| /* | |
| * 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<PixelValue, PixelValue> 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<int32_t>(); | |
| gemmlowp_output_stage_info.gemmlowp_max_bound = type_max.get<int32_t>(); | |
| 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<CpuGemmLowpMatrixMultiplyCore>(); | |
| _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<CpuGemm>(); | |
| _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<CpuFlatten>(); | |
| _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<kernels::CpuTransposeKernel>(); | |
| _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<CpuConvertFullyConnectedWeights>(); | |
| _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 |