IVGCVSW-7170 Add Concat support to TOSA Reference Backend
* Change comment for the unique tensor names in all tosa common operators
Signed-off-by: Kevin May <kevin.may@arm.com>
Change-Id: I247b4b2365d5f0173218c5dfd11fba12d2399959
diff --git a/src/backends/tosaCommon/operatorMappings/ConcatOperator.cpp b/src/backends/tosaCommon/operatorMappings/ConcatOperator.cpp
new file mode 100644
index 0000000..8c651be
--- /dev/null
+++ b/src/backends/tosaCommon/operatorMappings/ConcatOperator.cpp
@@ -0,0 +1,81 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ConcatOperator.hpp"
+
+TosaSerializationBasicBlock* ConvertConcatToTosaOperator(const Layer* layer,
+ const std::vector<const TensorInfo*>& inputs,
+ const std::vector<const TensorInfo*>& outputs,
+ const OriginsDescriptor* concatDescriptor)
+{
+ auto numInputs = inputs.size();
+ std::vector<std::string> inputNames;
+ inputNames.reserve(numInputs);
+ std::string outputName = std::string("output0_");
+ std::string blockName = std::string("Op_CONCAT_block_") + GetUniqueTosaMappingID();
+
+ // Set input names for validation purposes only.
+ if (layer == nullptr)
+ {
+ for (uint32_t i = 0; i < numInputs; ++i)
+ {
+ inputNames.push_back("input"+ std::to_string(i) +"_");
+ }
+ }
+ // If a layer is present then the block will be used for execution, so input and output names need to be determined
+ // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter.
+ else
+ {
+ // Get the layers connected to the input slots and determine unique tensor names.
+ for (uint32_t i = 0; i < numInputs; ++i)
+ {
+ Layer& connectedLayer = layer->GetInputSlot(i).GetConnectedOutputSlot()->GetOwningLayer();
+
+ std::string inputName = GenerateUniqueName(connectedLayer, i);
+ inputNames.push_back(inputName);
+ }
+
+ // Determine unique output tensor name.
+ outputName = GenerateUniqueOutputName(*layer, 0);
+ }
+
+ auto axis = static_cast<int32_t>(concatDescriptor->GetConcatAxis());
+ TosaAxisAttribute attribute(axis);
+
+ TosaSerializationOperator* op = new TosaSerializationOperator(Op_CONCAT,
+ Attribute_AxisAttribute,
+ &attribute,
+ inputNames,
+ {outputName});
+
+ std::vector<TosaSerializationTensor*> tensors;
+ tensors.reserve(numInputs);
+
+ for (uint32_t i = 0; i < numInputs; ++i)
+ {
+ // Only add input tensors for validation or when the connected layer is an input layer.
+ // As there can't be duplicate tensors and intermediate or constant tensors are created separately.
+ if(inputNames[i].find("input") != std::string::npos)
+ {
+ std::vector<int32_t> inputShape = GetTosaTensorShape(inputs[i]->GetShape());
+ DType inputDType = ArmNNToDType(inputs[i]->GetDataType());
+ tensors.push_back(new TosaSerializationTensor(inputNames[i], inputShape, inputDType, {}));
+ }
+ }
+
+ std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape());
+ DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType());
+
+ TosaSerializationTensor* outputTensor0 = new TosaSerializationTensor(outputName, outputShape0, outputDType0, {});
+ tensors.push_back(outputTensor0);
+
+ // operatorInputNames/operatorOutputNames ends up being the same as
+ // blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings
+ return new TosaSerializationBasicBlock(blockName, // name
+ {op}, // operators
+ tensors, // tensors
+ inputNames, // inputs
+ {outputName}); // outputs
+}
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