| /* |
| * Copyright (c) 2017, 2018 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/CLGEMM.h" |
| |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/CL/kernels/CLGEMMInterleave4x4Kernel.h" |
| #include "arm_compute/core/CL/kernels/CLGEMMMatrixAdditionKernel.h" |
| #include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h" |
| #include "arm_compute/core/CL/kernels/CLGEMMTranspose1xWKernel.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" |
| #include "arm_compute/runtime/ITensorAllocator.h" |
| |
| using namespace arm_compute; |
| |
| CLGEMM::CLGEMM(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false), |
| _is_first_run(true), _reshape_b_only_on_first_run(false) |
| { |
| } |
| |
| void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output); |
| 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"); |
| |
| if(c != nullptr) |
| { |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, c); |
| ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(1) != c->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) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix B"); |
| ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(0) != output->info()->dimension(0), "The C matrix must have the same number of rows as the output matrix"); |
| ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(1) != output->info()->dimension(1), "The C matrix must have the same number of columns as the output matrix"); |
| } |
| |
| 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"); |
| |
| // If the input tensor has less than 16 rows, we run a special version of GEMM without reshaping the input tensors |
| // For Bifrost architectures we do not reshape the input matrices |
| _is_interleaved_transposed = (a->info()->dimension(1) > 16 && CLScheduler::get().target() != GPUTarget::BIFROST); |
| |
| // Check if we need to reshape the matrix B only on the first run |
| _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); |
| |
| const ICLTensor *matrix_a = a; |
| const ICLTensor *matrix_b = b; |
| |
| // Set the target for the matrix multiply kernel |
| _mm_kernel.set_target(CLScheduler::get().target()); |
| |
| if(_is_interleaved_transposed) |
| { |
| matrix_a = &_tmp_a; |
| matrix_b = &_tmp_b; |
| |
| // _tmp_a and _tmp_n will be auto configured in _interleave_kernel and in _transpose_kernel |
| |
| // Configure interleave kernel |
| _interleave_kernel.configure(a, &_tmp_a); |
| |
| // Configure transpose kernel |
| _transpose_kernel.configure(b, &_tmp_b); |
| |
| // Manage intermediate buffers |
| _memory_group.manage(&_tmp_a); |
| _memory_group.manage(&_tmp_b); |
| } |
| |
| _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed); |
| |
| if(_is_interleaved_transposed) |
| { |
| // Allocate intermediate tensors |
| _tmp_a.allocator()->allocate(); |
| _tmp_b.allocator()->allocate(); |
| } |
| |
| // Configure matrix addition kernel |
| if(beta != 0 && c != nullptr) |
| { |
| _ma_kernel.configure(c, output, beta); |
| _run_addition = true; |
| } |
| } |
| |
| void CLGEMM::run() |
| { |
| _memory_group.acquire(); |
| |
| if(_is_interleaved_transposed) |
| { |
| // Run interleave kernel |
| CLScheduler::get().enqueue(_interleave_kernel, false); |
| |
| if(_is_first_run) |
| { |
| // Run transpose kernel |
| CLScheduler::get().enqueue(_transpose_kernel, false); |
| |
| _is_first_run = false; |
| } |
| else if(!_reshape_b_only_on_first_run) |
| { |
| // Run transpose kernel |
| CLScheduler::get().enqueue(_transpose_kernel, false); |
| } |
| } |
| |
| // Run matrix multiply kernel |
| CLScheduler::get().enqueue(_mm_kernel, !_run_addition); |
| |
| // Run matrix addition kernel |
| if(_run_addition) |
| { |
| CLScheduler::get().enqueue(_ma_kernel); |
| } |
| |
| _memory_group.release(); |
| } |