| /* |
| * 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/Error.h" |
| #include "arm_compute/core/GPUTarget.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| #include "arm_compute/runtime/ITensorAllocator.h" |
| |
| using namespace arm_compute; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| 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_is_in(gpu_target, GPUTarget::G52, GPUTarget::G52LIT, GPUTarget::G71, GPUTarget::G72, GPUTarget::G76)) |
| { |
| // COMPMID-852 |
| if(k > 256 && m > 4 && is_data_type_float(data_type) && reshape_b_only_on_first_run) |
| { |
| constexpr float alpha = 3.2f; |
| constexpr float fact0 = 1.51f; |
| constexpr float fact1 = 1.66f; |
| constexpr float ops = 12.0f; |
| const float scale = k > 1024 ? 1.07f : 1.0f; |
| flag = alpha + ((n * fact0) / ops) < ((fact1 * n * scale) / ops); |
| } |
| else |
| { |
| flag = false; |
| } |
| } |
| else |
| { |
| // We reshape the matrices only if we do not have the vector-by-matrix case and we reshape the matrix B only once |
| flag = m != 1 && reshape_b_only_on_first_run; |
| } |
| |
| 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(), |
| _original_b(nullptr), |
| _is_interleaved_transposed(false), |
| _run_addition(false), |
| _reshape_b_only_on_first_run(false), |
| _is_prepared(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_NULLPTR(a, b, output); |
| |
| // Perform validation step |
| ARM_COMPUTE_ERROR_THROW_ON(validate(a->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), alpha, beta, gemm_info)); |
| |
| // 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(); |
| _is_prepared = gemm_info.retain_internal_weights(); |
| _original_b = b; |
| |
| 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 |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const int m = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1); |
| const int n = b->info()->dimension(0); |
| const int k = a->info()->dimension(0); |
| const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| int mult_transpose1xW_width = 1; |
| int mult_interleave4x4_height = 1; |
| |
| if(get_arch_from_target(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 is set, force reinterpret_input_as_3d to be false as the output of CLGEMMInterleaveKernel will be 2D |
| if(_is_interleaved_transposed) |
| { |
| reinterpret_input_as_3d = false; |
| |
| matrix_a = &_tmp_a; |
| matrix_b = &_tmp_b; |
| |
| // Manage intermediate buffers |
| _memory_group.manage(&_tmp_a); |
| if(!_reshape_b_only_on_first_run) |
| { |
| _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, gemm_info.reinterpret_input_as_3d()); |
| |
| // Configure transpose kernel |
| _transpose_kernel.configure(b, &_tmp_b, mult_transpose1xW_width); |
| } |
| |
| // Configure and tune matrix multiply kernel |
| _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, |
| mult_transpose1xW_width, mult_interleave4x4_height, |
| depth_output_gemm3d, reinterpret_input_as_3d), |
| gemm_info.fp_mixed_precision()); |
| CLScheduler::get().tune_kernel_static(_mm_kernel); |
| |
| if(_is_interleaved_transposed) |
| { |
| // Allocate intermediate tensors |
| _tmp_a.allocator()->allocate(); |
| if(!_reshape_b_only_on_first_run) |
| { |
| _tmp_b.allocator()->allocate(); |
| } |
| } |
| |
| // Configure matrix addition kernel |
| if(beta != 0 && c != nullptr) |
| { |
| _ma_kernel.configure(c, output, beta); |
| _run_addition = true; |
| } |
| } |
| |
| Status CLGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) |
| { |
| ARM_COMPUTE_UNUSED(alpha); |
| ARM_COMPUTE_UNUSED(output); |
| |
| // Check if we need to reshape the matrix B only on the first run |
| const bool reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); |
| |
| const ITensorInfo *matrix_a_info = a; |
| const ITensorInfo *matrix_b_info = b; |
| |
| TensorInfo tmp_a_info{}; |
| TensorInfo tmp_b_info{}; |
| |
| // Get the GPU target |
| const GPUTarget gpu_target = CLScheduler::get().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 |
| bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| const int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); |
| const int n = b->dimension(0); |
| const int k = a->dimension(0); |
| int mult_transpose1xW_width = 1; |
| int mult_interleave4x4_height = 1; |
| const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| |
| if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST) |
| { |
| mult_transpose1xW_width = 4; |
| mult_interleave4x4_height = 2; |
| } |
| |
| // Check if we need to reshape the matrix A and matrix B |
| const bool run_interleave_transpose = is_interleaved_transposed(m, n, k, a->data_type(), reshape_b_only_on_first_run, gpu_target); |
| |
| // if _is_interleaved_transposed is set, force reinterpret_input_as_3d to be false as the output of CLGEMMInterleaveKernel will be 2D |
| if(run_interleave_transpose) |
| { |
| reinterpret_input_as_3d = false; |
| } |
| |
| const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, reinterpret_input_as_3d); |
| |
| if(run_interleave_transpose) |
| { |
| matrix_a_info = &tmp_a_info; |
| matrix_b_info = &tmp_b_info; |
| |
| // Validate interleave kernel |
| auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_interleaved_shape(*a, mult_interleave4x4_height, gemm_info.reinterpret_input_as_3d()))); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMInterleave4x4Kernel::validate(a, &tmp_a_info, mult_interleave4x4_height, gemm_info.reinterpret_input_as_3d())); |
| |
| // Validate transpose kernel |
| auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_transpose1xW_with_element_size_shape(*b, mult_transpose1xW_width))); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMTranspose1xWKernel::validate(b, &tmp_b_info, mult_transpose1xW_width)); |
| } |
| |
| // Validate matrix multiply |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, output, alpha, run_interleave_transpose, reshape_info, gpu_target, gemm_info.fp_mixed_precision())); |
| |
| if(beta != 0 && c != nullptr) |
| { |
| // Validate matrix addition kernel |
| ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixAdditionKernel::validate(c, output, beta)); |
| } |
| |
| return Status{}; |
| } |
| |
| void CLGEMM::run() |
| { |
| prepare(); |
| |
| _memory_group.acquire(); |
| |
| if(_is_interleaved_transposed) |
| { |
| // Run interleave kernel |
| CLScheduler::get().enqueue(_interleave_kernel, false); |
| |
| 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(); |
| } |
| |
| void CLGEMM::prepare() |
| { |
| if(!_is_prepared) |
| { |
| if(_is_interleaved_transposed && _reshape_b_only_on_first_run) |
| { |
| // Run transpose kernel and mark original weights tensor as unused |
| _tmp_b.allocator()->allocate(); |
| CLScheduler::get().enqueue(_transpose_kernel, false); |
| _original_b->mark_as_unused(); |
| } |
| CLScheduler::get().queue().finish(); |
| _is_prepared = true; |
| } |
| } |