From d9cdf1402fb7e1231f56c1d5549639b423e4e323 Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Fri, 2 Jul 2021 15:17:08 +0100 Subject: Port NEFullyConnectedLayer to memory injecting interface Resolves: COMPMID-4501 Change-Id: Ib61b3d06974009e501b3fb86467735427e13a94a Signed-off-by: Michele Di Giorgio Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5931 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Georgios Pinitas --- .../NEON/functions/NEFullyConnectedLayer.cpp | 442 ++------------------- 1 file changed, 42 insertions(+), 400 deletions(-) (limited to 'src/runtime/NEON/functions/NEFullyConnectedLayer.cpp') diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp index daa14b1b3a..d815a73b93 100644 --- a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp +++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp @@ -23,187 +23,41 @@ */ #include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h" -#include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensorPack.h" -#include "arm_compute/core/Size2D.h" #include "arm_compute/core/Validate.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/core/utils/quantization/AsymmHelpers.h" -#include "arm_compute/runtime/NEON/NEScheduler.h" -#include "src/core/cpu/kernels/CpuTransposeKernel.h" - -#include +#include "arm_compute/runtime/MemoryGroup.h" +#include "arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h" +#include "src/core/helpers/MemoryHelpers.h" +#include "src/runtime/cpu/operators/CpuFullyConnected.h" namespace arm_compute { -using namespace arm_compute::misc::shape_calculator; - -namespace -{ -// Get min, max bound of a quantized assymetric output tensor, with the effect of fused activation -std::pair get_quantized_asymmetric_output_min_max(const QuantizationInfo &q_info, const ActivationLayerInfo &act_info, DataType data_type) -{ - PixelValue type_min{}; - PixelValue type_max{}; - std::tie(type_min, type_max) = get_min_max(data_type); - const UniformQuantizationInfo q_unif = q_info.uniform(); - - if(act_info.enabled()) - { - switch(act_info.activation()) - { - case ActivationLayerInfo::ActivationFunction::RELU: - type_min = PixelValue(q_unif.offset); - break; - case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU: - type_min = PixelValue(q_unif.offset); - type_max = PixelValue(act_info.a(), data_type, q_info); - break; - case ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU: - type_min = PixelValue(act_info.b(), data_type, q_info); - type_max = PixelValue(act_info.a(), data_type, q_info); - break; - default: - ARM_COMPUTE_ERROR("Activation function not supported."); - break; - } - } - - return std::make_pair(type_min, type_max); -} +using namespace arm_compute::experimental; -Status get_gemmlowp_output_stage_info(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const ActivationLayerInfo &act, - GEMMLowpOutputStageInfo &gemmlowp_output_stage_info) +struct NEFullyConnectedLayer::Impl { - const auto data_type = input->data_type(); - const QuantizationInfo oq_info = output->quantization_info(); - const UniformQuantizationInfo iq_unif = input->quantization_info().uniform(); - const UniformQuantizationInfo wq_unif = weights->quantization_info().uniform(); - const UniformQuantizationInfo oq_unif = oq_info.uniform(); - - float multiplier = (iq_unif.scale * wq_unif.scale) / oq_unif.scale; - int32_t output_multiplier; - int32_t output_shift; + MemoryGroup memory_group{}; + IWeightsManager *weights_manager{ nullptr }; - ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); + std::unique_ptr op{ nullptr }; - PixelValue type_min{}; - PixelValue type_max{}; - std::tie(type_min, type_max) = get_quantized_asymmetric_output_min_max(oq_info, act, data_type); + const ITensor *original_weights{ nullptr }; - gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier; - gemmlowp_output_stage_info.gemmlowp_shift = output_shift; - gemmlowp_output_stage_info.gemmlowp_offset = oq_unif.offset; - gemmlowp_output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; - gemmlowp_output_stage_info.gemmlowp_min_bound = type_min.get(); - gemmlowp_output_stage_info.gemmlowp_max_bound = type_max.get(); + ITensorPack run_pack{}; + ITensorPack prep_pack{}; + WorkspaceData workspace{}; + experimental::MemoryRequirements aux_mem_req{}; - return Status{}; -} - -Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const ActivationLayerInfo &act) -{ - if(is_data_type_quantized_asymmetric(input->data_type())) - { - // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() - // Extract and negate input and weights offset - const QuantizationInfo input_quantization_info(input->quantization_info().uniform().scale, -input->quantization_info().uniform().offset); - const QuantizationInfo weights_quantization_info(weights->quantization_info().uniform().scale, -weights->quantization_info().uniform().offset); - - GEMMLowpOutputStageInfo gemmlowp_output_stage_info; - ARM_COMPUTE_RETURN_ON_ERROR(get_gemmlowp_output_stage_info(input, weights, output, act, gemmlowp_output_stage_info)); - - GEMMInfo gemm_info; - gemm_info.set_gemmlowp_output_stage(gemmlowp_output_stage_info); - - // Validate gemmlowp function - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(&input->clone()->set_quantization_info(input_quantization_info), - &weights->clone()->set_quantization_info(weights_quantization_info), - biases, - output, - gemm_info)); - } - else - { - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMM::validate(input, weights, biases, output, 1.f, 1.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */))); - } - - return Status{}; -} -} // namespace + bool is_prepared{ false }; +}; NEFullyConnectedLayer::~NEFullyConnectedLayer() = default; NEFullyConnectedLayer::NEFullyConnectedLayer(std::shared_ptr memory_manager, IWeightsManager *weights_manager) - : _memory_group(std::move(memory_manager)), _weights_manager(weights_manager), _flatten(), _convert_weights(), _convert_weights_managed(), _reshape_weights_function(), - _reshape_weights_managed_function(), _mm_gemm(nullptr, weights_manager), _mm_gemmlowp(nullptr, weights_manager), _flatten_output(), _converted_weights_output(), _reshape_weights_output(), - _original_weights(nullptr), _are_weights_converted(true), _are_weights_reshaped(false), _is_fc_after_conv(false), _is_quantized_asymmetric(false), _is_prepared(false) + : _impl(std::make_unique()) { -} - -void NEFullyConnectedLayer::configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act) -{ - if(_is_quantized_asymmetric) - { - // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() - // Extract and negate input and weights offset - const QuantizationInfo input_quantization_info = input->info()->quantization_info(); - const QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); - - input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset)); - weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset)); - - // Configure gemmlowp function and output stage for asymmetric quantized types - GEMMLowpOutputStageInfo gemmlowp_output_stage_info; - const Status status = get_gemmlowp_output_stage_info(input->info(), weights->info(), output->info(), act, gemmlowp_output_stage_info); - ARM_COMPUTE_ERROR_ON(status.error_code() != ErrorCode::OK); - - GEMMInfo gemm_info; - gemm_info.set_gemmlowp_output_stage(gemmlowp_output_stage_info); - gemm_info.set_activation_info(act); - _mm_gemmlowp.configure(input, weights, biases, output, gemm_info); - - // Revert back QuantizatioInfo as input and weights could be used in other fully connected layers - input->info()->set_quantization_info(input_quantization_info); - weights->info()->set_quantization_info(weights_quantization_info); - } - else - { - // Configure matrix multiply kernel - GEMMInfo gemm_info(false, false, true /* Reshape weights only for the first run */); - gemm_info.set_activation_info(act); - _mm_gemm.configure(input, weights, biases, output, 1.f, 1.0f, gemm_info); - } -} - -void NEFullyConnectedLayer::configure_conv_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act) -{ - ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)))); - - // If the fully connected layer is called after a convolution layer, the input tensor must be linearized - - // Initialize output tensor for flatten - TensorShape shape_flatten = compute_flatten_shape(input->info()); - _flatten_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_flatten)); - - // Configure flatten kernel - _memory_group.manage(&_flatten_output); - - _flatten.configure(input, &_flatten_output); - - // Configure matrix multiply kernel - configure_mm(&_flatten_output, weights, biases, output, act); - - // Allocate the output tensor for flatten once all the configure methods have been called - _flatten_output.allocator()->allocate(); -} - -void NEFullyConnectedLayer::configure_fc_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act) -{ - ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1)); - - // Configure matrix multiply kernel - configure_mm(input, weights, biases, output, act); + _impl->memory_group = MemoryGroup(std::move(memory_manager)); + _impl->weights_manager = weights_manager; } void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, @@ -217,266 +71,54 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh output->info(), fc_info)); - _are_weights_converted = true; - _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; - _is_fc_after_conv = true; - _is_quantized_asymmetric = is_data_type_quantized_asymmetric(input->info()->data_type()); - _original_weights = weights; - - if(_weights_manager) - { - _weights_manager->manage(weights); - } - - // With the Fully Connected layer we can have 4 different cases: - // 1) Convolution layer -> Fully Connected layer without batches - // 2) Fully Connected layer -> Fully Connected layer without batches - // 3) Convolution layer -> Fully Connected layer with batches - // 4) Fully Connected layer -> Fully Connected layer with batches - - const ITensor *weights_to_use = weights; - - // Check if we have a fully connected layer with batches - const bool is_batched_fc_layer = output->info()->dimension(1) > 1; - if(is_batched_fc_layer) - { - _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3, - input->info()->tensor_shape().cend(), - output->info()->tensor_shape().cbegin() + 1)); - } - else - { - _is_fc_after_conv = input->info()->num_dimensions() > 1; - } - - // Reshape weights if needed - if(!_are_weights_reshaped) - { - if(_weights_manager && _weights_manager->are_weights_managed(weights)) - { - _reshape_weights_managed_function.configure(weights); - weights_to_use = _weights_manager->acquire(weights, &_reshape_weights_managed_function); - } - else - { - // Reshape the weights - _reshape_weights_function.configure(weights, &_reshape_weights_output); - weights_to_use = &_reshape_weights_output; - } - } - - // Convert weights if needed - if(_is_fc_after_conv && (input->info()->data_layout() != fc_info.weights_trained_layout)) - { - if(_weights_manager && _weights_manager->are_weights_managed(weights_to_use)) - { - _convert_weights_managed.configure(weights_to_use, - input->info()->tensor_shape(), - fc_info.weights_trained_layout); - weights_to_use = _weights_manager->acquire(weights, &_convert_weights_managed); - } - else - { - // Convert weights - _convert_weights.configure(weights_to_use, - &_converted_weights_output, - input->info()->tensor_shape(), - fc_info.weights_trained_layout); + _impl->op = std::make_unique(); + _impl->original_weights = weights; + _impl->is_prepared = false; - weights_to_use = &_converted_weights_output; - } - _are_weights_converted = false; - } + _impl->op->configure(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), fc_info); - if(_is_fc_after_conv) - { - // Fully Connected layer after a Convolution Layer without batches - configure_conv_fc(input, weights_to_use, biases, output, fc_info.activation_info); - } - else - { - // Fully Connected layer after a Fully Connected Layer without batches - configure_fc_fc(input, weights_to_use, biases, output, fc_info.activation_info); - } - - _are_weights_reshaped = _are_weights_reshaped || fc_info.retain_internal_weights; + _impl->aux_mem_req = _impl->op->workspace(); + _impl->run_pack = { { ACL_SRC_0, input }, { ACL_SRC_1, weights }, { ACL_SRC_2, biases }, { ACL_DST, output } }; + _impl->prep_pack = { { ACL_SRC_1, weights }, { ACL_SRC_2, biases } }; + _impl->workspace = manage_workspace(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->prep_pack); } Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, FullyConnectedLayerInfo fc_info) { - ARM_COMPUTE_UNUSED(fc_info.retain_internal_weights); - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); - ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2); - ARM_COMPUTE_RETURN_ERROR_ON(biases != nullptr && biases->num_dimensions() > 1); - ARM_COMPUTE_RETURN_ERROR_ON(fc_info.activation_info.enabled() && is_data_type_quantized(input->data_type()) && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::RELU - && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU); - - bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; - bool is_fc_after_conv = true; - - const ITensorInfo &flatten_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(input))); - const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights))); - const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone()); - - // With the Fully Connected layer we can have 4 different cases: - // 1) Convolution layer -> Fully Connected layer without batches - // 2) Fully Connected layer -> Fully Connected layer without batches - // 3) Convolution layer -> Fully Connected layer with batches - // 4) Fully Connected layer -> Fully Connected layer with batches - - const ITensorInfo *input_to_use = input; - const ITensorInfo *weights_to_use = weights; - - // Check if we have a fully connected layer with batches - const bool is_batched_fc_layer = output->dimension(1) > 1; - - if(is_batched_fc_layer) - { - is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->tensor_shape().cbegin() + 3, - input->tensor_shape().cend(), - output->tensor_shape().cbegin() + 1)); - } - else - { - is_fc_after_conv = input->num_dimensions() > 1; - } - - if(!weights_reshaped) - { - // Validate reshape weights kernel - ARM_COMPUTE_RETURN_ON_ERROR(NETranspose::validate(weights, &reshaped_weights)); - weights_to_use = &reshaped_weights; - } - - if(is_fc_after_conv && (input->data_layout() != fc_info.weights_trained_layout)) - { - // Validate convert weights kernel - ARM_COMPUTE_RETURN_ON_ERROR(NEConvertFullyConnectedWeights::validate(weights_to_use, - &converted_weights, - input->tensor_shape(), - fc_info.weights_trained_layout)); - weights_to_use = &converted_weights; - } - - if(is_fc_after_conv) - { - // Fully Connected layer after a Convolution Layer without batches - ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(1) != (input->dimension(0) * input->dimension(1) * input->dimension(2)))); - - // Validate flatten kernel - ARM_COMPUTE_RETURN_ON_ERROR(NEFlattenLayer::validate(input, &flatten_input)); - input_to_use = &flatten_input; - } - else - { - // Fully Connected layer after a Fully Connected Layer without batches - ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != weights_to_use->dimension(1)); - } - // Validate matrix multiply kernel - ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(input_to_use, weights_to_use, biases, output, fc_info.activation_info)); - - return Status{}; + return cpu::CpuFullyConnected::validate(input, weights, biases, output, fc_info); } void NEFullyConnectedLayer::run() { prepare(); - MemoryGroupResourceScope scope_mg(_memory_group); - - // Linearize input if it comes from a convolutional layer - if(_is_fc_after_conv) - { - _flatten.run(); - } - - // Run matrix multiply - if(_is_quantized_asymmetric) - { - _mm_gemmlowp.run(); - } - else - { - _mm_gemm.run(); - } + MemoryGroupResourceScope scope_mg(_impl->memory_group); + _impl->op->run(_impl->run_pack); } void NEFullyConnectedLayer::prepare() { - if(!_is_prepared) + if(!_impl->is_prepared) { - if(!_weights_manager) - { - ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); - } - - auto release_unused = [](Tensor * w) - { - if(!w->is_used()) - { - w->allocator()->free(); - } - }; + _impl->op->prepare(_impl->prep_pack); - // Pointer to current weights - const ITensor *cur_weights = _original_weights; + 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; }); - // Reshape of the weights (happens only once) - if(!_are_weights_reshaped) + if(has_reshape != std::end(_impl->aux_mem_req)) { - if(_weights_manager && _weights_manager->are_weights_managed(_original_weights)) - { - cur_weights = _weights_manager->run(cur_weights, &_reshape_weights_managed_function); - } - else - { - // Reshape of the weights (happens only once) - if(!_are_weights_reshaped) - { - // Run reshape weights kernel and mark weights as unused - _reshape_weights_output.allocator()->allocate(); - _reshape_weights_function.run(); - } - cur_weights->mark_as_unused(); - cur_weights = &_reshape_weights_output; - } - _are_weights_reshaped = true; + _impl->original_weights->mark_as_unused(); } - - // Convert weights if needed (happens only once) - if(!_are_weights_converted) - { - if(_weights_manager && _weights_manager->are_weights_managed(cur_weights)) - { - _weights_manager->run(cur_weights, &_convert_weights_managed); - } - else - { - _converted_weights_output.allocator()->allocate(); - _convert_weights.run(); - cur_weights->mark_as_unused(); - } - - _are_weights_converted = true; - } - - // Release reshaped weights if unused - release_unused(&_reshape_weights_output); - - // Prepare GEMM prepare and release unused weights - if(!_is_quantized_asymmetric) + else { - _mm_gemm.prepare(); + _impl->run_pack.add_const_tensor(ACL_SRC_1, _impl->original_weights); } - // Release converted weights if unused - release_unused(&_reshape_weights_output); - release_unused(&_converted_weights_output); - - _is_prepared = true; + // Release temporary tensors that are only used in prepare stage + release_temporaries(_impl->aux_mem_req, _impl->workspace); + _impl->is_prepared = true; } } } // namespace arm_compute -- cgit v1.2.1