From 8efb48a6847c5cd166c561127ae6611150963ce3 Mon Sep 17 00:00:00 2001 From: Nikhil Raj Date: Fri, 19 May 2023 11:14:28 +0100 Subject: Update Doxygen docu for 23.05 Signed-off-by: Nikhil Raj Change-Id: I0a992286f14fa68fcc6e5eba31ac39fed003cbbe --- .../classarmnn_1_1_neon_lstm_float_workload.xhtml | 600 +++++++++++++++++++++ 1 file changed, 600 insertions(+) create mode 100644 23.05/classarmnn_1_1_neon_lstm_float_workload.xhtml (limited to '23.05/classarmnn_1_1_neon_lstm_float_workload.xhtml') diff --git a/23.05/classarmnn_1_1_neon_lstm_float_workload.xhtml b/23.05/classarmnn_1_1_neon_lstm_float_workload.xhtml new file mode 100644 index 0000000000..f39b74fac2 --- /dev/null +++ b/23.05/classarmnn_1_1_neon_lstm_float_workload.xhtml @@ -0,0 +1,600 @@ + + + + + + + + + + + + + +ArmNN: NeonLstmFloatWorkload Class Reference + + + + + + + + + + + + + + + + +
+
+ + + + ArmNN + + + +
+
+  23.05 +
+
+
+ + + + + + + +
+
+ +
+
+
+ +
+ +
+
+ + +
+ +
+ +
+
+Public Member Functions | +List of all members
+
+
NeonLstmFloatWorkload Class Reference
+
+
+ +

#include <NeonLstmFloatWorkload.hpp>

+
+Inheritance diagram for NeonLstmFloatWorkload:
+
+
+ + +TypedWorkload< QueueDescriptor, DataTypes > +BaseWorkload< QueueDescriptor > +IWorkload + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+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)
 
void ExecuteAsync (ExecutionData &executionData) 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
 
+

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 }
+
+

References ARMNN_REPORT_PROFILING_WORKLOAD_DESC, BaseWorkload< QueueDescriptor >::GetGuid(), armnn::info, 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_GUID("NeonLstmFloatWorkload_Execute", GetGuid());
+
250  m_LstmLayer.run();
+
251 }
+
+

References ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID, and BaseWorkload< QueueDescriptor >::GetGuid().

+ +
+
+ +

◆ 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: +
+
+
armnn::BaseWorkload::GetGuid
arm::pipe::ProfilingGuid GetGuid() const final
Definition: Workload.hpp:61
+
armnn::ConvertLstmActivationFuncToAclLayerInfo
arm_compute::ActivationLayerInfo ConvertLstmActivationFuncToAclLayerInfo(uint32_t activationFunction)
Definition: ArmComputeUtils.hpp:116
+
armnn::BaseWorkload::m_Data
QueueDescriptor m_Data
Definition: Workload.hpp:83
+
armnn::InitializeArmComputeTensorData
void InitializeArmComputeTensorData(arm_compute::Tensor &tensor, TensorInfo tensorInfo, const ITensorHandle *handle)
Definition: NeonWorkloadUtils.hpp:60
+ +
ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID
#define ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID(name, guid)
Definition: NeonWorkloadUtils.hpp:24
+ + +
ARMNN_REPORT_PROFILING_WORKLOAD_DESC
#define ARMNN_REPORT_PROFILING_WORKLOAD_DESC(name, desc, infos, guid)
Definition: Profiling.hpp:227
+
armnn::QueueDescriptor::m_Outputs
std::vector< ITensorHandle * > m_Outputs
Definition: WorkloadData.hpp:27
+
armnn::QueueDescriptor::m_Inputs
std::vector< ITensorHandle * > m_Inputs
Definition: WorkloadData.hpp:26
+ + + + + -- cgit v1.2.1