From e75a02b60736f37c34388c23c0ccee230f65da59 Mon Sep 17 00:00:00 2001 From: Gian Marco Date: Wed, 8 Nov 2017 12:24:09 +0000 Subject: COMPMID-675 - Reworked NEGEMMLowp interface/function The new interface makes NEGEMMLowp able to work with ASYMM8 data types. Implemented 2 new functions: - NEGEMMLowpMatrixMultiplyCore - NEGEMMLowpOutputStage These functions should make the integration in android NN doable For more information about GEMMLowp: https://github.com/google/gemmlowp/blob/master/doc/low-precision.md Change-Id: Ie2c775f45234f68ca53dba644b3a912b997fd890 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/95504 Tested-by: Kaizen Reviewed-by: Pablo Tello --- .../functions/NEGEMMLowpMatrixMultiplyCore.cpp | 84 ++++++++++++++++++++-- 1 file changed, 79 insertions(+), 5 deletions(-) (limited to 'src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp') diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp index 29104cc378..929ee41220 100644 --- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp +++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp @@ -47,19 +47,25 @@ namespace arm_compute using namespace arm_compute; NEGEMMLowpMatrixMultiplyCore::NEGEMMLowpMatrixMultiplyCore(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _mm_kernel(nullptr), _mtx_a_reshape_kernel(nullptr), _mtx_b_reshape_kernel(nullptr), _tmp_a(), _tmp_b(), _workspace() + : _memory_group(std::move(memory_manager)), _mm_kernel(nullptr), _mtx_a_reshape_kernel(nullptr), _mtx_b_reshape_kernel(nullptr), _mtx_a_reduction_kernel(), _mtx_b_reduction_kernel(), + _offset_contribution_kernel(), _vector_sum_col(), _vector_sum_row(), _tmp_a(), _tmp_b(), _workspace(), _a_offset(0), _b_offset(0) { } void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, ITensor *output) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::S8); + 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"); + bool dot_product_path = false; + + _a_offset = a->info()->quantization_info().offset; + _b_offset = b->info()->quantization_info().offset; + #ifdef ARM_COMPUTE_AARCH64_V8_2 // Check for DOT product instruction const struct CPUInfo ci = NEScheduler::get().cpu_info(); @@ -67,6 +73,13 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, if(cpu_has_dotprod != 0) { + dot_product_path = true; + + // If the DOT product instruction is available, the computation will be performed in int8_t + // In order to take into account this, we need to subtract -128 from a_offset and b_offset + _a_offset -= 128; + _b_offset -= 128; + // Configure matrix multiply kernel struct CPUInfo ci = NEScheduler::get().cpu_info(); const int M = output->info()->tensor_shape().y(); @@ -77,12 +90,11 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, constexpr size_t alignment = 4096; _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8)); _memory_group.manage(&_workspace); + // Configure matrix multiplication kernel auto k = arm_compute::support::cpp14::make_unique(); k->configure(a, b, output, &_workspace, 1.f, 1.f); _mm_kernel = std::move(k); - - _workspace.allocator()->allocate(); } else #endif /* ARM_COMPUTE_AARCH64_V8_2 */ @@ -124,11 +136,58 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, k->configure(&_tmp_a, &_tmp_b, output); _mm_kernel = std::move(k); } + } - // Allocate tensors + // 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 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(!dot_product_path) + { _tmp_a.allocator()->allocate(); _tmp_b.allocator()->allocate(); } + else + { + _workspace.allocator()->allocate(); + } + + if(_a_offset != 0) + { + _vector_sum_col.allocator()->allocate(); + } + + if(_b_offset != 0) + { + _vector_sum_row.allocator()->allocate(); + } } void NEGEMMLowpMatrixMultiplyCore::run() @@ -147,5 +206,20 @@ void NEGEMMLowpMatrixMultiplyCore::run() NEScheduler::get().schedule(_mm_kernel.get(), Window::DimY); + // 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 offset contribution kernel + NEScheduler::get().schedule(&_offset_contribution_kernel, Window::DimY); + _memory_group.release(); } -- cgit v1.2.1