From ab18212dd287cc0ec9b7c1a2c72455fe75ebd13d Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Mon, 9 Oct 2017 15:05:40 +0100 Subject: COMPMID-616 - Optimizing GEMMLowp on NEON intrinsics Change-Id: Ibbeff5d37249b6e8fc34ad496035a1511c9da5a3 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/94072 Tested-by: Kaizen Reviewed-by: Pablo Tello --- src/runtime/NEON/functions/NEGEMMLowp.cpp | 160 +++++++++----------- .../functions/NEGEMMLowpMatrixMultiplyCore.cpp | 163 +++++++++++++++++++++ 2 files changed, 235 insertions(+), 88 deletions(-) create mode 100644 src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp (limited to 'src/runtime/NEON') diff --git a/src/runtime/NEON/functions/NEGEMMLowp.cpp b/src/runtime/NEON/functions/NEGEMMLowp.cpp index 12136cbcb5..ab7fa079b1 100644 --- a/src/runtime/NEON/functions/NEGEMMLowp.cpp +++ b/src/runtime/NEON/functions/NEGEMMLowp.cpp @@ -31,120 +31,104 @@ #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; -#define NEGEMMLOWP_VALIDATE_DIMENSIONS(a, b, output) \ - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN((a), 1, DataType::U8); \ - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN((b), 1, DataType::U8); \ - 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 C 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 C matrix must have the same number of columns as the matrix C"); - NEGEMMLowp::NEGEMMLowp(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _mm_optimised_kernel(nullptr), _interleave_blocked(), _interleave_blocked_transposed(), _tmp_a(), - _tmp_b() + : _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) +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) { - NEGEMMLOWP_VALIDATE_DIMENSIONS(a, b, output); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32); + 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); - const struct CPUInfo ci = NEScheduler::get().cpu_info(); - const int cpu_has_dotprod = static_cast(ci.CPU) & static_cast(CPUTarget::DOT); - if(cpu_has_dotprod != 0) + // Initialize Matrix B reduction kernel only if _a_offset is not equal to 0 + if(_a_offset != 0) { -#ifdef ARM_COMPUTE_AARCH64_V8_2 - // NEGEMMLowpAArch64V8P4Kernel only compiled in AArch64 targets - _mm_optimised_kernel = support::cpp14::make_unique(); - TensorShape shape_a_int = a->info()->tensor_shape(); - shape_a_int.set(0, a->info()->dimension(0) * 8.f); - shape_a_int.set(1, std::ceil(a->info()->dimension(1) / 8.f)); - - TensorShape shape_b_int = b->info()->tensor_shape(); - shape_b_int.set(0, b->info()->dimension(0) * 12.f); - shape_b_int.set(1, std::ceil(b->info()->dimension(1) / 12.f)); - - TensorInfo info_a_int(shape_a_int, 1, a->info()->data_type()); - TensorInfo info_b_int(shape_b_int, 1, b->info()->data_type()); - _tmp_a.allocator()->init(info_a_int); - _tmp_b.allocator()->init(info_b_int); - - _memory_group.manage(&_tmp_a); - _memory_group.manage(&_tmp_b); - - _interleave_blocked.configure(a, &_tmp_a, 8, 4, false); - _interleave_blocked_transposed.configure(b, &_tmp_b, 12, 4, true); - _mm_optimised_kernel->configure(&_tmp_a, &_tmp_b, output); - - _tmp_a.allocator()->allocate(); - _tmp_b.allocator()->allocate(); -#endif /* ARM_COMPUTE_AARCH64_V8_2 */ + 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); } - else + + // Initialize Matrix A reduction kernel only if _b_offset is not equal to 0 + if(_b_offset != 0) { - ARM_COMPUTE_ERROR("Not implemented"); - //FIXME: This is in the process of being updated, for more info please refer to COMPMID-624. + 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); } -} -void NEGEMMLowp::run() -{ - _memory_group.acquire(); + // 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); - if(_mm_optimised_kernel != nullptr) + // Allocate tensors + _mm_output.allocator()->allocate(); + + if(_a_offset != 0) { - NEScheduler::get().schedule(&_interleave_blocked, Window::DimY); - NEScheduler::get().schedule(&_interleave_blocked_transposed, Window::DimY); - NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY); + _vector_sum_col.allocator()->allocate(); } - else + + if(_b_offset != 0) { - /* Run interleave kernel */ - NEScheduler::get().schedule(&_interleave_kernel, Window::DimY); - /* Run transpose kernel */ - NEScheduler::get().schedule(&_transpose_kernel, Window::DimY); - /* Run matrix multiply kernel */ - NEScheduler::get().schedule(&_mm_kernel, Window::DimY); + _vector_sum_row.allocator()->allocate(); } - - _memory_group.release(); } -void NEGEMMLowp::configure(const ITensor *a, const ITensor *b, ITensor *output, int32_t a_offset, int32_t b_offset, int32_t output_offset, int32_t output_mult_int, int32_t shift) +void NEGEMMLowp::run() { - NEGEMMLOWP_VALIDATE_DIMENSIONS(a, b, output); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8); - - /* 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)); - - 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)); + _memory_group.acquire(); - 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); + // 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); + } - // Manage intermediate buffers - _memory_group.manage(&_tmp_a); - _memory_group.manage(&_tmp_b); + // 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); + } - _interleave_kernel.configure(a, &_tmp_a); - _transpose_kernel.configure(b, &_tmp_b); - _mm_kernel.configure(&_tmp_a, &_tmp_b, output, a_offset, b_offset, output_offset, output_mult_int, shift); + // Run matrix multiply core function + _mm_func.run(); - _tmp_a.allocator()->allocate(); - _tmp_b.allocator()->allocate(); -} + // Run finalise kernel + NEScheduler::get().schedule(&_finalize_kernel, Window::DimY); -#undef NEGEMMLOWP_VALIDATE_DIMENSIONS + _memory_group.release(); +} \ No newline at end of file diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp new file mode 100644 index 0000000000..11ae054e11 --- /dev/null +++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp @@ -0,0 +1,163 @@ +/* + * 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/NEGEMMLowpMatrixMultiplyCore.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/NEGEMMInterleave4x4Kernel.h" +#include "arm_compute/core/NEON/kernels/NEGEMMInterleaveBlockedKernel.h" +#include "arm_compute/core/NEON/kernels/NEGEMMLowpAssemblyBaseKernel.h" +#include "arm_compute/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h" +#include "arm_compute/core/NEON/kernels/NEGEMMTranspose1xWKernel.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/TensorAllocator.h" +#include "support/ToolchainSupport.h" + +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() +{ +} + +void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, ITensor *output) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::U8); + 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"); + +#ifdef ARM_COMPUTE_AARCH64_V8_2 + // Check for DOT product instruction + const struct CPUInfo ci = NEScheduler::get().cpu_info(); + const int cpu_has_dotprod = static_cast(ci.CPU) & static_cast(CPUTarget::DOT); + + if(cpu_has_dotprod != 0) + { + TensorShape shape_a_int = a->info()->tensor_shape(); + shape_a_int.set(0, a->info()->dimension(0) * 8.f); + shape_a_int.set(1, std::ceil(a->info()->dimension(1) / 8.f)); + + TensorShape shape_b_int = b->info()->tensor_shape(); + shape_b_int.set(0, b->info()->dimension(0) * 12.f); + shape_b_int.set(1, std::ceil(b->info()->dimension(1) / 12.f)); + + TensorInfo info_a_int(shape_a_int, 1, a->info()->data_type()); + TensorInfo info_b_int(shape_b_int, 1, b->info()->data_type()); + _tmp_a.allocator()->init(info_a_int); + _tmp_b.allocator()->init(info_b_int); + _memory_group.manage(&_tmp_a); + _memory_group.manage(&_tmp_b); + + // Configure interleave blocked kernel for matrix A + { + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(a, &_tmp_a, 8, 4, false); + _mtx_a_reshape_kernel = std::move(k); + } + + // Configure interleave blocked kernel for matrix B + { + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(b, &_tmp_b, 12, 4, true); + _mtx_b_reshape_kernel = std::move(k); + } + + // Configure matrix multiply kernel + { + // NEGEMMLowpAArch64V8P4Kernel only compiled in AArch64 targets + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(&_tmp_a, &_tmp_b, output); + _mm_kernel = std::move(k); + } + } + else +#endif /* ARM_COMPUTE_AARCH64_V8_2 */ + { + // 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 + { + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(a, &_tmp_a); + _mtx_a_reshape_kernel = std::move(k); + } + + // Configure transpose kernel + { + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(b, &_tmp_b); + _mtx_b_reshape_kernel = std::move(k); + } + + // Configure matrix multiply kernel + { + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(&_tmp_a, &_tmp_b, output); + _mm_kernel = std::move(k); + } + } + + // Allocate tensors + _tmp_a.allocator()->allocate(); + _tmp_b.allocator()->allocate(); +} + +void NEGEMMLowpMatrixMultiplyCore::run() +{ + _memory_group.acquire(); + + // Run reshape matrix A + NEScheduler::get().schedule(_mtx_a_reshape_kernel.get(), Window::DimY); + + // Run reshape matrix B + NEScheduler::get().schedule(_mtx_b_reshape_kernel.get(), Window::DimY); + + // Run matrix multiply kernel + NEScheduler::get().schedule(_mm_kernel.get(), Window::DimY); + + _memory_group.release(); +} \ No newline at end of file -- cgit v1.2.1