Apply clang-format on repository
Code is formatted as per a revised clang format configuration
file(not part of this delivery). Version 14.0.6 is used.
Exclusion List:
- files with .cl extension
- files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...)
And the following directories
- compute_kernel_writer/validation/
- tests/
- include/
- src/core/NEON/kernels/convolution/
- src/core/NEON/kernels/arm_gemm/
- src/core/NEON/kernels/arm_conv/
- data/
There will be a follow up for formatting of .cl files and the
files under tests/ and compute_kernel_writer/validation/.
Signed-off-by: Felix Thomasmathibalan <felixjohnny.thomasmathibalan@arm.com>
Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
diff --git a/src/cpu/operators/CpuFullyConnected.cpp b/src/cpu/operators/CpuFullyConnected.cpp
index 395d8d2..85a0b03 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 @@
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 @@
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 @@
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 @@
// 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() = 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_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 @@
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 @@
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 @@
// 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 @@
}
// 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 @@
}
// 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 @@
_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 @@
}
// 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 @@
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 @@
}
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 @@
// 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 @@
}
}
- 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 @@
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 @@
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::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 @@
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 @@
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);
}