| /* |
| * 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/CL/functions/CLGEMMLowpMatrixMultiplyCore.h" |
| |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/Error.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/CL/CLScheduler.h" |
| |
| using namespace arm_compute; |
| |
| CLGEMMLowpMatrixMultiplyCore::CLGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _mm_kernel(), _mtx_a_reshape_kernel(), _mtx_b_reshape_kernel(), _mtx_a_reduction_kernel(), _mtx_b_reduction_kernel(), _offset_contribution_kernel(), |
| _vector_sum_col(), _vector_sum_row(), _tmp_a(), _tmp_b(), _a_offset(0), _b_offset(0), _is_interleaved_transposed(true), _is_first_run(true), _reshape_b_only_on_first_run(false) |
| { |
| } |
| |
| void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor *b, ICLTensor *output, const GEMMInfo &gemm_info) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b); |
| 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"); |
| 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"); |
| |
| _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); |
| _a_offset = a->info()->quantization_info().offset; |
| _b_offset = b->info()->quantization_info().offset; |
| |
| // If the input tensor has less than 16 rows, we run a special version of GEMMLowp without reshaping the input tensors |
| _is_interleaved_transposed = a->info()->dimension(1) > 16; |
| |
| const ICLTensor *matrix_a = a; |
| const ICLTensor *matrix_b = b; |
| |
| if(_is_interleaved_transposed) |
| { |
| matrix_a = &_tmp_a; |
| matrix_b = &_tmp_b; |
| |
| // The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ] |
| TensorShape shape_tmp_a = a->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.f)); |
| |
| // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ] |
| TensorShape shape_tmp_b = b->info()->tensor_shape(); |
| shape_tmp_b.set(0, b->info()->dimension(1) * 16); |
| shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / 16.f)); |
| |
| TensorInfo info_a(shape_tmp_a, 1, a->info()->data_type()); |
| TensorInfo info_b(shape_tmp_b, 1, b->info()->data_type()); |
| _tmp_a.allocator()->init(info_a); |
| _tmp_b.allocator()->init(info_b); |
| _memory_group.manage(&_tmp_a); |
| _memory_group.manage(&_tmp_b); |
| |
| // Configure interleave kernel |
| _mtx_a_reshape_kernel.configure(a, &_tmp_a); |
| |
| // Configure transpose kernel |
| _mtx_b_reshape_kernel.configure(b, &_tmp_b); |
| } |
| |
| // Configure matrix multiply kernel |
| _mm_kernel.configure(matrix_a, matrix_b, output, _is_interleaved_transposed); |
| |
| // 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(); |
| |
| if(shape_vector_sum_col.num_dimensions() > 1) |
| { |
| 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); |
| } |
| |
| // 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)); |
| if(a->info()->num_dimensions() > 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); |
| } |
| |
| // Configure offset contribution kernel |
| _offset_contribution_kernel.configure(output, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, a->info()->dimension(0), _a_offset, _b_offset); |
| |
| // Allocate tensors |
| if(_is_interleaved_transposed) |
| { |
| _tmp_a.allocator()->allocate(); |
| _tmp_b.allocator()->allocate(); |
| } |
| |
| if(_a_offset != 0) |
| { |
| _vector_sum_col.allocator()->allocate(); |
| } |
| |
| if(_b_offset != 0) |
| { |
| _vector_sum_row.allocator()->allocate(); |
| } |
| } |
| |
| void CLGEMMLowpMatrixMultiplyCore::run() |
| { |
| _memory_group.acquire(); |
| |
| if(_is_interleaved_transposed) |
| { |
| // Run reshape matrix A |
| CLScheduler::get().enqueue(_mtx_a_reshape_kernel, false); |
| |
| if(_is_first_run || !_reshape_b_only_on_first_run) |
| { |
| // Run reshape matrix B |
| CLScheduler::get().enqueue(_mtx_b_reshape_kernel, false); |
| } |
| } |
| |
| // Note: if _reshape_b_only_on_first_run = true, the reduction kernel can be executed only once |
| if(_is_first_run || !_reshape_b_only_on_first_run) |
| { |
| // Run matrix B reduction kernel only if _a_offset is not equal to 0 |
| if(_a_offset != 0) |
| { |
| CLScheduler::get().enqueue(_mtx_b_reduction_kernel, false); |
| } |
| } |
| |
| // Run matrix multiply |
| CLScheduler::get().enqueue(_mm_kernel, false); |
| |
| // Run matrix A reduction kernel only if _b_offset is not equal to 0 |
| if(_b_offset != 0) |
| { |
| CLScheduler::get().enqueue(_mtx_a_reduction_kernel, false); |
| } |
| |
| // Run offset contribution kernel |
| CLScheduler::get().enqueue(_offset_contribution_kernel, true); |
| |
| _memory_group.release(); |
| |
| _is_first_run = false; |
| } |