| /* |
| * 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; |
| |
| namespace |
| { |
| inline bool is_interleaved_transposed(int m, int n, int k, DataType data_type, bool reshape_b_only_on_first_run, GPUTarget gpu_target) |
| { |
| bool flag = true; |
| |
| if(gpu_target == GPUTarget::BIFROST) |
| { |
| // COMPMID-852 |
| if(k > 256 && m > 4 && data_type == DataType::F32 && reshape_b_only_on_first_run) |
| { |
| const float scale = k < 1024 ? 2.0f : 2.5f; |
| flag = (scale * n) > ((1.66f * n) + 38.4f); |
| } |
| else |
| { |
| flag = false; |
| } |
| } |
| |
| return flag; |
| } |
| } // namespace |
| |
| 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"); |
| |
| // 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; |
| |
| // Get the GPU target |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| |
| // Set the target for the kernels |
| _interleave_kernel.set_target(gpu_target); |
| _mm_kernel.set_target(gpu_target); |
| |
| // Arguments used by GEMMReshapeInfo |
| // If we pass the matrix A and matrix B reshaped to CLGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to CLGEMMReshapeInfo |
| // in order to know how the matrices have been reshaped |
| const int m = a->info()->dimension(1); |
| const int n = b->info()->dimension(0); |
| const int k = a->info()->dimension(0); |
| int mult_transpose1xW_width = 1; |
| int mult_interleave4x4_height = 1; |
| |
| if(gpu_target == GPUTarget::BIFROST) |
| { |
| mult_transpose1xW_width = 4; |
| mult_interleave4x4_height = 2; |
| } |
| |
| // Check if we need to reshape the matrix A and matrix B |
| _is_interleaved_transposed = is_interleaved_transposed(m, n, k, a->info()->data_type(), _reshape_b_only_on_first_run, gpu_target); |
| |
| if(_is_interleaved_transposed) |
| { |
| matrix_a = &_tmp_a; |
| matrix_b = &_tmp_b; |
| |
| // Manage intermediate buffers |
| _memory_group.manage(&_tmp_a); |
| _memory_group.manage(&_tmp_b); |
| |
| // _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel |
| |
| // Configure interleave kernel |
| _interleave_kernel.configure(a, &_tmp_a, mult_interleave4x4_height); |
| |
| // Configure transpose kernel |
| _transpose_kernel.configure(b, &_tmp_b, mult_transpose1xW_width); |
| } |
| |
| _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height)); |
| |
| 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(); |
| } |