/* * Copyright (c) 2018 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_NEGEMMASSEMBLYDISPATCH_H__ #define __ARM_COMPUTE_NEGEMMASSEMBLYDISPATCH_H__ #include "arm_compute/core/NEON/kernels/assembly/NEGEMMAssemblyWrapperKernel.h" #include "arm_compute/runtime/IFunction.h" #include "arm_compute/runtime/IMemoryManager.h" #include "arm_compute/runtime/MemoryGroup.h" #include "arm_compute/runtime/Tensor.h" #include "arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp" namespace arm_compute { /** Assembly kernel glue */ class NEGEMMAssemblyDispatch : public IFunction { public: /** Default constructor */ NEGEMMAssemblyDispatch(std::shared_ptr memory_manager = nullptr); /** Prevent instances of this class from being copy constructed */ NEGEMMAssemblyDispatch(const NEGEMMAssemblyDispatch &) = delete; /** Prevent instances of this class from being copied */ NEGEMMAssemblyDispatch &operator=(const NEGEMMAssemblyDispatch &) = delete; NEGEMMAssemblyDispatch(NEGEMMAssemblyDispatch &&) = default; NEGEMMAssemblyDispatch &operator=(NEGEMMAssemblyDispatch &&) = default; ~NEGEMMAssemblyDispatch() = default; class IFallback { public: virtual void run() = 0; virtual void prepare() = 0; virtual bool is_configured() const = 0; virtual ~IFallback() = default; }; private: /** ACL Function */ std::unique_ptr _function; /** If supported create the ACL function corresponding to the GemmMethod provided to process the other passed parameters * * @param[in] method GemmMethod to use to perform the matrix multiplication. * @param[in] a Input tensor (Matrix A). * @param[in] b Input tensor (Matrix B). * @param[out] d Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0. * @param[in] alpha Scalar multiplier to apply to AB matrix product. * @param[in] beta Scalar multiplier to apply to input D matrix before adding product. * @param[in] pretransposed_hint Can the B tensor can be pretransposed (ie shared across invocations)? * * @return True if the method is supported and the function was successfully created, false otherwise. */ bool create_function(arm_gemm::GemmMethod method, const ITensor *a, const ITensor *b, ITensor *d, float alpha, float beta, bool pretranspose_hint); /** Interface for the arm_gemm fallback */ std::unique_ptr _arm_gemm; MemoryGroup _memory_group; /**< Function memory group */ std::shared_ptr _memory_manager; /**< Copy of the memory manager used to create the memory group to be used when instantiating new functions */ public: /** If supported create an ACL function else fallback to the arm_gemm function. * * @param[in] a Input tensor (Matrix A) * @param[in] b Input tensor (Matrix B) * @param[out] d Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0. * @param[in] alpha Scalar multiplier to apply to AB matrix product. * @param[in] beta Scalar multiplier to apply to input D matrix before adding product. * @param[in] pretranspose_hint Can the B tensor can be pretransposed (ie shared across invocations)? */ void configure(const ITensor *a, const ITensor *b, ITensor *d, float alpha, float beta, bool pretranspose_hint); /** Indicates whether or not this function can be used to process the given parameters. * * @param[in] a Input tensor (Matrix A) * @param[in] b Input tensor (Matrix B) * @param[in] d Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0. * @param[in] alpha Scalar multiplier to apply to AB matrix product. * @param[in] beta Scalar multiplier to apply to input D matrix before adding product. * @param[in] pretranspose_hint Can the B tensor can be pretransposed (ie shared across invocations)? * * @return a status. */ static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *d, float alpha, float beta, bool pretranspose_hint); /** Was the function successfully configured ? * * @return True if the function is configured and ready to run */ bool is_configured() const; // Inherited methods overridden: /** Runs a preparation step, usually for pre-transposing matrix b */ void prepare() override; void run() override; }; } // namespace arm_compute #endif /* __ARM_COMPUTE_NEGEMMASSEMBLYDISPATCH_H__ */