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Diffstat (limited to 'src/runtime/cpu/operators/CpuGemm.cpp')
| -rw-r--r-- | src/runtime/cpu/operators/CpuGemm.cpp | 367 |
1 files changed, 0 insertions, 367 deletions
diff --git a/src/runtime/cpu/operators/CpuGemm.cpp b/src/runtime/cpu/operators/CpuGemm.cpp deleted file mode 100644 index bd3f231001..0000000000 --- a/src/runtime/cpu/operators/CpuGemm.cpp +++ /dev/null @@ -1,367 +0,0 @@ -/* - * Copyright (c) 2021 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 "src/runtime/cpu/operators/CpuGemm.h" - -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/runtime/NEON/NEScheduler.h" -#include "src/core/CPP/Validate.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/helpers/MemoryHelpers.h" -#include "src/runtime/cpu/utils/CpuAuxTensorHandler.h" - -using namespace arm_compute::experimental; -using namespace arm_compute::misc::shape_calculator; - -namespace arm_compute -{ -namespace cpu -{ -namespace -{ -cpu::AsmGemmInfo init_assembly_metadata(const GEMMInfo &info) -{ - cpu::AsmGemmInfo asm_info; - asm_info.method = cpu::AsmConvMethod::Im2Col; - asm_info.reinterpret_input_as_3d = info.reinterpret_input_as_3d(); - asm_info.depth_output_gemm3d = info.depth_output_gemm3d(); - asm_info.activation_info = info.activation_info(); - asm_info.fast_mode = info.fast_math(); - - return asm_info; -} -} // namespace - -void CpuGemm::configure(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, ITensorInfo *d, float alpha, float beta, const GEMMInfo &gemm_info) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, d); - ARM_COMPUTE_ERROR_THROW_ON(CpuGemm::validate(a, b, c, d, alpha, beta, gemm_info)); - - const cpu::AsmGemmInfo asm_info = init_assembly_metadata(gemm_info); - const bool is_c_bias = gemm_info.reshape_b_only_on_first_run(); - bool run_optimised = bool(cpu::CpuGemmAssemblyDispatch::validate(a, b, (is_c_bias) ? c : nullptr, d, asm_info)); - - // Check if we need to reshape the matrix B only on the first run - _is_prepared = false; - _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); - _run_vector_matrix_multiplication = a->dimension(1) < 2; - _run_alpha_scale = alpha != 1.f; - _run_bias_addition = c != nullptr && gemm_info.reshape_b_only_on_first_run(); - _run_addition = beta != 0 && c != nullptr && !gemm_info.reshape_b_only_on_first_run(); - _run_activation = gemm_info.activation_info().enabled() && (!run_optimised || (run_optimised - && !cpu::CpuGemmAssemblyDispatch::is_activation_supported(gemm_info.activation_info()))); - - if(run_optimised) - { - const ITensorInfo *c_to_use = is_c_bias ? c : nullptr; - _asm_glue = std::make_unique<cpu::CpuGemmAssemblyDispatch>(); - _asm_glue->configure(a, b, c_to_use, d, asm_info); - ARM_COMPUTE_ERROR_ON(!_asm_glue->is_configured()); - - auto asm_mem_req = _asm_glue->workspace(); - _aux_mem[AsmGemmWorkspace] = asm_mem_req[AsmGemmWorkspace]; - _aux_mem[Pretraspose] = asm_mem_req[Pretraspose]; - - // Scale product by alpha - if(_run_alpha_scale) - { - _alpha_scale_func = std::make_unique<cpu::CpuActivation>(); - _alpha_scale_func->configure(d, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, alpha, 0.f)); - } - } - else - { - // Pick output tensor in case bias addition should be performed - ITensorInfo *gemm_output_to_use = (_run_bias_addition) ? &_tmp_d : d; - - _mm_kernel = std::make_unique<cpu::kernels::CpuGemmMatrixMultiplyKernel>(); - - // Select between GEMV and GEMM - if(_run_vector_matrix_multiplication) - { - // Configure the matrix multiply kernel - _mm_kernel->configure(a, b, gemm_output_to_use, alpha, false); - } - else - { - const int m = a->dimension(1); - const int n = b->dimension(0); - const int k = a->dimension(0); - - // Configure interleave kernel - _interleave_kernel = std::make_unique<cpu::kernels::CpuGemmInterleave4x4Kernel>(); - _interleave_kernel->configure(a, &_tmp_a); - _aux_mem[InterleavedLHS] = MemoryInfo(offset_int_vec(InterleavedLHS), MemoryLifetime::Temporary, _tmp_a.total_size()); - - // Configure transpose kernel - _transpose_kernel = std::make_unique<cpu::kernels::CpuGemmTranspose1xWKernel>(); - _transpose_kernel->configure(b, &_tmp_b); - _aux_mem[TransposedRHS] = MemoryInfo(offset_int_vec(TransposedRHS), MemoryLifetime::Persistent, _tmp_b.total_size()); - - // Configure matrix multiplication kernel - _mm_kernel->configure(&_tmp_a, &_tmp_b, gemm_output_to_use, alpha, true, GEMMReshapeInfo(m, n, k)); - } - - if(_run_bias_addition) - { - _add_bias = std::make_unique<cpu::CpuAdd>(); - _add_bias->configure(gemm_output_to_use, c, d, ConvertPolicy::SATURATE); - _aux_mem[TempResult] = MemoryInfo(offset_int_vec(TempResult), MemoryLifetime::Temporary, _tmp_d.total_size()); - } - } - - // Configure matrix addition kernel - if(_run_addition) - { - _ma_kernel = std::make_unique<cpu::kernels::CpuGemmMatrixAdditionKernel>(); - _ma_kernel->configure(c, d, beta); - } - - // Configure activation - if(_run_activation) - { - _activation_func = std::make_unique<cpu::CpuActivation>(); - _activation_func->configure(d, nullptr, gemm_info.activation_info()); - } -} - -Status CpuGemm::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *d, float alpha, float beta, const GEMMInfo &gemm_info) -{ - ARM_COMPUTE_UNUSED(alpha); - const bool is_c_bias = gemm_info.reshape_b_only_on_first_run(); - - ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(a); - ARM_COMPUTE_RETURN_ERROR_ON_CPU_BF16_UNSUPPORTED(a); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::BFLOAT16, DataType::F16, DataType::F32); - 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(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"); - if(a->data_type() != DataType::BFLOAT16) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, d); - } - - if(c != nullptr && !is_c_bias) - { - ARM_COMPUTE_RETURN_ERROR_ON(gemm_info.depth_output_gemm3d() != 0); - ARM_COMPUTE_RETURN_ERROR_ON(gemm_info.reinterpret_input_as_3d()); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(c, d); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != c->dimension(1), "The C matrix must have the same number of rows as the matrix A"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(b->dimension(0) != c->dimension(0), "The C matrix must have the same number of columns as the matrix B"); - } - - if(d->total_size() != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON(b->dimension(0) != d->dimension(0)); - if(gemm_info.depth_output_gemm3d() != 0) - { - if(gemm_info.reinterpret_input_as_3d()) - { - ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != d->dimension(1)); - ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(2) != d->dimension(2)); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != d->dimension(1) * d->dimension(2)); - } - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != d->dimension(1)); - } - } - - // Check if we need to run the optimized assembly kernel - cpu::AsmGemmInfo asm_info = init_assembly_metadata(gemm_info); - const bool run_optimised = bool(cpu::CpuGemmAssemblyDispatch::validate(a, b, is_c_bias ? c : nullptr, d, asm_info)); - - if(!run_optimised) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.reinterpret_input_as_3d(), "CpuGemm cannot reinterpret the input tensor as 3D"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.depth_output_gemm3d() != 0, "CpuGemm cannot reinterpret the output tensor as 3D"); - - // Check if the first input tensor is a vector. - const bool run_vector_matrix_multiplication = a->dimension(1) < 2; - // Check if we need to reshape the matrix A and matrix B - const bool run_interleave_transpose = !run_vector_matrix_multiplication && !(gemm_info.reshape_b_only_on_first_run()); - - // Arguments used by GEMMReshapeInfo - // If we pass the matrix A and matrix B reshaped to CpuGemmMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to GEMMReshapeInfo - // in order to know how the matrices have been reshaped - const int m = 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 GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, gemm_info.depth_output_gemm3d()); - - const ITensorInfo *matrix_a_info = a; - const ITensorInfo *matrix_b_info = b; - - TensorInfo tmp_a_info{}; - TensorInfo tmp_b_info{}; - TensorInfo tmp_output_info = *d->clone(); - - 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(cpu::kernels::CpuGemmInterleave4x4Kernel::validate(a, &tmp_a_info)); - - // 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(cpu::kernels::CpuGemmTranspose1xWKernel::validate(b, &tmp_b_info)); - } - - // Validate matrix multiply - auto_init_if_empty(tmp_output_info, matrix_a_info->clone()->set_tensor_shape(compute_mm_shape(*matrix_a_info, *matrix_b_info, run_interleave_transpose, reshape_info))); - ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, &tmp_output_info, alpha, run_interleave_transpose, reshape_info)); - - if(c != nullptr && gemm_info.reshape_b_only_on_first_run()) - { - ARM_COMPUTE_RETURN_ON_ERROR(cpu::CpuAdd::validate(&tmp_output_info, c, d, ConvertPolicy::SATURATE)); - } - } - - // Validate matrix addition kernel - if(beta != 0 && c != nullptr && !is_c_bias) - { - ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmMatrixAdditionKernel::validate(c, d, beta)); - } - - // Validate activation - const ActivationLayerInfo &activation = gemm_info.activation_info(); - if(activation.enabled()) - { - ARM_COMPUTE_RETURN_ON_ERROR(cpu::CpuActivation::validate(d, nullptr, activation)); - } - - return Status{}; -} - -void CpuGemm::run(ITensorPack &tensors) -{ - prepare(tensors); - - auto a = tensors.get_const_tensor(ACL_SRC_0); - auto b = tensors.get_const_tensor(ACL_SRC_1); - auto c = tensors.get_const_tensor(ACL_SRC_2); - auto d = tensors.get_tensor(ACL_DST); - - if(_asm_glue->is_configured()) - { - // Pass c to asm dispatch only if it's the bias tensor - ITensorPack asm_pack = tensors; - asm_pack.add_const_tensor(ACL_SRC_2, (_reshape_b_only_on_first_run) ? c : nullptr); - _asm_glue->run(asm_pack); - if(_run_alpha_scale) - { - ITensorPack pack{ { ACL_SRC, d }, { ACL_DST, d } }; - _alpha_scale_func->run(pack); - } - } - else - { - CpuAuxTensorHandler interleaved_a(offset_int_vec(InterleavedLHS), _tmp_a, tensors, true); - CpuAuxTensorHandler transposed_b(offset_int_vec(TransposedRHS), _tmp_b, tensors, true); - CpuAuxTensorHandler temp_d(offset_int_vec(TempResult), _tmp_d, tensors, true); - - ITensorPack mm_pack{ { ACL_SRC_0, a }, { ACL_SRC_1, b }, { ACL_DST, (_run_bias_addition) ? temp_d.get() : d } }; - if(!_run_vector_matrix_multiplication) - { - // Run interleave kernel - ITensorPack interleave_pack{ { ACL_SRC, a }, { ACL_DST, interleaved_a.get() } }; - NEScheduler::get().schedule_op(_interleave_kernel.get(), Window::DimY, _interleave_kernel->window(), interleave_pack); - - if(!_reshape_b_only_on_first_run) - { - // Run transpose kernel - ITensorPack transpose_pack{ { ACL_SRC, b }, { ACL_DST, transposed_b.get() } }; - NEScheduler::get().schedule_op(_transpose_kernel.get(), Window::DimY, _transpose_kernel->window(), transpose_pack); - } - - // Use reshaped matrices - mm_pack.add_const_tensor(ACL_SRC_0, interleaved_a.get()); - mm_pack.add_const_tensor(ACL_SRC_1, transposed_b.get()); - } - - NEScheduler::get().schedule_op(_mm_kernel.get(), _run_vector_matrix_multiplication ? Window::DimX : Window::DimY, _mm_kernel->window(), mm_pack); - - // Run bias addition kernel - if(_run_bias_addition) - { - ITensorPack pack{ { ACL_SRC_0, temp_d.get() }, { ACL_SRC_1, c }, { ACL_DST, d } }; - _add_bias->run(pack); - } - } - - // Run matrix addition kernel - if(_run_addition) - { - ITensorPack c_add_pack{ { ACL_SRC, c }, { ACL_DST, d } }; - NEScheduler::get().schedule_op(_ma_kernel.get(), Window::DimY, _ma_kernel->window(), c_add_pack); - } - - // Run activation function - if(_run_activation) - { - ITensorPack pack{ { ACL_SRC, d }, { ACL_DST, d } }; - _activation_func->run(pack); - } -} - -void CpuGemm::prepare(ITensorPack &tensors) -{ - if(!_is_prepared) - { - if(_asm_glue->is_configured()) - { - _asm_glue->prepare(tensors); - } - else if(_reshape_b_only_on_first_run && !_run_vector_matrix_multiplication) - { - const ITensor *b = tensors.get_const_tensor(ACL_SRC_1); - ITensor *b_aux = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(TransposedRHS))); - ARM_COMPUTE_ERROR_ON_NULLPTR(b, b_aux); - - CpuAuxTensorHandler transposed_b(_tmp_b, *b_aux); - ITensorPack transpose_pack{ { ACL_SRC, b }, { ACL_DST, transposed_b.get() } }; - NEScheduler::get().schedule_op(_transpose_kernel.get(), Window::DimY, _transpose_kernel->window(), transpose_pack); - } - _is_prepared = true; - } -} - -experimental::MemoryRequirements CpuGemm::workspace() const -{ - return _aux_mem; -} -} // namespace cpu -} // namespace arm_compute |