/* * Copyright (c) 2017-2020 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/NEON/functions/NEFullyConnectedLayer.h" #include "arm_compute/core/Helpers.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 #include 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); } Status get_gemmlowp_output_stage_info(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const ActivationLayerInfo &act, GEMMLowpOutputStageInfo &gemmlowp_output_stage_info) { 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; ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); PixelValue type_min{}; PixelValue type_max{}; std::tie(type_min, type_max) = get_quantized_asymmetric_output_min_max(oq_info, act, data_type); 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(); 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 void NEFullyConnectedLayerReshapeWeights::configure(const ITensor *input, ITensor *output) { auto k = arm_compute::support::cpp14::make_unique(); k->configure(input, output); _kernel = std::move(k); } Status NEFullyConnectedLayerReshapeWeights::validate(const ITensorInfo *input, const ITensorInfo *output) { return NETransposeKernel::validate(input, output); } NEFullyConnectedLayer::NEFullyConnectedLayer(std::shared_ptr memory_manager, IWeightsManager *weights_manager) : _memory_group(std::move(memory_manager)), _weights_manager(weights_manager), _flatten_kernel(), _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) { } 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_kernel.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); } void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, FullyConnectedLayerInfo fc_info) { // Perform validate step ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); ARM_COMPUTE_ERROR_THROW_ON(NEFullyConnectedLayer::validate(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, 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); weights_to_use = &_converted_weights_output; } _are_weights_converted = false; } 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; } 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); 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(NEFullyConnectedLayerReshapeWeights::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(NEFlattenLayerKernel::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{}; } void NEFullyConnectedLayer::run() { prepare(); MemoryGroupResourceScope scope_mg(_memory_group); // Linearize input if it comes from a convolutional layer if(_is_fc_after_conv) { NEScheduler::get().schedule(&_flatten_kernel, Window::DimY); } // Run matrix multiply if(_is_quantized_asymmetric) { _mm_gemmlowp.run(); } else { _mm_gemm.run(); } } void NEFullyConnectedLayer::prepare() { if(!_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(); } }; // Pointer to current weights const ITensor *cur_weights = _original_weights; // Reshape of the weights (happens only once) if(!_are_weights_reshaped) { 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; } // 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) { _mm_gemm.prepare(); } // Release converted weights if unused release_unused(&_reshape_weights_output); release_unused(&_converted_weights_output); _is_prepared = true; } } } // namespace arm_compute