MLCE-91 LSTM doesn't support optional input

    * Add fix for optional NO_VALUE operands in ConversionUtils.hpp
    * Remove fail message for optional NO_VALUE in ConversionUtils.hpp
    * Add to existing tests and test helper to cover optional NO_VALUE

Signed-off-by: Kevin May <kevin.may@arm.com>
Change-Id: Icf36af1fc00d3fb33cdd77ff6d6618cc4700d3fd

2 files changed, 37 insertions, 12 deletions

diff --git a/test/DriverTestHelpers.hpp b/test/DriverTestHelpers.hpp
index 4d91ae22..394720ed 100644
--- a/test/DriverTestHelpers.hpp
+++ b/test/DriverTestHelpers.hpp
@@ -112,7 +112,8 @@ template<typename HalModel, typename T>
 void AddTensorOperand(HalModel& model,
                       const hidl_vec<uint32_t>& dimensions,
                       const T* values,
-                      OperandType operandType = OperandType::TENSOR_FLOAT32)
+                      OperandType operandType = OperandType::TENSOR_FLOAT32,
+                      OperandLifeTime operandLifeTime = OperandLifeTime::CONSTANT_COPY)
 {
     uint32_t totalElements = 1;
     for (uint32_t dim : dimensions)
@@ -121,9 +122,13 @@ void AddTensorOperand(HalModel& model,
     }
 
     DataLocation location = {};
-    location.offset = model.operandValues.size();
     location.length = totalElements * sizeof(T);
 
+    if(operandLifeTime == OperandLifeTime::CONSTANT_COPY)
+    {
+        location.offset = model.operandValues.size();
+    }
+
     Operand op    = {};
     op.type       = operandType;
     op.dimensions = dimensions;
@@ -143,9 +148,10 @@ template<typename HalModel, typename T>
 void AddTensorOperand(HalModel& model,
                       const hidl_vec<uint32_t>& dimensions,
                       const std::vector<T>& values,
-                      OperandType operandType = OperandType::TENSOR_FLOAT32)
+                      OperandType operandType = OperandType::TENSOR_FLOAT32,
+                      OperandLifeTime operandLifeTime = OperandLifeTime::CONSTANT_COPY)
 {
-    AddTensorOperand<HalModel, T>(model, dimensions, values.data(), operandType);
+    AddTensorOperand<HalModel, T>(model, dimensions, values.data(), operandType, operandLifeTime);
 }
 
 template<typename HalModel>
diff --git a/test/Lstm.cpp b/test/Lstm.cpp
index ea0405c6..66f2cf02 100644
--- a/test/Lstm.cpp
+++ b/test/Lstm.cpp
@@ -56,6 +56,17 @@ bool TolerantCompareEqual(float a, float b, float tolerance = 0.00001f)
     return rd < tolerance;
 }
 
+// Helper function to create an OperandLifeTime::NO_VALUE for testing.
+// To be used on optional input operands that have no values - these are valid and should be tested.
+OperandLifeTime CreateNoValueLifeTime(const hidl_vec<uint32_t>& dimensions)
+{
+    // Only create a NO_VALUE for optional operands that have no elements
+    if (dimensions.size() == 0 || dimensions[0] == 0)
+    {
+        return OperandLifeTime::NO_VALUE;
+    }
+    return OperandLifeTime::CONSTANT_COPY;
+}
 } // anonymous namespace
 
 // Add our own tests here since we fail the lstm tests which Google supplies (because of non-const weights)
@@ -126,7 +137,8 @@ void LstmTestImpl(const hidl_vec<uint32_t>&   inputDimensions,
 
     // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
     //     [num_units, input_size], where “num_units” corresponds to the number of cell units.
-    AddTensorOperand(model, inputToInputWeightsDimensions, inputToInputWeightsValue);
+    AddTensorOperand(model, inputToInputWeightsDimensions, inputToInputWeightsValue, OperandType::TENSOR_FLOAT32,
+                     CreateNoValueLifeTime(inputToInputWeightsDimensions));
     // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
     //     [num_units, input_size].
     AddTensorOperand(model, inputToForgetWeightsDimensions, inputToForgetWeightsValue);
@@ -138,7 +150,8 @@ void LstmTestImpl(const hidl_vec<uint32_t>&   inputDimensions,
     // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
     //     [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e.,
     //     “num_units”), or the second dimension of the “projection_weights”, if defined.
-    AddTensorOperand(model, recurrentToInputWeightsDimensions, recurrentToInputWeightsValue);
+    AddTensorOperand(model, recurrentToInputWeightsDimensions, recurrentToInputWeightsValue,
+                     OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(recurrentToInputWeightsDimensions));
     // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
     //     [num_units, output_size].
     AddTensorOperand(model, recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue);
@@ -149,13 +162,17 @@ void LstmTestImpl(const hidl_vec<uint32_t>&   inputDimensions,
     //     [num_units, output_size].
     AddTensorOperand(model, recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue);
     // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
-    AddTensorOperand(model, cellToInputWeightsDimensions, cellToInputWeightsValue);
+    AddTensorOperand(model, cellToInputWeightsDimensions, cellToInputWeightsValue,
+                     OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(cellToInputWeightsDimensions));
     // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
-    AddTensorOperand(model, cellToForgetWeightsDimensions, cellToForgetWeightsValue);
+    AddTensorOperand(model, cellToForgetWeightsDimensions, cellToForgetWeightsValue,
+                     OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(cellToForgetWeightsDimensions));
     // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
-    AddTensorOperand(model, cellToOutputWeightsDimensions, cellToOutputWeightsValue);
+    AddTensorOperand(model, cellToOutputWeightsDimensions, cellToOutputWeightsValue,
+                     OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(cellToOutputWeightsDimensions));
     // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
-    AddTensorOperand(model, inputGateBiasDimensions, inputGateBiasValue);
+    AddTensorOperand(model, inputGateBiasDimensions, inputGateBiasValue,
+                     OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(inputGateBiasDimensions));
     // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
     AddTensorOperand(model, forgetGateBiasDimensions, forgetGateBiasValue);
     // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units].
@@ -164,9 +181,11 @@ void LstmTestImpl(const hidl_vec<uint32_t>&   inputDimensions,
     AddTensorOperand(model, outputGateBiasDimensions, outputGateBiasValue);
     // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape
     //     [output_size, num_units].
-    AddTensorOperand(model, projectionWeightsDimensions, projectionWeightsValue);
+    AddTensorOperand(model, projectionWeightsDimensions, projectionWeightsValue,
+                     OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(projectionWeightsDimensions));
     // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size].
-    AddTensorOperand(model, projectionBiasDimensions, projectionBiasValue);
+    AddTensorOperand(model, projectionBiasDimensions, projectionBiasValue,
+                     OperandType::TENSOR_FLOAT32, CreateNoValueLifeTime(projectionBiasDimensions));
 
     // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size].
     AddInputOperand(model, outputStateInDimensions);