IVGCVSW-6806 Add Unidirectional Sequence Lstm support to Neon
* Corrected TensorInfo order for IsUnidirectionalSequenceLstmSupported
* outputStateOut TensorInfo is not optional.
* cellStateOut TensorInfo is not optional.
* TensorInfo Order matches other QLSTM/LSTM layers.
* Added missing parameters to UnidirectionalSequenceLstmOperator for
delegate.
* Added quantized UnidirectionalSequenceLstm support to Neon
!android-nn-driver:7457
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: I26dde1bb96793dd25eb9081ca5ae5f63752288c4
diff --git a/src/armnn/BackendHelper.cpp b/src/armnn/BackendHelper.cpp
index 056fbb0..e2aa672 100644
--- a/src/armnn/BackendHelper.cpp
+++ b/src/armnn/BackendHelper.cpp
@@ -1332,16 +1332,14 @@
bool LayerSupportHandle::IsUnidirectionalSequenceLstmSupported(const TensorInfo& input,
const TensorInfo& outputStateIn,
const TensorInfo& cellStateIn,
+ const TensorInfo& outputStateOut,
+ const TensorInfo& cellStateOut,
const TensorInfo& output,
- const Optional<TensorInfo>& hiddenStateOutput,
- const Optional<TensorInfo>& cellStateOutput,
const LstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo,
Optional<std::string&> reasonIfUnsupported)
{
- TensorInfo hiddenStateOutputVal = hiddenStateOutput.has_value() ? hiddenStateOutput.value() : TensorInfo();
- TensorInfo cellStateOutputVal = cellStateOutput.has_value() ? cellStateOutput.value() : TensorInfo();
- TensorInfos infos{input, outputStateIn, cellStateIn, hiddenStateOutputVal, cellStateOutputVal, output};
+ TensorInfos infos{input, outputStateIn, cellStateIn, outputStateOut, cellStateOut, output};
return m_LayerSupport->IsLayerSupported(LayerType::UnidirectionalSequenceLstm,
infos,
diff --git a/src/armnn/ILayerSupport.cpp b/src/armnn/ILayerSupport.cpp
index bf54223..5366b13 100644
--- a/src/armnn/ILayerSupport.cpp
+++ b/src/armnn/ILayerSupport.cpp
@@ -488,57 +488,15 @@
"hiddenStateOutputVal, cellStateOutputVal, output}");
}
auto desc = *(PolymorphicDowncast<const UnidirectionalSequenceLstmDescriptor*>(&descriptor));
-
- bool isHiddenStateOutputOptional = (infos[4] == TensorInfo());
- bool isCellStateOutput = (infos[5] == TensorInfo());
- if (isHiddenStateOutputOptional && isCellStateOutput)
- {
- return IsUnidirectionalSequenceLstmSupported(infos[0],
- infos[1],
- infos[2],
- infos[3],
- EmptyOptional(),
- EmptyOptional(),
- desc,
- lstmParamsInfo.value(),
- reasonIfUnsupported);
- }
- else if (isHiddenStateOutputOptional)
- {
- return IsUnidirectionalSequenceLstmSupported(infos[0],
- infos[1],
- infos[2],
- infos[3],
- EmptyOptional(),
- infos[5],
- desc,
- lstmParamsInfo.value(),
- reasonIfUnsupported);
- }
- else if (isCellStateOutput)
- {
- return IsUnidirectionalSequenceLstmSupported(infos[0],
- infos[1],
- infos[2],
- infos[3],
- infos[4],
- EmptyOptional(),
- desc,
- lstmParamsInfo.value(),
- reasonIfUnsupported);
- }
- else
- {
- return IsUnidirectionalSequenceLstmSupported(infos[0],
- infos[1],
- infos[2],
- infos[3],
- infos[4],
- infos[5],
- desc,
- lstmParamsInfo.value(),
- reasonIfUnsupported);
- }
+ return IsUnidirectionalSequenceLstmSupported(infos[0],
+ infos[1],
+ infos[2],
+ infos[3],
+ infos[4],
+ infos[5],
+ desc,
+ lstmParamsInfo.value(),
+ reasonIfUnsupported);
}
case LayerType::ChannelShuffle:
return IsChannelShuffleSupported(infos[0],
@@ -1285,9 +1243,9 @@
const TensorInfo& input,
const TensorInfo& outputStateIn,
const TensorInfo& cellStateIn,
+ const TensorInfo& outputStateOut,
+ const TensorInfo& cellStateOut,
const TensorInfo& output,
- const Optional<TensorInfo>& hiddenStateOutput,
- const Optional<TensorInfo>& cellStateOutput,
const LstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo,
Optional<std::string&> reasonIfUnsupported) const
@@ -1295,9 +1253,9 @@
IgnoreUnused(input,
outputStateIn,
cellStateIn,
+ outputStateOut,
+ cellStateOut,
output,
- hiddenStateOutput,
- cellStateOutput,
descriptor,
paramsInfo,
reasonIfUnsupported);
diff --git a/src/armnn/layers/UnidirectionalSequenceLstmLayer.cpp b/src/armnn/layers/UnidirectionalSequenceLstmLayer.cpp
index 1999614..e5f89bd 100644
--- a/src/armnn/layers/UnidirectionalSequenceLstmLayer.cpp
+++ b/src/armnn/layers/UnidirectionalSequenceLstmLayer.cpp
@@ -15,7 +15,7 @@
{
UnidirectionalSequenceLstmLayer::UnidirectionalSequenceLstmLayer(const LstmDescriptor& param, const char* name)
- : LayerWithParameters(3, 1, LayerType::UnidirectionalSequenceLstm, param, name)
+ : LayerWithParameters(3, 3, LayerType::UnidirectionalSequenceLstm, param, name)
{
}
@@ -171,7 +171,7 @@
{
VerifyLayerConnections(3, CHECK_LOCATION());
- const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
+ const TensorShape& outputShape = GetOutputSlot(2).GetTensorInfo().GetShape();
VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
diff --git a/src/armnnDeserializer/Deserializer.cpp b/src/armnnDeserializer/Deserializer.cpp
index 75c60cc..93fa99d 100644
--- a/src/armnnDeserializer/Deserializer.cpp
+++ b/src/armnnDeserializer/Deserializer.cpp
@@ -3616,7 +3616,7 @@
CHECK_VALID_SIZE(inputs.size(), 3);
auto outputs = GetOutputs(graph, layerIndex);
- CHECK_VALID_SIZE(outputs.size(), 1);
+ CHECK_VALID_SIZE(outputs.size(), 3);
auto flatBufferLayer = graph->layers()->Get(layerIndex)->layer_as_UnidirectionalSequenceLstmLayer();
auto layerName = GetLayerName(graph, layerIndex);
@@ -3714,8 +3714,14 @@
lstmInputParams,
layerName.c_str());
- armnn::TensorInfo outputTensorInfo1 = ToTensorInfo(outputs[0]);
- layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo1);
+ armnn::TensorInfo outputTensorInfo0 = ToTensorInfo(outputs[0]);
+ layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo0);
+
+ armnn::TensorInfo outputTensorInfo1 = ToTensorInfo(outputs[1]);
+ layer->GetOutputSlot(1).SetTensorInfo(outputTensorInfo1);
+
+ armnn::TensorInfo outputTensorInfo2 = ToTensorInfo(outputs[2]);
+ layer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo2);
RegisterInputSlots(graph, layerIndex, layer);
RegisterOutputSlots(graph, layerIndex, layer);
diff --git a/src/armnnSerializer/test/LstmSerializationTests.cpp b/src/armnnSerializer/test/LstmSerializationTests.cpp
index d8f8967..ae2d813 100644
--- a/src/armnnSerializer/test/LstmSerializationTests.cpp
+++ b/src/armnnSerializer/test/LstmSerializationTests.cpp
@@ -2299,6 +2299,8 @@
armnn::TensorInfo inputTensorInfo({ batchSize, timeSize, inputSize }, armnn::DataType::Float32);
armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
+ armnn::TensorInfo outputStateOutTensorInfo({ batchSize, timeSize, outputSize }, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateOutTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({ batchSize, timeSize, outputSize }, armnn::DataType::Float32);
inputLayer->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(0));
@@ -2310,8 +2312,10 @@
cellStateIn->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(2));
cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
- unidirectionalSequenceLstmLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
- unidirectionalSequenceLstmLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+ unidirectionalSequenceLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateOutTensorInfo);
+ unidirectionalSequenceLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateOutTensorInfo);
+ unidirectionalSequenceLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
+ unidirectionalSequenceLstmLayer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo);
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
CHECK(deserializedNetwork);
@@ -2319,7 +2323,7 @@
VerifyLstmLayer<armnn::UnidirectionalSequenceLstmDescriptor> checker(
layerName,
{inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
- {outputTensorInfo},
+ {outputStateOutTensorInfo, cellStateOutTensorInfo, outputTensorInfo},
descriptor,
params);
deserializedNetwork->ExecuteStrategy(checker);
@@ -2436,6 +2440,8 @@
armnn::TensorInfo inputTensorInfo({ batchSize, timeSize, inputSize }, armnn::DataType::Float32);
armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
+ armnn::TensorInfo outputStateOutTensorInfo({ batchSize, timeSize, outputSize }, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateOutTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({ batchSize, timeSize, outputSize }, armnn::DataType::Float32);
inputLayer->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(0));
@@ -2447,8 +2453,10 @@
cellStateIn->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(2));
cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
- unidirectionalSequenceLstmLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
- unidirectionalSequenceLstmLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+ unidirectionalSequenceLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateOutTensorInfo);
+ unidirectionalSequenceLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateOutTensorInfo);
+ unidirectionalSequenceLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
+ unidirectionalSequenceLstmLayer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo);
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
CHECK(deserializedNetwork);
@@ -2456,7 +2464,7 @@
VerifyLstmLayer<armnn::UnidirectionalSequenceLstmDescriptor> checker(
layerName,
{inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
- {outputTensorInfo},
+ {outputStateOutTensorInfo, cellStateOutTensorInfo, outputTensorInfo},
descriptor,
params);
deserializedNetwork->ExecuteStrategy(checker);
@@ -2592,6 +2600,8 @@
armnn::TensorInfo inputTensorInfo({ batchSize, timeSize, inputSize }, armnn::DataType::Float32);
armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
+ armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({ batchSize, timeSize, outputSize }, armnn::DataType::Float32);
inputLayer->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(0));
@@ -2603,8 +2613,10 @@
cellStateIn->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(2));
cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
- unidirectionalSequenceLstmLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
- unidirectionalSequenceLstmLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+ unidirectionalSequenceLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateOutTensorInfo);
+ unidirectionalSequenceLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateOutTensorInfo);
+ unidirectionalSequenceLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
+ unidirectionalSequenceLstmLayer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo);
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
CHECK(deserializedNetwork);
@@ -2612,7 +2624,7 @@
VerifyLstmLayer<armnn::UnidirectionalSequenceLstmDescriptor> checker(
layerName,
{inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
- {outputTensorInfo},
+ {outputStateOutTensorInfo, cellStateOutTensorInfo, outputTensorInfo},
descriptor,
params);
deserializedNetwork->ExecuteStrategy(checker);
@@ -2697,6 +2709,8 @@
armnn::TensorInfo inputTensorInfo({ timeSize, batchSize, inputSize }, armnn::DataType::Float32);
armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
+ armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({ timeSize, batchSize, outputSize }, armnn::DataType::Float32);
inputLayer->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(0));
@@ -2708,8 +2722,10 @@
cellStateIn->GetOutputSlot(0).Connect(unidirectionalSequenceLstmLayer->GetInputSlot(2));
cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
- unidirectionalSequenceLstmLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
- unidirectionalSequenceLstmLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+ unidirectionalSequenceLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateOutTensorInfo);
+ unidirectionalSequenceLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateOutTensorInfo);
+ unidirectionalSequenceLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
+ unidirectionalSequenceLstmLayer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo);
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
CHECK(deserializedNetwork);
@@ -2717,7 +2733,7 @@
VerifyLstmLayer<armnn::UnidirectionalSequenceLstmDescriptor> checker(
layerName,
{inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
- {outputTensorInfo},
+ {outputStateOutTensorInfo, cellStateOutTensorInfo, outputTensorInfo},
descriptor,
params);
deserializedNetwork->ExecuteStrategy(checker);
diff --git a/src/backends/backendsCommon/LayerSupportBase.cpp b/src/backends/backendsCommon/LayerSupportBase.cpp
index 89a0772..0010379 100644
--- a/src/backends/backendsCommon/LayerSupportBase.cpp
+++ b/src/backends/backendsCommon/LayerSupportBase.cpp
@@ -680,9 +680,9 @@
bool LayerSupportBase::IsUnidirectionalSequenceLstmSupported(const TensorInfo&, // input
const TensorInfo&, // outputStateIn
const TensorInfo&, // cellStateIn
+ const TensorInfo&, // outputStateOut
+ const TensorInfo&, // cellStateOut
const TensorInfo&, // output
- const Optional<TensorInfo>&, // hiddenStateOut
- const Optional<TensorInfo>&, // cellStateOut
const LstmDescriptor&, // descriptor
const LstmInputParamsInfo&, // paramsInfo
Optional<std::string&> reasonIfUnsupported) const
diff --git a/src/backends/backendsCommon/LayerSupportBase.hpp b/src/backends/backendsCommon/LayerSupportBase.hpp
index e911c00..618d21e 100644
--- a/src/backends/backendsCommon/LayerSupportBase.hpp
+++ b/src/backends/backendsCommon/LayerSupportBase.hpp
@@ -465,9 +465,9 @@
const TensorInfo& input,
const TensorInfo& outputStateIn,
const TensorInfo& cellStateIn,
+ const TensorInfo& outputStateOut,
+ const TensorInfo& cellStateOut,
const TensorInfo& output,
- const Optional<TensorInfo>& hiddenStateOutput,
- const Optional<TensorInfo>& cellStateOutput,
const LstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo,
Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override;
diff --git a/src/backends/backendsCommon/WorkloadData.cpp b/src/backends/backendsCommon/WorkloadData.cpp
index 70d7641..a2dcd63 100644
--- a/src/backends/backendsCommon/WorkloadData.cpp
+++ b/src/backends/backendsCommon/WorkloadData.cpp
@@ -3860,38 +3860,20 @@
{
throw InvalidArgumentException(descriptorName + ": Invalid number of inputs.");
}
- if (workloadInfo.m_OutputTensorInfos.size() != 1)
+ if (workloadInfo.m_OutputTensorInfos.size() != 3)
{
throw InvalidArgumentException(descriptorName + ": Invalid number of outputs.");
}
std::vector<DataType> supportedTypes =
{
- DataType::Float32
+ DataType::Float32,
+ DataType::QAsymmS8
};
// check for supported type of one input and match them with all the other input and output
ValidateDataTypes(workloadInfo.m_InputTensorInfos[0], supportedTypes, descriptorName);
- // type matches all other inputs
- for (uint32_t i = 1u; i < workloadInfo.m_InputTensorInfos.size(); ++i)
- {
- ValidateTensorDataTypesMatch(workloadInfo.m_InputTensorInfos[0],
- workloadInfo.m_InputTensorInfos[i],
- descriptorName,
- "input_0",
- "input_" + std::to_string(i));
- }
- // type matches all other outputs
- for (uint32_t i = 0u; i < workloadInfo.m_OutputTensorInfos.size(); ++i)
- {
- ValidateTensorDataTypesMatch(workloadInfo.m_InputTensorInfos[0],
- workloadInfo.m_OutputTensorInfos[i],
- "LstmQueueDescriptor",
- "input_0",
- "output_" + std::to_string(i));
- }
-
// Making sure clipping parameters have valid values.
// == 0 means no clipping
// > 0 means clipping
@@ -3936,7 +3918,7 @@
descriptorName + " input_2");
// outputTensor
- ValidateTensorNumDimNumElem(workloadInfo.m_OutputTensorInfos[0], 3, (timeStep * n_batch * n_output),
+ ValidateTensorNumDimNumElem(workloadInfo.m_OutputTensorInfos[2], 3, (timeStep * n_batch * n_output),
descriptorName + " output_0");
// check that dimensions of inputs/outputs and QueueDescriptor data match with each other
diff --git a/src/backends/backendsCommon/WorkloadFactory.cpp b/src/backends/backendsCommon/WorkloadFactory.cpp
index f955aec..5847e8c 100644
--- a/src/backends/backendsCommon/WorkloadFactory.cpp
+++ b/src/backends/backendsCommon/WorkloadFactory.cpp
@@ -1367,7 +1367,9 @@
const TensorInfo& cellStateIn = OverrideDataType(layer.GetInputSlot(2).GetConnection()->GetTensorInfo(),
dataType);
// Outputs
- const TensorInfo& output = OverrideDataType(layer.GetOutputSlot(0).GetTensorInfo(), dataType);
+ const TensorInfo& outputStateOut = OverrideDataType(layer.GetOutputSlot(0).GetTensorInfo(), dataType);
+ const TensorInfo& cellStateOut = OverrideDataType(layer.GetOutputSlot(1).GetTensorInfo(), dataType);
+ const TensorInfo& output = OverrideDataType(layer.GetOutputSlot(2).GetTensorInfo(), dataType);
// Basic parameters
const TensorInfo& inputToForgetWeights
@@ -1481,15 +1483,12 @@
paramsInfo.m_OutputLayerNormWeights = &optOutputLayerNormWeights;
}
- Optional<TensorInfo> hiddenStateOut;
- Optional<TensorInfo> cellStateOut;
-
result = layerSupportObject.IsUnidirectionalSequenceLstmSupported(input,
outputStateIn,
cellStateIn,
- output,
- hiddenStateOut,
+ outputStateOut,
cellStateOut,
+ output,
descriptor,
paramsInfo,
reason);
diff --git a/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp
index c719472..6effa9c 100644
--- a/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp
+++ b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp
@@ -31,7 +31,7 @@
armnn::DataType constantDataType = armnn::DataType::Float32)
{
IgnoreUnused(memoryManager);
- unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[1]);
+ unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[0]);
unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[2]);
unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]);
unsigned numUnits = outputSize;
@@ -39,7 +39,8 @@
armnn::TensorInfo inputTensorInfo({1, batchSize , inputSize}, ArmnnType, qScale, qOffset );
armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset);
armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset);
-
+ armnn::TensorInfo outputStateOutTensorInfo({ batchSize, 1, outputSize }, ArmnnType, qScale, qOffset);
+ armnn::TensorInfo cellStateOutTensorInfo({ batchSize, 1, outputSize }, ArmnnType, qScale, qOffset);
armnn::TensorInfo outputTensorInfo({1, batchSize, outputSize}, ArmnnType, qScale, qOffset);
std::vector<T> inputVector;
@@ -48,6 +49,8 @@
std::vector<T> cellStateInVector(batchSize * numUnits, T());
std::vector<T> outputStateInVector(batchSize * outputSize, T());
+ std::vector<T> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
+ std::vector<T> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
std::vector<T> outputVector;
@@ -59,6 +62,10 @@
std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
armnn::UnidirectionalSequenceLstmQueueDescriptor data;
@@ -68,6 +75,8 @@
AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
+ AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
+ AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
armnn::TensorInfo tensorInfo4({numUnits}, constantDataType , qScale, qOffset);
@@ -184,6 +193,8 @@
outputStateInHandle->Allocate();
cellStateInHandle->Allocate();
+ outputStateOutHandle->Allocate();
+ cellStateOutHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
@@ -192,6 +203,8 @@
workload->Execute();
+ CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
+ CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
return LayerTestResult<T, 3>(actualOutput,
@@ -222,7 +235,8 @@
armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, ArmnnType, qScale, qOffset);
armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);
armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
-
+ armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset);
+ armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset);
armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, ArmnnType, qScale, qOffset);
std::vector<T> inputVector;
@@ -231,6 +245,8 @@
std::vector<T> cellStateInVector(batchSize * numUnits, T());
std::vector<T> outputStateInVector(batchSize * outputSize, T());
+ std::vector<T> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
+ std::vector<T> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
std::vector<T> outputVector;
@@ -242,6 +258,10 @@
std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
armnn::UnidirectionalSequenceLstmQueueDescriptor data;
@@ -251,6 +271,8 @@
AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
+ AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
+ AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
armnn::TensorInfo tensorInfo4({numUnits}, constantDataType, qScale, qOffset);
@@ -359,6 +381,8 @@
outputStateInHandle->Allocate();
cellStateInHandle->Allocate();
+ outputStateOutHandle->Allocate();
+ cellStateOutHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
@@ -367,6 +391,8 @@
workload->Execute();
+ CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
+ CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
return LayerTestResult<T, 3>(actualOutput,
@@ -398,7 +424,8 @@
armnn::TensorInfo inputTensorInfo({timeSize, batchSize, inputSize}, ArmnnType, qScale, qOffset);
armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, ArmnnType, qScale, qOffset);
armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, ArmnnType, qScale, qOffset);
-
+ armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({timeSize, batchSize, outputSize}, ArmnnType, qScale, qOffset);
std::vector<T> inputVector;
@@ -407,6 +434,8 @@
std::vector<T> cellStateInVector(batchSize * numUnits, T());
std::vector<T> outputStateInVector(batchSize * outputSize, T());
+ std::vector<T> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
+ std::vector<T> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
std::vector<T> outputVector;
@@ -418,6 +447,10 @@
std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
armnn::UnidirectionalSequenceLstmQueueDescriptor data;
@@ -427,6 +460,8 @@
AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
+ AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
+ AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
armnn::TensorInfo tensorInfo4({numUnits}, constantDataType, qScale, qOffset);
@@ -535,6 +570,8 @@
outputStateInHandle->Allocate();
cellStateInHandle->Allocate();
+ outputStateOutHandle->Allocate();
+ cellStateOutHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
@@ -543,6 +580,8 @@
workload->Execute();
+ CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
+ CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
return LayerTestResult<T, 3>(actualOutput,
@@ -644,6 +683,8 @@
armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
@@ -654,6 +695,8 @@
std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
+ std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
+ std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
const std::vector<float> expectedOutput = { -0.0135612f, -0.0263441f, 0.0314008f, -0.00883455f, 0.00763052f,
@@ -668,6 +711,11 @@
tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
armnn::UnidirectionalSequenceLstmQueueDescriptor data;
@@ -676,6 +724,9 @@
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
+
+ AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
+ AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
armnn::TensorInfo tensorInfo5({outputSize}, armnn::DataType::Float32);
@@ -849,6 +900,9 @@
inputHandle->Allocate();
outputStateInHandle->Allocate();
cellStateInHandle->Allocate();
+
+ outputStateOutHandle->Allocate();
+ cellStateOutHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
@@ -857,6 +911,8 @@
workload->Execute();
+ CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
+ CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
return LayerTestResult<float, 3>(actualOutput,
@@ -880,6 +936,8 @@
armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
const std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
@@ -889,6 +947,8 @@
std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
+ std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
+ std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
const std::vector<float> expectedOutput = { 0.0642256f, 0.0343966f, 0.184122f, 0.114717f,
@@ -904,6 +964,10 @@
std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
armnn::UnidirectionalSequenceLstmQueueDescriptor data;
@@ -913,6 +977,8 @@
AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
+ AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
+ AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
armnn::TensorInfo tensorInfo4({outputSize}, armnn::DataType::Float32);
@@ -1074,6 +1140,9 @@
inputHandle->Allocate();
outputStateInHandle->Allocate();
cellStateInHandle->Allocate();
+
+ outputStateOutHandle->Allocate();
+ cellStateOutHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
@@ -1082,6 +1151,8 @@
workload->Execute();
+ CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
+ CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
return LayerTestResult<float, 3>(actualOutput,
@@ -1105,7 +1176,8 @@
armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
-
+ armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
std::vector<float> inputVector = { 1., 2., 3., 4., 5., 4.,
@@ -1115,6 +1187,8 @@
std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
+ std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
+ std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
std::vector<float> outputVector = { -0.0129257f, -0.070531f, -0.153508f, -0.0392391f,
@@ -1130,6 +1204,10 @@
std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
armnn::UnidirectionalSequenceLstmQueueDescriptor data;
@@ -1139,6 +1217,8 @@
AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
+ AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
+ AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
armnn::TensorInfo tensorInfo4({numUnits}, armnn::DataType::Float32);
@@ -1236,6 +1316,8 @@
outputStateInHandle->Allocate();
cellStateInHandle->Allocate();
+ outputStateOutHandle->Allocate();
+ cellStateOutHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
@@ -1244,6 +1326,8 @@
workload->Execute();
+ CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
+ CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
return LayerTestResult<float, 3>(actualOutput,
@@ -1267,7 +1351,8 @@
armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
-
+ armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
@@ -1277,6 +1362,8 @@
std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
+ std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
+ std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
const std::vector<float> outputVector = { -0.0142517f, -0.0198845f, -0.0120569f, -0.0116868f,
@@ -1292,8 +1379,13 @@
std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
+
armnn::UnidirectionalSequenceLstmQueueDescriptor data;
armnn::WorkloadInfo info;
@@ -1301,6 +1393,8 @@
AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
+ AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
+ AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32);
@@ -1376,6 +1470,8 @@
outputStateInHandle->Allocate();
cellStateInHandle->Allocate();
+ outputStateOutHandle->Allocate();
+ cellStateOutHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
@@ -1384,6 +1480,8 @@
workload->Execute();
+ CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
+ CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
return LayerTestResult<float, 3>(actualOutput,
@@ -1407,7 +1505,8 @@
armnn::TensorInfo inputTensorInfo({timeSize, batchSize, inputSize}, armnn::DataType::Float32);
armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
-
+ armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({timeSize, batchSize, outputSize}, armnn::DataType::Float32);
const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
@@ -1417,6 +1516,8 @@
std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
+ std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
+ std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
const std::vector<float> outputVector = { -0.0142517f, -0.0198845f, -0.0120122f, -0.0116868f,
@@ -1431,8 +1532,13 @@
std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
+
armnn::UnidirectionalSequenceLstmQueueDescriptor data;
armnn::WorkloadInfo info;
@@ -1440,6 +1546,8 @@
AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
+ AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
+ AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32);
@@ -1516,6 +1624,8 @@
outputStateInHandle->Allocate();
cellStateInHandle->Allocate();
+ outputStateOutHandle->Allocate();
+ cellStateOutHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
@@ -1524,6 +1634,8 @@
workload->Execute();
+ CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
+ CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
return LayerTestResult<float, 3>(actualOutput,
@@ -1547,6 +1659,8 @@
armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
@@ -1556,6 +1670,8 @@
std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
+ std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
+ std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
const std::vector<float> expectedOutput = { 0.612103f, 1.56788f, 0.31966f, 1.42956f,
@@ -1570,6 +1686,11 @@
tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+
+ std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
armnn::UnidirectionalSequenceLstmQueueDescriptor data;
@@ -1578,6 +1699,9 @@
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
+
+ AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
+ AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
armnn::TensorInfo tensorInfoOut({outputSize}, armnn::DataType::Float32);
@@ -1679,6 +1803,9 @@
inputHandle->Allocate();
outputStateInHandle->Allocate();
cellStateInHandle->Allocate();
+
+ outputStateOutHandle->Allocate();
+ cellStateOutHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
@@ -1687,6 +1814,8 @@
workload->Execute();
+ CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
+ CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
return LayerTestResult<float, 3>(actualOutput,
@@ -1710,6 +1839,8 @@
armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, armnn::DataType::Float32);
armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
const std::vector<float> inputVector = { 1., 8., 3., 4., 5., 4.,
@@ -1719,6 +1850,8 @@
std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
+ std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
+ std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
const std::vector<float> expectedOutput = { 0.0471276f, 0.0168155f, 0.0789885f, 0.16550f,
@@ -1734,6 +1867,10 @@
std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
armnn::UnidirectionalSequenceLstmQueueDescriptor data;
@@ -1743,6 +1880,8 @@
AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
+ AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
+ AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
armnn::TensorInfo tensorInfoOut({outputSize}, armnn::DataType::Float32);
@@ -1871,6 +2010,9 @@
inputHandle->Allocate();
outputStateInHandle->Allocate();
cellStateInHandle->Allocate();
+
+ outputStateOutHandle->Allocate();
+ cellStateOutHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
@@ -1879,6 +2021,8 @@
workload->Execute();
+ CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
+ CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
return LayerTestResult<float, 3>(actualOutput,
@@ -1902,7 +2046,8 @@
armnn::TensorInfo inputTensorInfo({batchSize, timeSize, inputSize}, armnn::DataType::Float32);
armnn::TensorInfo cellStateInTensorInfo({batchSize, numUnits}, armnn::DataType::Float32);
armnn::TensorInfo outputStateInTensorInfo({batchSize, outputSize}, armnn::DataType::Float32);
-
+ armnn::TensorInfo outputStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
+ armnn::TensorInfo cellStateOutTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({batchSize, timeSize, outputSize}, armnn::DataType::Float32);
const std::vector<float> inputVector = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f,
@@ -1912,6 +2057,8 @@
std::vector<float> cellStateInVector(batchSize * numUnits, 0.f);
std::vector<float> outputStateInVector(batchSize * outputSize, 0.f);
+ std::vector<float> actualOutputStateOut(outputStateOutTensorInfo.GetNumElements());
+ std::vector<float> actualCellStateOut(cellStateOutTensorInfo.GetNumElements());
std::vector<float> actualOutput(outputTensorInfo.GetNumElements());
const std::vector<float> outputVector = { -0.0072104f, -0.00991171f, -0.00650478f, -0.00713055f,
@@ -1927,6 +2074,10 @@
std::unique_ptr<armnn::ITensorHandle> outputStateInHandle =
tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> outputStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(outputStateOutTensorInfo);
+ std::unique_ptr<armnn::ITensorHandle> cellStateOutHandle =
+ tensorHandleFactory.CreateTensorHandle(cellStateOutTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
armnn::UnidirectionalSequenceLstmQueueDescriptor data;
@@ -1936,6 +2087,8 @@
AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get());
AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get());
+ AddOutputToWorkload(data, info, outputStateOutTensorInfo, outputStateOutHandle.get());
+ AddOutputToWorkload(data, info, cellStateOutTensorInfo, cellStateOutHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
armnn::TensorInfo tensorInfoNumFp({numUnits}, armnn::DataType::Float32);
@@ -2009,6 +2162,8 @@
outputStateInHandle->Allocate();
cellStateInHandle->Allocate();
+ outputStateOutHandle->Allocate();
+ cellStateOutHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputVector.data());
@@ -2017,10 +2172,12 @@
workload->Execute();
+ CopyDataFromITensorHandle(actualOutputStateOut.data(), outputStateOutHandle.get());
+ CopyDataFromITensorHandle(actualCellStateOut.data(), cellStateOutHandle.get());
CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
return LayerTestResult<float, 3>(actualOutput,
outputVector,
outputHandle->GetShape(),
outputTensorInfo.GetShape());
-}
\ No newline at end of file
+}
diff --git a/src/backends/cl/ClLayerSupport.cpp b/src/backends/cl/ClLayerSupport.cpp
index 4dcaca9..09b3f43 100644
--- a/src/backends/cl/ClLayerSupport.cpp
+++ b/src/backends/cl/ClLayerSupport.cpp
@@ -1444,9 +1444,9 @@
bool ClLayerSupport::IsUnidirectionalSequenceLstmSupported(const TensorInfo& input,
const TensorInfo& outputStateIn,
const TensorInfo& cellStateIn,
+ const TensorInfo& outputStateOut,
+ const TensorInfo& cellStateOut,
const TensorInfo& output,
- const Optional<TensorInfo>& hiddenStateOutput,
- const Optional<TensorInfo>& cellStateOutput,
const UnidirectionalSequenceLstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo,
Optional<std::string&> reasonIfUnsupported) const
@@ -1456,9 +1456,9 @@
input,
outputStateIn,
cellStateIn,
+ outputStateOut,
+ cellStateOut,
output,
- hiddenStateOutput,
- cellStateOutput,
descriptor,
paramsInfo);
}
diff --git a/src/backends/cl/ClLayerSupport.hpp b/src/backends/cl/ClLayerSupport.hpp
index b4d0e82..4f4e64e 100644
--- a/src/backends/cl/ClLayerSupport.hpp
+++ b/src/backends/cl/ClLayerSupport.hpp
@@ -334,9 +334,9 @@
bool IsUnidirectionalSequenceLstmSupported(const TensorInfo& input,
const TensorInfo& outputStateIn,
const TensorInfo& cellStateIn,
+ const TensorInfo& outputStateOut,
+ const TensorInfo& cellStateOut,
const TensorInfo& output,
- const Optional<TensorInfo>& hiddenStateOutput,
- const Optional<TensorInfo>& cellStateOutput,
const UnidirectionalSequenceLstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo,
Optional<std::string&> reasonIfUnsupported) const override;
diff --git a/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.cpp b/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.cpp
index cc9aea8..ac24120 100644
--- a/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.cpp
+++ b/src/backends/cl/workloads/ClUnidirectionalSequenceLstmFloatWorkload.cpp
@@ -41,17 +41,17 @@
GetGuid());
const arm_compute::ICLTensor& input = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
- arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
+ arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[2])->GetTensor();
TensorInfo inputInfo = info.m_InputTensorInfos[0];
- TensorInfo outputInfo = info.m_OutputTensorInfos[0];
+ TensorInfo outputInfo = info.m_OutputTensorInfos[2];
arm_compute::DataType armComputeDataType = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetDataType();
armnn::DataType armnnDataType = GetArmNNDataType(armComputeDataType);
TensorShape inputLayerShape = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetShape();
TensorShape cellStateLayerShape = static_cast<IClTensorHandle*>(m_Data.m_Inputs[2])->GetShape();
- TensorShape outputLayerShape = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetShape();
+ TensorShape outputLayerShape = static_cast<IClTensorHandle*>(m_Data.m_Outputs[2])->GetShape();
unsigned int maxTime = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1];
unsigned int batchSize = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0];
diff --git a/src/backends/neon/NeonLayerSupport.cpp b/src/backends/neon/NeonLayerSupport.cpp
index e2098a3..2105355 100644
--- a/src/backends/neon/NeonLayerSupport.cpp
+++ b/src/backends/neon/NeonLayerSupport.cpp
@@ -77,6 +77,7 @@
#include "workloads/NeonTransposeConvolution2dWorkload.hpp"
#include "workloads/NeonTransposeWorkload.hpp"
#include "workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp"
+#include "workloads/NeonUnidirectionalSequenceLstmWorkload.hpp"
#endif
namespace armnn
@@ -1436,23 +1437,44 @@
bool NeonLayerSupport::IsUnidirectionalSequenceLstmSupported(const TensorInfo& input,
const TensorInfo& outputStateIn,
const TensorInfo& cellStateIn,
+ const TensorInfo& outputStateOut,
+ const TensorInfo& cellStateOut,
const TensorInfo& output,
- const Optional<TensorInfo>& hiddenStateOutput,
- const Optional<TensorInfo>& cellStateOutput,
const UnidirectionalSequenceLstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo,
Optional<std::string&> reasonIfUnsupported) const
{
- FORWARD_WORKLOAD_VALIDATE_FUNC(NeonUnidirectionalSequenceLstmFloatWorkloadValidate,
- reasonIfUnsupported,
- input,
- outputStateIn,
- cellStateIn,
- output,
- hiddenStateOutput,
- cellStateOutput,
- descriptor,
- paramsInfo);
+ if (input.GetDataType() == armnn::DataType::QAsymmS8 &&
+ outputStateIn.GetDataType() == armnn::DataType::QAsymmS8 &&
+ cellStateIn.GetDataType() == armnn::DataType::QSymmS16 &&
+ outputStateOut.GetDataType() == armnn::DataType::QAsymmS8 &&
+ cellStateOut.GetDataType() == armnn::DataType::QSymmS16 &&
+ output.GetDataType() == armnn::DataType::QAsymmS8)
+ {
+ FORWARD_WORKLOAD_VALIDATE_FUNC(NeonUnidirectionalSequenceLstmWorkloadValidate,
+ reasonIfUnsupported,
+ input,
+ outputStateIn,
+ cellStateIn,
+ outputStateOut,
+ cellStateOut,
+ output,
+ descriptor,
+ paramsInfo);
+ }
+ else
+ {
+ FORWARD_WORKLOAD_VALIDATE_FUNC(NeonUnidirectionalSequenceLstmFloatWorkloadValidate,
+ reasonIfUnsupported,
+ input,
+ outputStateIn,
+ cellStateIn,
+ outputStateOut,
+ cellStateOut,
+ output,
+ descriptor,
+ paramsInfo);
+ }
}
} // namespace armnn
diff --git a/src/backends/neon/NeonLayerSupport.hpp b/src/backends/neon/NeonLayerSupport.hpp
index 1eef41f..511bb03 100644
--- a/src/backends/neon/NeonLayerSupport.hpp
+++ b/src/backends/neon/NeonLayerSupport.hpp
@@ -339,9 +339,9 @@
bool IsUnidirectionalSequenceLstmSupported(const TensorInfo& input,
const TensorInfo& outputStateIn,
const TensorInfo& cellStateIn,
+ const TensorInfo& outputStateOut,
+ const TensorInfo& cellStateOut,
const TensorInfo& output,
- const Optional<TensorInfo>& hiddenStateOutput,
- const Optional<TensorInfo>& cellStateOutput,
const UnidirectionalSequenceLstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo,
Optional<std::string&> reasonIfUnsupported) const override;
diff --git a/src/backends/neon/NeonWorkloadFactory.cpp b/src/backends/neon/NeonWorkloadFactory.cpp
index 7d94daf..c83e8b3 100644
--- a/src/backends/neon/NeonWorkloadFactory.cpp
+++ b/src/backends/neon/NeonWorkloadFactory.cpp
@@ -558,7 +558,20 @@
case LayerType::UnidirectionalSequenceLstm :
{
auto desc = PolymorphicDowncast<const UnidirectionalSequenceLstmQueueDescriptor*>(&descriptor);
- return MakeWorkloadHelper<NeonUnidirectionalSequenceLstmFloatWorkload, NullWorkload>(*desc, info);
+
+ if ((info.m_InputTensorInfos[0].GetDataType() == armnn::DataType::Float32) &&
+ (info.m_InputTensorInfos[1].GetDataType() == armnn::DataType::Float32) &&
+ (info.m_InputTensorInfos[2].GetDataType() == armnn::DataType::Float32) &&
+ (info.m_OutputTensorInfos[0].GetDataType() == armnn::DataType::Float32) &&
+ (info.m_OutputTensorInfos[1].GetDataType() == armnn::DataType::Float32) &&
+ (info.m_OutputTensorInfos[2].GetDataType() == armnn::DataType::Float32))
+ {
+ return std::make_unique<NeonUnidirectionalSequenceLstmFloatWorkload>(*desc, info);
+ }
+ else
+ {
+ return std::make_unique<NeonUnidirectionalSequenceLstmWorkload>(*desc, info);
+ }
}
default:
return nullptr;
diff --git a/src/backends/neon/backend.mk b/src/backends/neon/backend.mk
index d43426f..0d6fd80 100644
--- a/src/backends/neon/backend.mk
+++ b/src/backends/neon/backend.mk
@@ -85,7 +85,8 @@
workloads/NeonSubtractionWorkload.cpp \
workloads/NeonTransposeConvolution2dWorkload.cpp \
workloads/NeonTransposeWorkload.cpp \
- workloads/NeonUnidirectionalSequenceLstmFloatWorkload.cpp
+ workloads/NeonUnidirectionalSequenceLstmFloatWorkload.cpp \
+ workloads/NeonUnidirectionalSequenceLstmWorkload.cpp
else
diff --git a/src/backends/neon/workloads/CMakeLists.txt b/src/backends/neon/workloads/CMakeLists.txt
index 41c5f5a..33a18e3 100644
--- a/src/backends/neon/workloads/CMakeLists.txt
+++ b/src/backends/neon/workloads/CMakeLists.txt
@@ -133,6 +133,8 @@
NeonTransposeWorkload.hpp
NeonUnidirectionalSequenceLstmFloatWorkload.cpp
NeonUnidirectionalSequenceLstmFloatWorkload.hpp
+ NeonUnidirectionalSequenceLstmWorkload.cpp
+ NeonUnidirectionalSequenceLstmWorkload.hpp
NeonWorkloads.hpp
NeonWorkloadUtils.hpp
)
diff --git a/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.cpp b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.cpp
index c911afb..8dba719 100644
--- a/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.cpp
+++ b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.cpp
@@ -39,7 +39,7 @@
GetGuid());
const arm_compute::ITensor& input = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
- arm_compute::ITensor& output = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
+ arm_compute::ITensor& output = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[2])->GetTensor();
TensorInfo inputInfo = info.m_InputTensorInfos[0];
TensorInfo outputInfo = info.m_OutputTensorInfos[0];
@@ -49,7 +49,7 @@
TensorShape inputLayerShape = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetShape();
TensorShape cellStateLayerShape = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[2])->GetShape();
- TensorShape outputLayerShape = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetShape();
+ TensorShape outputLayerShape = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[2])->GetShape();
unsigned int maxTime = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1];
unsigned int batchSize = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0];
@@ -288,7 +288,7 @@
// LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo.
if (maxTime == 1 && m_Data.m_Parameters.m_TimeMajor)
{
- TensorShape inputShape = GetTensorShape((&input)->info()->tensor_shape(), 1U);
+ TensorShape inputShape = GetTensorShape(input.info()->tensor_shape(), 1U);
TensorShape outputShape = GetTensorShape((&output)->info()->tensor_shape(), 1U);
TensorShape inputShapeShrink({inputShape[1], inputShape[2]});
@@ -297,10 +297,10 @@
auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);
auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink);
- (&input)->info()->set_tensor_shape(acl_input_shape_shrink);
+ input.info()->set_tensor_shape(acl_input_shape_shrink);
inputLSTM = const_cast<arm_compute::ITensor*>(&input);
- (&output)->info()->set_tensor_shape(acl_output_shape_shrink);
+ output.info()->set_tensor_shape(acl_output_shape_shrink);
outputLSTM = &output;
}
// If there is only one LSTM batch major batch, we will not concat, only permute.
@@ -432,9 +432,9 @@
unsigned int aclAxisConcat = CalcAclAxis(concatDescriptor.GetNumDimensions(), concatDescriptor.GetConcatAxis());
if (!m_Data.m_Parameters.m_TimeMajor)
{
- TensorInfo concatOuputTensorInfo = outputInfo;
- concatOuputTensorInfo.SetShape(timeMajorShapeOutput);
- BuildArmComputeTensor(concat_out, concatOuputTensorInfo);
+ TensorInfo concatOutputTensorInfo = outputInfo;
+ concatOutputTensorInfo.SetShape(timeMajorShapeOutput);
+ BuildArmComputeTensor(concat_out, concatOutputTensorInfo);
armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_out);
m_Concat->configure(m_ConcatInputs, &concat_out, aclAxisConcat);
@@ -452,11 +452,11 @@
{
if (!m_Data.m_Parameters.m_TimeMajor)
{
- (&output)->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandBatchMajor));
+ output.info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandBatchMajor));
}
else
{
- (&output)->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));
+ output.info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));
}
}
@@ -510,14 +510,12 @@
NeonUnidirectionalSequenceLstmFloatWorkloadValidate(const TensorInfo& input,
const TensorInfo& outputStateIn,
const TensorInfo& cellStateIn,
+ const TensorInfo& outputStateOut,
+ const TensorInfo& cellStateOut,
const TensorInfo& output,
- const Optional<TensorInfo>& hiddenStateOutput,
- const Optional<TensorInfo>& cellStateOutput,
const UnidirectionalSequenceLstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo)
{
- IgnoreUnused(hiddenStateOutput, cellStateOutput);
-
TensorShape inputLayerShape = input.GetShape();
TensorShape outputLayerShape = outputStateIn.GetShape();
@@ -612,8 +610,6 @@
arm_compute::LSTMParams<arm_compute::ITensorInfo> lstm_params_info;
const TensorInfo& scratchBuffer = TensorInfo(cellStateIn.GetShape(), input.GetDataType());
- const TensorInfo& outputStateOut = TensorInfo(outputStateIn.GetShape(), input.GetDataType());
- const TensorInfo& cellStateOut = TensorInfo(cellStateIn.GetShape(), input.GetDataType());
// The inputs and outputs
const arm_compute::TensorInfo aclOutputStateInInfo = BuildArmComputeTensorInfo(outputStateIn);
@@ -704,7 +700,7 @@
aclOutputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetOutputLayerNormWeights());
lstm_params_info.set_layer_normalization_params(descriptor.m_CifgEnabled ? nullptr :
- &aclInputLayerNormWeightsInfo,
+ &aclInputLayerNormWeightsInfo,
&aclForgetLayerNormWeightsInfo,
&aclCellLayerNormWeightsInfo,
&aclOutputLayerNormWeightsInfo);
@@ -803,9 +799,9 @@
TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]});
TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]});
- TensorInfo concatOuputTensorInfo = TensorInfo(output);
- concatOuputTensorInfo.SetShape(timeMajorShapeOutput);
- arm_compute::TensorInfo aclConcatOuputTensorInfo= BuildArmComputeTensorInfo(concatOuputTensorInfo);
+ TensorInfo concatOutputTensorInfo = TensorInfo(output);
+ concatOutputTensorInfo.SetShape(timeMajorShapeOutput);
+ arm_compute::TensorInfo aclConcatOutputTensorInfo= BuildArmComputeTensorInfo(concatOutputTensorInfo);
if (maxTime != 1) // ACL concat does not work with only one element to concatenate.
{
@@ -819,7 +815,7 @@
if (!descriptor.m_TimeMajor)
{
statusConcat = arm_compute::NEConcatenateLayer::validate(concatInputsTensorInfosPtr,
- &aclConcatOuputTensorInfo,
+ &aclConcatOutputTensorInfo,
aclAxisConcat);
}
else
@@ -853,7 +849,7 @@
// Output now time major. Permute output back to batch major.
if (maxTime != 1)
{
- statusPermute2 = arm_compute::NEPermute::validate(&aclConcatOuputTensorInfo,
+ statusPermute2 = arm_compute::NEPermute::validate(&aclConcatOutputTensorInfo,
&aclOutputInfo,
arm_compute::PermutationVector(0U, 2U, 1U));
}
diff --git a/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp
index 776afd3..48cf7dc 100644
--- a/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp
+++ b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmFloatWorkload.hpp
@@ -10,7 +10,6 @@
#include <armnn/backends/Workload.hpp>
#include <armnn/backends/WorkloadData.hpp>
-#include "arm_compute/graph/Tensor.h"
#include "arm_compute/runtime/NEON/functions/NELSTMLayer.h"
#include "arm_compute/runtime/NEON/functions/NEPermute.h"
#include "arm_compute/runtime/NEON/functions/NESplit.h"
@@ -86,9 +85,9 @@
NeonUnidirectionalSequenceLstmFloatWorkloadValidate(const TensorInfo& input,
const TensorInfo& outputStateIn,
const TensorInfo& cellStateIn,
+ const TensorInfo& outputStateOut,
+ const TensorInfo& cellStateOut,
const TensorInfo& output,
- const Optional<TensorInfo>& hiddenStateOutput,
- const Optional<TensorInfo>& cellStateOutput,
const UnidirectionalSequenceLstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo);
diff --git a/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmWorkload.cpp b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmWorkload.cpp
new file mode 100644
index 0000000..dfbbb3c
--- /dev/null
+++ b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmWorkload.cpp
@@ -0,0 +1,879 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "NeonUnidirectionalSequenceLstmWorkload.hpp"
+#include "NeonWorkloadUtils.hpp"
+
+#include <aclCommon/ArmComputeUtils.hpp>
+#include <aclCommon/ArmComputeTensorUtils.hpp>
+
+#include <armnn/utility/NumericCast.hpp>
+#include <armnnUtils/Permute.hpp>
+#include <neon/test/NeonWorkloadFactoryHelper.hpp>
+#include <backendsCommon/WorkloadUtils.hpp>
+
+#include "neon/NeonTensorHandle.hpp"
+
+namespace
+{
+
+unsigned int CalcAclAxis(unsigned int numDimensions, unsigned int axis)
+{
+ return (numDimensions - axis) - 1;
+}
+} //namespace
+
+namespace armnn
+{
+using namespace armcomputetensorutils;
+
+NeonUnidirectionalSequenceLstmWorkload::NeonUnidirectionalSequenceLstmWorkload
+ (const UnidirectionalSequenceLstmQueueDescriptor& descriptor, const WorkloadInfo& info)
+ : NeonBaseWorkload<UnidirectionalSequenceLstmQueueDescriptor>(descriptor, info)
+{
+ // Report Profiling Details
+ ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonUnidirectionalSequenceLstmWorkload_Construct",
+ descriptor.m_Parameters,
+ info,
+ GetGuid());
+
+ // Input/Output tensors
+ const arm_compute::ITensor& input = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
+ arm_compute::ITensor& outputStateIn = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[1])->GetTensor();
+ const arm_compute::ITensor& cellStateIn = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[2])->GetTensor();
+
+ arm_compute::ITensor& outputStateOut = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
+ arm_compute::ITensor& cellStateOut = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[1])->GetTensor();
+ arm_compute::ITensor& output = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[2])->GetTensor();
+
+ TensorInfo inputInfo = info.m_InputTensorInfos[0];
+ TensorInfo outputInfo = info.m_OutputTensorInfos[2];
+
+ TensorShape inputLayerShape = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetShape();
+ TensorShape outputLayerShape = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[2])->GetShape();
+
+ unsigned int maxTime = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1];
+ unsigned int batchSize = m_Data.m_Parameters.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0];
+ unsigned int inputSize = inputLayerShape[2];
+ unsigned int outputSize = outputLayerShape[2];
+
+ const TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize});
+ const TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize});
+
+ //
+ // Permute: performed if Unidirectional Sequence Layer inputs/outputs are in batch major format.
+ //
+ if (!m_Data.m_Parameters.m_TimeMajor)
+ {
+ std::unique_ptr<arm_compute::NEPermute> layer(new arm_compute::NEPermute());
+
+ TensorInfo permuteOutInfo = inputInfo;
+ permuteOutInfo.SetShape(timeMajorShapeInput);
+ BuildArmComputeTensor(m_PermuteFirstOut, permuteOutInfo);
+ armcomputetensorutils::InitialiseArmComputeTensorEmpty(m_PermuteFirstOut);
+
+ // Permute to time major format.
+ layer->configure(&input, &m_PermuteFirstOut, arm_compute::PermutationVector(0U,2U,1U));
+ m_Permute1.reset(layer.release());
+ }
+
+ //
+ // Split and Concat Tensors
+ //
+ for (unsigned int i = 0; i < maxTime; ++i)
+ {
+ arm_compute::Tensor splitter_out;
+ arm_compute::Tensor concat_in;
+
+ auto splitterTensorInfo = inputInfo;
+ auto concatTensorInfo = outputInfo;
+ splitterTensorInfo.SetShape({batchSize, inputSize});
+ concatTensorInfo.SetShape({batchSize, outputSize});
+ BuildArmComputeTensor(splitter_out, splitterTensorInfo);
+ BuildArmComputeTensor(concat_in, concatTensorInfo);
+
+ armcomputetensorutils::InitialiseArmComputeTensorEmpty(splitter_out);
+ armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_in);
+
+ // append to std::vector<arm_compute::Tensor>
+ m_SplitterOutputsTensors.push_back(std::move(splitter_out));
+ m_ConcatInputsTensors.push_back(std::move(concat_in));
+ }
+
+ for (unsigned int i = 0; i < maxTime; ++i)
+ {
+ // append to std::vector<arm_compute::ITensor*>
+ m_SplitterOutputs.push_back(&m_SplitterOutputsTensors[i]);
+ m_ConcatInputs.push_back(&m_ConcatInputsTensors[i]);
+ }
+
+ //
+ // Split
+ //
+ unsigned int numberDimensions = 3;
+ unsigned int dimension = 0; // splitting on 0-dimension (i.e. maxTime dimension)
+
+ if (maxTime != 1) // ACL split does not work with only one element to split.
+ {
+ ViewsDescriptor splitterDesc(maxTime, numberDimensions);
+ unsigned int splitterDimSizes[3] = {1, batchSize, inputSize};
+ for (unsigned int outputIdx = 0u; outputIdx < maxTime; ++outputIdx)
+ {
+ splitterDesc.SetViewOriginCoord(outputIdx, dimension, splitterDimSizes[dimension] * outputIdx);
+ for (unsigned int dimIdx = 0u; dimIdx < numberDimensions; ++dimIdx)
+ {
+ splitterDesc.SetViewSize(outputIdx, dimIdx, splitterDimSizes[dimIdx]);
+ }
+ }
+
+ std::set<unsigned int> splitAxis = ComputeSplitAxis(splitterDesc, timeMajorShapeInput);
+
+ std::unique_ptr<arm_compute::NESplit> split_layer(new arm_compute::NESplit());
+ unsigned int aclAxisSplit = CalcAclAxis(splitterDesc.GetNumDimensions(),
+ *splitAxis.begin());
+ if (!m_Data.m_Parameters.m_TimeMajor)
+ {
+ split_layer->configure(&m_PermuteFirstOut, m_SplitterOutputs, aclAxisSplit);
+ } else
+ {
+ split_layer->configure(&input, m_SplitterOutputs, aclAxisSplit);
+ }
+
+ split_layer->prepare();
+ m_Splitter.reset(split_layer.release());
+ }
+
+ //
+ // Lstm
+ //
+ arm_compute::LSTMParams<arm_compute::ITensor> lstm_param;
+
+ lstm_param.set_cell_clip_params(descriptor.m_Parameters.m_ClippingThresCell);
+ lstm_param.set_projection_clip_params(descriptor.m_Parameters.m_ClippingThresProj);
+
+ lstm_param.set_matmul_scale_params(descriptor.m_Parameters.m_InputIntermediateScale,
+ descriptor.m_Parameters.m_ForgetIntermediateScale,
+ descriptor.m_Parameters.m_CellIntermediateScale,
+ descriptor.m_Parameters.m_OutputIntermediateScale);
+
+ lstm_param.set_hidden_state_params(descriptor.m_Parameters.m_HiddenStateZeroPoint,
+ descriptor.m_Parameters.m_HiddenStateScale);
+
+ m_InputToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights->GetTensorInfo());
+
+ m_InputToCellWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights->GetTensorInfo());
+
+ m_InputToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights->GetTensorInfo());
+
+ m_RecurrentToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights->GetTensorInfo());
+
+ m_RecurrentToCellWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights->GetTensorInfo());
+
+ m_RecurrentToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights->GetTensorInfo());
+
+ m_ForgetGateBiasTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias->GetTensorInfo());
+
+ m_CellBiasTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_CellBiasTensor, m_Data.m_CellBias->GetTensorInfo());
+
+ m_OutputGateBiasTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias->GetTensorInfo());
+
+ // for future reference: check the AndroidNN API for the logic here
+ if (!m_Data.m_Parameters.m_CifgEnabled)
+ {
+ m_InputToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights->GetTensorInfo());
+
+ m_RecurrentToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights->GetTensorInfo());
+
+ m_CellToInputWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ if (m_Data.m_CellToInputWeights != nullptr)
+ {
+ BuildArmComputeTensor(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights->GetTensorInfo());
+ }
+
+ m_InputGateBiasTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_InputGateBiasTensor, m_Data.m_InputGateBias->GetTensorInfo());
+ lstm_param.set_cifg_params(m_InputToInputWeightsTensor.get(),
+ m_RecurrentToInputWeightsTensor.get(),
+ m_Data.m_CellToInputWeights ? m_CellToInputWeightsTensor.get() : nullptr,
+ m_InputGateBiasTensor.get());
+ }
+
+ if (m_Data.m_Parameters.m_ProjectionEnabled)
+ {
+ m_ProjectionWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights->GetTensorInfo());
+
+ m_ProjectionBiasTensor = std::make_unique<arm_compute::Tensor>();
+ if (m_Data.m_ProjectionBias != nullptr)
+ {
+ BuildArmComputeTensor(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias->GetTensorInfo());
+ }
+
+ lstm_param.set_projection_params(m_ProjectionWeightsTensor.get(),
+ m_Data.m_ProjectionBias ? m_ProjectionBiasTensor.get() : nullptr);
+ }
+
+ if (m_Data.m_Parameters.m_PeepholeEnabled)
+ {
+ m_CellToForgetWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights->GetTensorInfo());
+
+ m_CellToOutputWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights->GetTensorInfo());
+
+ lstm_param.set_peephole_params(m_CellToForgetWeightsTensor.get(), m_CellToOutputWeightsTensor.get());
+ }
+
+ if (m_Data.m_Parameters.m_LayerNormEnabled)
+ {
+ m_InputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ if (!m_Data.m_Parameters.m_CifgEnabled)
+ {
+ BuildArmComputeTensor(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights->GetTensorInfo());
+ }
+
+ m_ForgetLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights->GetTensorInfo());
+
+ m_CellLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights->GetTensorInfo());
+
+ m_OutputLayerNormWeightsTensor = std::make_unique<arm_compute::Tensor>();
+ BuildArmComputeTensor(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights->GetTensorInfo());
+
+ auto inputNormWeightTensor = m_Data.m_Parameters.m_CifgEnabled ? nullptr : m_InputLayerNormWeightsTensor.get();
+ lstm_param.set_layer_normalization_params(inputNormWeightTensor,
+ m_ForgetLayerNormWeightsTensor.get(),
+ m_CellLayerNormWeightsTensor.get(),
+ m_OutputLayerNormWeightsTensor.get());
+ }
+
+ for (unsigned int i = 0; i != maxTime; ++i)
+ {
+ // Set LSTM input and output ITensors depending on:
+ // input format (timeMajor) & number of LSTM batches (maxTime).
+ arm_compute::ITensor* outputLSTM;
+ arm_compute::ITensor* inputLSTM;
+
+ // If there is only one LSTM time major batch, we will not concat OR permute.
+ // Set input of LSTM to be first input ITensor.
+ // Set output of LSTM to be final output ITensor.
+ // LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo.
+ if (maxTime == 1 && m_Data.m_Parameters.m_TimeMajor)
+ {
+ TensorShape inputShape = GetTensorShape(input.info()->tensor_shape(), 1U);
+ TensorShape outputShape = GetTensorShape(output.info()->tensor_shape(), 1U);
+
+ TensorShape inputShapeShrink({inputShape[1], inputShape[2]});
+ TensorShape outputShapeShrink({outputShape[1], outputShape[2]});
+
+ auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);
+ auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink);
+
+ input.info()->set_tensor_shape(acl_input_shape_shrink);
+ inputLSTM = const_cast<arm_compute::ITensor*>(&input);
+
+ output.info()->set_tensor_shape(acl_output_shape_shrink);
+ outputLSTM = &output;
+ }
+ // If there is only one LSTM batch major batch, we will not concat, only permute.
+ // Set input of LSTM to be output of initial permute.
+ // Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute.
+ // LSTM output cannot be > 2 dimensions so need to resize its TensorInfo.
+ else if (maxTime == 1 && !m_Data.m_Parameters.m_TimeMajor)
+ {
+ TensorShape inputShape = GetTensorShape(m_PermuteFirstOut.info()->tensor_shape(), 1U);
+ TensorShape inputShapeShrink({inputShape[1], inputShape[2]});
+ auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);
+ m_PermuteFirstOut.info()->set_tensor_shape(acl_input_shape_shrink);
+ inputLSTM = &m_PermuteFirstOut;
+
+ outputLSTM = const_cast<arm_compute::ITensor*>(m_ConcatInputs[i]);
+ }
+ // Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on.
+ else
+ {
+ inputLSTM = m_SplitterOutputs[i];
+ outputLSTM = const_cast<arm_compute::ITensor*>(m_ConcatInputs[i]);
+ }
+
+ std::unique_ptr<arm_compute::NEQLSTMLayer> lstm_layer(new arm_compute::NEQLSTMLayer());
+
+ lstm_layer->configure(inputLSTM,
+ m_InputToForgetWeightsTensor.get(),
+ m_InputToCellWeightsTensor.get(),
+ m_InputToOutputWeightsTensor.get(),
+ m_RecurrentToForgetWeightsTensor.get(),
+ m_RecurrentToCellWeightsTensor.get(),
+ m_RecurrentToOutputWeightsTensor.get(),
+ m_ForgetGateBiasTensor.get(),
+ m_CellBiasTensor.get(),
+ m_OutputGateBiasTensor.get(),
+ &cellStateIn,
+ &outputStateIn,
+ &cellStateOut,
+ &outputStateOut,
+ outputLSTM,
+ lstm_param);
+
+ m_Layers.emplace_back(std::move(lstm_layer));
+ }
+
+ InitializeArmComputeTensorData(*m_InputToForgetWeightsTensor, m_Data.m_InputToForgetWeights);
+ InitializeArmComputeTensorData(*m_InputToCellWeightsTensor, m_Data.m_InputToCellWeights);
+ InitializeArmComputeTensorData(*m_InputToOutputWeightsTensor, m_Data.m_InputToOutputWeights);
+ InitializeArmComputeTensorData(*m_RecurrentToForgetWeightsTensor, m_Data.m_RecurrentToForgetWeights);
+ InitializeArmComputeTensorData(*m_RecurrentToCellWeightsTensor, m_Data.m_RecurrentToCellWeights);
+ InitializeArmComputeTensorData(*m_RecurrentToOutputWeightsTensor, m_Data.m_RecurrentToOutputWeights);
+ InitializeArmComputeTensorData(*m_ForgetGateBiasTensor, m_Data.m_ForgetGateBias);
+ InitializeArmComputeTensorData(*m_CellBiasTensor, m_Data.m_CellBias);
+ InitializeArmComputeTensorData(*m_OutputGateBiasTensor, m_Data.m_OutputGateBias);
+
+ if (!m_Data.m_Parameters.m_CifgEnabled)
+ {
+ InitializeArmComputeTensorData(*m_InputToInputWeightsTensor, m_Data.m_InputToInputWeights);
+ InitializeArmComputeTensorData(*m_RecurrentToInputWeightsTensor, m_Data.m_RecurrentToInputWeights);
+ if (m_Data.m_CellToInputWeights != nullptr)
+ {
+ InitializeArmComputeTensorData(*m_CellToInputWeightsTensor, m_Data.m_CellToInputWeights);
+ }
+ InitializeArmComputeTensorData(*m_InputGateBiasTensor, m_Data.m_InputGateBias);
+ }
+
+ if (m_Data.m_Parameters.m_ProjectionEnabled)
+ {
+ InitializeArmComputeTensorData(*m_ProjectionWeightsTensor, m_Data.m_ProjectionWeights);
+ if (m_Data.m_ProjectionBias != nullptr)
+ {
+ InitializeArmComputeTensorData(*m_ProjectionBiasTensor, m_Data.m_ProjectionBias);
+ }
+ }
+
+ if (m_Data.m_Parameters.m_PeepholeEnabled)
+ {
+ InitializeArmComputeTensorData(*m_CellToForgetWeightsTensor, m_Data.m_CellToForgetWeights);
+ InitializeArmComputeTensorData(*m_CellToOutputWeightsTensor, m_Data.m_CellToOutputWeights);
+ }
+
+ if (m_Data.m_Parameters.m_LayerNormEnabled)
+ {
+ if (!m_Data.m_Parameters.m_CifgEnabled)
+ {
+ InitializeArmComputeTensorData(*m_InputLayerNormWeightsTensor, m_Data.m_InputLayerNormWeights);
+ }
+ InitializeArmComputeTensorData(*m_ForgetLayerNormWeightsTensor, m_Data.m_ForgetLayerNormWeights);
+ InitializeArmComputeTensorData(*m_CellLayerNormWeightsTensor, m_Data.m_CellLayerNormWeights);
+ InitializeArmComputeTensorData(*m_OutputLayerNormWeightsTensor, m_Data.m_OutputLayerNormWeights);
+ }
+
+ // Force Compute Library to perform the necessary copying and reshaping.
+ // After which delete all the input tensors that will no longer be needed.
+ for (uint32_t i = 0; i < m_Layers.size(); ++i)
+ {
+ m_Layers[i]->prepare();
+ }
+
+ //
+ // Concat
+ //
+
+ // Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs.
+ TensorShape shape = GetTensorShape(m_ConcatInputs[0]->info()->tensor_shape(), 1U);
+ TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]});
+ TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]});
+
+ if (maxTime != 1) // ACL concat does not work with only one element to concatenate.
+ {
+ for (unsigned int i = 0; i < maxTime; ++i)
+ {
+ m_ConcatInputs[i]->info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));
+ }
+ ConcatDescriptor concatDescriptor(maxTime, numberDimensions); // maxTime = num inputs (aka. number of views).
+
+ for (unsigned int inputIdx = 0u; inputIdx < maxTime; ++inputIdx)
+ {
+ concatDescriptor.SetViewOriginCoord(inputIdx, dimension, inputIdx);
+ concatDescriptor.SetConcatAxis(dimension);
+ }
+ m_Concat.reset(new arm_compute::NEConcatenateLayer());
+
+ unsigned int aclAxisConcat = CalcAclAxis(concatDescriptor.GetNumDimensions(), concatDescriptor.GetConcatAxis());
+ if (!m_Data.m_Parameters.m_TimeMajor)
+ {
+ TensorInfo concatOutputTensorInfo = outputInfo;
+ concatOutputTensorInfo.SetShape(timeMajorShapeOutput);
+ BuildArmComputeTensor(concat_out, concatOutputTensorInfo);
+ armcomputetensorutils::InitialiseArmComputeTensorEmpty(concat_out);
+
+ m_Concat->configure(m_ConcatInputs, &concat_out, aclAxisConcat);
+ }
+ else
+ {
+ m_Concat->configure(m_ConcatInputs, &output, aclAxisConcat);
+ }
+
+ m_Concat->prepare();
+ }
+ // If only one LSTM batch, we do not concat and/or permute.
+ // Must ensure final output info is expanded to correct batch major dimensions.
+ else
+ {
+ if (!m_Data.m_Parameters.m_TimeMajor)
+ {
+ output.info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandBatchMajor));
+ }
+ else
+ {
+ output.info()->set_tensor_shape(BuildArmComputeTensorShape(shapeExpandTimeMajor));
+ }
+ }
+
+ //
+ // Permute: only done if input/output are in batch major format.
+ //
+ if (!m_Data.m_Parameters.m_TimeMajor)
+ {
+ // Output now time major. Permute output back to batch major.
+ std::unique_ptr<arm_compute::NEPermute> layer(new arm_compute::NEPermute());
+ if (maxTime != 1)
+ {
+ layer->configure(&concat_out, &output, arm_compute::PermutationVector(0U, 2U, 1U));
+ }
+ else
+ {
+ layer->configure(m_ConcatInputs[0], &output, arm_compute::PermutationVector(0U, 2U, 1U));
+ }
+ m_Permute2.reset(layer.release());
+ }
+
+ FreeUnusedTensors();
+}
+
+void NeonUnidirectionalSequenceLstmWorkload::Execute() const
+{
+ ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonUnidirectionalSequenceLstmWorkload_Execute", GetGuid());
+ if (m_Permute1)
+ {
+ m_Permute1->run();
+ }
+ if (m_Splitter)
+ {
+ m_Splitter->run();
+ }
+ for (uint32_t i = 0; i < m_Layers.size(); ++i)
+ {
+ m_Layers[i]->run();
+ }
+ if (m_Concat)
+ {
+ m_Concat->run();
+ }
+ if (m_Permute2)
+ {
+ m_Permute2->run();
+ }
+}
+
+arm_compute::Status
+NeonUnidirectionalSequenceLstmWorkloadValidate(const TensorInfo& input,
+ const TensorInfo& outputStateIn,
+ const TensorInfo& cellStateIn,
+ const TensorInfo& outputStateOut,
+ const TensorInfo& cellStateOut,
+ const TensorInfo& output,
+ const UnidirectionalSequenceLstmDescriptor& descriptor,
+ const LstmInputParamsInfo& paramsInfo)
+{
+ TensorShape inputLayerShape = input.GetShape();
+ TensorShape outputLayerShape = output.GetShape();
+
+ unsigned int maxTime = descriptor.m_TimeMajor ? inputLayerShape[0] : inputLayerShape[1];
+ unsigned int batchSize = descriptor.m_TimeMajor ? inputLayerShape[1] : inputLayerShape[0];
+ unsigned int inputSize = inputLayerShape[2];
+ unsigned int outputSize = outputLayerShape[2];
+
+ const TensorShape timeMajorShapeInput({maxTime, batchSize, inputSize});
+ const TensorShape timeMajorShapeOutput({maxTime, batchSize, outputSize});
+
+ arm_compute::Status statusPermute1 = arm_compute::Status(arm_compute::ErrorCode::OK,
+ "Permute1 status");
+ arm_compute::Status statusSplit = arm_compute::Status(arm_compute::ErrorCode::OK,
+ "Split status");
+ arm_compute::Status statusLSTM = arm_compute::Status(arm_compute::ErrorCode::OK,
+ "LSTM status");
+ arm_compute::Status statusConcat = arm_compute::Status(arm_compute::ErrorCode::OK,
+ "Concat status");
+ arm_compute::Status statusPermute2 = arm_compute::Status(arm_compute::ErrorCode::OK,
+ "Permute2 status");
+
+ const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input);
+ const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
+
+ //
+ // Permute validate
+ //
+ TensorInfo permuteOutInfo = TensorInfo(input);
+ arm_compute::TensorInfo aclPermuteOutInfo = armcomputetensorutils::BuildArmComputeTensorInfo(permuteOutInfo);
+ if (!descriptor.m_TimeMajor)
+ {
+ statusPermute1 = arm_compute::NEPermute::validate(&aclInputInfo,
+ &aclPermuteOutInfo,
+ arm_compute::PermutationVector(0U, 2U, 1U));
+ }
+
+ //
+ // Split and Concat Tensors validate
+ //
+ std::vector<arm_compute::TensorInfo> splitterOutputsTensorInfos;
+ std::vector<arm_compute::TensorInfo> concatInputsTensorInfos;
+ std::vector<arm_compute::ITensorInfo*> splitterOutputsTensorInfosPtr;
+ std::vector<const arm_compute::ITensorInfo*> concatInputsTensorInfosPtr;
+ splitterOutputsTensorInfos.reserve(maxTime);
+ concatInputsTensorInfos.reserve(maxTime);
+ for (unsigned int i = 0; i < maxTime; ++i)
+ {
+ arm_compute::TensorInfo splitter_out;
+ arm_compute::TensorInfo concat_in;
+
+ auto splitterTensorInfo = TensorInfo(input);
+ auto concatTensorInfo = TensorInfo(output);
+ splitterTensorInfo.SetShape({batchSize, inputSize});
+ concatTensorInfo.SetShape({batchSize, outputSize});
+
+ arm_compute::TensorInfo aclSplitterTensorInfo
+ = armcomputetensorutils::BuildArmComputeTensorInfo(splitterTensorInfo);
+ arm_compute::TensorInfo aclConcatTensorInfo
+ = armcomputetensorutils::BuildArmComputeTensorInfo(concatTensorInfo);
+
+ splitterOutputsTensorInfos.emplace_back(aclSplitterTensorInfo);
+ concatInputsTensorInfos.emplace_back(aclConcatTensorInfo);
+ splitterOutputsTensorInfosPtr.emplace_back(&splitterOutputsTensorInfos[i]);
+ concatInputsTensorInfosPtr.emplace_back(&concatInputsTensorInfos[i]);
+ }
+
+ //
+ // Split validate
+ //
+ unsigned int numberDimensions = 3;
+ unsigned int dimension = 0; // splitting on 0-dimension (i.e. maxTime dimension)
+ unsigned int aclAxisSplit = CalcAclAxis(numberDimensions, dimension);
+
+ if (maxTime != 1) // ACL split does not work with only one element to split.
+ {
+ if (!descriptor.m_TimeMajor)
+ {
+ statusSplit = arm_compute::NESplit::validate(&aclPermuteOutInfo,
+ splitterOutputsTensorInfosPtr,
+ aclAxisSplit);
+ } else
+ {
+ statusSplit = arm_compute::NESplit::validate(&aclInputInfo, splitterOutputsTensorInfosPtr, aclAxisSplit);
+ }
+ }
+
+ //
+ // LSTM validate
+ //
+
+ arm_compute::LSTMParams<arm_compute::ITensorInfo> lstm_params_info;
+
+ const TensorInfo& scratchBuffer = TensorInfo(cellStateIn.GetShape(), input.GetDataType());
+
+ lstm_params_info.set_cell_clip_params(descriptor.m_ClippingThresCell);
+ lstm_params_info.set_projection_clip_params(descriptor.m_ClippingThresProj);
+ // The inputs and outputs
+ const arm_compute::TensorInfo aclOutputStateInInfo = BuildArmComputeTensorInfo(outputStateIn);
+ const arm_compute::TensorInfo aclCellStateInInfo = BuildArmComputeTensorInfo(cellStateIn);
+ const arm_compute::TensorInfo aclScratchBufferInfo = BuildArmComputeTensorInfo(scratchBuffer);
+ const arm_compute::TensorInfo aclOutputStateOutInfo = BuildArmComputeTensorInfo(outputStateOut);
+ const arm_compute::TensorInfo aclCellStateOutInfo = BuildArmComputeTensorInfo(cellStateOut);
+
+ // Basic parameters
+ const arm_compute::TensorInfo aclInputToForgetWeightsInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetInputToForgetWeights());
+ const arm_compute::TensorInfo aclInputToCellWeightsInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetInputToCellWeights());
+ const arm_compute::TensorInfo aclInputToOutputWeightsInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetInputToOutputWeights());
+ const arm_compute::TensorInfo aclRecurrentToForgetWeightsInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToForgetWeights());
+ const arm_compute::TensorInfo aclRecurrentToCellWeightsInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToCellWeights());
+ const arm_compute::TensorInfo aclRecurrentToOutputWeightsInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToOutputWeights());
+ const arm_compute::TensorInfo aclForgetGateBiasInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetForgetGateBias());
+ const arm_compute::TensorInfo aclCellBiasInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetCellBias());
+ const arm_compute::TensorInfo aclOutputGateBiasInfo
+ = BuildArmComputeTensorInfo(paramsInfo.GetOutputGateBias());
+
+ arm_compute::TensorInfo aclInputToInputWeightsInfo;
+ arm_compute::TensorInfo aclRecurrentToInputWeightsInfo;
+ arm_compute::TensorInfo aclCellToInputWeightsInfo;
+ arm_compute::TensorInfo aclInputGateBiasInfo;
+ arm_compute::TensorInfo aclProjectionWeightsInfo;
+ arm_compute::TensorInfo aclProjectionBiasInfo;
+ arm_compute::TensorInfo aclCellToForgetWeightsInfo;
+ arm_compute::TensorInfo aclCellToOutputWeightsInfo;
+
+ arm_compute::TensorInfo aclInputLayerNormWeightsInfo;
+ arm_compute::TensorInfo aclForgetLayerNormWeightsInfo;
+ arm_compute::TensorInfo aclCellLayerNormWeightsInfo;
+ arm_compute::TensorInfo aclOutputLayerNormWeightsInfo;
+
+ if (!descriptor.m_CifgEnabled)
+ {
+ if (descriptor.m_PeepholeEnabled)
+ {
+ aclCellToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToInputWeights());
+ }
+ aclInputToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputToInputWeights());
+ aclRecurrentToInputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetRecurrentToInputWeights());
+ aclInputGateBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputGateBias());
+
+ lstm_params_info.set_cifg_params(&aclInputToInputWeightsInfo,
+ &aclRecurrentToInputWeightsInfo,
+ descriptor.m_PeepholeEnabled ? &aclCellToInputWeightsInfo : nullptr,
+ &aclInputGateBiasInfo);
+ }
+
+ if (descriptor.m_ProjectionEnabled)
+ {
+ if (paramsInfo.m_ProjectionBias != nullptr)
+ {
+ aclProjectionBiasInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionBias());
+ }
+ aclProjectionWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetProjectionWeights());
+
+ lstm_params_info.set_projection_params(&aclProjectionWeightsInfo,
+ paramsInfo.m_ProjectionBias ? &aclProjectionBiasInfo : nullptr);
+ }
+
+ if (descriptor.m_PeepholeEnabled)
+ {
+ aclCellToForgetWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToForgetWeights());
+ aclCellToOutputWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellToOutputWeights());
+
+ lstm_params_info.set_peephole_params(&aclCellToForgetWeightsInfo, &aclCellToOutputWeightsInfo);
+ }
+
+ if (descriptor.m_LayerNormEnabled)
+ {
+ if (!descriptor.m_CifgEnabled)
+ {
+ aclInputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetInputLayerNormWeights());
+ }
+ aclForgetLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetForgetLayerNormWeights());
+ aclCellLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetCellLayerNormWeights());
+ aclOutputLayerNormWeightsInfo = BuildArmComputeTensorInfo(paramsInfo.GetOutputLayerNormWeights());
+
+ lstm_params_info.set_layer_normalization_params(descriptor.m_CifgEnabled ? nullptr :
+ &aclInputLayerNormWeightsInfo,
+ &aclForgetLayerNormWeightsInfo,
+ &aclCellLayerNormWeightsInfo,
+ &aclOutputLayerNormWeightsInfo);
+ }
+
+ lstm_params_info.set_matmul_scale_params(descriptor.m_InputIntermediateScale,
+ descriptor.m_ForgetIntermediateScale,
+ descriptor.m_CellIntermediateScale,
+ descriptor.m_OutputIntermediateScale);
+
+ lstm_params_info.set_hidden_state_params(descriptor.m_HiddenStateZeroPoint, descriptor.m_HiddenStateScale);
+
+ for (unsigned int i = 0; i != maxTime; ++i)
+ {
+
+ // Set LSTM input and output ITensors depending on:
+ // input format (timeMajor) & number of LSTM batches (maxTime).
+ arm_compute::ITensorInfo* outputLSTM;
+ arm_compute::ITensorInfo* inputLSTM;
+
+ // If there is only one LSTM time major batch, we will not concat OR permute.
+ // Set input of LSTM to be first input ITensor.
+ // Set output of LSTM to be final output ITensor.
+ // LSTM input/output cannot be > 2 dimensions so need to resize its TensorInfo.
+ if (maxTime == 1 && !descriptor.m_TimeMajor)
+ {
+ TensorShape inputShape = GetTensorShape(aclInputInfo.tensor_shape(), 1U);
+ TensorShape outputShape = GetTensorShape(aclOutputInfo.tensor_shape(), 1U);
+
+ TensorShape inputShapeShrink({inputShape[1], inputShape[2]});
+ TensorShape outputShapeShrink({outputShape[1], outputShape[2]});
+
+ auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);
+ auto acl_output_shape_shrink = BuildArmComputeTensorShape(outputShapeShrink);
+
+ const_cast<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape(acl_input_shape_shrink);
+ inputLSTM = const_cast<arm_compute::TensorInfo*>(&aclInputInfo);
+
+ const_cast<arm_compute::TensorInfo*>(&aclOutputInfo)->set_tensor_shape(acl_output_shape_shrink);
+ outputLSTM = const_cast<arm_compute::TensorInfo*>(&aclOutputInfo);
+ }
+ // If there is only one LSTM batch major batch, we will not concat, only permute.
+ // Set input of LSTM to be output of initial permute.
+ // Set output of LSTM to be first element of m_ConcatInputs & use that value later in permute.
+ // LSTM output cannot be > 2 dimensions so need to resize its TensorInfo.
+ else if (maxTime == 1 && !descriptor.m_TimeMajor)
+ {
+ TensorShape inputShape = GetTensorShape(aclPermuteOutInfo.tensor_shape(), 1U);
+ TensorShape inputShapeShrink({inputShape[1], inputShape[2]});
+ auto acl_input_shape_shrink = BuildArmComputeTensorShape(inputShapeShrink);
+ aclPermuteOutInfo.set_tensor_shape(acl_input_shape_shrink);
+ inputLSTM = &aclPermuteOutInfo;
+
+ outputLSTM = const_cast<arm_compute::ITensorInfo*>(concatInputsTensorInfosPtr[i]);
+ }
+ // Batch major AND/OR 2+ LSTM batches so will use concat AND/OR permute later on.
+ else
+ {
+ inputLSTM = splitterOutputsTensorInfosPtr[i];
+ outputLSTM = const_cast<arm_compute::ITensorInfo*>(concatInputsTensorInfosPtr[i]);
+ }
+
+ statusLSTM = arm_compute::NEQLSTMLayer::validate(inputLSTM,
+ &aclInputToForgetWeightsInfo,
+ &aclInputToCellWeightsInfo,
+ &aclInputToOutputWeightsInfo,
+ &aclRecurrentToForgetWeightsInfo,
+ &aclRecurrentToCellWeightsInfo,
+ &aclRecurrentToOutputWeightsInfo,
+ &aclForgetGateBiasInfo,
+ &aclCellBiasInfo,
+ &aclOutputGateBiasInfo,
+ &aclCellStateInInfo,
+ &aclOutputStateInInfo,
+ &aclCellStateOutInfo,
+ &aclOutputStateOutInfo,
+ outputLSTM,
+ lstm_params_info);
+ }
+
+ //
+ // Concat validate
+ //
+
+ // Expand dimensions of LSTM outputs adding one empty dimension to fit concatenate inputs.
+ TensorShape shape = GetTensorShape(concatInputsTensorInfosPtr[0]->tensor_shape(), 1U);
+ TensorShape shapeExpandTimeMajor({1, shape[0], shape[1]});
+ TensorShape shapeExpandBatchMajor({shape[0], 1, shape[1]});
+
+ TensorInfo concatOutputTensorInfo = TensorInfo(output);
+ concatOutputTensorInfo.SetShape(timeMajorShapeOutput);
+ arm_compute::TensorInfo aclConcatOutputTensorInfo= BuildArmComputeTensorInfo(concatOutputTensorInfo);
+
+ if (maxTime != 1) // ACL concat does not work with only one element to concatenate.
+ {
+ for (unsigned int i = 0; i < maxTime; ++i)
+ {
+ auto acl_shape_expand = BuildArmComputeTensorShape(shapeExpandTimeMajor);
+ concatInputsTensorInfos[i].set_tensor_shape(acl_shape_expand);
+ }
+
+ unsigned int aclAxisConcat = CalcAclAxis(numberDimensions, dimension);
+ if (!descriptor.m_TimeMajor)
+ {
+ statusConcat = arm_compute::NEConcatenateLayer::validate(concatInputsTensorInfosPtr,
+ &aclConcatOutputTensorInfo,
+ aclAxisConcat);
+ }
+ else
+ {
+ statusConcat = arm_compute::NEConcatenateLayer::validate(concatInputsTensorInfosPtr,
+ &aclOutputInfo,
+ aclAxisConcat);
+ }
+ }
+ // If only one LSTM batch, we do not concat and/or permute.
+ // Must ensure final output info is expanded to correct batch major dimensions.
+ else
+ {
+ if (!descriptor.m_TimeMajor)
+ {
+ const_cast<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape(
+ BuildArmComputeTensorShape(shapeExpandBatchMajor));
+ }
+ else
+ {
+ const_cast<arm_compute::TensorInfo*>(&aclInputInfo)->set_tensor_shape(
+ BuildArmComputeTensorShape(shapeExpandTimeMajor));
+ }
+ }
+
+ //
+ // Permute validate
+ //
+ if (!descriptor.m_TimeMajor)
+ {
+ // Output now time major. Permute output back to batch major.
+ if (maxTime != 1)
+ {
+ statusPermute2 = arm_compute::NEPermute::validate(&aclConcatOutputTensorInfo,
+ &aclOutputInfo,
+ arm_compute::PermutationVector(0U, 2U, 1U));
+ }
+ else
+ {
+ statusPermute2 = arm_compute::NEPermute::validate(concatInputsTensorInfosPtr[0],
+ &aclOutputInfo,
+ arm_compute::PermutationVector(0U, 2U, 1U));
+ }
+ }
+
+ auto okCode = arm_compute::ErrorCode::OK;
+ if (statusPermute1.error_code() == okCode &&
+ statusSplit.error_code() == okCode &&
+ statusLSTM .error_code() == okCode &&
+ statusConcat.error_code() == okCode &&
+ statusPermute2.error_code() == okCode)
+ {
+ return arm_compute::Status(arm_compute::ErrorCode::OK,
+ "All Unidirectional Sequence LSTM layer validate status OK.");
+ }
+ else
+ {
+ return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR,
+ "Unidirectional Sequence LSTM layer validate status failed.");
+ }
+}
+
+void NeonUnidirectionalSequenceLstmWorkload::FreeUnusedTensors()
+{
+ FreeTensorIfUnused(m_InputToInputWeightsTensor);
+ FreeTensorIfUnused(m_InputToForgetWeightsTensor);
+ FreeTensorIfUnused(m_InputToCellWeightsTensor);
+ FreeTensorIfUnused(m_InputToOutputWeightsTensor);
+ FreeTensorIfUnused(m_RecurrentToInputWeightsTensor);
+ FreeTensorIfUnused(m_RecurrentToForgetWeightsTensor);
+ FreeTensorIfUnused(m_RecurrentToCellWeightsTensor);
+ FreeTensorIfUnused(m_RecurrentToOutputWeightsTensor);
+ FreeTensorIfUnused(m_CellToInputWeightsTensor);
+ FreeTensorIfUnused(m_CellToForgetWeightsTensor);
+ FreeTensorIfUnused(m_CellToOutputWeightsTensor);
+ FreeTensorIfUnused(m_InputGateBiasTensor);
+ FreeTensorIfUnused(m_ForgetGateBiasTensor);
+ FreeTensorIfUnused(m_CellBiasTensor);
+ FreeTensorIfUnused(m_OutputGateBiasTensor);
+ FreeTensorIfUnused(m_ProjectionWeightsTensor);
+ FreeTensorIfUnused(m_ProjectionBiasTensor);
+ FreeTensorIfUnused(m_InputLayerNormWeightsTensor);
+ FreeTensorIfUnused(m_ForgetLayerNormWeightsTensor);
+ FreeTensorIfUnused(m_CellLayerNormWeightsTensor);
+ FreeTensorIfUnused(m_OutputLayerNormWeightsTensor);
+}
+
+} //namespace armnn
diff --git a/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmWorkload.hpp b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmWorkload.hpp
new file mode 100644
index 0000000..f012258
--- /dev/null
+++ b/src/backends/neon/workloads/NeonUnidirectionalSequenceLstmWorkload.hpp
@@ -0,0 +1,90 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <armnn/Descriptors.hpp>
+#include <armnn/LstmParams.hpp>
+#include <armnn/backends/Workload.hpp>
+#include <armnn/backends/WorkloadData.hpp>
+#include "NeonBaseWorkload.hpp"
+
+#include "arm_compute/runtime/NEON/functions/NEQLSTMLayer.h"
+#include "arm_compute/runtime/NEON/functions/NEPermute.h"
+#include "arm_compute/runtime/NEON/functions/NESplit.h"
+#include "arm_compute/runtime/NEON/functions/NEConcatenateLayer.h"
+
+namespace armnn
+{
+
+class NeonUnidirectionalSequenceLstmWorkload : public NeonBaseWorkload<UnidirectionalSequenceLstmQueueDescriptor>
+{
+public:
+ NeonUnidirectionalSequenceLstmWorkload(const UnidirectionalSequenceLstmQueueDescriptor& descriptor,
+ const WorkloadInfo& info);
+ virtual void Execute() const override;
+
+private:
+
+ //
+ // ACL layers required to fully form a Unidirectional Sequence LSTM layer.
+ //
+ mutable std::unique_ptr<arm_compute::NEPermute> m_Permute1;
+ mutable std::unique_ptr<arm_compute::IFunction> m_Splitter;
+ mutable std::vector<std::unique_ptr<arm_compute::NEQLSTMLayer>> m_Layers;
+ mutable std::unique_ptr<arm_compute::NEConcatenateLayer> m_Concat;
+ mutable std::unique_ptr<arm_compute::NEPermute> m_Permute2;
+
+ //
+ // ACL LSTM arm_compute::Tensors.
+ //
+ std::unique_ptr<arm_compute::Tensor> m_InputToInputWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_InputToForgetWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_InputToCellWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_InputToOutputWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_RecurrentToInputWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_RecurrentToForgetWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_RecurrentToCellWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_RecurrentToOutputWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_CellToInputWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_CellToForgetWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_CellToOutputWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_InputGateBiasTensor;
+ std::unique_ptr<arm_compute::Tensor> m_ForgetGateBiasTensor;
+ std::unique_ptr<arm_compute::Tensor> m_CellBiasTensor;
+ std::unique_ptr<arm_compute::Tensor> m_OutputGateBiasTensor;
+ std::unique_ptr<arm_compute::Tensor> m_ProjectionWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_ProjectionBiasTensor;
+
+ std::unique_ptr<arm_compute::Tensor> m_InputLayerNormWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_ForgetLayerNormWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_CellLayerNormWeightsTensor;
+ std::unique_ptr<arm_compute::Tensor> m_OutputLayerNormWeightsTensor;
+
+ //
+ // Additional ACL arm_compute::Tensors and std::vector<arm_compute::Tensor>.
+ // Required to perform splitting, concatenation and permutations.
+ //
+ arm_compute::Tensor m_PermuteFirstOut;
+ std::vector<arm_compute::Tensor> m_SplitterOutputsTensors;
+ std::vector<arm_compute::Tensor> m_ConcatInputsTensors;
+ std::vector<arm_compute::ITensor*> m_SplitterOutputs;
+ std::vector<const arm_compute::ITensor*> m_ConcatInputs;
+ arm_compute::Tensor concat_out;
+
+ void FreeUnusedTensors();
+};
+
+arm_compute::Status
+NeonUnidirectionalSequenceLstmWorkloadValidate(const TensorInfo& input,
+ const TensorInfo& outputStateIn,
+ const TensorInfo& cellStateIn,
+ const TensorInfo& outputStateOut,
+ const TensorInfo& cellStateOut,
+ const TensorInfo& output,
+ const UnidirectionalSequenceLstmDescriptor& descriptor,
+ const LstmInputParamsInfo& paramsInfo);
+
+} //namespace armnn
diff --git a/src/backends/neon/workloads/NeonWorkloads.hpp b/src/backends/neon/workloads/NeonWorkloads.hpp
index 4f5ba2d..8b99f03 100644
--- a/src/backends/neon/workloads/NeonWorkloads.hpp
+++ b/src/backends/neon/workloads/NeonWorkloads.hpp
@@ -69,3 +69,4 @@
#include "NeonTransposeConvolution2dWorkload.hpp"
#include "NeonTransposeWorkload.hpp"
#include "NeonUnidirectionalSequenceLstmFloatWorkload.hpp"
+#include "NeonUnidirectionalSequenceLstmWorkload.hpp"
\ No newline at end of file
diff --git a/src/backends/reference/RefLayerSupport.cpp b/src/backends/reference/RefLayerSupport.cpp
index 66661cb..919c6db 100644
--- a/src/backends/reference/RefLayerSupport.cpp
+++ b/src/backends/reference/RefLayerSupport.cpp
@@ -465,57 +465,15 @@
"hiddenStateOutputVal, cellStateOutputVal, output}");
}
auto desc = *(PolymorphicDowncast<const UnidirectionalSequenceLstmDescriptor*>(&descriptor));
-
- bool isHiddenStateOutputOptional = (infos[4] == TensorInfo());
- bool isCellStateOutput = (infos[5] == TensorInfo());
- if (isHiddenStateOutputOptional && isCellStateOutput)
- {
- return IsUnidirectionalSequenceLstmSupported(infos[0],
- infos[1],
- infos[2],
- infos[3],
- EmptyOptional(),
- EmptyOptional(),
- desc,
- lstmParamsInfo.value(),
- reasonIfUnsupported);
- }
- else if (isHiddenStateOutputOptional)
- {
- return IsUnidirectionalSequenceLstmSupported(infos[0],
- infos[1],
- infos[2],
- infos[3],
- EmptyOptional(),
- infos[5],
- desc,
- lstmParamsInfo.value(),
- reasonIfUnsupported);
- }
- else if (isCellStateOutput)
- {
- return IsUnidirectionalSequenceLstmSupported(infos[0],
- infos[1],
- infos[2],
- infos[3],
- infos[4],
- EmptyOptional(),
- desc,
- lstmParamsInfo.value(),
- reasonIfUnsupported);
- }
- else
- {
- return IsUnidirectionalSequenceLstmSupported(infos[0],
- infos[1],
- infos[2],
- infos[3],
- infos[4],
- infos[5],
- desc,
- lstmParamsInfo.value(),
- reasonIfUnsupported);
- }
+ return IsUnidirectionalSequenceLstmSupported(infos[0],
+ infos[1],
+ infos[2],
+ infos[3],
+ infos[4],
+ infos[5],
+ desc,
+ lstmParamsInfo.value(),
+ reasonIfUnsupported);
}
case LayerType::Pooling3d:
return IsPooling3dSupported(infos[0],
@@ -2841,9 +2799,9 @@
const TensorInfo& input,
const TensorInfo& outputStateIn,
const TensorInfo& cellStateIn,
+ const TensorInfo& outputStateOut,
+ const TensorInfo& cellStateOut,
const TensorInfo& output,
- const Optional<TensorInfo>& hiddenStateOutput,
- const Optional<TensorInfo>& cellStateOutput,
const UnidirectionalSequenceLstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo,
Optional<std::string&> reasonIfUnsupported) const
@@ -2852,17 +2810,14 @@
IgnoreUnused(paramsInfo);
IgnoreUnused(outputStateIn);
IgnoreUnused(cellStateIn);
+ IgnoreUnused(outputStateOut);
+ IgnoreUnused(cellStateOut);
bool supported = true;
- if (hiddenStateOutput.has_value() || cellStateOutput.has_value())
+ std::array<DataType, 2> supportedTypes =
{
- reasonIfUnsupported.value() += "Reference UnidirectionalSequenceLstm: hidden state output "
- "and cell state output are not supported at the moment.";
- }
-
- std::array<DataType, 1> supportedTypes =
- {
- DataType::Float32
+ DataType::Float32,
+ DataType::QAsymmS8
};
std::array<DataType, 2> supportedWeightTypes =
@@ -2871,16 +2826,19 @@
DataType::QAsymmS8
};
+ std::array<DataType, 3> supportedBiasTypes =
+ {
+ DataType::Float32,
+ DataType::QAsymmS8,
+ DataType::Signed32
+ };
+
// check inputs and outputs
supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported,
"Reference UnidirectionalSequenceLstm: input is not a supported type.");
- supported &= CheckSupportRule(TypesAreEqual(input, outputStateIn), reasonIfUnsupported,
- "Reference UnidirectionalSequenceLstm: input and outputStateIn types are mismatched");
- supported &= CheckSupportRule(TypesAreEqual(input, cellStateIn), reasonIfUnsupported,
- "Reference UnidirectionalSequenceLstm: input and cellStateIn types are mismatched");
+ supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: output is not a supported type.");
- supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported,
- "Reference UnidirectionalSequenceLstm: input and output types are mismatched");
// check layer parameters
supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetInputToForgetWeights(), supportedWeightTypes),
reasonIfUnsupported,
@@ -2905,14 +2863,13 @@
reasonIfUnsupported,
"Reference UnidirectionalSequenceLstm: RecurrentToOutputWeights "
"is not a supported type.");
- supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetForgetGateBias()), reasonIfUnsupported,
- "Reference UnidirectionalSequenceLstm: input and ForgetGateBias types "
- "are mismatched");
- supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetCellBias()), reasonIfUnsupported,
- "Reference UnidirectionalSequenceLstm: input and CellBias types are mismatched");
- supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetOutputGateBias()), reasonIfUnsupported,
- "Reference UnidirectionalSequenceLstm: input and OutputGateBias types "
- "are mismatched");
+
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetForgetGateBias(), supportedBiasTypes), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: ForgetGateBias is not a supported type.");
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetCellBias(), supportedBiasTypes), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: CellBias is not a supported type.");
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetOutputGateBias(), supportedBiasTypes), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: OutputGateBias is not a supported type.");
if (!descriptor.m_CifgEnabled)
{
supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetInputToInputWeights(), supportedWeightTypes),
@@ -2923,9 +2880,8 @@
reasonIfUnsupported,
"Reference UnidirectionalSequenceLstm: RecurrentToInputWeights "
"is not a supported type.");
- supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetInputGateBias()), reasonIfUnsupported,
- "Reference UnidirectionalSequenceLstm: input and InputGateBias types "
- "are mismatched");
+ supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetInputGateBias(), supportedBiasTypes), reasonIfUnsupported,
+ "Reference UnidirectionalSequenceLstm: InputGateBias is not a supported type.");
if (descriptor.m_PeepholeEnabled)
{
supported &= CheckSupportRule(TypeAnyOf(paramsInfo.GetCellToInputWeights(), supportedWeightTypes),
diff --git a/src/backends/reference/RefLayerSupport.hpp b/src/backends/reference/RefLayerSupport.hpp
index 98770ad..aa8bd8d 100644
--- a/src/backends/reference/RefLayerSupport.hpp
+++ b/src/backends/reference/RefLayerSupport.hpp
@@ -367,9 +367,9 @@
const TensorInfo& input,
const TensorInfo& outputStateIn,
const TensorInfo& cellStateIn,
+ const TensorInfo& outputStateOut,
+ const TensorInfo& cellStateOut,
const TensorInfo& output,
- const Optional<TensorInfo>& hiddenStateOutput,
- const Optional<TensorInfo>& cellStateOutput,
const UnidirectionalSequenceLstmDescriptor& descriptor,
const LstmInputParamsInfo& paramsInfo,
Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override;
diff --git a/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.cpp b/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.cpp
index d447a46..c4345d4 100644
--- a/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.cpp
+++ b/src/backends/reference/workloads/RefUnidirectionalSequenceLstmWorkload.cpp
@@ -59,7 +59,9 @@
TensorInfo inputInfo = GetTensorInfo(inputs[0]);
const TensorInfo& outputStateInfo = GetTensorInfo(inputs[1]);
const TensorInfo& cellStateInfo = GetTensorInfo(inputs[2]);
- TensorInfo outputInfo = GetTensorInfo(outputs[0]);
+ TensorInfo outputStateOutInfo = GetTensorInfo(outputs[0]);
+ TensorInfo cellStateOutInfo = GetTensorInfo(outputs[1]);
+ TensorInfo outputInfo = GetTensorInfo(outputs[2]);
TensorShape& inputShape = inputInfo.GetShape();
TensorShape& outputShape= outputInfo.GetShape();
auto inputTensor = reinterpret_cast<float*>(inputs[0]->Map());
@@ -140,7 +142,7 @@
auto currentInputData = reinterpret_cast<float*>(inputs[0]->Map());
std::unique_ptr<Decoder<float>> inputData = MakeDecoder<float>(lstmInputInfo, currentInputData);
- auto currentOutputData = reinterpret_cast<float*>(outputs[0]->Map());
+ auto currentOutputData = reinterpret_cast<float*>(outputs[2]->Map());
std::unique_ptr<Encoder<float>> output = MakeEncoder<float>(lstmOutputInfo, currentOutputData);
std::unique_ptr<Decoder<float>> outputDecoder = MakeDecoder<float>(lstmOutputInfo, currentOutputData);
@@ -296,7 +298,7 @@
{
// Permute Output back to batch major
const PermutationVector& mappings = {1U, 0U, 2U};
- auto outputData = reinterpret_cast<float*>(outputs[0]->Map());
+ auto outputData = reinterpret_cast<float*>(outputs[2]->Map());
std::vector<float> outputValue(outputData, outputData + outputInfo.GetNumElements());
outputShape = armnnUtils::Permuted(outputInfo.GetShape(), mappings);
outputInfo.SetShape(outputShape);