1 files changed, 68 insertions, 10 deletions
diff --git a/nn/common/operations/BidirectionalSequenceLSTM.cpp b/nn/common/operations/BidirectionalSequenceLSTM.cpp
index d4d32b964..12ac43f20 100644
--- a/nn/common/operations/BidirectionalSequenceLSTM.cpp
+++ b/nn/common/operations/BidirectionalSequenceLSTM.cpp
@@ -169,19 +169,24 @@ BidirectionalSequenceLSTM::BidirectionalSequenceLSTM(const Operation& operation,
     bw_cell_layer_norm_weights_ = GetInput(operation, operands, kBwCellLayerNormWeightsTensor);
     bw_output_layer_norm_weights_ = GetInput(operation, operands, kBwOutputLayerNormWeightsTensor);
 
-    params_.activation = static_cast<TfLiteFusedActivation>(
-            getScalarData<int32_t>(*GetInput(operation, operands, kActivationParam)));
+    const auto& activationOperand = *GetInput(operation, operands, kActivationParam);
+    params_.activation = static_cast<TfLiteFusedActivation>(getScalarDataWithDefault<int32_t>(
+            activationOperand, TfLiteFusedActivation::kTfLiteActNone));
+    const auto& clipOperand = *GetInput(operation, operands, kCellClipParam);
+    const auto& projOperand = *GetInput(operation, operands, kProjClipParam);
     if (input_->type == OperandType::TENSOR_FLOAT32) {
-        params_.cell_clip = getScalarData<float>(*GetInput(operation, operands, kCellClipParam));
-        params_.proj_clip = getScalarData<float>(*GetInput(operation, operands, kProjClipParam));
+        params_.cell_clip = getScalarDataWithDefault<float>(clipOperand, 0.0f);
+        params_.proj_clip = getScalarDataWithDefault<float>(projOperand, 0.0f);
     } else {
-        params_.cell_clip = static_cast<float>(
-                getScalarData<_Float16>(*GetInput(operation, operands, kCellClipParam)));
-        params_.proj_clip = static_cast<float>(
-                getScalarData<_Float16>(*GetInput(operation, operands, kProjClipParam)));
+        params_.cell_clip =
+                static_cast<float>(getScalarDataWithDefault<_Float16>(clipOperand, 0.0f));
+        params_.proj_clip =
+                static_cast<float>(getScalarDataWithDefault<_Float16>(projOperand, 0.0f));
     }
-    params_.merge_outputs = getScalarData<bool>(*GetInput(operation, operands, kMergeOutputsParam));
-    params_.time_major = getScalarData<bool>(*GetInput(operation, operands, kTimeMajorParam));
+    const auto& mergeOutputsOperand = *GetInput(operation, operands, kMergeOutputsParam);
+    params_.merge_outputs = getScalarDataWithDefault<bool>(mergeOutputsOperand, false);
+    const auto& timeMajorOperand = *GetInput(operation, operands, kTimeMajorParam);
+    params_.time_major = getScalarDataWithDefault<bool>(timeMajorOperand, false);
     params_.use_layer_norm = !IsNullInput(fw_input_layer_norm_weights_);
 
     fw_output_ = GetOutput(operation, operands, kFwOutputTensor);
@@ -205,6 +210,59 @@ bool BidirectionalSequenceLSTM::Prepare(const Operation& operation, RunTimeOpera
                                         Shape* fwOutputShape, Shape* bwOutputShape,
                                         Shape* fwOutputActivationState, Shape* fwOutputCellState,
                                         Shape* bwOutputActivationState, Shape* bwOutputCellState) {
+    // Check we have all the inputs and outputs we need.
+    constexpr int requiredInputs[] = {
+            kInputTensor,
+            kFwInputToForgetWeightsTensor,
+            kFwInputToCellWeightsTensor,
+            kFwInputToOutputWeightsTensor,
+            kFwRecurrentToForgetWeightsTensor,
+            kFwRecurrentToCellWeightsTensor,
+            kFwRecurrentToOutputWeightsTensor,
+            kFwForgetGateBiasTensor,
+            kFwCellGateBiasTensor,
+            kFwOutputGateBiasTensor,
+            kBwInputToForgetWeightsTensor,
+            kBwInputToCellWeightsTensor,
+            kBwInputToOutputWeightsTensor,
+            kBwRecurrentToForgetWeightsTensor,
+            kBwRecurrentToCellWeightsTensor,
+            kBwRecurrentToOutputWeightsTensor,
+            kBwForgetGateBiasTensor,
+            kBwCellGateBiasTensor,
+            kBwOutputGateBiasTensor,
+            kFwInputActivationStateTensor,
+            kFwInputCellStateTensor,
+            kBwInputActivationStateTensor,
+            kBwInputCellStateTensor,
+            kActivationParam,
+            kCellClipParam,
+            kProjClipParam,
+            kMergeOutputsParam,
+            kTimeMajorParam,
+    };
+    for (const int requiredInput : requiredInputs) {
+        NN_RET_CHECK(!IsNullInput(GetInput(operation, operands, requiredInput)))
+                << "required input " << requiredInput << " is omitted";
+    }
+
+    // Check that the scalar operands' buffers are large enough.
+    const auto& activationOperand = *GetInput(operation, operands, kActivationParam);
+    NN_RET_CHECK(activationOperand.length >= sizeof(int32_t));
+    const auto& cellOperand = *GetInput(operation, operands, kCellClipParam);
+    const auto& projOperand = *GetInput(operation, operands, kProjClipParam);
+    if (input_->type == OperandType::TENSOR_FLOAT32) {
+        NN_RET_CHECK(cellOperand.length >= sizeof(float));
+        NN_RET_CHECK(projOperand.length >= sizeof(float));
+    } else {
+        NN_RET_CHECK(cellOperand.length >= sizeof(_Float16));
+        NN_RET_CHECK(projOperand.length >= sizeof(_Float16));
+    }
+    const auto& mergeOutputsOperand = *GetInput(operation, operands, kMergeOutputsParam);
+    NN_RET_CHECK(mergeOutputsOperand.length >= sizeof(bool));
+    const auto& timeMajorOperand = *GetInput(operation, operands, kTimeMajorParam);
+    NN_RET_CHECK(timeMajorOperand.length >= sizeof(bool));
+
     // Inferring batch size, number of outputs and number of cells from the
     // input tensors.
     NN_CHECK(NumDimensions(input_) == 3);