IVGCVSW-6806 Fixed issue with missing TensorInfos in UnidirectionalSequenceLSTM
* Corrected TensorInfo order for IsUnidirectionalSequenceLstmSupported
* outputStateOut TensorInfo is not optional.
* cellStateOut TensorInfo is not optional.
* TensorInfo Order matches other QLSTM/LSTM layers.
!armnn:7455
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: I5b0e0fa4b6e1c3da6689d9aefc9b959172c2e7d4
diff --git a/ConversionUtils_1_2.hpp b/ConversionUtils_1_2.hpp
index 1bcd9f2..0ff50cf 100644
--- a/ConversionUtils_1_2.hpp
+++ b/ConversionUtils_1_2.hpp
@@ -3455,6 +3455,18 @@
// Outputs
const TensorInfo& outputInfo = GetTensorInfoForOperand(*output);
+ unsigned int batchSize = inputInfo.GetShape()[0];
+ unsigned int outputSize = outputInfo.GetShape()[2];
+ unsigned int numUnits = cellStateInInfo.GetShape()[1];
+
+ armnn::DataType dataType = inputInfo.GetDataType();
+ float qScale = inputInfo.GetQuantizationScale();
+ int qOffset = inputInfo.GetQuantizationOffset();
+
+ armnn::TensorInfo cellStateOutInfo({batchSize, numUnits}, cellStateInInfo.GetDataType(),
+ cellStateInInfo.GetQuantizationScale(), cellStateInInfo.GetQuantizationOffset());
+ armnn::TensorInfo outputStateOutInfo({batchSize, outputSize}, dataType, qScale, qOffset);
+
// Basic parameters
LstmInputParamsInfo paramsInfo;
paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo());
@@ -3505,9 +3517,6 @@
paramsInfo.m_OutputLayerNormWeights = &(params.m_OutputLayerNormWeights->GetInfo());
}
- auto hiddenStateOutInfo = EmptyOptional();
- auto cellStateOutInfo = EmptyOptional();
-
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported)
{
@@ -3518,9 +3527,9 @@
inputInfo,
outputStateInInfo,
cellStateInInfo,
- outputInfo,
- hiddenStateOutInfo,
+ outputStateOutInfo,
cellStateOutInfo,
+ outputInfo,
desc,
paramsInfo);
};
@@ -3552,12 +3561,12 @@
if (!isDynamic)
{
- return (SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, 0, model, data));
+ return (SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, 2, model, data));
}
else
{
- return (SetupAndTrackLayerOutputSlot<HalPolicy>(
- operation, 0, *layer, 0, model, data, nullptr, validateFunc, ActivationFn::kActivationNone, true));
+ return (SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, 2, model, data, nullptr,
+ validateFunc, ActivationFn::kActivationNone, true));
}
}
diff --git a/test/UnidirectionalSequenceLstm.hpp b/test/UnidirectionalSequenceLstm.hpp
index d03f8ab..75b7a8d 100644
--- a/test/UnidirectionalSequenceLstm.hpp
+++ b/test/UnidirectionalSequenceLstm.hpp
@@ -138,10 +138,10 @@
const std::vector<float>& outputLayerNormWeightsValue,
const hidl_vec<uint32_t>& outputDimensions,
const std::vector<float>& outputValue,
- const hidl_vec<uint32_t>&,
- const std::vector<float>&,
- const hidl_vec<uint32_t>&,
- const std::vector<float>&,
+ const hidl_vec<uint32_t>&, // outputStateOutDimensions,
+ const std::vector<float>&, // outputStateOutValue,
+ const hidl_vec<uint32_t>&, // cellStateOutDimensions,
+ const std::vector<float>&, // cellStateOutValue,
armnn::Compute compute,
float epsilonValue = 0)
{
@@ -519,12 +519,12 @@
// 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape
// [batch_size, output_size]. This output is optional and can be omitted. If this output
// is present then output #2 must be present as well.
- hidl_vec<uint32_t> hiddenStateOutDimensions{0};
- std::vector<float> hiddenStateOutValue;
+ hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize};
+ std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f);
// 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape
// [batch_size, num_units]. This output is optional and can be omitted.
- hidl_vec<uint32_t> cellStateOutDimensions{0};
- std::vector<float> cellStateOutValue;
+ hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits};
+ std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f);
UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue,
inputToInputWeightsDimensions, inputToInputWeightsValue,
@@ -724,12 +724,12 @@
// 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape
// [batch_size, output_size]. This output is optional and can be omitted. If this output
// is present then output #2 must be present as well.
- hidl_vec<uint32_t> hiddenStateOutDimensions{0};
- std::vector<float> hiddenStateOutValue;
+ hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize};
+ std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f);
// 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape
// [batch_size, num_units]. This output is optional and can be omitted.
- hidl_vec<uint32_t> cellStateOutDimensions{0};
- std::vector<float> cellStateOutValue;
+ hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits};
+ std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f);
UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue,
inputToInputWeightsDimensions, inputToInputWeightsValue,
@@ -964,12 +964,12 @@
// 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape
// [batch_size, output_size]. This output is optional and can be omitted. If this output
// is present then output #2 must be present as well.
- hidl_vec<uint32_t> hiddenStateOutDimensions{0};
- std::vector<float> hiddenStateOutValue;
+ hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize};
+ std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f);
// 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape
// [batch_size, num_units]. This output is optional and can be omitted.
- hidl_vec<uint32_t> cellStateOutDimensions{0};
- std::vector<float> cellStateOutValue;
+ hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits};
+ std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f);
UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue,
inputToInputWeightsDimensions, inputToInputWeightsValue,
@@ -1179,12 +1179,12 @@
// 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape
// [batch_size, output_size]. This output is optional and can be omitted. If this output
// is present then output #2 must be present as well.
- hidl_vec<uint32_t> hiddenStateOutDimensions{0};
- std::vector<float> hiddenStateOutValue;
+ hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize};
+ std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f);
// 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape
// [batch_size, num_units]. This output is optional and can be omitted.
- hidl_vec<uint32_t> cellStateOutDimensions{0};
- std::vector<float> cellStateOutValue;
+ hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits};
+ std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f);
UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue,
inputToInputWeightsDimensions, inputToInputWeightsValue,
@@ -1376,12 +1376,12 @@
// 1: The hidden state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape
// [batch_size, output_size]. This output is optional and can be omitted. If this output
// is present then output #2 must be present as well.
- hidl_vec<uint32_t> hiddenStateOutDimensions{0};
- std::vector<float> hiddenStateOutValue;
+ hidl_vec<uint32_t> hiddenStateOutDimensions{batchSize, outputSize};
+ std::vector<float> hiddenStateOutValue(batchSize * outputSize, 0.f);
// 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32/16, of shape
// [batch_size, num_units]. This output is optional and can be omitted.
- hidl_vec<uint32_t> cellStateOutDimensions{0};
- std::vector<float> cellStateOutValue;
+ hidl_vec<uint32_t> cellStateOutDimensions{batchSize, numUnits};
+ std::vector<float> cellStateOutValue(batchSize * numUnits, 0.f);
UnidirectionalSequenceLstmTestImpl<HalPolicy>(inputDimensions, inputValue,
inputToInputWeightsDimensions, inputToInputWeightsValue,