/* * Copyright (c) 2016-2022 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 ARM_COMPUTE_CLHELPERS_H #define ARM_COMPUTE_CLHELPERS_H #include "arm_compute/core/CL/CLTypes.h" #include "arm_compute/core/CL/OpenCL.h" #include "arm_compute/core/Types.h" #include #include namespace arm_compute { class CLCompileContext; class CLBuildOptions; enum class DataType; /** Max vector width of an OpenCL vector */ static constexpr unsigned int max_cl_vector_width = 16; /** Max number of manual loop unrolling */ static constexpr int max_manual_loop_unrolling = 128; /** Translates a tensor data type to the appropriate OpenCL type. * * @param[in] dt @ref DataType to be translated to OpenCL type. * * @return The string specifying the OpenCL type to be used. */ std::string get_cl_type_from_data_type(const DataType &dt); /** Translates a tensor data type to the appropriate OpenCL promoted type. * * @param[in] dt @ref DataType to be used to get the promoted OpenCL type. * * @return The string specifying the OpenCL type to be used. */ std::string get_cl_promoted_type_from_data_type(const DataType &dt); /** Translates the element size to an unsigned integer data type * * @param[in] element_size Size in bytes of an element. * * @return The string specifying the OpenCL type to be used. */ std::string get_cl_unsigned_type_from_element_size(size_t element_size); /** Translates the element size to an signed integer data type * * @param[in] element_size Size in bytes of an element. * * @return The string specifying the OpenCL type to be used. */ std::string get_cl_signed_type_from_element_size(size_t element_size); /** Translates a tensor data type to the appropriate OpenCL select type. * * @param[in] dt @ref DataType to be translated to OpenCL select type. * * @return The string specifying the OpenCL select type to be used. */ std::string get_cl_select_type_from_data_type(const DataType &dt); /** Translates a tensor data type to the appropriate OpenCL dot8 accumulator type. * * @param[in] dt @ref DataType to be translated to OpenCL dot8 accumulator type. * * @return The string specifying the OpenCL dot8 accumulator type to be used. */ std::string get_cl_dot8_acc_type_from_data_type(const DataType &dt); /** Get the size of a data type in number of bits. * * @param[in] dt @ref DataType. * * @return Number of bits in the data type specified. */ std::string get_data_size_from_data_type(const DataType &dt); /** Helper function to get the GPU target from CL device * * @param[in] device A CL device * * @return the GPU target */ GPUTarget get_target_from_device(const cl::Device &device); /** Helper function to get the highest OpenCL version supported * * @param[in] device A CL device * * @return the highest OpenCL version supported */ CLVersion get_cl_version(const cl::Device &device); /** Helper function to get the cl_image pitch alignment in pixels * * @param[in] device A CL device * * @return the cl_image pitch alignment in pixels. If an error occurs, the function will return 0 */ size_t get_cl_image_pitch_alignment(const cl::Device &device); /** Helper function to check whether non-uniform work group is supported * * @param[in] device A CL device * * @return True if the feature is supported */ bool get_cl_non_uniform_work_group_supported(const cl::Device &device); /** Helper function to check whether a given extension is supported * * @param[in] device A CL device * @param[in] extension_name Name of the extension to be checked * * @return True if the extension is supported */ bool device_supports_extension(const cl::Device &device, const char *extension_name); /** Helper function to check whether the cl_khr_fp16 extension is supported * * @param[in] device A CL device * * @return True if the extension is supported */ bool fp16_supported(const cl::Device &device); /** Helper function to check whether the arm_non_uniform_work_group_size extension is supported * * @param[in] device A CL device * * @return True if the extension is supported */ bool arm_non_uniform_workgroup_supported(const cl::Device &device); /** Helper function to check whether the cl_arm_integer_dot_product_int8 extension is supported * * @param[in] device A CL device * * @return True if the extension is supported */ bool dot8_supported(const cl::Device &device); /** Helper function to check whether the cl_arm_integer_dot_product_accumulate_int8 extension is supported * * @param[in] device A CL device * * @return True if the extension is supported */ bool dot8_acc_supported(const cl::Device &device); /** This function checks if the Winograd configuration (defined through the output tile, kernel size and the data layout) is supported on OpenCL * * @param[in] output_tile Output tile for the Winograd filtering algorithm * @param[in] kernel_size Kernel size for the Winograd filtering algorithm * @param[in] data_layout Data layout of the input tensor * * @return True if the configuration is supported */ bool cl_winograd_convolution_layer_supported(const Size2D &output_tile, const Size2D &kernel_size, DataLayout data_layout); /** Helper function to get the preferred native vector width size for built-in scalar types that can be put into vectors * * @param[in] device A CL device * @param[in] dt data type * * @return preferred vector width */ size_t preferred_vector_width(const cl::Device &device, DataType dt); /** Helper function to check if "dummy work-items" are preferred to have a power of two NDRange * In case dummy work-items is enabled, it is OpenCL kernel responsibility to check if the work-item is out-of range or not * * @param[in] device A CL device * * @return True if dummy work-items should be preferred to dispatch the NDRange */ bool preferred_dummy_work_items_support(const cl::Device &device); /** Helper function to check whether the cl_khr_image2d_from_buffer extension is supported * * @param[in] device A CL device * * @return True if the extension is supported */ bool image2d_from_buffer_supported(const cl::Device &device); /** Creates an opencl kernel using a compile context * * @param[in] ctx A compile context to be used to create the opencl kernel. * @param[in] kernel_name The kernel name. * @param[in] build_opts The build options to be used for the opencl kernel compilation. * * @return An opencl kernel */ cl::Kernel create_kernel(const CLCompileContext &ctx, const std::string &kernel_name, const std::set &build_opts = std::set()); /** Creates a suitable LWS hint object for parallel implementations. Sets the number of WG based on the input size. * If input width is smaller than 128 we can use fewer threads than 8. * * @param[in] input_dimension number of elements along the dimension to apply the parallellization * @param[in] vector_size size of the vector in OpenCL * * @return An LWS hint object */ cl::NDRange create_lws_hint_parallel_implementations(unsigned int input_dimension, unsigned int vector_size); /* Helper function to check if the workgroup batch size modifier parameter is supported on the cl device * * @param[in] device cl device to check for support * * @return true if the workgroup batch size modifier parameter is supported, false otherwise */ bool get_wbsm_support_info(const cl::Device &device); /* Helper function to set the workgroup batch size modifier parameter in the kernel * * @param[in] kernel cl kernel to set the workgroup batch size modifier parameter * @param[in] wbsm_hint workgroup batch size modifier to use */ void set_wbsm(cl::Kernel &kernel, cl_int wbsm_hint); /* Helper function to check if we can export the tensor to cl_image * * @param[in] input tensor * * @return true if we can export the tensor to cl_image */ bool export_to_cl_image(const ITensorInfo *tensor); /* Helper function to force unroll with pragma when any of the input values (iterations) are greater than @ref max_manual_loop_unrolling * * This function passes UNROLL_WITH_PRAGMA at compile time when any of the input values are greater than @ref max_manual_loop_unrolling * * @param[in] built_opts OpenCL kernel build options * @param[in] values Input values (iterations) * */ void set_unroll_with_pragma(CLBuildOptions &built_opts, std::initializer_list values); /** Helper function to check whether the cl_arm_matrix_multiply extension is supported * * @param[in] device A CL device * * @return True if the extension is supported */ bool arm_matrix_multiply_supported(const cl::Device &device); } // namespace arm_compute #endif /* ARM_COMPUTE_CLHELPERS_H */