1 files changed, 121 insertions, 0 deletions
diff --git a/src/backends/tosaCommon/operatorMappings/ReluOperator.cpp b/src/backends/tosaCommon/operatorMappings/ReluOperator.cpp
new file mode 100644
index 0000000000..bd1a59670e
--- /dev/null
+++ b/src/backends/tosaCommon/operatorMappings/ReluOperator.cpp
@@ -0,0 +1,121 @@
+//
+// Copyright © 2024 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+//
+// Copyright © 2020 The TensorFlow Authors. All Rights Reserved.
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "LeakyReluOperator.hpp"
+#include "TosaRescaleOperatorUtils.hpp"
+
+#include <layers/ActivationLayer.hpp>
+
+// This function is paraphrased from:
+// tensorflow/compiler/mlir/tosa/transforms/legalize_tfl.cc from function ConvertTFLReluOp
+TosaSerializationBasicBlock* ConvertReluToTosaOperator(const Layer* layer,
+                                                       const std::vector<const TensorInfo*>& inputs,
+                                                       const std::vector<const TensorInfo*>& outputs,
+                                                       const ActivationDescriptor*)
+{
+    if (inputs.size() != 1)
+    {
+        throw armnn::Exception("ConvertReluToTosaOperator: 1 input tensors required.");
+    }
+
+    if (outputs.size() != 1)
+    {
+        throw armnn::Exception("ConvertReluToTosaOperator: 1 output tensor required.");
+    }
+
+    std::string inputName  = std::string("input_");
+    std::string outputName = std::string("output0_");
+    std::string blockName  = std::string("Op_RELU_block_") + GetUniqueTosaMappingID();
+
+    // 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.
+    if (layer != nullptr)
+    {
+        inputName  = GenerateUniqueInputName(layer->GetInputSlot(0));
+        outputName = GenerateUniqueOutputName(*layer);
+    }
+
+    std::vector<TosaSerializationTensor*> tensors;
+    std::vector<TosaSerializationOperator*> operators;
+
+    // Only add input tensors if connected layer is an input layer.
+    // As intermediate or constant tensors will be created separately.
+    // There also can't be duplicate tensor.
+    std::vector<int32_t> inputShape0;
+    DType inputDType0 = DType::DType_UNKNOWN;
+    if(inputName.find("input_") != std::string::npos)
+    {
+        inputShape0 = GetTosaTensorShape(inputs[0]->GetShape());
+        inputDType0 = ArmNNToDType(inputs[0]->GetDataType());
+        tensors.push_back(new TosaSerializationTensor(inputName, inputShape0, inputDType0, {}));
+    }
+
+    std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape());
+    DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType());
+    tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}));
+
+    int32_t clamp_min = 0;
+    int32_t clamp_max = std::numeric_limits<int32_t>::max();
+    std::string clampInputNameStr = inputName;
+    if (inputDType0 == tosa::DType::DType_INT8 || inputDType0 == tosa::DType::DType_INT16)
+    {
+        std::string outputNameRescale = std::string("intermediate0_") + GetUniqueTosaMappingID();
+        clampInputNameStr = outputNameRescale;
+
+        double scale = inputs[0]->GetQuantizationScale() / outputs[0]->GetQuantizationScale();
+        int32_t input_zp = inputs[0]->GetQuantizationOffset();
+        int32_t output_zp = outputs[0]->GetQuantizationOffset();
+
+        clamp_min = outputs[0]->GetQuantizationOffset();
+        if (inputDType0 == tosa::DType::DType_INT8)
+        {
+            clamp_min =
+                clamp_min < std::numeric_limits<int8_t>::min() ? std::numeric_limits<int8_t>::min() : clamp_min;
+            clamp_max = std::numeric_limits<int8_t>::max();
+        }
+        else
+        {
+            clamp_min =
+                clamp_min < std::numeric_limits<int16_t>::min() ? std::numeric_limits<int16_t>::min() : clamp_min;
+            clamp_max = std::numeric_limits<int16_t>::max();
+        }
+
+        TosaSerializationOperator* rescaleOp = nullptr;
+        CreateRescaleTosaOperator(inputName,
+                                  outputNameRescale,
+                                  scale,
+                                  input_zp,
+                                  output_zp,
+                                  false,
+                                  true,
+                                  &rescaleOp);
+        operators.push_back(rescaleOp);
+        tensors.push_back(new TosaSerializationTensor(outputNameRescale,
+                                                      inputShape0,
+                                                      inputDType0,
+                                                      {}));
+    }
+
+    TosaClampAttribute attribute(clamp_min, clamp_max, 0, std::numeric_limits<float>::max());
+    auto* clamp_op = new TosaSerializationOperator(Op_CLAMP,
+                                                   Attribute_ClampAttribute,
+                                                   &attribute,
+                                                   {clampInputNameStr},
+                                                   {outputName});
+    operators.push_back(clamp_op);
+
+    // operatorInputNames/operatorOutputNames ends up being the same as
+    // blockInputNames/blockOutputNames for one-to-one ArmNN to Tosa mappings
+    return new TosaSerializationBasicBlock(blockName,      // name
+                                           mainName,       // region name
+                                           operators,      // operators
+                                           tensors,        // tensors
+                                           {inputName},    // inputs
+                                           {outputName});  // outputs
+}