ArmNN
 25.11
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ClLstmFloatWorkload Class Reference

#include <ClLstmFloatWorkload.hpp>

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

 ClLstmFloatWorkload (const LstmQueueDescriptor &descriptor, const WorkloadInfo &info, const arm_compute::CLCompileContext &clCompileContext)
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, armnn::DataType::Float16, armnn::DataType::Float32 >
 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 18 of file ClLstmFloatWorkload.hpp.

Constructor & Destructor Documentation

◆ ClLstmFloatWorkload()

ClLstmFloatWorkload ( const LstmQueueDescriptor & descriptor,
const WorkloadInfo & info,
const arm_compute::CLCompileContext & clCompileContext )

Definition at line 23 of file ClLstmFloatWorkload.cpp.

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

References ARMNN_REPORT_PROFILING_WORKLOAD_DESC, ARMNN_SCOPED_PROFILING_EVENT_CL_NAME_GUID, armnn::ConvertLstmActivationFuncToAclLayerInfo(), armnn::Float32, BaseWorkload< QueueDescriptor >::GetGuid(), TensorInfo::GetShape(), armnn::info, armnn::InitializeArmComputeClTensorData(), BaseWorkload< QueueDescriptor >::m_Data, QueueDescriptorWithParameters< LayerDescriptor >::m_Parameters, and armnn::numeric_cast().

Member Function Documentation

◆ Execute()

void Execute ( ) const
overridevirtual

Implements IWorkload.

Definition at line 238 of file ClLstmFloatWorkload.cpp.

239{
240 ARMNN_SCOPED_PROFILING_EVENT_CL_NAME_GUID("ClLstmFloatWorkload_Execute");
241 RunClFunction(m_LstmLayer, CHECK_LOCATION());
242}
CHECK_LOCATION
#define CHECK_LOCATION()
Definition Exceptions.hpp:203
armnn::RunClFunction
void RunClFunction(arm_compute::IFunction &function, const CheckLocation &location)
Definition ClWorkloadUtils.hpp:167

References ARMNN_SCOPED_PROFILING_EVENT_CL_NAME_GUID, CHECK_LOCATION, and armnn::RunClFunction().

◆ ReplaceInputTensorHandle()

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

Reimplemented from BaseWorkload< QueueDescriptor >.

Definition at line 396 of file ClLstmFloatWorkload.cpp.

397{
398 ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot];
399 this->m_Data.m_Inputs[slot] = tensorHandle;
400 try
401 {
402 Reconfigure();
403 }
404 catch(armnn::UnimplementedException& e)
405 {
406 // Cannot reconfigure, revert the slot back and throw the exception.
407 this->m_Data.m_Inputs[slot] = backupHandle;
408 throw e;
409 }
410}

References BaseWorkload< QueueDescriptor >::m_Data.

◆ ReplaceOutputTensorHandle()

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

Reimplemented from BaseWorkload< QueueDescriptor >.

Definition at line 413 of file ClLstmFloatWorkload.cpp.

414{
415 ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot];
416 this->m_Data.m_Inputs[slot] = tensorHandle;
417 try
418 {
419 Reconfigure();
420 }
421 catch(armnn::UnimplementedException& e)
422 {
423 // Cannot reconfigure, revert the slot back and throw the exception.
424 this->m_Data.m_Inputs[slot] = backupHandle;
425 throw e;
426 }
427}

References BaseWorkload< QueueDescriptor >::m_Data.

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