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Public Member Functions | List of all members
NeonLstmFloatWorkload Class Reference

#include <NeonLstmFloatWorkload.hpp>

Inheritance diagram for NeonLstmFloatWorkload:
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Collaboration diagram for NeonLstmFloatWorkload:
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Public Member Functions

 NeonLstmFloatWorkload (const LstmQueueDescriptor &descriptor, const WorkloadInfo &info)
 
virtual void Execute () const override
 
void ReplaceInputTensorHandle (ITensorHandle *tensorHandle, unsigned int slot) override
 
void ReplaceOutputTensorHandle (ITensorHandle *tensorHandle, unsigned int slot) override
 
- Public Member Functions inherited from TypedWorkload< QueueDescriptor, DataTypes >
 TypedWorkload (const QueueDescriptor &descriptor, const WorkloadInfo &info)
 
- Public Member Functions inherited from BaseWorkload< QueueDescriptor >
 BaseWorkload (const QueueDescriptor &descriptor, const WorkloadInfo &info)
 
virtual const std::string & GetName () const override
 
void PostAllocationConfigure () override
 
const QueueDescriptor & GetData () const
 
arm::pipe::ProfilingGuid GetGuid () const final
 
virtual bool SupportsTensorHandleReplacement () const override
 
- Public Member Functions inherited from IWorkload
virtual ~IWorkload ()
 
virtual void RegisterDebugCallback (const DebugCallbackFunction &)
 
virtual armnn::Optional< armnn::MemoryRequirements > GetMemoryRequirements ()
 

Additional Inherited Members

- Protected Attributes inherited from BaseWorkload< QueueDescriptor >
QueueDescriptor m_Data
 
const arm::pipe::ProfilingGuid m_Guid
 
const std::string m_Name
 

Detailed Description

Definition at line 19 of file NeonLstmFloatWorkload.hpp.

Constructor & Destructor Documentation

◆ NeonLstmFloatWorkload()

NeonLstmFloatWorkload ( const LstmQueueDescriptor & descriptor,
const WorkloadInfo & info )

Definition at line 20 of file NeonLstmFloatWorkload.cpp.

21 : FloatWorkload<LstmQueueDescriptor>(descriptor, info)
22{
23 // Report Profiling Details
24 ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonLstmFloatWorkload_Construct",
25 descriptor.m_Parameters,
26 info,
27 GetGuid());
28
29 arm_compute::LSTMParams<arm_compute::ITensor> lstm_param;
30
31 // Basic parameters
32 m_InputToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();
33 BuildArmComputeTensor(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights->GetTensorInfo());
34
35 m_InputToCellWeightsTensor = std::make_unique<arm_compute::Tensor>();
36 BuildArmComputeTensor(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights->GetTensorInfo());
37
38 m_InputToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();
39 BuildArmComputeTensor(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights->GetTensorInfo());
40
41 m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();
42 BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights->GetTensorInfo());
43
44 m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::Tensor>();
45 BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights->GetTensorInfo());
46
47 m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();
48 BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights->GetTensorInfo());
49
50 m_ForgetGateBiasTensor = std::make_unique<arm_compute::Tensor>();
51 BuildArmComputeTensor(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias->GetTensorInfo());
52
53 m_CellBiasTensor = std::make_unique<arm_compute::Tensor>();
54 BuildArmComputeTensor(*m_CellBiasTensor, m_Data.m_CellBias->GetTensorInfo());
55
56 m_OutputGateBiasTensor = std::make_unique<arm_compute::Tensor>();
57 BuildArmComputeTensor(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias->GetTensorInfo());
58
59 // for future reference: check the AndroidNN API for the logic here
60 if (!m_Data.m_Parameters.m_CifgEnabled)
61 {
62 m_InputToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();
63 BuildArmComputeTensor(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights->GetTensorInfo());
64
65 m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();
66 BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights->GetTensorInfo());
67
68 m_CellToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();
69 if (m_Data.m_CellToInputWeights != nullptr)
70 {
71 BuildArmComputeTensor(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights->GetTensorInfo());
72 }
73
74 m_InputGateBiasTensor = std::make_unique<arm_compute::Tensor>();
75 BuildArmComputeTensor(*m_InputGateBiasTensor, m_Data.m_InputGateBias->GetTensorInfo());
76
77 lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),
78 m_RecurrentToInputWeightsTensor.get(),
79 m_Data.m_CellToInputWeights != nullptr ? m_CellToInputWeightsTensor.get() : nullptr,
80 m_InputGateBiasTensor.get());
81 }
82
83 if (m_Data.m_Parameters.m_ProjectionEnabled)
84 {
85 m_ProjectionWeightsTensor = std::make_unique<arm_compute::Tensor>();
86 BuildArmComputeTensor(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights->GetTensorInfo());
87
88 m_ProjectionBiasTensor = std::make_unique<arm_compute::Tensor>();
89 if (m_Data.m_ProjectionBias != nullptr)
90 {
91 BuildArmComputeTensor(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias->GetTensorInfo());
92 }
93
94 lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),
95 m_Data.m_ProjectionBias != nullptr ? m_ProjectionBiasTensor.get() : nullptr);
96 }
97
98 if (m_Data.m_Parameters.m_PeepholeEnabled)
99 {
100 m_CellToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();
101 BuildArmComputeTensor(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights->GetTensorInfo());
102
103 m_CellToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();
104 BuildArmComputeTensor(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights->GetTensorInfo());
105
106 lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());
107 }
108
109 if (m_Data.m_Parameters.m_LayerNormEnabled)
110 {
111 m_InputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();
112 if (!m_Data.m_Parameters.m_CifgEnabled)
113 {
114 BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights->GetTensorInfo());
115 }
116
117 m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();
118 BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights->GetTensorInfo());
119
120 m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();
121 BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights->GetTensorInfo());
122
123 m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();
124 BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights->GetTensorInfo());
125
126 lstm_param.set_layer_normalization_params(m_Data.m_Parameters.m_CifgEnabled ?
127 nullptr : m_InputLayerNormWeightsTensor.get(),
128 m_ForgetLayerNormWeightsTensor.get(),
129 m_CellLayerNormWeightsTensor.get(),
130 m_OutputLayerNormWeightsTensor.get());
131 }
132
133 const arm_compute::ITensor& input = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
134 const arm_compute::ITensor& output_state_in = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[1])->GetTensor();
135 const arm_compute::ITensor& cell_state_in = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[2])->GetTensor();
136
137 arm_compute::ITensor& output_state_out = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[1])->GetTensor();
138 arm_compute::ITensor& cell_state_out = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[2])->GetTensor();
139 arm_compute::ITensor& output = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[3])->GetTensor();
140
141 // Get the batch_size and the num_units from the cellStateIn dimensions
142 const TensorInfo& inputTensorInfo = info.m_InputTensorInfos[2];
143 const unsigned int batch_size = armnn::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[0]);
144 const unsigned int num_units = armnn::numeric_cast<unsigned int>(inputTensorInfo.GetShape()[1]);
145
146 m_ScratchBuffer = std::make_unique<arm_compute::Tensor>();
147 if (m_Data.m_Parameters.m_CifgEnabled)
148 {
149 // 2D tensor with dimensions [num_units * 3, batch_size] with CIFG
150 armnn::TensorInfo scratchBuffer1({ batch_size, num_units * 3 }, DataType::Float32);
151 BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer1);
152 }
153 else
154 {
155 // scratch_buffer [num_units * 4, batch_size] without CIFG
156 armnn::TensorInfo scratchBuffer2({ batch_size, num_units * 4 }, DataType::Float32);
157 BuildArmComputeTensor(*m_ScratchBuffer, scratchBuffer2);
158 }
159
160 float cell_threshold = m_Data.m_Parameters.m_ClippingThresCell;
161 float projection_threshold = m_Data.m_Parameters.m_ClippingThresProj;
162
163 // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations
164 arm_compute::ActivationLayerInfo activationLayerInfo =
165 ConvertLstmActivationFuncToAclLayerInfo(m_Data.m_Parameters.m_ActivationFunc);
166
167 m_LstmLayer.configure(&input, m_InputToForgetWeightsTensor.get(), m_InputToCellWeightsTensor.get(),
168 m_InputToOutputWeightsTensor.get(), m_RecurrentToForgetWeightsTensor.get(),
169 m_RecurrentToCellWeightsTensor.get(), m_RecurrentToOutputWeightsTensor.get(),
170 m_ForgetGateBiasTensor.get(), m_CellBiasTensor.get(), m_OutputGateBiasTensor.get(),
171 &output_state_in, &cell_state_in, m_ScratchBuffer.get(), &output_state_out,
172 &cell_state_out, &output, lstm_param, activationLayerInfo,
173 cell_threshold, projection_threshold);
174
175 armcomputetensorutils::InitialiseArmComputeTensorEmpty(*m_ScratchBuffer);
176
177 InitializeArmComputeTensorData(*m_InputToForgetWeightsTensor,
178 m_Data.m_InputToForgetWeights);
179 InitializeArmComputeTensorData(*m_InputToCellWeightsTensor,
180 m_Data.m_InputToCellWeights);
181 InitializeArmComputeTensorData(*m_InputToOutputWeightsTensor,
182 m_Data.m_InputToOutputWeights);
183 InitializeArmComputeTensorData(*m_RecurrentToForgetWeightsTensor,
184 m_Data.m_RecurrentToForgetWeights);
185 InitializeArmComputeTensorData(*m_RecurrentToCellWeightsTensor,
186 m_Data.m_RecurrentToCellWeights);
187 InitializeArmComputeTensorData(*m_RecurrentToOutputWeightsTensor,
188 m_Data.m_RecurrentToOutputWeights);
189 InitializeArmComputeTensorData(*m_ForgetGateBiasTensor,
190 m_Data.m_ForgetGateBias);
191 InitializeArmComputeTensorData(*m_CellBiasTensor,
192 m_Data.m_CellBias);
193 InitializeArmComputeTensorData(*m_OutputGateBiasTensor,
194 m_Data.m_OutputGateBias);
195
196 if (!m_Data.m_Parameters.m_CifgEnabled)
197 {
198 InitializeArmComputeTensorData(*m_InputToInputWeightsTensor,
199 m_Data.m_InputToInputWeights);
200 InitializeArmComputeTensorData(*m_RecurrentToInputWeightsTensor,
201 m_Data.m_RecurrentToInputWeights);
202 if (m_Data.m_CellToInputWeights != nullptr)
203 {
204 InitializeArmComputeTensorData(*m_CellToInputWeightsTensor,
205 m_Data.m_CellToInputWeights);
206 }
207 InitializeArmComputeTensorData(*m_InputGateBiasTensor,
208 m_Data.m_InputGateBias);
209 }
210
211 if (m_Data.m_Parameters.m_ProjectionEnabled)
212 {
213 InitializeArmComputeTensorData(*m_ProjectionWeightsTensor,
214 m_Data.m_ProjectionWeights);
215 if (m_Data.m_ProjectionBias != nullptr)
216 {
217 InitializeArmComputeTensorData(*m_ProjectionBiasTensor,
218 m_Data.m_ProjectionBias);
219 }
220 }
221
222 if (m_Data.m_Parameters.m_PeepholeEnabled)
223 {
224 InitializeArmComputeTensorData(*m_CellToForgetWeightsTensor,
225 m_Data.m_CellToForgetWeights);
226 InitializeArmComputeTensorData(*m_CellToOutputWeightsTensor,
227 m_Data.m_CellToOutputWeights);
228 }
229
230 if (m_Data.m_Parameters.m_LayerNormEnabled)
231 {
232 if (!m_Data.m_Parameters.m_CifgEnabled)
233 {
234 InitializeArmComputeTensorData(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights);
235 }
236 InitializeArmComputeTensorData(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights);
237 InitializeArmComputeTensorData(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights);
238 InitializeArmComputeTensorData(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights);
239 }
240
241 // Force Compute Library to perform the necessary copying and reshaping, after which
242 // delete all the input tensors that will no longer be needed
243 m_LstmLayer.prepare();
244 FreeUnusedTensors();
245}
ARMNN_REPORT_PROFILING_WORKLOAD_DESC
#define ARMNN_REPORT_PROFILING_WORKLOAD_DESC(name, desc, infos, guid)
Definition Profiling.hpp:227
armnn::BaseWorkload::GetGuid
arm::pipe::ProfilingGuid GetGuid() const final
Definition Workload.hpp:52
armnn::BaseWorkload::m_Data
QueueDescriptor m_Data
Definition Workload.hpp:74
armnn::ConvertLstmActivationFuncToAclLayerInfo
arm_compute::ActivationLayerInfo ConvertLstmActivationFuncToAclLayerInfo(uint32_t activationFunction)
Definition ArmComputeUtils.hpp:118
armnn::InitializeArmComputeTensorData
void InitializeArmComputeTensorData(arm_compute::Tensor &tensor, TensorInfo tensorInfo, const ITensorHandle *handle)
Definition NeonWorkloadUtils.hpp:69
armnn::QueueDescriptor::m_Inputs
std::vector< ITensorHandle * > m_Inputs
Definition WorkloadData.hpp:26
armnn::QueueDescriptor::m_Outputs
std::vector< ITensorHandle * > m_Outputs
Definition WorkloadData.hpp:27

References ARMNN_REPORT_PROFILING_WORKLOAD_DESC, armnn::ConvertLstmActivationFuncToAclLayerInfo(), armnn::Float32, BaseWorkload< QueueDescriptor >::GetGuid(), TensorInfo::GetShape(), armnn::info, armnn::InitializeArmComputeTensorData(), BaseWorkload< QueueDescriptor >::m_Data, QueueDescriptor::m_Inputs, QueueDescriptor::m_Outputs, and QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters.

Member Function Documentation

◆ Execute()

void Execute ( ) const
overridevirtual

Implements IWorkload.

Definition at line 247 of file NeonLstmFloatWorkload.cpp.

248{
249 ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID("NeonLstmFloatWorkload_Execute");
250 m_LstmLayer.run();
251}
ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID
#define ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID(label)
Creates a profiling event that uses GetGuid() and GetName() from the calling class.
Definition NeonWorkloadUtils.hpp:33

References ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID.

◆ ReplaceInputTensorHandle()

void ReplaceInputTensorHandle ( ITensorHandle * tensorHandle,
unsigned int slot )
overridevirtual

Reimplemented from BaseWorkload< QueueDescriptor >.

Definition at line 417 of file NeonLstmFloatWorkload.cpp.

418{
419 ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot];
420 this->m_Data.m_Inputs[slot] = tensorHandle;
421 try
422 {
423 Reconfigure();
424 }
425 catch(armnn::UnimplementedException& e)
426 {
427 // Cannot reconfigure, revert the slot back and throw the exception.
428 this->m_Data.m_Inputs[slot] = backupHandle;
429 throw e;
430 }
431}

References BaseWorkload< QueueDescriptor >::m_Data, and QueueDescriptor::m_Inputs.

◆ ReplaceOutputTensorHandle()

void ReplaceOutputTensorHandle ( ITensorHandle * tensorHandle,
unsigned int slot )
overridevirtual

Reimplemented from BaseWorkload< QueueDescriptor >.

Definition at line 434 of file NeonLstmFloatWorkload.cpp.

435{
436 ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot];
437 this->m_Data.m_Inputs[slot] = tensorHandle;
438 try
439 {
440 Reconfigure();
441 }
442 catch(armnn::UnimplementedException& e)
443 {
444 // Cannot reconfigure, revert the slot back and throw the exception.
445 this->m_Data.m_Inputs[slot] = backupHandle;
446 throw e;
447 }
448}

References BaseWorkload< QueueDescriptor >::m_Data, and QueueDescriptor::m_Inputs.

The documentation for this class was generated from the following files: