IVGCVSW-7168 Support simple model in the TOSA Reference Backend
* Fixed issue where duplicate tensors where being created.
* Fixed issue where output name could be generated with the wrong id.
* Updated bias tensor for Conv2d, so the size matches the channel.
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I1de6947e036b3e629ec6446d24d69e50603a5593
diff --git a/src/backends/tosaCommon/operatorMappings/AdditionOperator.cpp b/src/backends/tosaCommon/operatorMappings/AdditionOperator.cpp
index f1fb34c..20ba146 100644
--- a/src/backends/tosaCommon/operatorMappings/AdditionOperator.cpp
+++ b/src/backends/tosaCommon/operatorMappings/AdditionOperator.cpp
@@ -26,8 +26,7 @@
input1Name = GenerateUniqueName(connectedLayer1, 1);
// Get the layer connected to the output slot and determine unique layer name.
- Layer& connectedOutputLayer = layer->GetOutputSlot().GetConnection(0)->GetOwningLayer();
- outputName = GenerateUniqueName(connectedOutputLayer, 0);
+ outputName = GenerateUniqueOutputName(*layer, 0);
}
auto* op = new TosaSerializationOperator(Op_ADD,
@@ -36,24 +35,38 @@
{input0Name, input1Name},
{outputName});
- std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape());
- DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType());
- std::vector<int32_t> inputShape1 = GetTosaTensorShape(inputs[1]->GetShape());
- DType inputDType1 = ArmNNToDType(inputs[1]->GetDataType());
+ std::vector<TosaSerializationTensor*> tensors;
+
+ // 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.
+ 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, {}));
+ }
+
+ if(input1Name.find("input1_") != std::string::npos)
+ {
+ std::vector<int32_t> inputShape1 = GetTosaTensorShape(inputs[1]->GetShape());
+ DType inputDType1 = ArmNNToDType(inputs[1]->GetDataType());
+
+ tensors.push_back(new TosaSerializationTensor(input1Name, inputShape1, inputDType1, {}));
+ }
std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape());
DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType());
- auto* inputTensor0 = new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {});
- auto* inputTensor1 = new TosaSerializationTensor(input1Name, inputShape1, inputDType1, {});
- auto* outputTensor0 = new TosaSerializationTensor(outputName, outputShape0, outputDType0, {});
+ tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}));
// 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
- {inputTensor0, inputTensor1, outputTensor0}, // tensors
+ tensors, // tensors
{input0Name, input1Name}, // inputs
{outputName}); // outputs
}
\ No newline at end of file
diff --git a/src/backends/tosaCommon/operatorMappings/AvgPool2DIgnoreValueOperator.cpp b/src/backends/tosaCommon/operatorMappings/AvgPool2DIgnoreValueOperator.cpp
index 7e7631d..d268c2f 100644
--- a/src/backends/tosaCommon/operatorMappings/AvgPool2DIgnoreValueOperator.cpp
+++ b/src/backends/tosaCommon/operatorMappings/AvgPool2DIgnoreValueOperator.cpp
@@ -24,8 +24,7 @@
padInputName = GenerateUniqueName(connectedInputLayer, 0);
// Get the layer connected to the output slot and determine unique layer name.
- Layer& connectedOutputLayer = layer->GetOutputSlot().GetConnection(0)->GetOwningLayer();
- poolOutputName = GenerateUniqueName(connectedOutputLayer, 0);
+ poolOutputName = GenerateUniqueOutputName(*layer, 0);
}
std::vector<int> paddings;
@@ -74,9 +73,19 @@
{padOutputName},
{poolOutputName});
+ std::vector<TosaSerializationTensor*> tensors;
+
std::vector<int32_t> inputShape = GetTosaTensorShape(inputs[0]->GetShape());
DType inputDType = ArmNNToDType(inputs[0]->GetDataType());
+ // 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.
+ if(padInputName.find("input0_") != std::string::npos)
+ {
+ tensors.push_back(new TosaSerializationTensor(padInputName, inputShape, inputDType, {}));
+ }
+
std::vector<int32_t> outputShape = GetTosaTensorShape(outputs[0]->GetShape());
DType outputDType = ArmNNToDType(outputs[0]->GetDataType());
@@ -96,15 +105,14 @@
inputShape[3] + paddings[6] + paddings[7]};
}
- auto* inputTensor = new TosaSerializationTensor(padInputName, inputShape, inputDType, {});
- auto* intermediateTensor = new TosaSerializationTensor(padOutputName, intermediateShape, inputDType, {});
- auto* outputTensor = new TosaSerializationTensor(poolOutputName, outputShape, outputDType, {});
+ tensors.push_back(new TosaSerializationTensor(padOutputName, intermediateShape, inputDType, {}));
+ tensors.push_back(new TosaSerializationTensor(poolOutputName, outputShape, outputDType, {}));
// operatorInputNames/operatorOutputNames ends up being the same as
// blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings
return new TosaSerializationBasicBlock(blockName, // name
{opPad, opPool}, // operators
- {inputTensor, intermediateTensor, outputTensor}, // tensors
+ tensors, // tensors
{padInputName}, // inputs
{poolOutputName}); // outputs
}
\ No newline at end of file
diff --git a/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp b/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp
index 9c095d6..dadd91b 100644
--- a/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp
+++ b/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp
@@ -39,19 +39,23 @@
}
// Get the layer connected to the output slot and determine unique layer name.
- Layer& connectedLayer = layer->GetOutputSlot().GetConnection(0)->GetOwningLayer();
-
- outputName = GenerateUniqueName(connectedLayer, 0);
+ outputName = GenerateUniqueOutputName(*layer, 0);
}
std::vector<TosaSerializationTensor*> tensors;
std::vector<TosaSerializationOperator*> operators;
// Setup input Tensor
- std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape());
- DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType());
+ // 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(inputNames[0].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(inputNames[0], inputShape0, inputDType0, {}));
+ tensors.push_back(new TosaSerializationTensor(inputNames[0], inputShape0, inputDType0, {}));
+ }
// Only add input tensors if weights and bias are not constant or if running validation.
// Constant tensors will be created in the ConvertConstantToTosaOperator function.
@@ -80,12 +84,18 @@
operators.push_back(new TosaSerializationOperator(Op_CONST, Attribute_NONE, nullptr, {}, {constantName}));
+ // The size of the bias must match the channels dimension, so get the correct index.
+ unsigned int index = (conv2dDescriptor->m_DataLayout == DataLayout::NHWC) ? 3 : 1;
+
std::vector<uint8_t> uint8Data;
- std::vector<float> data = { 0.0 };
+ std::vector<float> data(outputs[0]->GetShape()[index], 0.0f);
TosaSerializationHandler::ConvertF32toU8(data, uint8Data);
- tensors.push_back(new TosaSerializationTensor(constantName, {1}, DType_FP32, uint8Data));
+ tensors.push_back(new TosaSerializationTensor(constantName,
+ {static_cast<int32_t>(outputs[0]->GetShape()[index])},
+ DType_FP32,
+ uint8Data));
inputNames.emplace_back(constantName);
}
diff --git a/src/backends/tosaCommon/operatorMappings/Pooling2DOperator.cpp b/src/backends/tosaCommon/operatorMappings/Pooling2DOperator.cpp
index 265901e..ee02425 100644
--- a/src/backends/tosaCommon/operatorMappings/Pooling2DOperator.cpp
+++ b/src/backends/tosaCommon/operatorMappings/Pooling2DOperator.cpp
@@ -26,8 +26,7 @@
input0Name = GenerateUniqueName(connectedInputLayer, 0);
// Get the layer connected to the output slot and determine unique layer name.
- Layer& connectedOutputLayer = layer->GetOutputSlot().GetConnection(0)->GetOwningLayer();
- outputName = GenerateUniqueName(connectedOutputLayer, 0);
+ outputName = GenerateUniqueOutputName(*layer, 0);
}
std::vector<int> pad = {static_cast<int>(poolDescriptor->m_PadTop),
@@ -46,20 +45,29 @@
{input0Name},
{outputName});
- std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape());
- DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType());
+ std::vector<TosaSerializationTensor*> tensors;
+
+ // 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.
+ 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, {}));
+ }
std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape());
DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType());
- auto* inputTensor0 = new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {});
- auto* outputTensor0 = new TosaSerializationTensor(outputName, outputShape0, outputDType0, {});
+ tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}));
// 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
- {inputTensor0, outputTensor0}, // tensors
+ tensors, // tensors
{input0Name}, // inputs
{outputName}); // outputs
}
\ No newline at end of file
diff --git a/src/backends/tosaCommon/operatorMappings/ReshapeOperator.cpp b/src/backends/tosaCommon/operatorMappings/ReshapeOperator.cpp
index b88a6ef..3027e2e 100644
--- a/src/backends/tosaCommon/operatorMappings/ReshapeOperator.cpp
+++ b/src/backends/tosaCommon/operatorMappings/ReshapeOperator.cpp
@@ -23,8 +23,7 @@
inputName = GenerateUniqueName(connectedLayer, 0);
// Get the layer connected to the output slot and determine unique layer name.
- Layer& connectedOutputLayer = layer->GetOutputSlot().GetConnection(0)->GetOwningLayer();
- outputName = GenerateUniqueName(connectedOutputLayer, 0);
+ outputName = GenerateUniqueOutputName(*layer, 0);
}
TosaReshapeAttribute attribute(GetTosaTensorShape(reshapeDescriptor->m_TargetShape));
@@ -35,20 +34,29 @@
{inputName},
{outputName});
- std::vector<int32_t> inputShape = GetTosaTensorShape(inputs[0]->GetShape());
- DType inputDType = ArmNNToDType(inputs[0]->GetDataType());
+ std::vector<TosaSerializationTensor*> tensors;
+
+ // 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.
+ if(inputName.find("input0_") != std::string::npos)
+ {
+ std::vector<int32_t> inputShape = GetTosaTensorShape(inputs[0]->GetShape());
+ DType inputDType = ArmNNToDType(inputs[0]->GetDataType());
+
+ tensors.push_back(new TosaSerializationTensor(inputName, inputShape, inputDType, {}));
+ }
std::vector<int32_t> outputShape = GetTosaTensorShape(outputs[0]->GetShape());
DType outputDType = ArmNNToDType(outputs[0]->GetDataType());
- auto* inputTensor = new TosaSerializationTensor(inputName, inputShape, inputDType, {});
- auto* outputTensor = new TosaSerializationTensor(outputName, outputShape, outputDType, {});
+ tensors.push_back(new TosaSerializationTensor(outputName, outputShape, outputDType, {}));
// 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
- {inputTensor, outputTensor}, // tensors
+ tensors, // tensors
{inputName}, // inputs
{outputName}); // outputs
}
\ No newline at end of file
diff --git a/src/backends/tosaCommon/operatorMappings/SliceOperator.cpp b/src/backends/tosaCommon/operatorMappings/SliceOperator.cpp
index fc2e40a..742ba88 100644
--- a/src/backends/tosaCommon/operatorMappings/SliceOperator.cpp
+++ b/src/backends/tosaCommon/operatorMappings/SliceOperator.cpp
@@ -23,8 +23,7 @@
inputName = GenerateUniqueName(connectedLayer, 0);
// Get the layer connected to the output slot and determine unique layer name.
- Layer& connectedOutputLayer = layer->GetOutputSlot().GetConnection(0)->GetOwningLayer();
- outputName = GenerateUniqueName(connectedOutputLayer, 0);
+ outputName = GenerateUniqueOutputName(*layer, 0);
}
std::vector<int32_t> begin(sliceDescriptor->m_Begin.begin(), sliceDescriptor->m_Begin.end());
@@ -38,20 +37,29 @@
{inputName},
{outputName});
- std::vector<int32_t> inputShape = GetTosaTensorShape(inputs[0]->GetShape());
- DType inputDType = ArmNNToDType(inputs[0]->GetDataType());
+ std::vector<TosaSerializationTensor*> tensors;
+
+ // 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.
+ if(inputName.find("input0_") != std::string::npos)
+ {
+ std::vector<int32_t> inputShape = GetTosaTensorShape(inputs[0]->GetShape());
+ DType inputDType = ArmNNToDType(inputs[0]->GetDataType());
+
+ tensors.push_back(new TosaSerializationTensor(inputName, inputShape, inputDType, {}));
+ }
std::vector<int32_t> outputShape = GetTosaTensorShape(outputs[0]->GetShape());
DType outputDType = ArmNNToDType(outputs[0]->GetDataType());
- auto* inputTensor = new TosaSerializationTensor(inputName, inputShape, inputDType, {});
- auto* outputTensor = new TosaSerializationTensor(outputName, outputShape, outputDType, {});
+ tensors.push_back(new TosaSerializationTensor(outputName, outputShape, outputDType, {}));
// 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
- {inputTensor, outputTensor}, // tensors
+ tensors, // tensors
{inputName}, // inputs
{outputName}); // outputs
}
\ No newline at end of file
diff --git a/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp
index a0d58e2..1ad8c95 100644
--- a/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp
+++ b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp
@@ -94,8 +94,7 @@
{
// 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];
+ unsigned int index = (descriptor->m_DataLayout == DataLayout::NHWC) ? 3 : 1;
std::vector<uint8_t> uint8Data;
std::vector<float> data(outputs[0]->GetShape()[index], 0.0f);