diff options
Diffstat (limited to 'src/runtime/CL/functions/CLGEMM.cpp')
| -rw-r--r-- | src/runtime/CL/functions/CLGEMM.cpp | 892 |
1 files changed, 76 insertions, 816 deletions
diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp index cf1a82bc5a..871a1d6e27 100644 --- a/src/runtime/CL/functions/CLGEMM.cpp +++ b/src/runtime/CL/functions/CLGEMM.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2022 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -23,870 +23,130 @@ */ #include "arm_compute/runtime/CL/functions/CLGEMM.h" +#include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.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/KernelDescriptors.h" -#include "arm_compute/core/Log.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" -#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h" -#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h" -#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h" -#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h" -#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h" -#include "src/core/helpers/AutoConfiguration.h" -#include "src/core/utils/helpers/float_ops.h" -#include "src/runtime/CL/gemm/CLGEMMKernelSelection.h" -#include "src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.h" -#include "support/Cast.h" -#include "utils/TypePrinter.h" -namespace arm_compute -{ -using namespace arm_compute::misc::shape_calculator; -using namespace arm_compute::cl_gemm; -using namespace arm_compute::utils::cast; - -namespace weights_transformations -{ -CLGEMMReshapeRHSMatrixKernelManaged::CLGEMMReshapeRHSMatrixKernelManaged() - : _kernel(std::make_unique<CLGEMMReshapeRHSMatrixKernel>()) -{ -} - -CLGEMMReshapeRHSMatrixKernelManaged::~CLGEMMReshapeRHSMatrixKernelManaged() = default; - -void CLGEMMReshapeRHSMatrixKernelManaged::run() -{ - _output.allocator()->allocate(); - CLScheduler::get().enqueue(*_kernel, false); - _reshape_run = true; -} - -void CLGEMMReshapeRHSMatrixKernelManaged::release() -{ - _output.allocator()->free(); -} - -ICLTensor *CLGEMMReshapeRHSMatrixKernelManaged::get_weights() -{ - return &_output; -} +#include "src/core/helpers/MemoryHelpers.h" +#include "src/gpu/cl/operators/ClGemm.h" -uint32_t CLGEMMReshapeRHSMatrixKernelManaged::uid() -{ - return _uid; -} - -void CLGEMMReshapeRHSMatrixKernelManaged::configure(const ICLTensor *input, GEMMRHSMatrixInfo info) -{ - configure(CLKernelLibrary::get().get_compile_context(), input, info); -} - -void CLGEMMReshapeRHSMatrixKernelManaged::configure(const CLCompileContext &compile_context, const ICLTensor *input, GEMMRHSMatrixInfo info) -{ - _kernel->configure(compile_context, input, &_output, info); -} -} // namespace weights_transformations - -namespace -{ -inline bool validate_gemm_kernel(CLGEMMKernelType kernel_type) -{ - switch(kernel_type) - { - case CLGEMMKernelType::NATIVE_V1: - case CLGEMMKernelType::RESHAPED_ONLY_RHS: - case CLGEMMKernelType::RESHAPED_V1: - case CLGEMMKernelType::RESHAPED: - { - return true; - } - default: - { - return false; - } - } -} -//Automatically select between mlgo (prioritized) and default heuristics for gemm kernel type -inline CLGEMMKernelType auto_select_gemm_kernel(auto_heuristics::CommonQuery query, bool reshape_b_only_on_first_run) -{ - auto gemm_kernel = auto_heuristics::select_mlgo_gemm_kernel(query, reshape_b_only_on_first_run); - if(bool(gemm_kernel)) - { - if(validate_gemm_kernel(gemm_kernel.gemm_type)) - { - ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from mlgo heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str()); - return gemm_kernel.gemm_type; - } - } - gemm_kernel = auto_heuristics::select_default_gemm_kernel(query, reshape_b_only_on_first_run); - ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from default heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str()); - return gemm_kernel.gemm_type; -} -// Validate lhs_info and rhs_info for reshaped only rhs kernel -inline bool validate_lhs_rhs_info_reshaped_only_rhs(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, - const ITensorInfo *output, GEMMKernelInfo gemm_kernel_info) -{ - // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel - TensorInfo tmp_b_info{}; - // Validate reshape RHS kernel - auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); - if(!bool(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info))) - { - return false; - } - // Validate mm kernel - gemm_kernel_info.lhs_info = lhs_info; - gemm_kernel_info.rhs_info = rhs_info; - gemm_kernel_info.has_pad_y = false; - if(!bool(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info))) - { - return false; - } - gemm_kernel_info.has_pad_y = true; - if(!bool(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info))) - { - return false; - } - return true; -} - -//Automatically select between mlgo (prioritized) and default heuristics for reshaped only rhs kernel configs -inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery query, GEMMKernelInfo kernel_info, const ITensorInfo *a, - const ITensorInfo *b, - const ITensorInfo *c, const ITensorInfo *output) -{ - auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(query); - if(config) - { - if(validate_lhs_rhs_info_reshaped_only_rhs(config.lhs_info, config.rhs_info, a, b, c, output, kernel_info)) - { - ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); - return { config.lhs_info, config.rhs_info }; - } - } - config = auto_heuristics::select_default_gemm_config_reshaped_only_rhs(query); - ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); - return { config.lhs_info, config.rhs_info }; -} - -// Validate lhs_info and rhs_info for reshaped kernel -inline bool validate_lhs_rhs_info_reshaped(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, - const ITensorInfo *output, GEMMKernelInfo gemm_kernel_info, bool reinterpret_input_as_3d) +namespace arm_compute { - // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped kernel - TensorInfo tmp_a_info{}; - TensorInfo tmp_b_info{}; - - // Validate reshape LHS kernel - auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, reinterpret_input_as_3d))); - if(!bool(CLGEMMReshapeLHSMatrixKernel::validate(a, &tmp_a_info, lhs_info, reinterpret_input_as_3d))) - { - return false; - } - - // Validate reshape RHS kernel - auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); - if(!bool(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info))) - { - return false; - } - // Validate mm kernel - gemm_kernel_info.lhs_info = lhs_info; - gemm_kernel_info.rhs_info = rhs_info; - if(!bool(CLGEMMMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info))) - { - return false; - } - return true; -} +using namespace arm_compute::experimental; +using OperatorType = opencl::ClGemm; -//Automatically select between mlgo (prioritized) and default heuristics for reshaped kernel configs -inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery query, GEMMKernelInfo kernel_info, const ITensorInfo *a, const ITensorInfo *b, - const ITensorInfo *c, const ITensorInfo *output, bool reinterpret_input_as_3d) +struct CLGEMM::Impl { - auto config = auto_heuristics::select_mlgo_gemm_config_reshaped(query); - if(config) - { - if(validate_lhs_rhs_info_reshaped(config.lhs_info, config.rhs_info, a, b, c, output, kernel_info, reinterpret_input_as_3d)) - { - ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); - return { config.lhs_info, config.rhs_info }; - } - } - config = auto_heuristics::select_default_gemm_config_reshaped(query); - ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); - return { config.lhs_info, config.rhs_info }; -} - -} // namespace + const ICLTensor *b{nullptr}; + std::unique_ptr<OperatorType> op{nullptr}; + MemoryGroup memory_group{}; + IWeightsManager *weights_manager{nullptr}; + ITensorPack run_pack{}; + ITensorPack prep_pack{}; + MemoryRequirements aux_mem_req{}; + WorkspaceData<CLTensor> workspace_tensors{}; + bool is_prepared{false}; +}; CLGEMM::CLGEMM(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager) - : _memory_group(std::move(memory_manager)), - _weights_manager(weights_manager), - _mm_kernel(std::make_unique<CLGEMMMatrixMultiplyKernel>()), - _reshape_lhs_kernel(std::make_unique<CLGEMMReshapeLHSMatrixKernel>()), - _reshape_rhs_kernel(std::make_unique<CLGEMMReshapeRHSMatrixKernel>()), - _reshape_rhs_kernel_managed(std::make_unique<weights_transformations::CLGEMMReshapeRHSMatrixKernelManaged>()), - _mm_reshaped_kernel(std::make_unique<CLGEMMMatrixMultiplyReshapedKernel>()), - _mm_reshaped_only_rhs_kernel(std::make_unique<CLGEMMMatrixMultiplyReshapedOnlyRHSKernel>()), - _mm_reshaped_only_rhs_fallback_kernel(std::make_unique<CLGEMMMatrixMultiplyReshapedOnlyRHSKernel>()), - _tmp_a(), - _tmp_b(), - _original_b(nullptr), - _lhs(nullptr), - _dst(nullptr), - _reshape_b_only_on_first_run(false), - _is_prepared(false), - _gemm_kernel_type(CLGEMMKernelType::NATIVE_V1) + : _impl(std::make_unique<Impl>()) { + _impl->memory_group = MemoryGroup(memory_manager); + _impl->weights_manager = weights_manager; } CLGEMM::~CLGEMM() = default; -void CLGEMM::configure_native_v1(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, - const GEMMInfo &gemm_info) -{ - const unsigned int m = gemm_info.reinterpret_input_as_3d() ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1); - const unsigned int n = b->info()->dimension(0); - const unsigned int k = a->info()->dimension(0); - const GPUTarget gpu_target = CLScheduler::get().target(); - - // Set the target for the kernels - _mm_kernel->set_target(gpu_target); - - GEMMReshapeInfo reshape_info(m, n, k, 1, 1, gemm_info.depth_output_gemm3d(), gemm_info.reinterpret_input_as_3d(), gemm_info.broadcast_bias()); - - // Configure and tune matrix multiply kernel - _mm_kernel->configure(compile_context, a, b, c, output, alpha, beta, false, reshape_info, gemm_info.fp_mixed_precision(), gemm_info.activation_info()); - - // Tune kernel statically - CLScheduler::get().tune_kernel_static(*_mm_kernel); -} - -void CLGEMM::configure_reshaped_v1(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, - const GEMMInfo &gemm_info) +void CLGEMM::configure(const ICLTensor *a, + const ICLTensor *b, + const ICLTensor *c, + ICLTensor *output, + float alpha, + float beta, + const GEMMInfo &gemm_info) { - bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1); - const unsigned int n = b->info()->dimension(0); - const unsigned int k = a->info()->dimension(0); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - const GPUTarget gpu_target = CLScheduler::get().target(); - int mult_transpose1xW_width = 1; - int mult_interleave4x4_height = 1; - - // Set the target for the kernels - _reshape_lhs_kernel->set_target(gpu_target); - _mm_kernel->set_target(gpu_target); - - if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST) - { - mult_transpose1xW_width = 4; - mult_interleave4x4_height = 2; - } - - GEMMRHSMatrixInfo rhs_info; - rhs_info.n0 = 16 / b->info()->element_size(); - rhs_info.k0 = 1; - rhs_info.h0 = mult_transpose1xW_width; - rhs_info.interleave = false; - rhs_info.transpose = false; - - GEMMLHSMatrixInfo lhs_info; - lhs_info.m0 = 4; - lhs_info.k0 = 4; - lhs_info.v0 = mult_interleave4x4_height; - lhs_info.interleave = true; - lhs_info.transpose = true; - - GEMMReshapeInfo reshape_info(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, false, gemm_info.broadcast_bias()); - - const bool use_mm_b = (!_weights_manager || !_weights_manager->are_weights_managed(b)); - - // Manage intermediate buffers - _memory_group.manage(&_tmp_a); - - if(!_reshape_b_only_on_first_run && use_mm_b) - { - _memory_group.manage(&_tmp_b); - } - - // Configure interleave kernel - _reshape_lhs_kernel->configure(compile_context, a, &_tmp_a, lhs_info, reinterpret_input_as_3d); - - // Configure transpose kernel - ICLTensor *reshaped_rhs = &_tmp_b; - if(_weights_manager && _weights_manager->are_weights_managed(b)) - { - _reshape_rhs_kernel_managed->configure(compile_context, b, rhs_info); - reshaped_rhs = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(b, _reshape_rhs_kernel_managed.get())); - } - else - { - _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info); - } - - // Configure and tune matrix multiply kernel - _mm_kernel->configure(compile_context, &_tmp_a, reshaped_rhs, c, output, alpha, beta, true, reshape_info, gemm_info.fp_mixed_precision(), gemm_info.activation_info()); - - CLScheduler::get().tune_kernel_static(*_mm_kernel); - - // Allocate intermediate tensors - _tmp_a.allocator()->allocate(); - - if(!_reshape_b_only_on_first_run && use_mm_b) - { - _tmp_b.allocator()->allocate(); - } -} - -void CLGEMM::configure_reshaped_v2(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, - const GEMMInfo &gemm_info) -{ - DataType data_type = a->info()->data_type(); - bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1); - const unsigned int n = b->info()->dimension(0); - const unsigned int k = a->info()->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->dimension(2); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - const GPUTarget gpu_target = CLScheduler::get().target(); - bool broadcast_bias = gemm_info.broadcast_bias(); - - GEMMKernelInfo kernel_info; - kernel_info.m = m; - kernel_info.n = n; - kernel_info.k = k; - kernel_info.depth_output_gemm3d = depth_output_gemm3d; - kernel_info.reinterpret_input_as_3d = false; - kernel_info.broadcast_bias = broadcast_bias; - kernel_info.activation_info = gemm_info.activation_info(); - - // Set the target for the kernels - _reshape_lhs_kernel->set_target(gpu_target); - _mm_kernel->set_target(gpu_target); - - const bool use_mm_b = (!_weights_manager || !_weights_manager->are_weights_managed(b)); - - // Manage intermediate buffers - _memory_group.manage(&_tmp_a); - - if(!_reshape_b_only_on_first_run && use_mm_b) - { - _memory_group.manage(&_tmp_b); - } - - // _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel - - GEMMLHSMatrixInfo lhs_info{}; - GEMMRHSMatrixInfo rhs_info{}; - - // Pick up the GEMM configuration - std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }, kernel_info, a->info(), b->info(), - c == nullptr ? nullptr : c->info(), output->info(), gemm_info.reinterpret_input_as_3d()); - - _reshape_lhs_kernel->configure(compile_context, a, &_tmp_a, lhs_info, gemm_info.reinterpret_input_as_3d()); - - ICLTensor *reshaped_rhs = &_tmp_b; - if(_weights_manager && _weights_manager->are_weights_managed(b)) - { - _reshape_rhs_kernel_managed->configure(compile_context, b, rhs_info); - reshaped_rhs = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(b, _reshape_rhs_kernel_managed.get())); - } - else - { - _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info); - } - - // Configure and tune matrix multiply kernel - _mm_reshaped_kernel->configure(compile_context, &_tmp_a, reshaped_rhs, c, output, alpha, beta, lhs_info, rhs_info, kernel_info); - - // Allocate intermediate tensors - _tmp_a.allocator()->allocate(); - - if(!_reshape_b_only_on_first_run && use_mm_b) - { - _tmp_b.allocator()->allocate(); - } + configure(CLKernelLibrary::get().get_compile_context(), a, b, c, output, alpha, beta, gemm_info); } -void CLGEMM::configure_reshaped_only_rhs(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, - const GEMMInfo &gemm_info) +void CLGEMM::configure(const CLCompileContext &compile_context, + const ICLTensor *a, + const ICLTensor *b, + const ICLTensor *c, + ICLTensor *output, + float alpha, + float beta, + const GEMMInfo &gemm_info) { - DataType data_type = a->info()->data_type(); - bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1); - const unsigned int n = b->info()->dimension(0); - const unsigned int k = a->info()->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->dimension(2); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - const GPUTarget gpu_target = CLScheduler::get().target(); - bool broadcast_bias = gemm_info.broadcast_bias(); - - GEMMKernelInfo kernel_info; - kernel_info.m = m; - kernel_info.n = n; - kernel_info.k = k; - kernel_info.depth_output_gemm3d = depth_output_gemm3d; - kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; - kernel_info.broadcast_bias = broadcast_bias; - kernel_info.activation_info = gemm_info.activation_info(); - - // Set the target for the kernels - _mm_kernel->set_target(gpu_target); - - const bool use_mm_b = (!_weights_manager || !_weights_manager->are_weights_managed(b)); - - // Manage intermediate buffers - if(!_reshape_b_only_on_first_run && use_mm_b) - { - _memory_group.manage(&_tmp_b); - } + ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); - GEMMLHSMatrixInfo lhs_info{}; - GEMMRHSMatrixInfo rhs_info{}; + _impl->b = b; + _impl->op = std::make_unique<OperatorType>(); + _impl->is_prepared = gemm_info.retain_internal_weights(); - // Pick up the GEMM configuration - std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }, kernel_info, a->info(), b->info(), - c == nullptr ? nullptr : c->info(), output->info()); + _impl->op->configure(compile_context, a->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), + alpha, beta, gemm_info); + _impl->aux_mem_req = _impl->op->workspace(); - ICLTensor *reshaped_rhs = &_tmp_b; - if(_weights_manager && _weights_manager->are_weights_managed(b)) + // Manage/allocate auxilairy tensors + if (_impl->is_prepared) { - _reshape_rhs_kernel_managed->configure(compile_context, b, rhs_info); - reshaped_rhs = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(b, _reshape_rhs_kernel_managed.get())); + _impl->run_pack.add_const_tensor(ACL_SRC_0, a); + _impl->run_pack.add_tensor(ACL_DST, output); } else { - _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info); - } + _impl->run_pack = {{ACL_SRC_0, a}, {ACL_SRC_2, c}, {ACL_DST, output}}; + _impl->prep_pack = {{ACL_SRC_1, _impl->b}}; - // Configure two variants of CLGEMMMatrixMultiplyReshapedOnlyRHSKernel (has_pad_y = false/true) - // During the prepare stage we check the padding requirement for the lhs and dst tensors. If they do not have - // pad y, we dispatch CLGEMMMatrixMultiplyReshapedOnlyRHSKernel with has_pad_y = false - - // Configure matrix multiply kernel with no y padding support - kernel_info.has_pad_y = false; - _mm_reshaped_only_rhs_kernel->configure(compile_context, a, reshaped_rhs, c, output, alpha, beta, lhs_info, rhs_info, kernel_info); - - // Configure matrix multiply kernel with y padding support - kernel_info.has_pad_y = true; - _mm_reshaped_only_rhs_fallback_kernel->configure(compile_context, a, reshaped_rhs, c, output, alpha, beta, lhs_info, rhs_info, kernel_info); - - if(!_reshape_b_only_on_first_run && use_mm_b) - { - _tmp_b.allocator()->allocate(); + _impl->workspace_tensors = + manage_workspace<CLTensor>(_impl->op->workspace(), _impl->memory_group, _impl->run_pack, _impl->prep_pack); } } -Status CLGEMM::validate_native_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) +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); - - // Get the GPU target - const GPUTarget gpu_target = CLScheduler::get().target(); - bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - - const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d, gemm_info.broadcast_bias()); - - // Validate matrix multiply - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(a, b, c, output, alpha, beta, - false, reshape_info, gpu_target, gemm_info.fp_mixed_precision(), gemm_info.activation_info())); - - return Status{}; -} - -Status CLGEMM::validate_reshaped_v1(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); - - TensorInfo tmp_a_info{}; - TensorInfo tmp_b_info{}; - - // Get the GPU target - const GPUTarget gpu_target = CLScheduler::get().target(); - const unsigned int m = gemm_info.reinterpret_input_as_3d() ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned 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; - } - - GEMMRHSMatrixInfo rhs_info; - rhs_info.n0 = 16 / b->element_size(); - rhs_info.k0 = 1; - rhs_info.h0 = mult_transpose1xW_width; - rhs_info.interleave = false; - rhs_info.transpose = false; - - GEMMLHSMatrixInfo lhs_info; - lhs_info.m0 = 4; - lhs_info.k0 = 4; - lhs_info.v0 = mult_interleave4x4_height; - lhs_info.interleave = true; - lhs_info.transpose = true; - - const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, false, gemm_info.broadcast_bias()); - - // Validate interleave kernel - auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d()))); - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeLHSMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d())); - - // Validate transpose kernel - auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info)); - - // Validate matrix multiply - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, beta, - true, reshape_info, gpu_target, gemm_info.fp_mixed_precision(), gemm_info.activation_info())); - - return Status{}; -} - -Status CLGEMM::validate_reshaped(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); - - TensorInfo tmp_a_info{}; - TensorInfo tmp_b_info{}; - - // Get the GPU target - const GPUTarget gpu_target = CLScheduler::get().target(); - DataType data_type = a->data_type(); - bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - const bool broadcast_bias = gemm_info.broadcast_bias(); - - GEMMKernelInfo kernel_info; - kernel_info.m = m; - kernel_info.n = n; - kernel_info.k = k; - kernel_info.depth_output_gemm3d = depth_output_gemm3d; - kernel_info.reinterpret_input_as_3d = false; - kernel_info.broadcast_bias = broadcast_bias; - kernel_info.activation_info = gemm_info.activation_info(); - - GEMMLHSMatrixInfo lhs_info; - GEMMRHSMatrixInfo rhs_info; - - // Pick up the GEMM configuration - // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails - const auto gemm_config = select_default_gemm_config_reshaped(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); - lhs_info = gemm_config.lhs_info; - rhs_info = gemm_config.rhs_info; - - auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d()))); - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeLHSMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d())); - - auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info)); - - // Validate matrix multiply - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); - - return Status{}; -} - -Status CLGEMM::validate_reshaped_only_rhs(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); - - TensorInfo tmp_b_info{}; - - // Get the GPU target - const GPUTarget gpu_target = CLScheduler::get().target(); - const DataType data_type = a->data_type(); - bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); - const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); - const bool broadcast_bias = gemm_info.broadcast_bias(); - - GEMMKernelInfo kernel_info; - kernel_info.m = m; - kernel_info.n = n; - kernel_info.k = k; - kernel_info.depth_output_gemm3d = depth_output_gemm3d; - kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; - kernel_info.broadcast_bias = broadcast_bias; - kernel_info.activation_info = gemm_info.activation_info(); - - GEMMLHSMatrixInfo lhs_info; - GEMMRHSMatrixInfo rhs_info; - - // Pick up the GEMM configuration - // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails - const auto gemm_config = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); - lhs_info = gemm_config.lhs_info; - rhs_info = gemm_config.rhs_info; - - auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info)); - - // Validate matrix multiply - kernel_info.has_pad_y = false; - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); - - kernel_info.has_pad_y = true; - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); - - return Status{}; -} - -void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info) -{ - configure(CLKernelLibrary::get().get_compile_context(), a, b, c, output, alpha, beta, gemm_info); + return OperatorType::validate(a, b, c, output, alpha, beta, gemm_info); } -void CLGEMM::configure(const CLCompileContext &compile_context, 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; - _lhs = a; - _dst = output; - - bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1); - const unsigned int n = b->info()->dimension(0); - const unsigned int k = a->info()->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->dimension(2); - - // Select GEMMType - _gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery{ CLScheduler::get().target(), a->info()->data_type(), m, n, k, batch_size }, _reshape_b_only_on_first_run); - - const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr); - - const ICLTensor *c_to_use = fuse_add_c ? c : nullptr; - - switch(_gemm_kernel_type) - { - case CLGEMMKernelType::NATIVE_V1: - { - configure_native_v1(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info); - break; - } - case CLGEMMKernelType::RESHAPED_V1: - { - configure_reshaped_v1(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info); - break; - } - case CLGEMMKernelType::RESHAPED: - { - configure_reshaped_v2(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info); - break; - } - case CLGEMMKernelType::RESHAPED_ONLY_RHS: - { - configure_reshaped_only_rhs(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info); - break; - } - default: - { - ARM_COMPUTE_ERROR("GEMMType not supported"); - } - } -} - -Status CLGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) +void CLGEMM::run() { - // Get the GPU target - bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); - const unsigned int n = b->dimension(0); - const unsigned int k = a->dimension(0); - const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); - - // Select GEMMType - CLGEMMKernelType gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery - { - CLScheduler::get().target(), a->data_type(), m, n, k, batch_size, - }, - gemm_info.reshape_b_only_on_first_run()); - - const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr); - - const ITensorInfo *c_to_use = fuse_add_c ? c : nullptr; + prepare(); - switch(gemm_kernel_type) - { - case CLGEMMKernelType::NATIVE_V1: - { - ARM_COMPUTE_RETURN_ON_ERROR(validate_native_v1(a, b, c_to_use, output, alpha, beta, gemm_info)); - break; - } - case CLGEMMKernelType::RESHAPED_V1: - { - ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_v1(a, b, c_to_use, output, alpha, beta, gemm_info)); - break; - } - case CLGEMMKernelType::RESHAPED: - { - ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped(a, b, c_to_use, output, alpha, beta, gemm_info)); - break; - } - case CLGEMMKernelType::RESHAPED_ONLY_RHS: - { - ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_only_rhs(a, b, c_to_use, output, alpha, beta, gemm_info)); - break; - } - default: - { - ARM_COMPUTE_RETURN_ERROR_MSG("GEMMType not supported"); - } - } + MemoryGroupResourceScope scope_mg(_impl->memory_group); - return Status{}; + _impl->op->run(_impl->run_pack); } -void CLGEMM::run() +void CLGEMM::prepare() { - prepare(); - MemoryGroupResourceScope scope_mg(_memory_group); - - // Run matrix multiply kernel - switch(_gemm_kernel_type) + if (!_impl->is_prepared) { - case CLGEMMKernelType::NATIVE_V1: - { - CLScheduler::get().enqueue(*_mm_kernel, true); - break; - } - case CLGEMMKernelType::RESHAPED_V1: - { - // Run interleave kernel - CLScheduler::get().enqueue(*_reshape_lhs_kernel, false); - - if(!_reshape_b_only_on_first_run) - { - // Run transpose kernel - if(_weights_manager && _weights_manager->are_weights_managed(_original_b)) - { - _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get()); - } - else - { - CLScheduler::get().enqueue(*_reshape_rhs_kernel, false); - } - } - - CLScheduler::get().enqueue(*_mm_kernel, true); - break; - } - case CLGEMMKernelType::RESHAPED: - { - // Run interleave kernel - CLScheduler::get().enqueue(*_reshape_lhs_kernel, false); + _impl->op->prepare(_impl->prep_pack); - if(!_reshape_b_only_on_first_run) - { - // Run transpose kernel - if(_weights_manager && _weights_manager->are_weights_managed(_original_b)) - { - _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get()); - } - else - { - CLScheduler::get().enqueue(*_reshape_rhs_kernel, false); - } - } + 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; }); - CLScheduler::get().enqueue(*_mm_reshaped_kernel, true); - break; - } - case CLGEMMKernelType::RESHAPED_ONLY_RHS: + if (has_reshape != std::end(_impl->aux_mem_req)) { - if(!_reshape_b_only_on_first_run) - { - // Run transpose kernel - if(_weights_manager && _weights_manager->are_weights_managed(_original_b)) - { - _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get()); - } - else - { - CLScheduler::get().enqueue(*_reshape_rhs_kernel, false); - } - } - // In case of RESHAPED_ONLY_RHS, we need to check the padding requirement - // Check if the lhs or dst tensors have padding - const unsigned int cross_plane_pad_lhs = _lhs->info()->padding().top + _lhs->info()->padding().bottom; - const unsigned int cross_plane_pad_dst = _dst->info()->padding().top + _dst->info()->padding().bottom; - - bool has_pad_y = (cross_plane_pad_lhs != 0) || (cross_plane_pad_dst != 0); - if(has_pad_y) - { - CLScheduler::get().enqueue(*_mm_reshaped_only_rhs_fallback_kernel, true); - } - else - { - CLScheduler::get().enqueue(*_mm_reshaped_only_rhs_kernel, true); - } - break; + _impl->b->mark_as_unused(); } - default: - { - ARM_COMPUTE_ERROR("GEMMType not supported"); - } - } -} - -void CLGEMM::prepare() -{ - if(!_is_prepared) - { - if(_gemm_kernel_type != CLGEMMKernelType::NATIVE_V1 && _reshape_b_only_on_first_run) + else { - if(_weights_manager && _weights_manager->are_weights_managed(_original_b)) - { - _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get()); - } - else - { - // Run transpose kernel and mark original weights tensor as unused - _tmp_b.allocator()->allocate(); - CLScheduler::get().enqueue(*_reshape_rhs_kernel, false); - _original_b->mark_as_unused(); - } + // Pack the B matrix to be used as the underlying GEMM performs no reshapes + _impl->run_pack.add_const_tensor(ACL_SRC_1, _impl->b); } - CLScheduler::get().queue().finish(); - _is_prepared = true; + _impl->is_prepared = true; } } } // namespace arm_compute |