/* * Copyright (c) 2017-2018 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. */ #include "arm_compute/runtime/GLES_COMPUTE/functions/GCGEMM.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/GLES_COMPUTE/GCHelpers.h" #include "arm_compute/core/GLES_COMPUTE/IGCTensor.h" #include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMInterleave4x4Kernel.h" #include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixAdditionKernel.h" #include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h" #include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMTranspose1xWKernel.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Validate.h" #include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.h" #include "arm_compute/runtime/ITensorAllocator.h" using namespace arm_compute; using namespace arm_compute::gles_compute; GCGEMM::GCGEMM(std::shared_ptr memory_manager) : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false) { } void GCGEMM::configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor *c, IGCTensor *output, float alpha, float beta, const GEMMInfo &gemm_info) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output); ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported"); ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported"); ARM_COMPUTE_ERROR_ON_MSG(gemm_info.reshape_b_only_on_first_run(), "Reshape matrix B only on first run is not supported"); ARM_COMPUTE_UNUSED(gemm_info); if(c != nullptr) { ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, c); ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(1) != c->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A"); ARM_COMPUTE_ERROR_ON_MSG(b->info()->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix C"); ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(0) != output->info()->dimension(0), "The C matrix must have the same number of rows as the output matrix"); ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(1) != output->info()->dimension(1), "The C matrix must have the same number of columns as the output matrix"); } ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(0) != b->info()->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B"); // If the input tensor has less than 16 rows, we run a special version of GEMM without reshaping the input tensors _is_interleaved_transposed = a->info()->dimension(1) > 16; const IGCTensor *matrix_a = a; const IGCTensor *matrix_b = b; if(_is_interleaved_transposed) { matrix_a = &_tmp_a; matrix_b = &_tmp_b; TensorShape shape_tmp_a = a->info()->tensor_shape(); TensorShape shape_tmp_b = b->info()->tensor_shape(); shape_tmp_a.set(0, a->info()->dimension(0) * 4); shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.0f)); const unsigned int transpose_w = max_gc_vector_width / data_size_from_type(b->info()->data_type()); shape_tmp_b.set(0, b->info()->dimension(1) * transpose_w); shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / static_cast(transpose_w))); TensorInfo info_a(shape_tmp_a, 1, a->info()->data_type(), a->info()->fixed_point_position()); _tmp_a.allocator()->init(info_a); _memory_group.manage(&_tmp_a); TensorInfo info_b(shape_tmp_b, 1, b->info()->data_type(), b->info()->fixed_point_position()); _tmp_b.allocator()->init(info_b); // Configure interleave kernel _interleave_kernel.configure(a, &_tmp_a); // Configure transpose kernel _transpose_kernel.configure(b, &_tmp_b); } _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed); if(_is_interleaved_transposed) { // Allocate intermediate tensors _tmp_a.allocator()->allocate(); _tmp_b.allocator()->allocate(); } // Configure matrix addition kernel if(beta != 0 && c != nullptr) { _ma_kernel.configure(c, output, beta); _run_addition = true; } } void GCGEMM::run() { _memory_group.acquire(); if(_is_interleaved_transposed) { // Run interleave kernel GCScheduler::get().dispatch(_interleave_kernel, false); // Run transpose kernel GCScheduler::get().dispatch(_transpose_kernel, false); GCScheduler::get().memory_barrier(); } // Run matrix multiply kernel GCScheduler::get().dispatch(_mm_kernel, !_run_addition); // Run matrix addition kernel if(_run_addition) { GCScheduler::get().memory_barrier(); GCScheduler::get().dispatch(_ma_kernel); } _memory_group.release(); }