/* * Copyright (c) 2021-2023 Arm Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "src/cpu/operators/CpuFullyConnected.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensorPack.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/common/utils/Log.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/MemoryHelpers.h" #include "src/cpu/kernels/CpuTransposeKernel.h" #include "src/cpu/operators/CpuConvertFullyConnectedWeights.h" #include "src/cpu/operators/CpuFlatten.h" #include "src/cpu/operators/CpuGemm.h" #include "src/cpu/operators/CpuGemmLowpMatrixMultiplyCore.h" #include "src/cpu/utils/CpuAuxTensorHandler.h" namespace arm_compute { namespace cpu { using namespace arm_compute::experimental; using namespace arm_compute::misc::shape_calculator; namespace { // Get min, max bound of a quantized asymmetric dst 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 *src, const ITensorInfo *weights, const ITensorInfo *dst, const ActivationLayerInfo &act, GEMMLowpOutputStageInfo &gemmlowp_output_stage_info) { const auto data_type = src->data_type(); const QuantizationInfo oq_info = dst->quantization_info(); const UniformQuantizationInfo iq_unif = src->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 *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const ActivationLayerInfo &act, bool enable_fast_math, WeightFormat weight_format) { if(is_data_type_quantized_asymmetric(src->data_type())) { // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() // Extract and negate src and weights offset const QuantizationInfo src_quantization_info(src->quantization_info().uniform().scale, -src->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(src, weights, dst, act, gemmlowp_output_stage_info)); GEMMInfo gemm_info; gemm_info.set_gemmlowp_output_stage(gemmlowp_output_stage_info); gemm_info.set_fast_math(enable_fast_math); // Validate gemmlowp function TensorInfo src_info = src->clone()->set_quantization_info(src_quantization_info); TensorInfo weights_info = weights->clone()->set_quantization_info(weights_quantization_info); ARM_COMPUTE_RETURN_ON_ERROR(CpuGemmLowpMatrixMultiplyCore::validate(&src_info, &weights_info, biases, dst, gemm_info)); } else { GEMMInfo gemm_info(false, false, true /* Reshape weights only for the first run */); gemm_info.set_weight_format(weight_format); gemm_info.set_fixed_format(weight_format != WeightFormat::UNSPECIFIED); gemm_info.set_fast_math(enable_fast_math); ARM_COMPUTE_RETURN_ON_ERROR(CpuGemm::validate(src, weights, biases, dst, 1.f, 1.0f, gemm_info)); } return Status{}; } } // namespace CpuFullyConnected::CpuFullyConnected() : _flatten(nullptr), _convert_weights(nullptr), _transpose_weights(nullptr), _mm_gemm(nullptr), _mm_gemmlowp(nullptr), _flattened_src(), _converted_weights(), _reshaped_weights(), _trans_weights(), _trans_weights_idx(AuxTensorIdx::Count), _aux_mem(Count), _needs_weights_conversion(false), _needs_weights_reshape(false), _is_fc_after_conv(false), _is_quantized_asymmetric(false), _is_prepared(false), _enable_fast_math(false), _fixed_format(false), _weight_format(arm_compute::WeightFormat::UNSPECIFIED) { } CpuFullyConnected::~CpuFullyConnected() = default; void CpuFullyConnected::configure_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const ActivationLayerInfo &act) { if(_is_quantized_asymmetric) { // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() // Extract and negate src and weights offset const QuantizationInfo src_quantization_info(src->quantization_info().uniform().scale, -src->quantization_info().uniform().offset); const QuantizationInfo weights_quantization_info(weights->quantization_info().uniform().scale, -weights->quantization_info().uniform().offset); TensorInfo src_info = src->clone()->set_quantization_info(src_quantization_info); TensorInfo weights_info = weights->clone()->set_quantization_info(weights_quantization_info); // Configure gemmlowp function and output stage for asymmetric quantized types GEMMLowpOutputStageInfo gemmlowp_output_stage_info; const Status status = get_gemmlowp_output_stage_info(&src_info, &weights_info, dst, 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); gemm_info.set_fast_math(_enable_fast_math); _mm_gemmlowp = std::make_unique(); _mm_gemmlowp->configure(&src_info, &weights_info, biases, dst, gemm_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); gemm_info.set_fast_math(_enable_fast_math); gemm_info.set_fixed_format(_fixed_format); gemm_info.set_weight_format(_weight_format); _mm_gemm = std::make_unique(); _mm_gemm->configure(src, weights, biases, dst, 1.f, 1.0f, gemm_info); } } void CpuFullyConnected::configure_conv_fc(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const ActivationLayerInfo &act) { ARM_COMPUTE_ERROR_ON((weights->dimension(1) != (src->dimension(0) * src->dimension(1) * src->dimension(2)))); // If the fully connected layer is called after a convolution layer, the src tensor must be linearized // Initialize output tensor for flatten auto_init_if_empty(_flattened_src, src->clone()->set_tensor_shape(compute_flatten_shape(src))); _flatten = std::make_unique(); _flatten->configure(src, &_flattened_src); // Configure matrix multiply kernel configure_mm(&_flattened_src, weights, biases, dst, act); } void CpuFullyConnected::configure_fc_fc(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const ActivationLayerInfo &act) { ARM_COMPUTE_ERROR_ON(src->dimension(0) != weights->dimension(1)); // Configure matrix multiply kernel configure_mm(src, weights, biases, dst, act); } void CpuFullyConnected::configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, FullyConnectedLayerInfo fc_info, const WeightsInfo &weights_info) { // Perform validate step ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); ARM_COMPUTE_ERROR_THROW_ON(CpuFullyConnected::validate(src, weights, biases != nullptr ? biases : nullptr, dst, fc_info, weights_info)); ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, fc_info); _needs_weights_conversion = false; _needs_weights_reshape = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false; _needs_weights_reshape = _needs_weights_reshape && !fc_info.retain_internal_weights; _is_fc_after_conv = true; _is_quantized_asymmetric = is_data_type_quantized_asymmetric(src->data_type()); _is_prepared = false; _trans_weights_idx = AuxTensorIdx::Count; _enable_fast_math = fc_info.enable_fast_math; _fixed_format = weights_info.weight_format() != WeightFormat::UNSPECIFIED; _weight_format = weights_info.weight_format(); // 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 *weights_to_use = weights; // Check if we have a fully connected layer with batches const bool is_batched_fc_layer = dst->dimension(1) > 1; if(is_batched_fc_layer) { _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(src->tensor_shape().cbegin() + 3, src->tensor_shape().cend(), dst->tensor_shape().cbegin() + 1)); } else { _is_fc_after_conv = src->num_dimensions() > 1; } // Reshape weights if needed if(_needs_weights_reshape) { // Reshape the weights _transpose_weights = std::make_unique(); _transpose_weights->configure(weights, &_reshaped_weights); weights_to_use = &_reshaped_weights; _trans_weights_idx = AuxTensorIdx::TransposedWeights; } // Convert weights if needed if(_is_fc_after_conv && (src->data_layout() != fc_info.weights_trained_layout)) { // Convert weights _convert_weights = std::make_unique(); _convert_weights->configure(weights_to_use, &_converted_weights, src->tensor_shape(), fc_info.weights_trained_layout); weights_to_use = &_converted_weights; _needs_weights_conversion = true; _trans_weights_idx = AuxTensorIdx::ConvertedWeights; } if(_is_fc_after_conv) { // Fully Connected layer after a Convolution Layer without batches configure_conv_fc(src, weights_to_use, biases, dst, fc_info.activation_info); } else { // Fully Connected layer after a Fully Connected Layer without batches configure_fc_fc(src, weights_to_use, biases, dst, fc_info.activation_info); } // Retain the tensorinfo with the weights to use if(_needs_weights_reshape || _needs_weights_conversion) { _trans_weights = *weights_to_use; } // Set auxiliary memory requirements auto gemm_mem_req = (_is_quantized_asymmetric) ? _mm_gemmlowp->workspace() : _mm_gemm->workspace(); for(unsigned int i = 0; i < gemm_mem_req.size(); ++i) { _aux_mem[i] = gemm_mem_req[i]; } if(_aux_mem[Pretranspose].size > 0) { // Release permuted weights at the end of prepare as they are further transposed by the assembly dispatch // Do not release them if biases are dynamic and data type is quantized, since the weights tensor will be used for biases offset calculation _aux_mem[TransposedWeights] = MemoryInfo(offset_int_vec(TransposedWeights), (_is_quantized_asymmetric && biases && !(biases->are_values_constant())) ? MemoryLifetime::Persistent : MemoryLifetime::Prepare, _reshaped_weights.total_size()); _aux_mem[ConvertedWeights] = MemoryInfo(offset_int_vec(ConvertedWeights), MemoryLifetime::Prepare, _converted_weights.total_size()); } else { _aux_mem[TransposedWeights] = MemoryInfo(offset_int_vec(TransposedWeights), _needs_weights_conversion ? MemoryLifetime::Prepare : MemoryLifetime::Persistent, _reshaped_weights.total_size()); _aux_mem[ConvertedWeights] = MemoryInfo(offset_int_vec(ConvertedWeights), MemoryLifetime::Persistent, _converted_weights.total_size()); } _aux_mem[FlattenedSrc] = MemoryInfo(offset_int_vec(FlattenedSrc), MemoryLifetime::Temporary, _flattened_src.total_size()); } Status CpuFullyConnected::has_opt_impl(arm_compute::WeightFormat &expected_weight_format, const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, FullyConnectedLayerInfo fc_info, WeightsInfo weights_info) { GEMMInfo gemm_info(false, false, true /* Reshape weights only for the first run */); gemm_info.set_activation_info(fc_info.activation_info); gemm_info.set_fast_math(fc_info.enable_fast_math); gemm_info.set_fixed_format(weights_info.weight_format() != WeightFormat::UNSPECIFIED); gemm_info.set_weight_format(weights_info.weight_format()); return CpuGemm::has_opt_impl(expected_weight_format, src, weights, biases, dst, gemm_info); } Status CpuFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, FullyConnectedLayerInfo fc_info, const WeightsInfo &weights_info) { ARM_COMPUTE_UNUSED(fc_info.retain_internal_weights); ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); if (is_fixed_format_fast_math(weights_info.weight_format())) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(src, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(weights, DataType::BFLOAT16); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(dst, DataType::F32); } else { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights, dst); } ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2); ARM_COMPUTE_RETURN_ERROR_ON(fc_info.activation_info.enabled() && is_data_type_quantized(src->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); ARM_COMPUTE_RETURN_ERROR_ON(!weights->are_values_constant() && (!fc_info.are_weights_reshaped || fc_info.transpose_weights)); bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; bool is_fc_after_conv = true; const ITensorInfo &flatten_src = TensorInfo(src->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(src))); 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 *src_to_use = src; const ITensorInfo *weights_to_use = weights; // Check if we have a fully connected layer with batches const bool is_batched_fc_layer = dst->dimension(1) > 1; if(biases != nullptr) { ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); if(is_data_type_quantized(src->data_type())) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); } else { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases); } } if(is_batched_fc_layer) { is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(src->tensor_shape().cbegin() + 3, src->tensor_shape().cend(), dst->tensor_shape().cbegin() + 1)); } else { is_fc_after_conv = src->num_dimensions() > 1; } if(!weights_reshaped) { // Validate reshape weights kernel ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuTransposeKernel::validate(weights, &reshaped_weights)); weights_to_use = &reshaped_weights; } if(is_fc_after_conv && (src->data_layout() != fc_info.weights_trained_layout)) { // Validate convert weights kernel ARM_COMPUTE_RETURN_ON_ERROR(CpuConvertFullyConnectedWeights::validate(weights_to_use, &converted_weights, src->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) != (src->dimension(0) * src->dimension(1) * src->dimension(2)))); // Validate flatten kernel ARM_COMPUTE_RETURN_ON_ERROR(CpuFlatten::validate(src, &flatten_src)); src_to_use = &flatten_src; } else { // Fully Connected layer after a Fully Connected Layer without batches ARM_COMPUTE_RETURN_ERROR_ON(src->dimension(0) != weights_to_use->dimension(1)); } // Validate matrix multiply kernel ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(src_to_use, weights_to_use, biases, dst, fc_info.activation_info, fc_info.enable_fast_math, weights_info.weight_format())); return Status{}; } void CpuFullyConnected::run(ITensorPack &tensors) { prepare(tensors); auto src = tensors.get_const_tensor(ACL_SRC_0); CpuAuxTensorHandler flattened_src(offset_int_vec(FlattenedSrc), _flattened_src, tensors, false); CpuAuxTensorHandler transformed_wei(offset_int_vec(_trans_weights_idx), _trans_weights, tensors, false); // Linearize src if it comes from a convolutional layer if(_is_fc_after_conv) { ITensorPack flatten_pack{ { ACL_SRC, src }, { ACL_DST, flattened_src.get() } }; _flatten->run(flatten_pack); } ITensorPack gemm_pack = tensors; gemm_pack.add_const_tensor(ACL_SRC_0, (_is_fc_after_conv) ? flattened_src.get() : src); if(_needs_weights_reshape || _needs_weights_conversion) { gemm_pack.add_const_tensor(ACL_SRC_1, transformed_wei.get()); } // Run matrix multiply if(_is_quantized_asymmetric) { _mm_gemmlowp->run(gemm_pack); } else { _mm_gemm->run(gemm_pack); } } void CpuFullyConnected::prepare(ITensorPack &tensors) { if(!_is_prepared) { auto weights = tensors.get_const_tensor(ACL_SRC_1); CpuAuxTensorHandler reshaped_weights(offset_int_vec(TransposedWeights), _reshaped_weights, tensors, false); CpuAuxTensorHandler converted_weights(offset_int_vec(ConvertedWeights), _converted_weights, tensors, false); // Pointer to current weights const ITensor *cur_weights = weights; // Reshape of the weights (happens only once) if(_needs_weights_reshape) { // Run reshape weights kernel and mark weights as unused ITensorPack transpose_pack{ { ACL_SRC, weights }, { ACL_DST, reshaped_weights.get() } }; NEScheduler::get().schedule_op(_transpose_weights.get(), Window::DimY, _transpose_weights->window(), transpose_pack); cur_weights->mark_as_unused(); cur_weights = reshaped_weights.get(); } // Convert weights if needed (happens only once) if(_needs_weights_conversion) { ITensorPack convert_pack{ { ACL_SRC, cur_weights }, { ACL_DST, converted_weights.get() } }; _convert_weights->run(convert_pack); cur_weights->mark_as_unused(); cur_weights = converted_weights.get(); } ITensorPack gemm_pack = tensors; gemm_pack.add_const_tensor(ACL_SRC_1, cur_weights); // Prepare GEMM prepare and release unused weights if(!_is_quantized_asymmetric) { _mm_gemm->prepare(gemm_pack); } else { _mm_gemmlowp->prepare(gemm_pack); } _is_prepared = true; } } experimental::MemoryRequirements CpuFullyConnected::workspace() const { return _aux_mem; } } // namespace cpu } // namespace arm_compute