| /* |
| * 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/CLGEMMLowpMatrixMultiplyCore.h" |
| |
| #include "arm_compute/core/CL/ICLTensor.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/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| |
| using namespace arm_compute; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| namespace |
| { |
| inline bool is_interleaved_transposed(int m, int n, int k, bool reshape_b_only_on_first_run, GPUTarget gpu_target) |
| { |
| bool flag = true; |
| |
| if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX)) |
| { |
| // COMPMID-852 |
| if(k > 256 && m > 4 && reshape_b_only_on_first_run) |
| { |
| flag = ((0.72f + n * 0.10766f) < (n * 0.1284f)); |
| } |
| else |
| { |
| flag = false; |
| } |
| } |
| |
| return flag; |
| } |
| } // namespace |
| |
| CLGEMMLowpMatrixMultiplyCore::CLGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _mm_kernel(), _mtx_a_reshape_kernel(), _mtx_b_reshape_kernel(), _mtx_a_reduction_kernel(), _mtx_b_reduction_kernel(), _offset_contribution_kernel(), |
| _vector_sum_col(), _vector_sum_row(), _tmp_a(), _tmp_b(), _a_offset(0), _b_offset(0), _is_interleaved_transposed(true), _is_first_run(true), _reshape_b_only_on_first_run(false) |
| { |
| } |
| |
| void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor *b, ICLTensor *output, const GEMMInfo &gemm_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); |
| ARM_COMPUTE_UNUSED(gemm_info); |
| ARM_COMPUTE_ERROR_THROW_ON(CLGEMMLowpMatrixMultiplyCore::validate(a->info(), b->info(), output->info(), gemm_info)); |
| |
| _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); |
| _a_offset = a->info()->quantization_info().offset; |
| _b_offset = b->info()->quantization_info().offset; |
| |
| // Get the GPU target |
| const GPUTarget gpu_target = CLScheduler::get().target(); |
| |
| // Set the target for the kernels |
| _mtx_a_reshape_kernel.set_target(gpu_target); |
| _mm_kernel.set_target(gpu_target); |
| |
| const ICLTensor *matrix_a = a; |
| const ICLTensor *matrix_b = b; |
| |
| // 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); |
| constexpr int mult_transpose1xW_width = 1; |
| constexpr int mult_interleave4x4_height = 1; |
| |
| // Check if we need to reshape the matrix A and matrix B |
| _is_interleaved_transposed = is_interleaved_transposed(m, n, k, _reshape_b_only_on_first_run, gpu_target); |
| |
| if(_is_interleaved_transposed) |
| { |
| matrix_a = &_tmp_a; |
| matrix_b = &_tmp_b; |
| |
| _memory_group.manage(&_tmp_a); |
| if(!_reshape_b_only_on_first_run) |
| { |
| _memory_group.manage(&_tmp_b); |
| } |
| |
| // Configure interleave kernel |
| _mtx_a_reshape_kernel.configure(a, &_tmp_a, mult_interleave4x4_height); |
| |
| // Configure transpose kernel |
| _mtx_b_reshape_kernel.configure(b, &_tmp_b, mult_transpose1xW_width); |
| } |
| |
| // Configure matrix multiply kernel |
| _mm_kernel.configure(matrix_a, matrix_b, output, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height)); |
| |
| // Initialize matrix B reduction kernel only if _a_offset is not equal to 0 |
| if(_a_offset != 0) |
| { |
| TensorInfo info_vector_sum_col(compute_reductionA_shape(*b->info()), 1, DataType::S32); |
| _vector_sum_col.allocator()->init(info_vector_sum_col); |
| if(!_reshape_b_only_on_first_run) |
| { |
| _memory_group.manage(&_vector_sum_col); |
| } |
| |
| // Configure Matrix B reduction kernel |
| _mtx_b_reduction_kernel.configure(b, &_vector_sum_col); |
| } |
| |
| // Initialize Matrix A reduction kernel only if _b_offset is not equal to 0 |
| if(_b_offset != 0) |
| { |
| TensorInfo info_vector_sum_row(compute_reductionB_shape(*a->info()), 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); |
| } |
| |
| // 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(_is_interleaved_transposed) |
| { |
| _tmp_a.allocator()->allocate(); |
| _tmp_b.allocator()->allocate(); |
| } |
| |
| if(_a_offset != 0) |
| { |
| _vector_sum_col.allocator()->allocate(); |
| } |
| |
| if(_b_offset != 0) |
| { |
| _vector_sum_row.allocator()->allocate(); |
| } |
| } |
| |
| Status CLGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output, const GEMMInfo &gemm_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((a)->dimension(0) != (b)->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_RETURN_ERROR_ON_MSG((a)->dimension(1) != (output)->dimension(1), |
| "The output matrix must have the same number of rows as the matrix A"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((b)->dimension(0) != (output)->dimension(0), |
| "The output matrix must have the same number of columns as the matrix B"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported"); |
| |
| int32_t a_offset = a->quantization_info().offset; |
| int32_t b_offset = b->quantization_info().offset; |
| |
| const int m = a->dimension(1); |
| const int n = b->dimension(0); |
| const int k = a->dimension(0); |
| constexpr int mult_transpose1xW_width = 1; |
| constexpr int mult_interleave4x4_height = 1; |
| const GEMMReshapeInfo reshape_info(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height); |
| |
| bool reshape_matrices = is_interleaved_transposed(m, n, k, gemm_info.reshape_b_only_on_first_run(), CLScheduler::get().target()); |
| |
| if(reshape_matrices) |
| { |
| TensorInfo info_a(compute_interleaved_shape(*a, mult_interleave4x4_height), 1, a->data_type()); |
| TensorInfo info_b(compute_transpose1xW_with_element_size_shape(*b, mult_transpose1xW_width), 1, b->data_type()); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMInterleave4x4Kernel::validate(a, &info_a, mult_interleave4x4_height)); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMTranspose1xWKernel::validate(b, &info_b, mult_transpose1xW_width)); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyKernel::validate(&info_a, &info_b, output, reshape_matrices, reshape_info)); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixMultiplyKernel::validate(a, b, output, reshape_matrices, reshape_info)); |
| } |
| |
| TensorInfo info_vector_sum_col, info_vector_sum_row; |
| |
| // Validate matrix B reduction kernel only if _a_offset is not equal to 0 |
| if(a_offset != 0) |
| { |
| info_vector_sum_col = TensorInfo(compute_reductionA_shape(*b), 1, DataType::S32); |
| |
| // Configure Matrix B reduction kernel |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixBReductionKernel::validate(b, &info_vector_sum_col)); |
| } |
| |
| // Validate Matrix A reduction kernel only if _b_offset is not equal to 0 |
| if(b_offset != 0) |
| { |
| info_vector_sum_row = TensorInfo(compute_reductionB_shape(*a), 1, DataType::S32); |
| |
| // Configure matrix A reduction kernel |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpMatrixAReductionKernel::validate(a, &info_vector_sum_row)); |
| } |
| |
| // Validate offset contribution kernel |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOffsetContributionKernel::validate(output, |
| a_offset == 0 ? nullptr : &info_vector_sum_col, |
| b_offset == 0 ? nullptr : &info_vector_sum_row, |
| a_offset, b_offset)); |
| |
| return Status{}; |
| } |
| |
| void CLGEMMLowpMatrixMultiplyCore::run() |
| { |
| _memory_group.acquire(); |
| |
| if(_is_interleaved_transposed) |
| { |
| // Run reshape matrix A |
| CLScheduler::get().enqueue(_mtx_a_reshape_kernel, false); |
| |
| if(_is_first_run || !_reshape_b_only_on_first_run) |
| { |
| // Run reshape matrix B |
| CLScheduler::get().enqueue(_mtx_b_reshape_kernel, false); |
| } |
| } |
| |
| // Note: if _reshape_b_only_on_first_run = true, the reduction kernel can be executed only once |
| if(_is_first_run || !_reshape_b_only_on_first_run) |
| { |
| // Run matrix B reduction kernel only if _a_offset is not equal to 0 |
| if(_a_offset != 0) |
| { |
| CLScheduler::get().enqueue(_mtx_b_reduction_kernel, false); |
| } |
| } |
| |
| // Run matrix multiply |
| CLScheduler::get().enqueue(_mm_kernel, false); |
| |
| // Run matrix A reduction kernel only if _b_offset is not equal to 0 |
| if(_b_offset != 0) |
| { |
| CLScheduler::get().enqueue(_mtx_a_reduction_kernel, false); |
| } |
| |
| // Run offset contribution kernel |
| CLScheduler::get().enqueue(_offset_contribution_kernel, true); |
| |
| _memory_group.release(); |
| |
| _is_first_run = false; |
| } |