diff options
Diffstat (limited to 'src/runtime/NEON/functions/NEGEMM.cpp')
| -rw-r--r-- | src/runtime/NEON/functions/NEGEMM.cpp | 418 |
1 files changed, 88 insertions, 330 deletions
diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp index 7318c3e492..934a8250cc 100644 --- a/src/runtime/NEON/functions/NEGEMM.cpp +++ b/src/runtime/NEON/functions/NEGEMM.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -23,382 +23,140 @@ */ #include "arm_compute/runtime/NEON/functions/NEGEMM.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/ITensor.h" +#include "arm_compute/core/ITensorPack.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/NEON/NEScheduler.h" -#include "arm_compute/runtime/TensorAllocator.h" -#include "src/core/CPP/Validate.h" -#include "src/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h" -#include "src/core/NEON/kernels/NEGEMMMatrixAdditionKernel.h" -#include "src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h" -#include "src/core/NEON/kernels/NEGEMMTranspose1xWKernel.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.h" +#include "arm_compute/runtime/MemoryGroup.h" +#include "arm_compute/runtime/Tensor.h" -#include <cmath> +#include "src/core/CPP/Validate.h" +#include "src/core/helpers/MemoryHelpers.h" +#include "src/cpu/operators/CpuGemm.h" -using namespace arm_compute::misc::shape_calculator; +using namespace arm_compute::experimental; namespace arm_compute { -namespace +struct NEGEMM::Impl { -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(); + MemoryGroup memory_group{}; + IWeightsManager *weights_manager{nullptr}; - return asm_info; -} -} // namespace + std::unique_ptr<cpu::CpuGemm> op{nullptr}; + + const ITensor *original_b{nullptr}; + bool is_prepared{false}; + + ITensorPack run_pack{}; + ITensorPack prep_pack{}; + WorkspaceData<Tensor> workspace{}; + experimental::MemoryRequirements aux_mem_req{}; +}; NEGEMM::NEGEMM(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager) - : _memory_group(memory_manager), _weights_manager(weights_manager), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _asm_glue(std::make_unique<cpu::CpuGemmAssemblyDispatch>()), _ma_kernel(), - _alpha_scale_func(nullptr), _add_bias(), _activation_func(), _tmp_a(), _tmp_b(), _tmp_d(), _original_b(nullptr), _run_vector_matrix_multiplication(false), _run_alpha_scale(false), - _run_addition(false), _run_bias_addition(false), _run_activation(false), _reshape_b_only_on_first_run(false), _is_prepared(false) + : _impl(std::make_unique<Impl>()) { + _impl->memory_group = MemoryGroup(std::move(memory_manager)); + _impl->weights_manager = weights_manager; } NEGEMM::~NEGEMM() = default; -void NEGEMM::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *d, float alpha, float beta, const GEMMInfo &gemm_info) +void NEGEMM::configure(const ITensor *a, + const ITensor *b, + const ITensor *c, + ITensor *d, + float alpha, + float beta, + const GEMMInfo &gemm_info) { - ARM_COMPUTE_ERROR_THROW_ON(NEGEMM::validate(a->info(), b->info(), (c != nullptr) ? c->info() : nullptr, d->info(), 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->info(), b->info(), (is_c_bias && c != nullptr) ? c->info() : nullptr, d->info(), asm_info)); + ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, d); + ARM_COMPUTE_ERROR_THROW_ON(cpu::CpuGemm::validate(a->info(), b->info(), (c != nullptr) ? c->info() : nullptr, + d->info(), alpha, beta, gemm_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->info()->dimension(1) < 2; - _original_b = b; - _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()))); + _impl->is_prepared = false; + _impl->original_b = b; + _impl->op = std::make_unique<cpu::CpuGemm>(); - if(run_optimised) + // Make the B matrix dynamic values. + auto b_info_to_use = b->info()->clone(); + if (!gemm_info.reshape_b_only_on_first_run()) { - const ITensor *c_to_use = is_c_bias ? c : nullptr; - const ITensorInfo *c_info_to_use = c_to_use != nullptr ? c_to_use->info() : nullptr; - _asm_glue->configure(a->info(), b->info(), c_info_to_use, d->info(), asm_info); - ARM_COMPUTE_ERROR_ON(!_asm_glue->is_configured()); - - _asm_glue_tensors = - { - { ACL_SRC_0, a }, - { ACL_SRC_1, b }, - { ACL_SRC_2, c_to_use }, - { ACL_DST, d }, - }; - - // Scale product by alpha - if(_run_alpha_scale) - { - _alpha_scale_func.configure(d, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, alpha, 0.f)); - } + b_info_to_use->set_are_values_constant(false); } - else - { - // Pick output tensor in case bias addition should be performed - ITensor *gemm_output_to_use = d; - if(_run_bias_addition) - { - gemm_output_to_use = &_tmp_d; - _memory_group.manage(&_tmp_d); - } - - _mm_kernel = std::make_unique<NEGEMMMatrixMultiplyKernel>(); - - // 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 - { - TensorShape shape_tmp_a = a->info()->tensor_shape(); - TensorShape shape_tmp_b = b->info()->tensor_shape(); - - shape_tmp_a.set(0, a->info()->dimension(0) * 4); - shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.0f)); - - const unsigned int transpose_w = 16 / data_size_from_type(b->info()->data_type()); - shape_tmp_b.set(0, b->info()->dimension(1) * transpose_w); - shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / static_cast<float>(transpose_w))); - - TensorInfo info_a = a->info()->clone()->set_tensor_shape(shape_tmp_a).set_is_resizable(true); - TensorInfo info_b = b->info()->clone()->set_tensor_shape(shape_tmp_b).set_is_resizable(true); - - _tmp_a.allocator()->init(info_a); - _tmp_b.allocator()->init(info_b); - - // Manage intermediate buffers - _memory_group.manage(&_tmp_a); - if(!_reshape_b_only_on_first_run) - { - _memory_group.manage(&_tmp_b); - } - int m = a->info()->dimension(1); - int n = b->info()->dimension(0); - int k = a->info()->dimension(0); + _impl->op->configure(a->info(), b_info_to_use.get(), (c != nullptr) ? c->info() : nullptr, d->info(), alpha, beta, + gemm_info); - // Configure interleave kernel - _interleave_kernel = std::make_unique<NEGEMMInterleave4x4Kernel>(); - _interleave_kernel->configure(a, &_tmp_a); - - // Configure transpose kernel - _transpose_kernel = std::make_unique<NEGEMMTranspose1xWKernel>(); - _transpose_kernel->configure(b, &_tmp_b); - - // Configure matrix multiplication kernel - _mm_kernel->configure(&_tmp_a, &_tmp_b, gemm_output_to_use, alpha, true, GEMMReshapeInfo(m, n, k)); - - // Allocate once the all configure methods have been called - _tmp_a.allocator()->allocate(); - if(!_reshape_b_only_on_first_run) - { - _tmp_b.allocator()->allocate(); - } - } - - if(_run_bias_addition) - { - _add_bias.configure(gemm_output_to_use, c, d, ConvertPolicy::SATURATE); - _tmp_d.allocator()->allocate(); - } - } - - // Configure matrix addition kernel - if(_run_addition) - { - _ma_kernel = std::make_unique<NEGEMMMatrixAdditionKernel>(); - _ma_kernel->configure(c, d, beta); - } - - // Configure activation - const ActivationLayerInfo &activation = gemm_info.activation_info(); - if(_run_activation) - { - _activation_func.configure(d, nullptr, activation); - } + _impl->aux_mem_req = _impl->op->workspace(); + _impl->run_pack = {{ACL_SRC_0, a}, {ACL_SRC_1, b}, {ACL_SRC_2, c}, {ACL_DST, d}}; + _impl->prep_pack = {{ACL_SRC_1, b}, {ACL_SRC_2, c}}; + _impl->workspace = + manage_workspace<Tensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->prep_pack); } -Status NEGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) +Status NEGEMM::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); - 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, output); - } - - 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, output); - 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(output->total_size() != 0) - { - ARM_COMPUTE_RETURN_ERROR_ON(b->dimension(0) != output->dimension(0)); - if(gemm_info.depth_output_gemm3d() != 0) - { - if(gemm_info.reinterpret_input_as_3d()) - { - ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1)); - ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(2) != output->dimension(2)); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1) * output->dimension(2)); - } - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->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, output, asm_info)); - - if(!run_optimised) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.reinterpret_input_as_3d(), "NEGEMM cannot reinterpret the input tensor as 3D"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.depth_output_gemm3d() != 0, "NEGEMM 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 NEGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to NEGEMMReshapeInfo - // 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 = *output->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(NEGEMMInterleave4x4Kernel::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(NEGEMMTranspose1xWKernel::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(NEGEMMMatrixMultiplyKernel::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(NEArithmeticAddition::validate(&tmp_output_info, c, output, ConvertPolicy::SATURATE)); - } - } - - // Validate matrix addition kernel - if(beta != 0 && c != nullptr && !is_c_bias) + // Make the B matrix dynamic values. + auto b_to_use = b->clone(); + if (!gemm_info.reshape_b_only_on_first_run()) { - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixAdditionKernel::validate(c, output, beta)); + b_to_use->set_are_values_constant(false); } - // Validate activation - const ActivationLayerInfo &activation = gemm_info.activation_info(); - if(activation.enabled()) - { - ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(output, nullptr, activation)); - } + return cpu::CpuGemm::validate(a, b_to_use.get(), c, output, alpha, beta, gemm_info); +} - return Status{}; +Status NEGEMM::has_opt_impl(arm_compute::WeightFormat &expected_weight_format, + const ITensorInfo *a, + const ITensorInfo *b, + const ITensorInfo *c, + const ITensorInfo *output, + float alpha, + float beta, + const GEMMInfo &gemm_info) +{ + ARM_COMPUTE_UNUSED(alpha, beta); + return cpu::CpuGemm::has_opt_impl(expected_weight_format, a, b, c, output, gemm_info); } void NEGEMM::run() { prepare(); - MemoryGroupResourceScope scope_mg(_memory_group); - - if(_asm_glue->is_configured()) - { - _asm_glue->run(_asm_glue_tensors); - if(_run_alpha_scale) - { - _alpha_scale_func.run(); - } - } - else - { - if(!_run_vector_matrix_multiplication) - { - // Run interleave kernel - NEScheduler::get().schedule(_interleave_kernel.get(), Window::DimY); - - if(!_reshape_b_only_on_first_run) - { - // Run transpose kernel - NEScheduler::get().schedule(_transpose_kernel.get(), Window::DimY); - } - } - - NEScheduler::get().schedule(_mm_kernel.get(), _run_vector_matrix_multiplication ? Window::DimX : Window::DimY); - - // Run bias addition kernel - if(_run_bias_addition) - { - _add_bias.run(); - } - } - - // Run matrix addition kernel - if(_run_addition) - { - NEScheduler::get().schedule(_ma_kernel.get(), Window::DimY); - } - - // Run activation function - if(_run_activation) - { - _activation_func.run(); - } + MemoryGroupResourceScope scope_mg(_impl->memory_group); + _impl->op->run(_impl->run_pack); } void NEGEMM::prepare() { - if(!_is_prepared) + if (!_impl->is_prepared) { - const bool original_b_managed_by_weights_manager = _weights_manager && _weights_manager->are_weights_managed(_original_b); - if(_asm_glue->is_configured()) - { - if(!original_b_managed_by_weights_manager) - { - ARM_COMPUTE_ERROR_ON(!_original_b->is_used()); - } + _impl->op->prepare(_impl->prep_pack); + + auto has_reshape = + std::find_if(_impl->aux_mem_req.begin(), _impl->aux_mem_req.end(), + [](const MemoryInfo &m) -> bool { return m.lifetime == MemoryLifetime::Persistent; }); - _asm_glue->prepare(_asm_glue_tensors); - if(!original_b_managed_by_weights_manager) - { - _original_b->mark_as_unused(); - } + if (has_reshape != std::end(_impl->aux_mem_req)) + { + _impl->original_b->mark_as_unused(); } - else if(_reshape_b_only_on_first_run && !_run_vector_matrix_multiplication && !_asm_glue->is_configured()) + else { - if(!original_b_managed_by_weights_manager) - { - ARM_COMPUTE_ERROR_ON(!_original_b->is_used()); - } - - _tmp_b.allocator()->allocate(); - NEScheduler::get().schedule(_transpose_kernel.get(), Window::DimY); - if(!original_b_managed_by_weights_manager) - { - _original_b->mark_as_unused(); - } + _impl->run_pack.add_const_tensor(ACL_SRC_1, _impl->original_b); } - _is_prepared = true; + // Release temporary tensors that are only used in prepare stage + release_temporaries<Tensor>(_impl->aux_mem_req, _impl->workspace); + _impl->is_prepared = true; } } } // namespace arm_compute |