/* * Copyright (c) 2017-2019 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; namespace { Status validate_arguments(const ITensorInfo *a, const ITensorInfo *b, const IGCTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info = GEMMInfo()) { ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F16, 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"); if(c != nullptr) { ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, c->info()); ARM_COMPUTE_ERROR_ON_MSG(a->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->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix B"); } if(output->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MSG(b->dimension(0) != output->dimension(0), "The output matrix must have the same number of columns as the matrix B"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != output->dimension(1), "The output matrix must have the same number of rows as the matrix A"); } ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(0) != b->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B"); ARM_COMPUTE_UNUSED(alpha); ARM_COMPUTE_UNUSED(beta); ARM_COMPUTE_UNUSED(gemm_info); return Status{}; } } // namespace 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(), _original_b(nullptr), _is_interleaved_transposed(false), _run_addition(false), _reshape_b_only_on_first_run(false), _is_prepared(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_NULLPTR(a, b, output); // Perform validation step ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(a->info(), b->info(), c, output->info(), alpha, beta, gemm_info)); // Check if we need to reshape the matrix B only on the first run _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); _is_prepared = false; _original_b = b; const IGCTensor *matrix_a = a; const IGCTensor *matrix_b = b; // Get the GPU target const GPUTarget gpu_target = GCScheduler::get().get_target(); // Set the target for the kernels _interleave_kernel.set_target(gpu_target); _mm_kernel.set_target(gpu_target); // Arguments used by GEMMReshapeInfo // If we pass the matrix A and matrix B reshaped to GCGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to GCGEMMReshapeInfo // in order to know how the matrices have been reshaped const int m = a->info()->dimension(1); const int n = b->info()->dimension(0); const int k = a->info()->dimension(0); int mult_transpose1xW_width = 1; int mult_interleave4x4_height = 1; // 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; if(_is_interleaved_transposed) { matrix_a = &_tmp_a; matrix_b = &_tmp_b; // Manage intermediate buffers _memory_group.manage(&_tmp_a); if(!_reshape_b_only_on_first_run) { _memory_group.manage(&_tmp_b); } // _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel // 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, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height)); if(_is_interleaved_transposed) { // Allocate intermediate tensors _tmp_a.allocator()->allocate(); if(!_reshape_b_only_on_first_run) { _tmp_b.allocator()->allocate(); } } // Configure matrix addition kernel if(beta != 0 && c != nullptr) { _ma_kernel.configure(c, output, beta); _run_addition = true; } } Status GCGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const IGCTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(a, b, c, output, alpha, beta, gemm_info)); return Status{}; } void GCGEMM::run() { prepare(); MemoryGroupResourceScope scope_mg(_memory_group); if(_is_interleaved_transposed) { // Run interleave kernel GCScheduler::get().dispatch(_interleave_kernel, false); if(!_reshape_b_only_on_first_run) { // 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); } } void GCGEMM::prepare() { if(!_is_prepared) { if(_is_interleaved_transposed && _reshape_b_only_on_first_run) { ARM_COMPUTE_ERROR_ON(!_original_b->is_used()); // Run transpose kernel _tmp_b.allocator()->allocate(); GCScheduler::get().dispatch(_transpose_kernel, false); GCScheduler::get().memory_barrier(); // Mark original weights tensor as unused _original_b->mark_as_unused(); } _is_prepared = true; } }