| /* 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/NEGEMMLowpAssemblyMatrixMultiplyCore.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/NEGEMMAssemblyBaseKernel.h" |
| #include "arm_compute/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h" |
| #include "arm_compute/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.h" |
| #include "arm_compute/core/NEON/kernels/NEGEMMTranspose1xWKernel.h" |
| #include "arm_compute/core/NEON/kernels/arm64/NEGEMMLowpAArch64A53Kernel.h" |
| #include "arm_compute/core/NEON/kernels/arm64/NEGEMMLowpAArch64Kernel.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" |
| |
| namespace arm_compute |
| { |
| #include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp" |
| #include "arm_compute/core/NEON/kernels/assembly/kernels/a64_gemm_s16_12x8.hpp" |
| #include "arm_compute/core/NEON/kernels/assembly/kernels/a64_gemm_s8_12x8.hpp" |
| #include "arm_compute/core/NEON/kernels/assembly/kernels/a64_gemm_s8_4x4.hpp" |
| #include "arm_compute/core/NEON/kernels/assembly/kernels/a64_gemm_u16_12x8.hpp" |
| #include "arm_compute/core/NEON/kernels/assembly/kernels/a64_gemm_u8_4x4.hpp" |
| } // namespace arm_compute |
| |
| using namespace arm_compute; |
| |
| NEGEMMLowpAssemblyMatrixMultiplyCore::NEGEMMLowpAssemblyMatrixMultiplyCore(std::shared_ptr<IMemoryManager> 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() |
| { |
| } |
| |
| void NEGEMMLowpAssemblyMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, ITensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::U8, DataType::S8); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32, 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 __aarch64__ |
| const int M = output->info()->tensor_shape().y(); |
| const int N = output->info()->tensor_shape().x(); |
| const int K = a->info()->tensor_shape().x(); |
| constexpr size_t workspace_alignment = 4096; |
| const struct CPUInfo ci = NEScheduler::get().cpu_info(); |
| #endif /* __aarch64__ */ |
| |
| #ifdef ARM_COMPUTE_AARCH64_V8_2 |
| if(ci.CPU == CPUTarget::A75_DOT) |
| { |
| // Configure matrix multiply kernel |
| GemmInterleaved<gemm_s8_12x8, int8_t, int32_t> gemm(&ci, M, N, K, false, false); |
| _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + workspace_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<NEGEMMLowpAArch64V8P4Kernel>(); |
| k->configure(a, b, output, &_workspace, 1.f, 1.f); |
| _mm_kernel = std::move(k); |
| _workspace.allocator()->allocate(); |
| } |
| else if(ci.CPU == CPUTarget::A55_DOT) |
| { |
| ARM_COMPUTE_ERROR_ON("WIP"); |
| } |
| else |
| #elif defined(ARM_COMPUTE_AARCH64_V8A) |
| if(ci.CPU == CPUTarget::A53) |
| { |
| switch(a->info()->data_type()) |
| { |
| case DataType::S8: |
| { |
| // Configure matrix multiply kernel |
| GemmInterleaved<gemm_s16_12x8, int8_t, int32_t> gemm(&ci, M, N, K, false, false); |
| _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + workspace_alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8)); |
| } |
| break; |
| case DataType::U8: |
| { |
| // Configure matrix multiply kernel |
| GemmInterleaved<gemm_u16_12x8, uint8_t, uint32_t> gemm(&ci, M, N, K, false, false); |
| _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + workspace_alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8)); |
| } |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Datatype not supported"); |
| } |
| |
| _memory_group.manage(&_workspace); |
| // Configure matrix multiplication kernel |
| auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpAArch64A53Kernel>(); |
| k->configure(a, b, output, &_workspace, 1.f, 1.f); |
| _mm_kernel = std::move(k); |
| _workspace.allocator()->allocate(); |
| } |
| else if(1) // Generic v8a kernel |
| { |
| switch(a->info()->data_type()) |
| { |
| case DataType::S8: |
| { |
| // Configure matrix multiply kernel |
| GemmInterleaved<gemm_s8_4x4, int8_t, int32_t> gemm(&ci, M, N, K, false, false); |
| _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + workspace_alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8)); |
| } |
| break; |
| case DataType::U8: |
| { |
| // Configure matrix multiply kernel |
| GemmInterleaved<gemm_u8_4x4, uint8_t, uint32_t> gemm(&ci, M, N, K, false, false); |
| _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + workspace_alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8)); |
| } |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Datatype not supported"); |
| } |
| _memory_group.manage(&_workspace); |
| // Configure matrix multiplication kernel |
| auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpAArch64Kernel>(); |
| 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 */ |
| { |
| // 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<NEGEMMInterleave4x4Kernel>(); |
| k->configure(a, &_tmp_a); |
| _mtx_a_reshape_kernel = std::move(k); |
| } |
| |
| // Configure transpose kernel |
| { |
| auto k = arm_compute::support::cpp14::make_unique<NEGEMMTranspose1xWKernel>(); |
| k->configure(b, &_tmp_b); |
| _mtx_b_reshape_kernel = std::move(k); |
| } |
| |
| // Configure matrix multiply kernel |
| { |
| auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpMatrixMultiplyKernel>(); |
| k->configure(&_tmp_a, &_tmp_b, output); |
| _mm_kernel = std::move(k); |
| } |
| |
| // Allocate tensors |
| _tmp_a.allocator()->allocate(); |
| _tmp_b.allocator()->allocate(); |
| } |
| } |
| |
| void NEGEMMLowpAssemblyMatrixMultiplyCore::run() |
| { |
| _memory_group.acquire(); |
| if(_mtx_a_reshape_kernel) |
| { |
| NEScheduler::get().schedule(_mtx_a_reshape_kernel.get(), Window::DimY); |
| } |
| |
| if(_mtx_b_reshape_kernel) |
| { |
| NEScheduler::get().schedule(_mtx_b_reshape_kernel.get(), Window::DimY); |
| } |
| |
| NEScheduler::get().schedule(_mm_kernel.get(), Window::DimY); |
| |
| _memory_group.release(); |
| } |