diff options
Diffstat (limited to 'src/cpu/operators/CpuFullyConnected.cpp')
| -rw-r--r-- | src/cpu/operators/CpuFullyConnected.cpp | 225 |
1 files changed, 134 insertions, 91 deletions
diff --git a/src/cpu/operators/CpuFullyConnected.cpp b/src/cpu/operators/CpuFullyConnected.cpp index 395d8d2aa5..85a0b0311b 100644 --- a/src/cpu/operators/CpuFullyConnected.cpp +++ b/src/cpu/operators/CpuFullyConnected.cpp @@ -25,10 +25,11 @@ #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/core/Validate.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" @@ -49,8 +50,11 @@ using namespace arm_compute::misc::shape_calculator; namespace { -Status get_gemmlowp_output_stage_info(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst, const ActivationLayerInfo &act, - GEMMLowpOutputStageInfo &gemmlowp_output_stage_info) +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(); @@ -62,10 +66,11 @@ Status get_gemmlowp_output_stage_info(const ITensorInfo *src, const ITensorInfo int32_t output_multiplier; int32_t output_shift; - ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); + ARM_COMPUTE_RETURN_ON_ERROR( + quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); - int32_t type_min = 0; - int32_t type_max = 0; + int32_t type_min = 0; + int32_t type_max = 0; std::tie(type_min, type_max) = quantization::get_quantized_asymmetric_output_min_max(oq_info, act, data_type); gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier; @@ -78,14 +83,22 @@ Status get_gemmlowp_output_stage_info(const ITensorInfo *src, const ITensorInfo 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) +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())) + 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); + 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)); @@ -97,11 +110,8 @@ Status validate_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITe // 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)); + ARM_COMPUTE_RETURN_ON_ERROR( + CpuGemmLowpMatrixMultiplyCore::validate(&src_info, &weights_info, biases, dst, gemm_info)); } else { @@ -142,21 +152,28 @@ CpuFullyConnected::CpuFullyConnected() CpuFullyConnected::~CpuFullyConnected() = default; -void CpuFullyConnected::configure_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const ActivationLayerInfo &act) +void CpuFullyConnected::configure_mm(const ITensorInfo *src, + const ITensorInfo *weights, + const ITensorInfo *biases, + ITensorInfo *dst, + const ActivationLayerInfo &act) { - if(_is_quantized_asymmetric) + 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); + 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); + 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; @@ -179,7 +196,11 @@ void CpuFullyConnected::configure_mm(const ITensorInfo *src, const ITensorInfo * } } -void CpuFullyConnected::configure_conv_fc(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst, const ActivationLayerInfo &act) +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)))); @@ -195,7 +216,11 @@ void CpuFullyConnected::configure_conv_fc(const ITensorInfo *src, const ITensorI 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) +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)); @@ -203,17 +228,17 @@ void CpuFullyConnected::configure_fc_fc(const ITensorInfo *src, const ITensorInf 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) +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_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; @@ -238,9 +263,11 @@ void CpuFullyConnected::configure(const ITensorInfo *src, const ITensorInfo *wei // 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) + 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)); + _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 { @@ -248,7 +275,7 @@ void CpuFullyConnected::configure(const ITensorInfo *src, const ITensorInfo *wei } // Reshape weights if needed - if(_needs_weights_reshape) + if (_needs_weights_reshape) { // Reshape the weights _transpose_weights = std::make_unique<kernels::CpuTransposeKernel>(); @@ -260,13 +287,11 @@ void CpuFullyConnected::configure(const ITensorInfo *src, const ITensorInfo *wei } // Convert weights if needed - if(_is_fc_after_conv && (src->data_layout() != fc_info.weights_trained_layout)) + 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(), + _convert_weights->configure(weights_to_use, &_converted_weights, src->tensor_shape(), fc_info.weights_trained_layout); _converted_weights.set_are_values_constant(weights_to_use->are_values_constant()); @@ -275,7 +300,7 @@ void CpuFullyConnected::configure(const ITensorInfo *src, const ITensorInfo *wei _trans_weights_idx = AuxTensorIdx::ConvertedWeights; } - if(_is_fc_after_conv) + 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); @@ -287,54 +312,57 @@ void CpuFullyConnected::configure(const ITensorInfo *src, const ITensorInfo *wei } // Retain the tensorinfo with the weights to use - if(_needs_weights_reshape || _needs_weights_conversion) + 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) + for (unsigned int i = 0; i < gemm_mem_req.size(); ++i) { _aux_mem[i] = gemm_mem_req[i]; } - if(_aux_mem[Pretranspose].size > 0) + 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 // Keep all the auxiliary tensors in case of dynamic weights as they are recalculated every time. _aux_mem[TransposedWeights] = MemoryInfo( offset_int_vec(TransposedWeights), - _dynamic_weights ? MemoryLifetime::Temporary : - (_is_quantized_asymmetric && biases && !(biases->are_values_constant())) ? MemoryLifetime::Persistent : - MemoryLifetime::Prepare, + _dynamic_weights ? MemoryLifetime::Temporary + : (_is_quantized_asymmetric && biases && !(biases->are_values_constant())) ? MemoryLifetime::Persistent + : MemoryLifetime::Prepare, _reshaped_weights.total_size()); - _aux_mem[ConvertedWeights] = MemoryInfo( - offset_int_vec(ConvertedWeights), - _dynamic_weights ? MemoryLifetime::Temporary : MemoryLifetime::Prepare, - _converted_weights.total_size()); + _aux_mem[ConvertedWeights] = MemoryInfo(offset_int_vec(ConvertedWeights), + _dynamic_weights ? MemoryLifetime::Temporary : MemoryLifetime::Prepare, + _converted_weights.total_size()); } else { - _aux_mem[TransposedWeights] = MemoryInfo( - offset_int_vec(TransposedWeights), - _dynamic_weights ? MemoryLifetime::Temporary : - _needs_weights_conversion ? MemoryLifetime::Prepare : - MemoryLifetime::Persistent, - _reshaped_weights.total_size()); + _aux_mem[TransposedWeights] = MemoryInfo(offset_int_vec(TransposedWeights), + _dynamic_weights ? MemoryLifetime::Temporary + : _needs_weights_conversion ? MemoryLifetime::Prepare + : MemoryLifetime::Persistent, + _reshaped_weights.total_size()); _aux_mem[ConvertedWeights] = MemoryInfo( - offset_int_vec(ConvertedWeights), - _dynamic_weights ? MemoryLifetime::Temporary : MemoryLifetime::Persistent, + offset_int_vec(ConvertedWeights), _dynamic_weights ? MemoryLifetime::Temporary : MemoryLifetime::Persistent, _converted_weights.total_size()); } - _aux_mem[FlattenedSrc] = MemoryInfo(offset_int_vec(FlattenedSrc), MemoryLifetime::Temporary, _flattened_src.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) +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; gemm_info.set_activation_info(fc_info.activation_info); @@ -345,12 +373,17 @@ Status CpuFullyConnected::has_opt_impl(arm_compute::WeightFormat &expected_weigh 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) +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); + 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())) { @@ -364,15 +397,22 @@ Status CpuFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *we } 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( + 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); 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()); + 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 @@ -386,10 +426,10 @@ Status CpuFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *we // Check if we have a fully connected layer with batches const bool is_batched_fc_layer = dst->dimension(1) > 1; - if(biases != nullptr) + if (biases != nullptr) { ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - if(is_data_type_quantized(src->data_type())) + if (is_data_type_quantized(src->data_type())) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); } @@ -399,36 +439,37 @@ Status CpuFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *we } } - if(is_batched_fc_layer) + 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)); + 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) + 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)) + 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)); + 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) + 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)))); + 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)); @@ -440,7 +481,8 @@ Status CpuFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *we 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())); + 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{}; } @@ -460,21 +502,21 @@ void CpuFullyConnected::run(ITensorPack &tensors) 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) + if (_is_fc_after_conv) { - ITensorPack flatten_pack{ { ACL_SRC, src }, { ACL_DST, flattened_src.get() } }; + 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) + 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) + if (_is_quantized_asymmetric) { _mm_gemmlowp->run(gemm_pack); } @@ -486,7 +528,7 @@ void CpuFullyConnected::run(ITensorPack &tensors) void CpuFullyConnected::prepare(ITensorPack &tensors) { - if(!_is_prepared || _dynamic_weights) + if (!_is_prepared || _dynamic_weights) { #ifdef ARM_COMPUTE_ASSERTS_ENABLED ++_asrt_prepare_count; @@ -502,20 +544,21 @@ void CpuFullyConnected::prepare(ITensorPack &tensors) const ITensor *cur_weights = weights; // Reshape of the weights (happens only once) - if(_needs_weights_reshape) + 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); + 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) + if (_needs_weights_conversion) { - ITensorPack convert_pack{ { ACL_SRC, cur_weights }, { ACL_DST, converted_weights.get() } }; + ITensorPack convert_pack{{ACL_SRC, cur_weights}, {ACL_DST, converted_weights.get()}}; _convert_weights->run(convert_pack); cur_weights->mark_as_unused(); @@ -526,7 +569,7 @@ void CpuFullyConnected::prepare(ITensorPack &tensors) gemm_pack.add_const_tensor(ACL_SRC_1, cur_weights); // Prepare GEMM prepare and release unused weights - if(!_is_quantized_asymmetric) + if (!_is_quantized_asymmetric) { _mm_gemm->prepare(gemm_pack); } |