClKernelRuntime.h new file mode 100644 index 0000000000..acc2380031 --- /dev/null +++ b/src/dynamic_fusion/runtime/gpu/cl/ClKernelRuntime.h @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2022 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef SRC_DYNAMIC_FUSION_RUNTIME_GPU_CL_CLKERNELRUNTIME +#define SRC_DYNAMIC_FUSION_RUNTIME_GPU_CL_CLKERNELRUNTIME + +#include "src/dynamic_fusion/sketch/gpu/GpuKernelArgument.h" +#include "src/dynamic_fusion/sketch/gpu/GpuKernelSourceCode.h" +#include "src/gpu/cl/ClCompileContext.h" +#include "src/gpu/cl/IClKernel.h" + +namespace arm_compute +{ +namespace experimental +{ +namespace dynamic_fusion +{ +struct GpuKernelSourceCode; + +/** OpenCL runtime to run a single kernel */ +class ClKernelRuntime final : public opencl::IClKernel +{ +public: + /** Configure the kernel runtime + * + * @param[in] compile_ctx OpenCL compile context + * @param[in] code Kernel source code + */ + void configure(const opencl::ClCompileContext &compile_ctx, const GpuKernelSourceCode &code); + /** Run the kernel + * + * @param[in,out] tensors @ref ITensorPack object containing run-time tensor memories + * @param[in] window Execution window + * @param[in] queue OpenCL command queue + */ + virtual void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override; + +private: + /** Set a kernel tensor argument + * + * @param[in,out] idx Index at which to start adding the tensor's arguments. Will be incremented by the number of kernel arguments set. + * @param[in] arg Kernel argument descriptor accompanying @p tensor + * @param[in] tensor Tensor to set as an argument of the object's kernel + * @param[in] arg_slice Window the kernel will be run on + * @param[out] cl_images Extra cl images created from the tensor (will need to be retained until the kernel is enqueued) + */ + inline void add_tensor_argument(unsigned int &idx, const GpuKernelArgumentInfo &arg, const ICLTensor *tensor, const Window &arg_slice, std::vector<cl::Image2D> &cl_images); + +private: + GpuKernelArgumentList _arguments{}; /** All kernel arguments required by the runtime */ +}; + +} // namespace dynamic_fusion +} // namespace experimental +} // namespace arm_compute +#endif /* SRC_DYNAMIC_FUSION_RUNTIME_GPU_CL_CLKERNELRUNTIME */ diff --git a/src/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.cpp b/src/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.cpp new file mode 100644 index 0000000000..549c6d4abb --- /dev/null +++ b/src/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.cpp @@ -0,0 +1,351 @@ +/* + * Copyright (c) 2022 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h" + +#include "arm_compute/core/experimental/Types.h" +#include "arm_compute/runtime/CL/CLTensor.h" +#include "src/dynamic_fusion/runtime/gpu/cl/ClKernelRuntime.h" +#include "src/dynamic_fusion/sketch/gpu/GpuWorkloadSketchImpl.h" +#include "src/dynamic_fusion/sketch/gpu/GpuWorkloadSourceCode.h" +#include "support/Cast.h" + +#include <algorithm> + +namespace arm_compute +{ +namespace experimental +{ +namespace dynamic_fusion +{ +namespace +{ +/** Holder of any auxiliary @ref CLTensor required by a @ref GpuWorkloadSourceCode. + * + * @note The tensors are not allocated by default, and require the user to explicitly allocate them using the associated @ref TensorInfo and @ref AuxMemoryInfo + * + * @note This data holder must remain valid until the @ref ClWorkloadRuntime that uses it, is out of scope + */ +class ClAuxTensors +{ +public: + /** A view of a single auxiliary data and the associated @ref TensorInfo and @ref AuxMemoryInfo + */ + struct DataView + { + DataView() = default; + DataView(CLTensor *tensor, const TensorInfo &tensor_info, const AuxMemoryInfo &memory_info) + : tensor{ tensor }, tensor_info{ tensor_info }, memory_info{ memory_info } + { + } + ~DataView() = default; + DataView(const DataView &other) = default; + DataView &operator=(const DataView &other) = default; + DataView(DataView &&other) = default; + DataView &operator=(DataView &&other) = default; + CLTensor *tensor{}; /**< Pointer to the auxiliary tensor */ + TensorInfo tensor_info{}; /**< Associated tensor info */ + AuxMemoryInfo memory_info{}; /**< Memory requirement */ + }; + + /** Get views of all auxiliary tensors. This is mainly used for allocating the auxiliary tensors. */ + std::vector<DataView> get_tensors() + { + return _tensors; + } + std::vector<DataView> get_tensors() const + { + return _tensors; + } + + friend Status create_aux_tensors(ClAuxTensors *aux_tensors, const GpuWorkloadSourceCode &code); + +private: + /** Add auxiliary tensor. + * + * @param[in] tensor_info @ref ITensorInfo of the auxiliary tensor + * @param[in] memory_info Memory requirements of the auxiliary tensor + * + * @return CLTensor* Corresponding tensor memory if successfully added, otherwise nullptr + */ + CLTensor *add_aux_tensor(const ITensorInfo &tensor_info, const AuxMemoryInfo &aux_memory_info) + { + const auto t_id = tensor_info.id(); + auto find_tensor_pair = _owned_tensors.find(t_id); + if(find_tensor_pair == _owned_tensors.end()) + { + return find_tensor_pair->second.get(); + } + else + { + auto tensor = std::make_unique<CLTensor>(); + auto inserted_pair = _owned_tensors.emplace(t_id, std::move(tensor)).first; + auto new_tensor = inserted_pair->second.get(); + _tensors.emplace_back(new_tensor, tensor_info, aux_memory_info); + return new_tensor; + } + } + + std::map<ITensorInfo::Id, std::unique_ptr<CLTensor>> _owned_tensors{}; + std::vector<DataView> _tensors{}; +}; +/** Construct auxiliary tensors required by @ref GpuWorkloadSourceCode + * + * @note This is the only recommended method for user to create @ref ClAuxTensors + * + * @param[out] aux_tensors Auxiliary tensors required by the workload code + * @param[in] code @ref GpuWorkloadSourceCode which all tensors bind to + * + * @return Status + */ +Status create_aux_tensors(ClAuxTensors *aux_tensors, const GpuWorkloadSourceCode &code) +{ + for(auto t_id : code.tensors()) + { + // Get tensor object + const auto workload_arg = code.query_tensor(t_id); + ICLTensor *tensor_object = nullptr; + if(workload_arg->memory_descriptor()->memory_type == MemoryType::Auxiliary) + { + // Create aux tensor CLTensor object + const TensorInfo tensor_info = *workload_arg->tensor_info(); + ARM_COMPUTE_ERROR_ON(tensor_info.id() != t_id); + const auto aux_memory_info = workload_arg->memory_descriptor()->aux_memory_info; + tensor_object = aux_tensors->add_aux_tensor(tensor_info, aux_memory_info); + } + if(tensor_object == nullptr) + { + return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Failed to construct an auxiliary tensor"); + } + } + return Status{}; +} + +/** A fast tensor lookup table for runtime tensor objects retrieval + */ +class ClTensorLUT +{ +public: + /** Find a tensor pack associated with the @ref UnitWorkloadId @p uwk_id + * + * @param[in] uwk_id @ref UnitWorkloadId associated with the tensor pack + * + * @return ITensorPack* + */ + ITensorPack *find_tensor_pack(UnitWorkloadId uwk_id) + { + auto tensor_pack = _tensor_packs.find(uwk_id); + if(tensor_pack != _tensor_packs.end()) + { + return &(tensor_pack->second); + } + return nullptr; + } + /** Get a tensor pack associated with @p uwk_id. Throws a exception if it cannot be found. + * + * @param[in] uwk_id @ref UnitWorkloadId associated with the tensor pack + * + * @return ITensorPack* + */ + ITensorPack &get_tensor_pack(UnitWorkloadId uwk_id) + { + return _tensor_packs.at(uwk_id); + } + + friend Status create_tensor_lut(ClTensorLUT *tensor_lut, const GpuWorkloadSourceCode &code, const std::vector<CLTensor *> &user_tensors, const ClAuxTensors &aux_tensors); + +private: + /** Add a tensor pack and associate it with @ref UnitWorkloadId @p uwk_id + * + * @param[in] uwk_id @ref UnitWorkloadId associated with the tensor pack + * @param[in] tensor_pack Tensor pack to be added + */ + void add_tensor_pack(UnitWorkloadId uwk_id, const ITensorPack &tensor_pack) + { + _tensor_packs[uwk_id] = tensor_pack; + } + std::map<UnitWorkloadId, ITensorPack> _tensor_packs{}; +}; + +/** Create a fast tensor lookup table for runtime tensor retrieval + * + * @param[out] tensor_lut @ref ClTensorLUT used by the runtime to feed tensor memories to underlying kernels + * @param[in] code @ref GpuWorkloadSourceCode which all tensors bind to + * @param[in] user_tensors User tensors + * @param[in] aux_tensors Auxiliary tensors required by the workload code + * + * @return Status + */ +Status create_tensor_lut(ClTensorLUT *tensor_lut, const GpuWorkloadSourceCode &code, const std::vector<CLTensor *> &user_tensors, const ClAuxTensors &aux_tensors) +{ + // Combine user tensors and aux tensors + std::map<ITensorInfo::Id, CLTensor *> tensor_map; + for(auto tensor : user_tensors) + { + const auto t_id = tensor->info()->id(); + if(tensor_map.find(t_id) != tensor_map.end()) + { + return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Clashing tensor ids"); + } + tensor_map[t_id] = tensor; + } + for(const auto &data : aux_tensors.get_tensors()) + { + const auto t_id = data.tensor_info.id(); + const auto tensor = data.tensor; + if(tensor_map.find(t_id) != tensor_map.end()) + { + return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Clashing tensor ids"); + } + tensor_map[t_id] = tensor; + } + + // Add tensor objects into corresponding tensor packs + for(auto id_tensor : tensor_map) + { + const auto t_id = id_tensor.first; + const auto tensor_object = id_tensor.second; + if(tensor_object == nullptr) + { + return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Trying to add a nullptr into the tensor packs"); + } + if(tensor_object->allocator()->info().total_size() == 0U) + { + return ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "No allocated memory found in tensor"); + } + + for(auto uwk_id : code.get_unit_workloads_from_tensor(t_id)) + { + ITensorPack *tensor_pack = tensor_lut->find_tensor_pack(uwk_id); + if(tensor_pack == nullptr) + { + tensor_lut->add_tensor_pack(uwk_id, ITensorPack{ { t_id, tensor_object } }); + } + else + { + tensor_pack->add_tensor(t_id, tensor_object); + } + } + } + return Status{}; +} + +} // namespace + +struct ClWorkloadRuntime::Implementation +{ + std::map<UnitWorkloadId, std::unique_ptr<ClKernelRuntime>> _kernels{}; + std::map<UnitWorkloadId, std::unique_ptr<ClKernelRuntime>> _kernels_prep{}; + bool _is_configured{ false }; + bool _is_prepared{ false }; + ClTensorLUT _tensor_lut{}; + ClAuxTensors _aux_tensors{}; + GpuWorkloadSourceCode _source_code{}; +}; + +ClWorkloadRuntime::ClWorkloadRuntime() + : _impl{ std::make_unique<Implementation>() } +{ +} + +ClWorkloadRuntime::~ClWorkloadRuntime() = default; + +Status ClWorkloadRuntime::configure(const GpuWorkloadSketch &sketch) +{ + ARM_COMPUTE_RETURN_ERROR_ON_MSG(_impl->_is_configured, "ClWorkloadRuntime cannot be re-configured"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(sketch.gpu_context()->gpu_language() != GpuLanguage::OpenCL, "ClWorkloadRuntime cannot be configured with non-OpenCL workload sketch"); + // Generate source code + _impl->_source_code = sketch.implementation().generate_source_code(); + // Configure unit workload from source code + for(auto uwk_id : _impl->_source_code.unit_workloads()) + { + const auto work = _impl->_source_code.query_unit_workload(uwk_id); + const auto stage = work.stage().stage; + auto k = std::make_unique<ClKernelRuntime>(); + k->configure(*sketch.gpu_context()->cl_compile_context(), work.code()); + + switch(stage) + { + case UnitWorkloadStage::Stage::Run: + _impl->_kernels.emplace(work.id(), std::move(k)); + break; + case UnitWorkloadStage::Stage::Prepare: + _impl->_kernels_prep.emplace(work.id(), std::move(k)); + break; + default: + ARM_COMPUTE_ERROR("Invalid unit workload stage"); + } + break; + } + // Create auxiliary tensor objects + create_aux_tensors(&_impl->_aux_tensors, _impl->_source_code); + _impl->_is_configured = true; + return Status{}; +} + +void ClWorkloadRuntime::prepare() +{ + if(!_impl->_is_prepared) + { + for(auto &id_kernel_pair : _impl->_kernels_prep) + { + const bool flush_queue = false; + const auto uwk_id = id_kernel_pair.first; + auto kernel = id_kernel_pair.second.get(); + CLScheduler::get().enqueue_op(*kernel, _impl->_tensor_lut.get_tensor_pack(uwk_id), flush_queue); + } + + _impl->_is_prepared = true; + } +} + +Status ClWorkloadRuntime::run(const std::vector<CLTensor *> &tensors) +{ + // Need to create the tensor lut in every run, unless the user can guarantee the binding remains fixed, + // in which case the lut can be cached during prepare + const auto st = create_tensor_lut(&_impl->_tensor_lut, _impl->_source_code, tensors, _impl->_aux_tensors); + ARM_COMPUTE_RETURN_ON_ERROR(st); + prepare(); + for(auto &id_kernel_pair : _impl->_kernels) + { + // Flush the command queue on the last kernel + const bool flush_queue = false; + const auto uwk_id = id_kernel_pair.first; + auto kernel = id_kernel_pair.second.get(); + CLScheduler::get().enqueue_op(*kernel, _impl->_tensor_lut.get_tensor_pack(uwk_id), flush_queue); + } + return Status{}; +} + +std::vector<std::pair<CLTensor *, AuxMemoryInfo>> ClWorkloadRuntime::get_auxiliary_tensors() +{ + std::vector<std::pair<CLTensor *, AuxMemoryInfo>> aux_tensors; + for(const auto &data : _impl->_aux_tensors.get_tensors()) + { + aux_tensors.emplace_back(data.tensor, data.memory_info); + } + return aux_tensors; +} +} // namespace dynamic_fusion +} // namespace experimental +} // namespace arm_compute |