From 05288a2b871ef99f544771621c3bba409b2f70df Mon Sep 17 00:00:00 2001 From: Gian Marco Date: Tue, 21 Nov 2017 10:57:50 +0000 Subject: COMPMID-697 - Rework GEMMLowp interface on OpenCL Reworked the interface of GemmLowp in order to make easy the integration in Android NN - Added support for different output stage - Added validation for both matrix multiplication and output stage - Added bounded relu support in the output stage - Added in32_t bias support - Added optimized path for vector by matrix case This rework is required for: - Convolution quantized - Fully connected quantized Change-Id: I512283d406099cf8c614dd89d0a97ed411143afc Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110625 Reviewed-by: Georgios Pinitas Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com --- .../CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp | 178 +++++++++++++++++++++ 1 file changed, 178 insertions(+) create mode 100644 src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp (limited to 'src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp') diff --git a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp new file mode 100644 index 0000000000..5d2d13e243 --- /dev/null +++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp @@ -0,0 +1,178 @@ +/* + * 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 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) +{ +} + +void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor *b, ICLTensor *output) +{ + 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"); + + _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(b->info()->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); + + // Run reshape matrix B + CLScheduler::get().enqueue(_mtx_b_reshape_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 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 offset contribution kernel + CLScheduler::get().enqueue(_offset_contribution_kernel, true); + + _memory_group.release(); +} -- cgit v1.2.1