IVGCVSW-7204 Add TransposeConv2d support to TOSA Reference Backend

Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I9bfd597afd41468f304edfbe5d7141378ce60d4f

5 files changed, 202 insertions, 1 deletions

diff --git a/src/backends/tosaCommon/operatorMappings/CMakeLists.txt b/src/backends/tosaCommon/operatorMappings/CMakeLists.txt
index cb1d68e625..90c1a4f958 100644
--- a/src/backends/tosaCommon/operatorMappings/CMakeLists.txt
+++ b/src/backends/tosaCommon/operatorMappings/CMakeLists.txt
@@ -19,6 +19,8 @@ list(APPEND armnnTosaBackendOperators_sources
         SliceOperator.hpp
         SliceOperator.cpp
         TosaOperatorUtils.hpp
+        TransposeConv2dOperator.hpp
+        TransposeConv2dOperator.cpp
     )
 
 add_library(armnnTosaBackendOperators OBJECT ${armnnTosaBackendOperators_sources})
diff --git a/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp b/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp
index a3597f0461..1a9d6be9c0 100644
--- a/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp
+++ b/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp
@@ -11,4 +11,5 @@
 #include "AvgPool2DIgnoreValueOperator.hpp"
 #include "Pooling2DOperator.hpp"
 #include "ReshapeOperator.hpp"
-#include "SliceOperator.hpp"
\ No newline at end of file
+#include "SliceOperator.hpp"
+#include "TransposeConv2dOperator.hpp"
\ No newline at end of file
diff --git a/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp b/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp
index 288966badd..be2f53e413 100644
--- a/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp
+++ b/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp
@@ -75,6 +75,23 @@ inline std::string GenerateUniqueName(const Layer& layer, uint32_t layerSlot)
     }
 }
 
+// Function that generates unique output name using the layer type, input slot and layer guid.
+inline std::string GenerateUniqueOutputName(const Layer& layer, uint32_t layerSlot)
+{
+    Layer& connectedLayer = layer.GetOutputSlot().GetConnection(0)->GetOwningLayer();
+
+    // Get the layer connected to the output slot, if output use that layer and id,
+    // otherwise use current layer and id.
+    if(connectedLayer.GetType() == LayerType::Output)
+    {
+        return GenerateUniqueName(connectedLayer, layerSlot);
+    }
+    else
+    {
+        return GenerateUniqueName(layer, layerSlot);
+    }
+}
+
 // Function to return unique int as a string to ensure uniqueness between all input, output and block names.
 static int uniqueTosaMappingID = 0;
 inline std::string GetUniqueTosaMappingID()
diff --git a/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp
new file mode 100644
index 0000000000..a0d58e2fa8
--- /dev/null
+++ b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp
@@ -0,0 +1,161 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "TransposeConv2dOperator.hpp"
+
+#include "layers/TransposeConvolution2dLayer.hpp"
+
+TosaSerializationBasicBlock* ConvertTransposeConv2dToTosaOperator(const Layer* layer,
+                                                                  const std::vector<const TensorInfo*>& inputs,
+                                                                  const std::vector<const TensorInfo*>& outputs,
+                                                                  const TransposeConvolution2dDescriptor* descriptor)
+{
+    std::string input0Name = std::string("input0_");
+    std::string input1Name = std::string("constant_") + GetUniqueTosaMappingID();
+    std::string input2Name = std::string("constant_") + GetUniqueTosaMappingID();
+    std::string outputName = std::string("output0_");
+    std::string blockName  = std::string("Op_TRANSPOSE_CONV2D_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)
+    {
+        Layer& connectedInputLayer = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer();
+        input0Name = GenerateUniqueName(connectedInputLayer, 0);
+
+        outputName = GenerateUniqueOutputName(*layer, 0);
+    }
+
+    std::vector<TosaSerializationTensor*> tensors;
+    std::vector<TosaSerializationOperator*> operators;
+
+    // Setup input tensor
+    // Only add tensor if connected layer is an input layer.
+    // As intermediate or constant tensors will be created separately.
+    // There also can't be duplicate tensors.
+    if(input0Name.find("input0_") != std::string::npos)
+    {
+        std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape());
+        DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType());
+
+        tensors.push_back(new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {}));
+    }
+
+    // Setup weights tensor, constant data will get copied during SetConstantTensorData
+    operators.push_back(new TosaSerializationOperator(Op_CONST, Attribute_NONE, nullptr, {}, {input1Name}));
+
+    // During validation the TensorInfo can be retrieved from the inputs.
+    // During execution, it is only available through the layer so use m_Weight.
+    if(layer == nullptr)
+    {
+        std::vector<int32_t> inputShape1 = GetTosaTensorShape(inputs[1]->GetShape());
+        DType inputDType1 = ArmNNToDType(inputs[1]->GetDataType());
+
+        tensors.push_back(new TosaSerializationTensor(input1Name, inputShape1, inputDType1, {}));
+    }
+    else
+    {
+        auto transposeConv2dLayer = PolymorphicDowncast<const TransposeConvolution2dLayer*>(layer);
+
+        std::vector<int32_t> inputShape1 = GetTosaTensorShape(
+                transposeConv2dLayer->m_Weight->GetTensorInfo().GetShape());
+        DType inputDType1 = ArmNNToDType(transposeConv2dLayer->m_Weight->GetTensorInfo().GetDataType());
+
+        std::vector<uint8_t> uint8Data = ConvertConstantTensorDataToBuffer(transposeConv2dLayer->m_Weight);
+        tensors.push_back(new TosaSerializationTensor(input1Name, inputShape1, inputDType1, uint8Data));
+    }
+
+    // Setup bias operator and tensor, constant data will get copied during SetConstantTensorData
+    operators.push_back(new TosaSerializationOperator(Op_CONST, Attribute_NONE, nullptr, {}, {input2Name}));
+
+    // During validation the TensorInfo can be retrieved from the inputs.
+    // During execution, it is only available through the layer so use m_Bias.
+    if(layer == nullptr && descriptor->m_BiasEnabled)
+    {
+        std::vector<int32_t> inputShape2 = GetTosaTensorShape(inputs[2]->GetShape());
+        DType inputDType2 = ArmNNToDType(inputs[2]->GetDataType());
+
+        tensors.push_back(new TosaSerializationTensor(input2Name, inputShape2, inputDType2, {}));
+    }
+    else if(descriptor->m_BiasEnabled)
+    {
+        auto transposeConv2dLayer = PolymorphicDowncast<const TransposeConvolution2dLayer*>(layer);
+
+        std::vector<int32_t> inputShape2 = GetTosaTensorShape(
+                transposeConv2dLayer->m_Bias->GetTensorInfo().GetShape());
+        DType inputDType2 = ArmNNToDType(transposeConv2dLayer->m_Bias->GetTensorInfo().GetDataType());
+
+        std::vector<uint8_t> uint8Data = ConvertConstantTensorDataToBuffer(transposeConv2dLayer->m_Bias);
+        tensors.push_back(new TosaSerializationTensor(input2Name, inputShape2, inputDType2, uint8Data));
+    }
+    else
+    {
+        // If bias is disabled, create a constant bias tensor of 0's as three inputs are required.
+        // The size of the bias must match the channels dimension, so get the correct index.
+        unsigned int index = (descriptor->m_DataLayout == DataLayout::NHWC) ?
+                outputs[0]->GetShape()[3] : outputs[0]->GetShape()[1];
+
+        std::vector<uint8_t> uint8Data;
+        std::vector<float> data(outputs[0]->GetShape()[index], 0.0f);
+
+        TosaSerializationHandler::ConvertF32toU8(data, uint8Data);
+
+        tensors.push_back(new TosaSerializationTensor(input2Name,
+                                                      {static_cast<int32_t>(outputs[0]->GetShape()[index])},
+                                                      DType_FP32,
+                                                      uint8Data));
+    }
+
+    // Setup Output Tensor
+    std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape());
+    DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType());
+
+    tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}));
+
+    // Set up TRANSPOSE_CONV2D operator
+    // The TOSA Reference Model pads the output shape, so it is added to output shape.
+    // In Arm NN we pad the input shape, so it is taken away.
+    // To offset this the negative padding value can be used.
+    std::vector<int> pad = {-static_cast<int>(descriptor->m_PadTop),
+                            -static_cast<int>(descriptor->m_PadBottom),
+                            -static_cast<int>(descriptor->m_PadLeft),
+                            -static_cast<int>(descriptor->m_PadRight)};
+    std::vector<int> stride = {static_cast<int>(descriptor->m_StrideY),
+                               static_cast<int>(descriptor->m_StrideX)};
+
+    std::vector<int> outputShape;
+    // If available use shape in descriptor otherwise use output shape.
+    if (descriptor->m_OutputShape.size() == 4)
+    {
+        for (uint32_t i = 0; i < descriptor->m_OutputShape.size(); ++i)
+        {
+            outputShape.push_back(static_cast<int>(descriptor->m_OutputShape[i]));
+        }
+    }
+    else
+    {
+        for (uint32_t i = 0; i < outputs[0]->GetNumDimensions(); ++i)
+        {
+            outputShape.push_back(static_cast<int>(outputs[0]->GetShape()[i]));
+        }
+    }
+
+    TosaTransposeConvAttribute attribute(pad, stride, outputShape, 0, 0, ArmNNToDType(inputs[0]->GetDataType()));
+
+    auto* op = new TosaSerializationOperator(Op_TRANSPOSE_CONV2D,
+                                             Attribute_TransposeConvAttribute,
+                                             &attribute,
+                                             {input0Name, input1Name, input2Name},
+                                             {outputName});
+    operators.push_back(op);
+
+    // operatorInputNames/operatorOutputNames ends up being the same as
+    // blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings
+    return new TosaSerializationBasicBlock(blockName,                            // name
+                                           operators,                            // operators
+                                           tensors,                              // tensors
+                                           {input0Name, input1Name, input2Name}, // inputs
+                                           {outputName});                        // outputs
+}
\ No newline at end of file
diff --git a/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.hpp b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.hpp
new file mode 100644
index 0000000000..eb911a1195
--- /dev/null
+++ b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.hpp
@@ -0,0 +1,20 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TosaOperatorUtils.hpp"
+
+#include <Layer.hpp>
+
+#include <tosa_serialization_handler.h>
+
+using namespace armnn;
+using namespace tosa;
+
+TosaSerializationBasicBlock* ConvertTransposeConv2dToTosaOperator(const Layer* layer,
+                                                                  const std::vector<const TensorInfo*>& inputs,
+                                                                  const std::vector<const TensorInfo*>& outputs,
+                                                                  const TransposeConvolution2dDescriptor* descriptor);