| /* |
| * Copyright (c) 2017 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/NEON/functions/NEGEMMLowp.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/ITensor.h" |
| #include "arm_compute/core/NEON/kernels/arm64/NEGEMMLowpAArch64V8P4Kernel.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| #include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h" |
| #include "arm_compute/runtime/TensorAllocator.h" |
| #include "support/ToolchainSupport.h" |
| |
| using namespace arm_compute; |
| |
| NEGEMMLowp::NEGEMMLowp(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _mm_func(), _mtx_a_reduction_kernel(), _mtx_b_reduction_kernel(), _finalize_kernel(), _vector_sum_col(), _vector_sum_row(), _mm_output(), _a_offset(0), |
| _b_offset(0) |
| { |
| } |
| |
| void NEGEMMLowp::configure(const ITensor *a, const ITensor *b, ITensor *output, int32_t a_offset, int32_t b_offset, int32_t c_offset, int32_t output_mult_int, int32_t shift) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN((a), 1, DataType::U8); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output); |
| 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"); |
| ARM_COMPUTE_ERROR_ON_MSG((a)->info()->dimension(1) != (output)->info()->dimension(1), "The output matrix must have the same number of rows as the matrix A"); |
| ARM_COMPUTE_ERROR_ON_MSG((b)->info()->dimension(0) != (output)->info()->dimension(0), "The output matrix must have the same number of columns as the matrix B"); |
| |
| _a_offset = a_offset; |
| _b_offset = b_offset; |
| |
| // Initialize matrix multiply output tensor |
| const TensorShape &shape_mm_output = output->info()->tensor_shape(); |
| TensorInfo info_mm_output(shape_mm_output, 1, DataType::S32); |
| _mm_output.allocator()->init(info_mm_output); |
| _memory_group.manage(&_mm_output); |
| |
| // Initialize Matrix B reduction kernel only if _a_offset is not equal to 0 |
| if(_a_offset != 0) |
| { |
| TensorShape shape_vector_sum_col = b->info()->tensor_shape(); |
| shape_vector_sum_col.remove_dimension(1); |
| TensorInfo info_vector_sum_col(shape_vector_sum_col, 1, DataType::S32); |
| _vector_sum_col.allocator()->init(info_vector_sum_col); |
| _memory_group.manage(&_vector_sum_col); |
| |
| // Configure Matrix B reduction kernel |
| _mtx_b_reduction_kernel.configure(b, &_vector_sum_col, a->info()->dimension(0), false); |
| } |
| |
| // Initialize Matrix A reduction kernel only if _b_offset is not equal to 0 |
| if(_b_offset != 0) |
| { |
| TensorShape shape_vector_sum_row = a->info()->tensor_shape(); |
| shape_vector_sum_row.set(Window::DimX, a->info()->dimension(1)); |
| shape_vector_sum_row.remove_dimension(1); |
| TensorInfo info_vector_sum_row(shape_vector_sum_row, 1, DataType::S32); |
| _vector_sum_row.allocator()->init(info_vector_sum_row); |
| _memory_group.manage(&_vector_sum_row); |
| |
| // Configure Matrix A reduction kernel |
| _mtx_a_reduction_kernel.configure(a, &_vector_sum_row, a->info()->dimension(0), false); |
| } |
| |
| // Configure matrix multiply function |
| _mm_func.configure(a, b, &_mm_output); |
| |
| // Configure finalize kernel |
| _finalize_kernel.configure(_a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, &_mm_output, output, a->info()->dimension(0), a_offset, b_offset, c_offset, |
| output_mult_int, shift); |
| |
| // Allocate tensors |
| _mm_output.allocator()->allocate(); |
| |
| if(_a_offset != 0) |
| { |
| _vector_sum_col.allocator()->allocate(); |
| } |
| |
| if(_b_offset != 0) |
| { |
| _vector_sum_row.allocator()->allocate(); |
| } |
| } |
| |
| void NEGEMMLowp::run() |
| { |
| _memory_group.acquire(); |
| |
| // Run matrix A reduction kernel only if _b_offset is not equal to 0 |
| if(_b_offset != 0) |
| { |
| NEScheduler::get().schedule(&_mtx_a_reduction_kernel, Window::DimX); |
| } |
| |
| // Run matrix B reduction kernel only if _a_offset is not equal to 0 |
| if(_a_offset != 0) |
| { |
| NEScheduler::get().schedule(&_mtx_b_reduction_kernel, Window::DimX); |
| } |
| |
| // Run matrix multiply core function |
| _mm_func.run(); |
| |
| // Run finalise kernel |
| NEScheduler::get().schedule(&_finalize_kernel, Window::DimY); |
| |
| _memory_group.release(); |
| } |