1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
|
//
// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "NeonFullyConnectedWorkload.hpp"
#include "NeonWorkloadUtils.hpp"
#include <aclCommon/ArmComputeTensorUtils.hpp>
#include <aclCommon/ArmComputeUtils.hpp>
#include <armnn/utility/PolymorphicDowncast.hpp>
#include <armnn/backends/TensorHandle.hpp>
#include <arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h>
namespace armnn
{
using namespace armcomputetensorutils;
using ACLMemManagerOnDemand = std::shared_ptr<arm_compute::MemoryManagerOnDemand>;
arm_compute::Status NeonFullyConnectedWorkloadValidate(const TensorInfo& input,
const TensorInfo& output,
const TensorInfo& weights,
const Optional<TensorInfo>& biases,
const FullyConnectedDescriptor& descriptor,
const ActivationDescriptor* activationDescriptor)
{
const arm_compute::TensorInfo aclInput = BuildArmComputeTensorInfo(input);
const arm_compute::TensorInfo aclOutput = BuildArmComputeTensorInfo(output);
const arm_compute::TensorInfo aclWeights = BuildArmComputeTensorInfo(weights);
arm_compute::TensorInfo aclBiases;
arm_compute::TensorInfo* optionalAclBiases = nullptr;
if (descriptor.m_BiasEnabled)
{
ARMNN_ASSERT(biases.has_value());
aclBiases = BuildArmComputeTensorInfo(biases.value());
optionalAclBiases = &aclBiases;
}
const arm_compute::FullyConnectedLayerInfo fullyConnectedLayerInfo =
ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(descriptor, activationDescriptor);
return arm_compute::NEFullyConnectedLayer::validate(&aclInput,
&aclWeights,
optionalAclBiases,
&aclOutput,
fullyConnectedLayerInfo);
}
NeonFullyConnectedWorkload::NeonFullyConnectedWorkload(const FullyConnectedQueueDescriptor& descriptor,
const WorkloadInfo& info,
ACLMemManagerOnDemand& memoryManager)
: NeonBaseWorkload<FullyConnectedQueueDescriptor>(descriptor, info)
{
m_Data.ValidateInputsOutputs("NeonFullyConnectedWorkload", 1, 1);
arm_compute::ITensor& input = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
m_WeightsTensor = std::make_unique<arm_compute::Tensor>();
BuildArmComputeTensor(*m_WeightsTensor, m_Data.m_Weight->GetTensorInfo());
if (m_Data.m_Parameters.m_BiasEnabled)
{
m_BiasesTensor = std::make_unique<arm_compute::Tensor>();
BuildArmComputeTensor(*m_BiasesTensor, m_Data.m_Bias->GetTensorInfo());
}
const arm_compute::ActivationLayerInfo activationInfo = ConvertAdditionalInfoToAclActivationLayerInfo(descriptor);
arm_compute::FullyConnectedLayerInfo fc_info =
ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(descriptor.m_Parameters, activationInfo);
auto layer = std::make_unique<arm_compute::NEFullyConnectedLayer>(memoryManager);
layer->configure(&input, m_WeightsTensor.get(), m_BiasesTensor.get(), &output, fc_info);
m_FullyConnectedLayer.reset(layer.release());
// Allocate
if (m_Data.m_Weight->GetTensorInfo().GetDataType() == DataType::QAsymmU8)
{
InitializeArmComputeTensorData(*m_WeightsTensor, m_Data.m_Weight);
}
else
{
InitializeArmComputeTensorData(*m_WeightsTensor, m_Data.m_Weight);
}
if (m_BiasesTensor)
{
if (m_Data.m_Bias->GetTensorInfo().GetDataType() == DataType::Signed32)
{
InitializeArmComputeTensorData(*m_BiasesTensor, m_Data.m_Bias);
}
else
{
InitializeArmComputeTensorData(*m_BiasesTensor, m_Data.m_Bias);
}
}
// Add details for profiling output
WorkloadInfo detailsInfo;
detailsInfo.m_InputTensorInfos = info.m_InputTensorInfos;
detailsInfo.m_OutputTensorInfos = info.m_OutputTensorInfos;
detailsInfo.m_WeightsTensorInfo = armnn::Optional<armnn::TensorInfo>(descriptor.m_Weight->GetTensorInfo());
if (descriptor.m_Parameters.m_BiasEnabled)
{
detailsInfo.m_BiasTensorInfo = armnn::Optional<armnn::TensorInfo>(descriptor.m_Bias->GetTensorInfo());
}
// Report Profiling Details
ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonFullyConnectedWorkload_Construct",
descriptor.m_Parameters,
detailsInfo,
this->GetGuid());
// Force Compute Library to perform the necessary copying and reshaping, after which
// delete all the input tensors that will no longer be needed
m_FullyConnectedLayer->prepare();
FreeUnusedTensors();
}
void NeonFullyConnectedWorkload::Execute() const
{
ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonFullyConnectedWorkload_Execute", this->GetGuid());
m_FullyConnectedLayer->run();
}
void NeonFullyConnectedWorkload::FreeUnusedTensors()
{
FreeTensorIfUnused(m_WeightsTensor);
FreeTensorIfUnused(m_BiasesTensor);
}
} //namespace armnn
|
