ArmNN
 24.02
RefQLstmWorkload.cpp
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1 //
2 // Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 
6 #include "RefQLstmWorkload.hpp"
7 #include "Activation.hpp"
8 #include "Encoders.hpp"
9 #include "Decoders.hpp"
10 #include "LstmUtils.hpp"
11 #include "RefWorkloadUtils.hpp"
12 
13 namespace armnn
14 {
15 
16 RefQLstmWorkload::RefQLstmWorkload(const QLstmQueueDescriptor &descriptor, const WorkloadInfo &info)
17  : RefBaseWorkload<QLstmQueueDescriptor>(descriptor, info)
18  , m_InputToInputWeightsTensor (AssignScopedTensorHandle(descriptor.m_InputToInputWeights))
19  , m_InputToForgetWeightsTensor (AssignScopedTensorHandle(descriptor.m_InputToForgetWeights))
20  , m_InputToCellWeightsTensor (AssignScopedTensorHandle(descriptor.m_InputToCellWeights))
21  , m_InputToOutputWeightsTensor (AssignScopedTensorHandle(descriptor.m_InputToOutputWeights))
22 
23  , m_RecurrentToInputWeightsTensor (AssignScopedTensorHandle(descriptor.m_RecurrentToInputWeights))
24  , m_RecurrentToForgetWeightsTensor(AssignScopedTensorHandle(descriptor.m_RecurrentToForgetWeights))
25  , m_RecurrentToCellWeightsTensor (AssignScopedTensorHandle(descriptor.m_RecurrentToCellWeights))
26  , m_RecurrentToOutputWeightsTensor(AssignScopedTensorHandle(descriptor.m_RecurrentToOutputWeights))
27 
28  , m_CellToInputWeightsTensor (AssignScopedTensorHandle(descriptor.m_CellToInputWeights))
29  , m_CellToForgetWeightsTensor (AssignScopedTensorHandle(descriptor.m_CellToForgetWeights))
30  , m_CellToOutputWeightsTensor (AssignScopedTensorHandle(descriptor.m_CellToOutputWeights))
31 
32  , m_InputGateBiasTensor (AssignScopedTensorHandle(descriptor.m_InputGateBias))
33  , m_ForgetGateBiasTensor (AssignScopedTensorHandle(descriptor.m_ForgetGateBias))
34  , m_CellBiasTensor (AssignScopedTensorHandle(descriptor.m_CellBias))
35  , m_OutputGateBiasTensor (AssignScopedTensorHandle(descriptor.m_OutputGateBias))
36 
37  , m_ProjectionWeightsTensor (AssignScopedTensorHandle(descriptor.m_ProjectionWeights))
38  , m_ProjectionBiasTensor (AssignScopedTensorHandle(descriptor.m_ProjectionBias))
39 
40  , m_InputLayerNormWeightsTensor (AssignScopedTensorHandle(descriptor.m_InputLayerNormWeights))
41  , m_ForgetLayerNormWeightsTensor (AssignScopedTensorHandle(descriptor.m_ForgetLayerNormWeights))
42  , m_CellLayerNormWeightsTensor (AssignScopedTensorHandle(descriptor.m_CellLayerNormWeights))
43  , m_OutputLayerNormWeightsTensor (AssignScopedTensorHandle(descriptor.m_OutputLayerNormWeights))
44 {}
45 
46 void RefQLstmWorkload::Execute() const
47 {
48  Execute(m_Data.m_Inputs, m_Data.m_Outputs);
49 }
50 
51 void RefQLstmWorkload::ExecuteAsync(ExecutionData& executionData)
52 {
53  WorkingMemDescriptor* workingMemDescriptor = static_cast<WorkingMemDescriptor*>(executionData.m_Data);
54  Execute(workingMemDescriptor->m_Inputs, workingMemDescriptor->m_Outputs);
55 }
56 
57 void RefQLstmWorkload::Execute(std::vector<ITensorHandle*> inputs, std::vector<ITensorHandle*> outputs) const
58 {
59  ARMNN_SCOPED_PROFILING_EVENT_REF_NAME_GUID("RefQLstmWorkload_Execute");
60 
61  // This is a porting of the QLSTM::Execute(std::vector<ITensorHandle*> inputs, std::vector<ITensorHandle*> outputs)
62  // method in the Android code base
63  // Note: this implementation wraps the arithmetic functions of the LSTM cell in Quantize/Dequantize ops, so all
64  // computation is done in the floating point domain. Arithmetic functions are found in LstmUtils.cpp.
65  // Refer to: android/frameworks/ml/nn/common/operations/QLSTM.cpp
66  const DataType& internalType = armnn::DataType::QSymmS16;
67 
68  const TensorInfo& inputInfo = GetTensorInfo(inputs[0]);
69  const TensorInfo& outputStateInInfo = GetTensorInfo(inputs[1]);
70  const TensorInfo& cellStateInInfo = GetTensorInfo(inputs[2]);
71 
72  const TensorInfo& outputStateOutInfo = GetTensorInfo(outputs[0]);
73  const TensorInfo& cellStateOutInfo = GetTensorInfo(outputs[1]);
74  const TensorInfo& outputInfo = GetTensorInfo(outputs[2]);
75 
76  const TensorShape& inputShape = inputInfo.GetShape();
77  const TensorShape& outputStateInShape = outputStateInInfo.GetShape();
78  const TensorShape& cellStateInShape = cellStateInInfo.GetShape();
79 
80  // Infer numBatches, inputSize, outputSize and numUnits
81  const uint32_t numBatches = inputShape[0];
82  const uint32_t inputSize = inputShape[1];
83  const uint32_t outputSize = outputStateInShape[1];
84  const uint32_t numUnits = cellStateInShape[1];
85 
86  // Optional param settings
87  const bool cifgEnabled = m_Data.m_Parameters.m_CifgEnabled;
88  const bool peepholeEnabled = m_Data.m_Parameters.m_PeepholeEnabled;
89  const bool projectionEnabled = m_Data.m_Parameters.m_ProjectionEnabled;
90  const bool layerNormEnabled = m_Data.m_Parameters.m_LayerNormEnabled;
91 
92  // Input decoders
93  std::unique_ptr<Decoder<float>> inputDecoder =
94  MakeDecoder<float>(inputInfo, inputs[0]->Map());
95  std::unique_ptr<Decoder<float>> outputStateInDecoder =
96  MakeDecoder<float>(outputStateInInfo, inputs[1]->Map());
97  std::unique_ptr<Decoder<float>> cellStateInDecoder =
98  MakeDecoder<float>(cellStateInInfo, inputs[2]->Map());
99 
100  // Output decoders
101  std::unique_ptr<Decoder<float>> outputStateOutDecoder =
102  MakeDecoder<float>(outputStateOutInfo, outputs[0]->Map());
103  std::unique_ptr<Decoder<float>> cellStateOutDecoder =
104  MakeDecoder<float>(cellStateOutInfo, outputs[1]->Map());
105  std::unique_ptr<Decoder<float>> outputDecoder =
106  MakeDecoder<float>(outputInfo, outputs[2]->Map());
107 
108  // Output encoders
109  std::unique_ptr<Encoder<float>> outputStateOutEncoder =
110  MakeEncoder<float>(outputStateOutInfo, outputs[0]->Map());
111  std::unique_ptr<Encoder<float>> cellStateOutEncoder =
112  MakeEncoder<float>(cellStateOutInfo, outputs[1]->Map());
113  std::unique_ptr<Encoder<float>> outputEncoder =
114  MakeEncoder<float>(outputInfo, outputs[2]->Map());
115 
116  // Weights decoders
117  std::unique_ptr<Decoder<float>> inputToForgetWeightsDecoder = MakeDecoder<float>(
118  m_InputToForgetWeightsTensor->GetTensorInfo(), m_InputToForgetWeightsTensor->GetConstTensor<void>());
119  std::unique_ptr<Decoder<float>> inputToCellWeightsDecoder = MakeDecoder<float>(
120  m_InputToCellWeightsTensor->GetTensorInfo(), m_InputToCellWeightsTensor->GetConstTensor<void>());
121  std::unique_ptr<Decoder<float>> inputToOutputWeightsDecoder = MakeDecoder<float>(
122  m_InputToOutputWeightsTensor->GetTensorInfo(), m_InputToOutputWeightsTensor->GetConstTensor<void>());
123 
124  std::unique_ptr<Decoder<float>> recurrentToForgetWeightsDecoder = MakeDecoder<float>(
125  m_RecurrentToForgetWeightsTensor->GetTensorInfo(),
126  m_RecurrentToForgetWeightsTensor->GetConstTensor<void>());
127  std::unique_ptr<Decoder<float>> recurrentToCellWeightsDecoder = MakeDecoder<float>(
128  m_RecurrentToCellWeightsTensor->GetTensorInfo(), m_RecurrentToCellWeightsTensor->GetConstTensor<void>());
129  std::unique_ptr<Decoder<float>> recurrentToOutputWeightsDecoder = MakeDecoder<float>(
130  m_RecurrentToOutputWeightsTensor->GetTensorInfo(),
131  m_RecurrentToOutputWeightsTensor->GetConstTensor<void>());
132 
133  // Optional CIFG params
134  std::unique_ptr<Decoder<float>> inputToInputWeightsDecoder;
135  std::unique_ptr<Decoder<float>> recurrentToInputWeightsDecoder;
136  std::unique_ptr<Decoder<float>> inputGateBiasDecoder;
137 
138  // Optional Peephole params
139  std::unique_ptr<Decoder<float>> cellToInputWeightsDecoder;
140  std::unique_ptr<Decoder<float>> cellToForgetWeightsDecoder;
141  std::unique_ptr<Decoder<float>> cellToOutputWeightsDecoder;
142 
143  // Optional Projection params
144  std::unique_ptr<Decoder<float>> projectionWeightsDecoder;
145  std::unique_ptr<Decoder<float>> projectionBiasDecoder;
146 
147  // Optional Layer Norm params
148  std::unique_ptr<Decoder<float>> inputLayerNormWeightsDecoder;
149  std::unique_ptr<Decoder<float>> forgetLayerNormWeightsDecoder;
150  std::unique_ptr<Decoder<float>> cellLayerNormWeightsDecoder;
151  std::unique_ptr<Decoder<float>> outputLayerNormWeightsDecoder;
152 
153  // Biases are only used when Layer Norm is enabled. Scale is defined as (XLayerNormWeights Scale / 1024)
154  std::unique_ptr<Decoder<float>> forgetGateBiasDecoder;
155  std::unique_ptr<Decoder<float>> cellGateBiasDecoder;
156  std::unique_ptr<Decoder<float>> outputGateBiasDecoder;
157 
158  // Int16 vectors for internal state data (to be decoded/encoded)
159  const uint32_t stateTensorSize = numBatches * numUnits;
160  std::vector<int16_t> inputGateData(stateTensorSize);
161  std::vector<int16_t> cellGateData(stateTensorSize);
162  std::vector<int16_t> forgetGateData(stateTensorSize);
163  std::vector<int16_t> outputGateData(stateTensorSize);
164  std::vector<int32_t> hiddenStateData(stateTensorSize);
165  std::vector<int16_t> outputInt16Data(numBatches * outputSize);
166 
167  armnn::TensorInfo inputGateInfo(
168  {numBatches , numUnits}, armnn::DataType::QSymmS16, m_Data.m_Parameters.m_InputIntermediateScale, 0);
169  armnn::TensorInfo cellGateInfo(
170  {numBatches , numUnits}, armnn::DataType::QSymmS16, m_Data.m_Parameters.m_CellIntermediateScale, 0);
171  armnn::TensorInfo forgetGateInfo(
172  {numBatches , numUnits}, armnn::DataType::QSymmS16, m_Data.m_Parameters.m_ForgetIntermediateScale, 0);
173  armnn::TensorInfo outputGateInfo(
174  {numBatches , numUnits}, armnn::DataType::QSymmS16, m_Data.m_Parameters.m_OutputIntermediateScale, 0);
175  armnn::TensorInfo hiddenStateInfo({numBatches, numUnits},
176  armnn::DataType::QAsymmS8,
177  m_Data.m_Parameters.m_HiddenStateScale,
178  m_Data.m_Parameters.m_HiddenStateZeroPoint);
179  armnn::TensorInfo outputInt16Info({numBatches , outputSize},
180  armnn::DataType::QSymmS16,
181  outputInfo.GetQuantizationScale(),
182  outputInfo.GetQuantizationOffset());
183 
184  // Decoders/Encoders for internal states
185  std::unique_ptr<Decoder<float>> inputGateDecoder =
186  MakeDecoder<float>(inputGateInfo, inputGateData.data());
187  std::unique_ptr<Decoder<float>> cellGateDecoder =
188  MakeDecoder<float>(cellGateInfo, cellGateData.data());
189  std::unique_ptr<Decoder<float>> forgetGateDecoder =
190  MakeDecoder<float>(forgetGateInfo, forgetGateData.data());
191  std::unique_ptr<Decoder<float>> outputGateDecoder =
192  MakeDecoder<float>(outputGateInfo, outputGateData.data());
193  std::unique_ptr<Decoder<float>> hiddenStateDecoder =
194  MakeDecoder<float>(hiddenStateInfo, hiddenStateData.data());
195 
196  std::unique_ptr<Encoder<float>> inputGateEncoder =
197  MakeEncoder<float>(inputGateInfo, inputGateData.data());
198  std::unique_ptr<Encoder<float>> cellGateEncoder =
199  MakeEncoder<float>(cellGateInfo, cellGateData.data());
200  std::unique_ptr<Encoder<float>> forgetGateEncoder =
201  MakeEncoder<float>(forgetGateInfo, forgetGateData.data());
202  std::unique_ptr<Encoder<float>> outputGateEncoder =
203  MakeEncoder<float>(outputGateInfo, outputGateData.data());
204  std::unique_ptr<Encoder<float>> hiddenStateEncoder =
205  MakeEncoder<float>(hiddenStateInfo, hiddenStateData.data());
206 
207  // Int16 used to accumulate output to prevent overflowing (after Projection MatMul)
208  std::unique_ptr<Decoder<float>> outputInt16Decoder =
209  MakeDecoder<float>(outputInt16Info, outputInt16Data.data());
210  std::unique_ptr<Encoder<float>> outputInt16Encoder =
211  MakeEncoder<float>(outputInt16Info, outputInt16Data.data());
212 
213  // Create decoders for optional params if they are enabled
214  if (!cifgEnabled)
215  {
216  inputToInputWeightsDecoder = MakeDecoder<float>(
217  m_InputToInputWeightsTensor->GetTensorInfo(), m_InputToInputWeightsTensor->GetConstTensor<void>());
218  recurrentToInputWeightsDecoder = MakeDecoder<float>(m_RecurrentToInputWeightsTensor->GetTensorInfo(),
219  m_RecurrentToInputWeightsTensor->GetConstTensor<void>());
220  }
221 
222  if (peepholeEnabled)
223  {
224  if (!cifgEnabled)
225  {
226  cellToInputWeightsDecoder = MakeDecoder<float>(
227  m_CellToInputWeightsTensor->GetTensorInfo(), m_CellToInputWeightsTensor->GetConstTensor<void>());
228  }
229  cellToForgetWeightsDecoder = MakeDecoder<float>(
230  m_CellToForgetWeightsTensor->GetTensorInfo(), m_CellToForgetWeightsTensor->GetConstTensor<void>());
231  cellToOutputWeightsDecoder = MakeDecoder<float>(
232  m_CellToOutputWeightsTensor->GetTensorInfo(), m_CellToOutputWeightsTensor->GetConstTensor<void>());
233  }
234 
235  if (projectionEnabled)
236  {
237  projectionWeightsDecoder = MakeDecoder<float>(
238  m_ProjectionWeightsTensor->GetTensorInfo(), m_ProjectionWeightsTensor->GetConstTensor<void>());
239  if (m_ProjectionBiasTensor)
240  {
241  projectionBiasDecoder = MakeDecoder<float>(
242  m_ProjectionBiasTensor->GetTensorInfo(), m_ProjectionBiasTensor->GetConstTensor<void>());
243  }
244  }
245 
246  if (layerNormEnabled)
247  {
248  if (!cifgEnabled)
249  {
250  inputLayerNormWeightsDecoder = MakeDecoder<float>(m_InputLayerNormWeightsTensor->GetTensorInfo(),
251  m_InputLayerNormWeightsTensor->GetConstTensor<void>());
252 
253  // Bias only used if layer norm enabled
254  armnn::TensorInfo inputGateBiasTensorInfo({outputSize}, armnn::DataType::Signed32,
255  m_InputLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() / 1024, 0);
256  inputGateBiasDecoder = MakeDecoder<float>(
257  inputGateBiasTensorInfo, m_InputGateBiasTensor->GetConstTensor<void>());
258  }
259 
260  forgetLayerNormWeightsDecoder = MakeDecoder<float>(
261  m_ForgetLayerNormWeightsTensor->GetTensorInfo(),
262  m_ForgetLayerNormWeightsTensor->GetConstTensor<void>());
263  cellLayerNormWeightsDecoder = MakeDecoder<float>(
264  m_CellLayerNormWeightsTensor->GetTensorInfo(), m_CellLayerNormWeightsTensor->GetConstTensor<void>());
265  outputLayerNormWeightsDecoder = MakeDecoder<float>(
266  m_OutputLayerNormWeightsTensor->GetTensorInfo(),
267  m_OutputLayerNormWeightsTensor->GetConstTensor<void>());
268 
269  // Bias only used if layer norm enabled
270  armnn::TensorInfo forgetGateBiasTensorInfo({outputSize}, armnn::DataType::Signed32,
271  m_ForgetLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() / 1024, 0);
272  forgetGateBiasDecoder = MakeDecoder<float>(
273  forgetGateBiasTensorInfo, m_ForgetGateBiasTensor->GetConstTensor<void>());
274 
275  armnn::TensorInfo cellGateBiasTensorInfo({outputSize}, armnn::DataType::Signed32,
276  m_CellLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() / 1024, 0);
277  cellGateBiasDecoder = MakeDecoder<float>(
278  cellGateBiasTensorInfo, m_CellBiasTensor->GetConstTensor<void>());
279 
280  armnn::TensorInfo outputGateBiasTensorInfo({outputSize}, armnn::DataType::Signed32,
281  m_OutputLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() / 1024, 0);
282  outputGateBiasDecoder = MakeDecoder<float>(
283  outputGateBiasTensorInfo, m_OutputGateBiasTensor->GetConstTensor<void>());
284  }
285 
286  // Initialize internal state tensors with zeroes.
287  if (!cifgEnabled)
288  {
289  ZeroVector(*inputGateEncoder, stateTensorSize);
290  }
291  ZeroVector(*forgetGateEncoder, stateTensorSize);
292  ZeroVector(*cellGateEncoder, stateTensorSize);
293  ZeroVector(*outputGateEncoder, stateTensorSize);
294  ZeroVector(*hiddenStateEncoder, stateTensorSize);
295 
296  // Input weights * Input
297  if (!cifgEnabled)
298  {
299  MatrixBatchVectorMultiplyAccumulate(*inputToInputWeightsDecoder,
300  numUnits, inputSize, *inputDecoder, numBatches, *inputGateEncoder);
301  }
302 
303  MatrixBatchVectorMultiplyAccumulate(*inputToForgetWeightsDecoder,
304  numUnits, inputSize, *inputDecoder, numBatches, *forgetGateEncoder);
305 
306  MatrixBatchVectorMultiplyAccumulate(*inputToCellWeightsDecoder,
307  numUnits, inputSize, *inputDecoder, numBatches, *cellGateEncoder);
308 
309  MatrixBatchVectorMultiplyAccumulate(*inputToOutputWeightsDecoder,
310  numUnits, inputSize, *inputDecoder, numBatches, *outputGateEncoder);
311 
312  // Recurrent weights * OutputStateIn
313  if (!cifgEnabled)
314  {
315  MatrixBatchVectorMultiplyAccumulate(*recurrentToInputWeightsDecoder,
316  numUnits, outputSize, *outputStateInDecoder, numBatches, *inputGateEncoder);
317  }
318 
319  MatrixBatchVectorMultiplyAccumulate(*recurrentToForgetWeightsDecoder,
320  numUnits, outputSize, *outputStateInDecoder, numBatches, *forgetGateEncoder);
321 
322  MatrixBatchVectorMultiplyAccumulate(*recurrentToCellWeightsDecoder,
323  numUnits, outputSize, *outputStateInDecoder, numBatches, *cellGateEncoder);
324 
325  MatrixBatchVectorMultiplyAccumulate(*recurrentToOutputWeightsDecoder,
326  numUnits, outputSize, *outputStateInDecoder, numBatches, *outputGateEncoder);
327 
328  // Input gate.
329  if (!cifgEnabled)
330  {
331  if (peepholeEnabled)
332  {
333  VectorBatchVectorCwiseProductAccumulate(*cellToInputWeightsDecoder,
334  numUnits, *cellStateInDecoder, numBatches, *inputGateEncoder);
335  }
336 
337  if (layerNormEnabled)
338  {
339  inputGateInfo.SetQuantizationScale(inputInfo.GetQuantizationScale() *
340  m_InputLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() *
341  1024);
342  inputGateEncoder = MakeEncoder<float>(inputGateInfo, inputGateData.data());
343 
344  MeanStddevNormalization(*inputGateDecoder,
345  *inputGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);
346 
347  inputGateDecoder = MakeDecoder<float>(inputGateInfo, inputGateData.data());
348 
349  VectorBatchVectorCwiseProduct(*inputLayerNormWeightsDecoder,
350  numUnits, *inputGateDecoder, numBatches, *inputGateEncoder);
351 
352  inputGateInfo.SetQuantizationScale(1.f / 4096);
353  inputGateEncoder = MakeEncoder<float>(inputGateInfo, inputGateData.data());
354 
355  VectorBatchVectorAdd(*inputGateBiasDecoder,
356  numUnits, *inputGateDecoder, numBatches, *inputGateEncoder);
357 
358  inputGateDecoder = MakeDecoder<float>(inputGateInfo, inputGateData.data());
359  }
360 
361  inputGateInfo.SetQuantizationScale(cellStateOutInfo.GetQuantizationScale());
362  inputGateEncoder = MakeEncoder<float>(inputGateInfo, inputGateData.data());
363 
364  // Input gate sigmoid
365  Activation(*inputGateDecoder, *inputGateEncoder,
366  TensorInfo({numUnits, numBatches}, internalType),
367  ActivationFunction::Sigmoid, 0, 0);
368 
369  inputGateDecoder = MakeDecoder<float>(inputGateInfo, inputGateData.data());
370  }
371 
372  // Forget gate
373  if (peepholeEnabled)
374  {
375  VectorBatchVectorCwiseProductAccumulate(*cellToForgetWeightsDecoder, numUnits,
376  *cellStateInDecoder, numBatches, *forgetGateEncoder);
377  }
378 
379  if (layerNormEnabled)
380  {
381  // Quantize layer norm output to Input Scale * m_ForgetLayerNormWeightsTensor * 1024
382  forgetGateInfo.SetQuantizationScale(inputInfo.GetQuantizationScale() *
383  m_ForgetLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() *
384  1024);
385  forgetGateEncoder = MakeEncoder<float>(forgetGateInfo, forgetGateData.data());
386 
387 
388 
389  MeanStddevNormalization(*forgetGateDecoder,
390  *forgetGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);
391 
392 
393  forgetGateDecoder = MakeDecoder<float>(forgetGateInfo, forgetGateData.data());
394 
395  VectorBatchVectorCwiseProduct(*forgetLayerNormWeightsDecoder,
396  numUnits, *forgetGateDecoder, numBatches, *forgetGateEncoder);
397 
398 
399  // Dequantize layer norm output to (1 / 4096)
400  forgetGateInfo.SetQuantizationScale(1.f / 4096);
401  forgetGateEncoder = MakeEncoder<float>(forgetGateInfo, forgetGateData.data());
402 
403  VectorBatchVectorAdd(*forgetGateBiasDecoder,
404  numUnits, *forgetGateDecoder, numBatches, *forgetGateEncoder);
405 
406 
407  forgetGateDecoder = MakeDecoder<float>(forgetGateInfo, forgetGateData.data());
408  }
409 
410  forgetGateInfo.SetQuantizationScale(cellStateOutInfo.GetQuantizationScale());
411  forgetGateEncoder = MakeEncoder<float>(forgetGateInfo, forgetGateData.data());
412 
413  // Forget gate sigmoid
414  Activation(*forgetGateDecoder, *forgetGateEncoder,
415  TensorInfo({numUnits, numBatches}, internalType),
416  ActivationFunction::Sigmoid, 0, 0);
417 
418  forgetGateDecoder = MakeDecoder<float>(forgetGateInfo, forgetGateData.data());
419 
420  // Cell (Modulation) gate
421  if (layerNormEnabled)
422  {
423  cellGateInfo.SetQuantizationScale(inputInfo.GetQuantizationScale() *
424  m_CellLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() *
425  1024);
426  cellGateEncoder = MakeEncoder<float>(cellGateInfo, cellGateData.data());
427 
428  MeanStddevNormalization(*cellGateDecoder, *cellGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);
429 
430  cellGateDecoder = MakeDecoder<float>(cellGateInfo, cellGateData.data());
431 
432  VectorBatchVectorCwiseProduct(*cellLayerNormWeightsDecoder,
433  numUnits, *cellGateDecoder, numBatches, *cellGateEncoder);
434 
435  cellGateInfo.SetQuantizationScale(1.f / 4096);
436  cellGateEncoder = MakeEncoder<float>(cellGateInfo, cellGateData.data());
437 
438  VectorBatchVectorAdd(*cellGateBiasDecoder,
439  numUnits, *cellGateDecoder, numBatches, *cellGateEncoder);
440 
441  cellGateDecoder = MakeDecoder<float>(cellGateInfo, cellGateData.data());
442  }
443 
444  cellGateInfo.SetQuantizationScale(cellStateOutInfo.GetQuantizationScale());
445  cellGateEncoder = MakeEncoder<float>(cellGateInfo, cellGateData.data());
446 
447  // Cell (Modulation) gate tanH
448  Activation(*cellGateDecoder, *cellGateEncoder,
449  TensorInfo({numUnits, numBatches}, internalType),
450  ActivationFunction::TanH, 1.0f, 1.0f);
451 
452  cellGateDecoder = MakeDecoder<float>(cellGateInfo, cellGateData.data());
453 
454  VectorVectorCwiseProduct(*forgetGateDecoder, *cellStateInDecoder, stateTensorSize, *cellStateOutEncoder);
455 
456  if (cifgEnabled)
457  {
458  Sub1Vector(*forgetGateDecoder, stateTensorSize, *forgetGateEncoder);
459  VectorVectorCwiseProductAccumulate(
460  *cellGateDecoder, *forgetGateDecoder, stateTensorSize, *cellStateOutEncoder);
461  }
462  else
463  {
464  VectorVectorCwiseProductAccumulate(
465  *cellGateDecoder, *inputGateDecoder, stateTensorSize, *cellStateOutEncoder);
466  }
467 
468  // Final cell state out calculated here
469  if (m_Data.m_Parameters.m_CellClip > 0.0)
470  {
471  ClipVector(*cellStateOutDecoder, stateTensorSize, m_Data.m_Parameters.m_CellClip, *cellStateOutEncoder);
472  }
473 
474  // Output gate.
475  if (peepholeEnabled)
476  {
477  VectorBatchVectorCwiseProductAccumulate(*cellToOutputWeightsDecoder,
478  numUnits, *cellStateOutDecoder, numBatches, *outputGateEncoder);
479  }
480 
481  if (layerNormEnabled)
482  {
483  outputGateInfo.SetQuantizationScale(inputInfo.GetQuantizationScale() *
484  m_OutputLayerNormWeightsTensor->GetTensorInfo().GetQuantizationScale() *
485  1024);
486  outputGateEncoder = MakeEncoder<float>(outputGateInfo, outputGateData.data());
487 
488  MeanStddevNormalization(*outputGateDecoder, *outputGateEncoder, numUnits, numBatches, m_LayerNormEpsilon);
489 
490  outputGateDecoder = MakeDecoder<float>(outputGateInfo, outputGateData.data());
491 
492  VectorBatchVectorCwiseProduct(*outputLayerNormWeightsDecoder, numUnits, *outputGateDecoder,
493  numBatches, *outputGateEncoder);
494 
495  outputGateInfo.SetQuantizationScale(1.f / 4096);
496  outputGateEncoder = MakeEncoder<float>(outputGateInfo, outputGateData.data());
497 
498  VectorBatchVectorAdd(*outputGateBiasDecoder, numUnits, *outputGateDecoder, numBatches, *outputGateEncoder);
499 
500  outputGateDecoder = MakeDecoder<float>(outputGateInfo, outputGateData.data());
501  }
502 
503  outputGateInfo.SetQuantizationScale(cellStateOutInfo.GetQuantizationScale());
504  outputGateEncoder = MakeEncoder<float>(outputGateInfo, outputGateData.data());
505 
506  // Output gate sigmoid
507  Activation(*outputGateDecoder, *outputGateEncoder,
508  TensorInfo({numUnits, numBatches}, internalType),
509  ActivationFunction::Sigmoid, 0, 0);
510 
511  outputGateDecoder = MakeDecoder<float>(outputGateInfo, outputGateData.data());
512 
513  // Hidden state tanH
514  Activation(*cellStateOutDecoder, *cellGateEncoder,
515  TensorInfo({numUnits, numBatches}, internalType),
516  ActivationFunction::TanH, 1.0f, 1.0f);
517 
518  // Final hidden state output
519  VectorVectorCwiseProduct(*outputGateDecoder, *cellGateDecoder, stateTensorSize, *hiddenStateEncoder);
520 
521  // Projection
522  if (m_Data.m_Parameters.m_ProjectionEnabled)
523  {
524  if (m_ProjectionBiasTensor)
525  {
526  VectorBatchVectorAssign(*projectionBiasDecoder, outputSize, numBatches, *outputInt16Encoder);
527  }
528 
529  MatrixBatchVectorMultiplyAccumulate(*projectionWeightsDecoder, outputSize, numUnits, *hiddenStateDecoder,
530  numBatches, *outputInt16Encoder);
531 
532  CopyVector(*outputInt16Decoder, numBatches * outputSize, *outputEncoder);
533 
534  if (m_Data.m_Parameters.m_ProjectionClip > 0.0)
535  {
536  ClipVector(*outputDecoder, numBatches * outputSize, m_Data.m_Parameters.m_ProjectionClip, *outputEncoder);
537  }
538  }
539  else
540  {
541  // Output has same quantization scale as hidden state if projection is disabled
542  CopyVector(*hiddenStateDecoder, numBatches * outputSize, *outputEncoder);
543  }
544 
545  // output == outputStateOut
546  CopyVector(*outputDecoder, numBatches * outputSize, *outputStateOutEncoder);
547 }
548 
549 } //namespace armnn
VectorVectorCwiseProduct
void VectorVectorCwiseProduct(armnn::Decoder< float > &vector1, armnn::Decoder< float > &vector2, uint32_t vSize, armnn::Encoder< float > &outResult)
Definition: LstmUtils.cpp:187
armnn::QLstmDescriptor::m_ForgetIntermediateScale
float m_ForgetIntermediateScale
Forget intermediate quantization scale.
Definition: Descriptors.hpp:1428
armnn::QLstmDescriptor::m_ProjectionEnabled
bool m_ProjectionEnabled
Enable/disable the projection layer.
Definition: Descriptors.hpp:1422
MeanStddevNormalization
void MeanStddevNormalization(armnn::Decoder< float > &input_vector, armnn::Encoder< float > &output_vector, uint32_t v_size, uint32_t n_batch, float normalization_epsilon)
Definition: LstmUtils.cpp:40
VectorBatchVectorCwiseProductAccumulate
void VectorBatchVectorCwiseProductAccumulate(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Decoder< float > &batchVector, uint32_t nBatch, armnn::Encoder< float > &outResult)
Definition: LstmUtils.cpp:131
VectorBatchVectorAdd
void VectorBatchVectorAdd(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Decoder< float > &batchVector, uint32_t nBatch, armnn::Encoder< float > &outResult)
Definition: LstmUtils.cpp:16
armnn::experimental::ExecutionData::m_Data
void * m_Data
Definition: ExecutionData.hpp:16
armnn::TensorInfo::GetQuantizationScale
float GetQuantizationScale() const
Definition: Tensor.cpp:461
armnn::TensorInfo
Definition: Tensor.hpp:152
armnn::RefQLstmWorkload::RefQLstmWorkload
RefQLstmWorkload(const QLstmQueueDescriptor &descriptor, const WorkloadInfo &info)
Definition: RefQLstmWorkload.cpp:16
armnn::ActivationFunction::TanH
@ TanH
VectorBatchVectorCwiseProduct
void VectorBatchVectorCwiseProduct(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Decoder< float > &batchVector, uint32_t nBatch, armnn::Encoder< float > &outResult)
Definition: LstmUtils.cpp:152
ClipVector
void ClipVector(armnn::Decoder< float > &vector, uint32_t vSize, float absLimit, armnn::Encoder< float > &outResult)
Definition: LstmUtils.cpp:229
AssignScopedTensorHandle
std::unique_ptr< armnn::ScopedTensorHandle > AssignScopedTensorHandle(const armnn::ConstTensorHandle *ptr)
Definition: LstmUtils.cpp:299
MatrixBatchVectorMultiplyAccumulate
void MatrixBatchVectorMultiplyAccumulate(armnn::Decoder< float > &matrix, uint32_t mRows, uint32_t mCols, armnn::Decoder< float > &vector, uint32_t nBatch, armnn::Encoder< float > &outResult)
Definition: LstmUtils.cpp:87
ARMNN_SCOPED_PROFILING_EVENT_REF_NAME_GUID
#define ARMNN_SCOPED_PROFILING_EVENT_REF_NAME_GUID(label)
Creates a profiling event that uses GetGuid() and GetName() from the calling class.
Definition: RefWorkloadUtils.hpp:22
armnn::QLstmDescriptor::m_InputIntermediateScale
float m_InputIntermediateScale
Input intermediate quantization scale.
Definition: Descriptors.hpp:1426
CopyVector
void CopyVector(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Encoder< float > &outResult)
Definition: LstmUtils.cpp:244
Sub1Vector
void Sub1Vector(armnn::Decoder< float > &vector, uint32_t vSize, armnn::Encoder< float > &result)
Definition: LstmUtils.cpp:173
armnn::QLstmDescriptor::m_CellIntermediateScale
float m_CellIntermediateScale
Cell intermediate quantization scale.
Definition: Descriptors.hpp:1430
armnn::DataType::QSymmS16
@ QSymmS16
RefQLstmWorkload.hpp
armnn::TensorShape
Definition: Tensor.hpp:20
armnn::QueueDescriptorWithParameters::m_Parameters
LayerDescriptor m_Parameters
Definition: WorkloadData.hpp:66
armnn::WorkloadInfo
Contains information about TensorInfos of a layer.
Definition: WorkloadInfo.hpp:16
armnn::DataType
DataType
Definition: Types.hpp:48
armnn::GetTensorInfo
const TensorInfo & GetTensorInfo(const ITensorHandle *tensorHandle)
float32 helpers
Definition: RefWorkloadUtils.hpp:33
Activation.hpp
armnn::QLstmDescriptor::m_ProjectionClip
float m_ProjectionClip
Clipping threshold value for the projection.
Definition: Descriptors.hpp:1416
armnn::QLstmQueueDescriptor
Definition: WorkloadData.hpp:562
armnn::BoostLogSeverityMapping::info
@ info
armnn::QLstmDescriptor::m_OutputIntermediateScale
float m_OutputIntermediateScale
Output intermediate quantization scale.
Definition: Descriptors.hpp:1432
armnn::QueueDescriptor::m_Outputs
std::vector< ITensorHandle * > m_Outputs
Definition: WorkloadData.hpp:27
armnn::DataType::Signed32
@ Signed32
ZeroVector
void ZeroVector(armnn::Encoder< float > &vector, uint32_t vSize)
Definition: LstmUtils.cpp:76
armnn::DataType::QAsymmS8
@ QAsymmS8
RefWorkloadUtils.hpp
armnn::BaseWorkload< QLstmQueueDescriptor >::m_Data
QLstmQueueDescriptor m_Data
Definition: Workload.hpp:89
armnn::TensorInfo::GetShape
const TensorShape & GetShape() const
Definition: Tensor.hpp:193
armnn::QLstmDescriptor::m_HiddenStateZeroPoint
int32_t m_HiddenStateZeroPoint
Hidden State zero point.
Definition: Descriptors.hpp:1434
armnn::RefQLstmWorkload::Execute
void Execute() const override
Definition: RefQLstmWorkload.cpp:46
armnn::QLstmDescriptor::m_CifgEnabled
bool m_CifgEnabled
Enable/disable CIFG (coupled input & forget gate).
Definition: Descriptors.hpp:1418
Decoders.hpp
armnn::LayerType::Map
@ Map
VectorVectorCwiseProductAccumulate
void VectorVectorCwiseProductAccumulate(armnn::Decoder< float > &vector1, armnn::Decoder< float > &vector2, uint32_t vSize, armnn::Encoder< float > &outResult)
Definition: LstmUtils.cpp:204
armnn::experimental::WorkingMemDescriptor::m_Inputs
std::vector< ITensorHandle * > m_Inputs
Definition: WorkingMemDescriptor.hpp:20
armnn
Copyright (c) 2021 ARM Limited and Contributors.
Definition: 01_00_quick_start.dox:6
armnn::experimental::WorkingMemDescriptor
Definition: WorkingMemDescriptor.hpp:18
armnn::QLstmDescriptor::m_HiddenStateScale
float m_HiddenStateScale
Hidden State quantization scale.
Definition: Descriptors.hpp:1436
armnn::QLstmDescriptor::m_CellClip
float m_CellClip
Clipping threshold value for the cell state.
Definition: Descriptors.hpp:1414
armnn::Activation
float Activation(float in, ActivationFunction function, float a, float b)
Definition: Activation.cpp:13
armnn::QLstmDescriptor::m_LayerNormEnabled
bool m_LayerNormEnabled
Enable/disable layer normalization.
Definition: Descriptors.hpp:1424
LstmUtils.hpp
armnn::TensorInfo::GetQuantizationOffset
int32_t GetQuantizationOffset() const
Definition: Tensor.cpp:478
Encoders.hpp
armnn::RefBaseWorkload
Definition: RefBaseWorkload.hpp:13
armnn::QLstmDescriptor::m_PeepholeEnabled
bool m_PeepholeEnabled
Enable/disable peephole.
Definition: Descriptors.hpp:1420
armnn::RefQLstmWorkload::ExecuteAsync
void ExecuteAsync(ExecutionData &executionData) override
Definition: RefQLstmWorkload.cpp:51
armnn::experimental::WorkingMemDescriptor::m_Outputs
std::vector< ITensorHandle * > m_Outputs
Definition: WorkingMemDescriptor.hpp:21
armnn::QueueDescriptor::m_Inputs
std::vector< ITensorHandle * > m_Inputs
Definition: WorkloadData.hpp:26
armnn::ActivationFunction::Sigmoid
@ Sigmoid
VectorBatchVectorAssign
void VectorBatchVectorAssign(armnn::Decoder< float > &vector, uint32_t vSize, uint32_t nBatch, armnn::Encoder< float > &outBatchVector)
Definition: LstmUtils.cpp:113
armnn::experimental::ExecutionData
Definition: ExecutionData.hpp:14