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// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package ai.vespa.rankingexpression.importer.onnx;
import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
import ai.vespa.rankingexpression.importer.IntermediateGraph;
import ai.vespa.rankingexpression.importer.OrderedTensorType;
import ai.vespa.rankingexpression.importer.operations.Argument;
import ai.vespa.rankingexpression.importer.operations.ConcatV2;
import ai.vespa.rankingexpression.importer.operations.Constant;
import ai.vespa.rankingexpression.importer.operations.Identity;
import ai.vespa.rankingexpression.importer.operations.IntermediateOperation;
import ai.vespa.rankingexpression.importer.operations.Join;
import ai.vespa.rankingexpression.importer.operations.Map;
import ai.vespa.rankingexpression.importer.operations.MatMul;
import ai.vespa.rankingexpression.importer.operations.NoOp;
import ai.vespa.rankingexpression.importer.operations.Reshape;
import ai.vespa.rankingexpression.importer.operations.Shape;
import com.yahoo.tensor.functions.ScalarFunctions;
import onnx.Onnx;
import java.util.List;
import java.util.stream.Collectors;
/**
* Converts an ONNX graph to a Vespa IntermediateGraph which is the basis
* for generating Vespa ranking expressions.
*
* @author lesters
*/
class GraphImporter {
private static IntermediateOperation mapOperation(Onnx.NodeProto node,
List<IntermediateOperation> inputs,
IntermediateGraph graph) {
String nodeName = node.getName();
String modelName = graph.name();
switch (node.getOpType().toLowerCase()) {
case "abs": return new Map(modelName, nodeName, inputs, ScalarFunctions.abs());
case "add": return new Join(modelName, nodeName, inputs, ScalarFunctions.add());
case "acos": return new Map(modelName, nodeName, inputs, ScalarFunctions.acos());
case "asin": return new Map(modelName, nodeName, inputs, ScalarFunctions.asin());
case "atan": return new Map(modelName, nodeName, inputs, ScalarFunctions.atan());
case "ceil": return new Map(modelName, nodeName, inputs, ScalarFunctions.ceil());
case "concat": return new ConcatV2(modelName, nodeName, inputs);
case "cos": return new Map(modelName, nodeName, inputs, ScalarFunctions.cos());
case "div": return new Join(modelName, nodeName, inputs, ScalarFunctions.divide());
case "elu": return new Map(modelName, nodeName, inputs, ScalarFunctions.elu());
case "equal": return new Join(modelName, nodeName, inputs, ScalarFunctions.equal());
case "exp": return new Map(modelName, nodeName, inputs, ScalarFunctions.exp());
case "floor": return new Map(modelName, nodeName, inputs, ScalarFunctions.floor());
case "greater": return new Join(modelName, nodeName, inputs, ScalarFunctions.greater());
case "identity": return new Identity(modelName, nodeName, inputs);
case "less": return new Join(modelName, nodeName, inputs, ScalarFunctions.less());
case "log": return new Map(modelName, nodeName, inputs, ScalarFunctions.log());
case "matmul": return new MatMul(modelName, nodeName, inputs);
case "max": return new Join(modelName, nodeName, inputs, ScalarFunctions.max());
case "min": return new Join(modelName, nodeName, inputs, ScalarFunctions.min());
case "mean": return new Join(modelName, nodeName, inputs, ScalarFunctions.mean());
case "mul": return new Join(modelName, nodeName, inputs, ScalarFunctions.multiply());
case "neg": return new Map(modelName, nodeName, inputs, ScalarFunctions.neg());
case "pow": return new Join(modelName, nodeName, inputs, ScalarFunctions.pow());
case "reshape": return new Reshape(modelName, nodeName, inputs);
case "reciprocal": return new Map(modelName, nodeName, inputs, ScalarFunctions.reciprocal());
case "relu": return new Map(modelName, nodeName, inputs, ScalarFunctions.relu());
case "selu": return new Map(modelName, nodeName, inputs, ScalarFunctions.selu());
case "shape": return new Shape(modelName, nodeName, inputs);
case "sin": return new Map(modelName, nodeName, inputs, ScalarFunctions.sin());
case "sqrt": return new Map(modelName, nodeName, inputs, ScalarFunctions.sqrt());
case "sigmoid": return new Map(modelName, nodeName, inputs, ScalarFunctions.sigmoid());
case "sub": return new Join(modelName, nodeName, inputs, ScalarFunctions.subtract());
case "tan": return new Map(modelName, nodeName, inputs, ScalarFunctions.tan());
case "tanh": return new Map(modelName, nodeName, inputs, ScalarFunctions.tanh());
}
IntermediateOperation op = new NoOp(modelName, node.getName(), inputs);
op.warning("Operation '" + node.getOpType() + "' is currently not implemented");
return op;
}
static IntermediateGraph importGraph(String modelName, Onnx.ModelProto model) {
Onnx.GraphProto onnxGraph = model.getGraph();
IntermediateGraph intermediateGraph = new IntermediateGraph(modelName);
importOperations(onnxGraph, intermediateGraph);
verifyOutputTypes(onnxGraph, intermediateGraph);
return intermediateGraph;
}
private static void importOperations(Onnx.GraphProto onnxGraph, IntermediateGraph intermediateGraph) {
for (Onnx.ValueInfoProto valueInfo : onnxGraph.getOutputList()) {
importOperation(valueInfo.getName(), onnxGraph, intermediateGraph);
}
}
private static IntermediateOperation importOperation(String name,
Onnx.GraphProto onnxGraph,
IntermediateGraph intermediateGraph) {
if (intermediateGraph.alreadyImported(name)) {
return intermediateGraph.get(name);
}
IntermediateOperation operation;
if (isArgumentTensor(name, onnxGraph)) {
Onnx.ValueInfoProto valueInfoProto = getArgumentTensor(name, onnxGraph);
if (valueInfoProto == null)
throw new IllegalArgumentException("Could not find argument tensor: " + name);
OrderedTensorType type = TypeConverter.fromOnnxType(valueInfoProto.getType());
operation = new Argument(intermediateGraph.name(), valueInfoProto.getName(), type);
intermediateGraph.inputs(intermediateGraph.defaultSignature())
.put(IntermediateOperation.namePartOf(name), operation.vespaName());
} else if (isConstantTensor(name, onnxGraph)) {
Onnx.TensorProto tensorProto = getConstantTensor(name, onnxGraph);
OrderedTensorType defaultType = OrderedTensorType.fromDimensionList(tensorProto.getDimsList());
operation = new Constant(intermediateGraph.name(), name, defaultType);
operation.setConstantValueFunction(type -> new TensorValue(TensorConverter.toVespaTensor(tensorProto, type)));
} else {
Onnx.NodeProto node = getNodeFromGraph(name, onnxGraph);
List<IntermediateOperation> inputs = importOperationInputs(node, onnxGraph, intermediateGraph);
operation = mapOperation(node, inputs, intermediateGraph);
if (isOutputNode(name, onnxGraph)) {
intermediateGraph.outputs(intermediateGraph.defaultSignature())
.put(IntermediateOperation.namePartOf(name), operation.vespaName());
}
}
intermediateGraph.put(operation.vespaName(), operation);
return operation;
}
private static boolean isArgumentTensor(String name, Onnx.GraphProto graph) {
Onnx.ValueInfoProto value = getArgumentTensor(name, graph);
Onnx.TensorProto tensor = getConstantTensor(name, graph);
return value != null && tensor == null;
}
private static boolean isConstantTensor(String name, Onnx.GraphProto graph) {
Onnx.ValueInfoProto value = getArgumentTensor(name, graph);
Onnx.TensorProto tensor = getConstantTensor(name, graph);
return value != null && tensor != null;
}
private static Onnx.ValueInfoProto getArgumentTensor(String name, Onnx.GraphProto graph) {
for (Onnx.ValueInfoProto valueInfo : graph.getInputList()) {
if (valueInfo.getName().equals(name)) {
return valueInfo;
}
}
return null;
}
private static Onnx.TensorProto getConstantTensor(String name, Onnx.GraphProto graph) {
for (Onnx.TensorProto tensorProto : graph.getInitializerList()) {
if (tensorProto.getName().equals(name)) {
return tensorProto;
}
}
return null;
}
private static boolean isOutputNode(String name, Onnx.GraphProto graph) {
return getOutputNode(name, graph) != null;
}
private static Onnx.ValueInfoProto getOutputNode(String name, Onnx.GraphProto graph) {
for (Onnx.ValueInfoProto valueInfo : graph.getOutputList()) {
if (valueInfo.getName().equals(name)) {
return valueInfo;
}
String nodeName = IntermediateOperation.namePartOf(valueInfo.getName());
if (nodeName.equals(name)) {
return valueInfo;
}
}
return null;
}
private static List<IntermediateOperation> importOperationInputs(Onnx.NodeProto node,
Onnx.GraphProto onnxGraph,
IntermediateGraph intermediateGraph) {
return node.getInputList().stream()
.map(nodeName -> importOperation(nodeName, onnxGraph, intermediateGraph))
.collect(Collectors.toList());
}
private static void verifyOutputTypes(Onnx.GraphProto onnxGraph, IntermediateGraph intermediateGraph) {
for (String outputName : intermediateGraph.outputs(intermediateGraph.defaultSignature()).values()) {
IntermediateOperation operation = intermediateGraph.get(outputName);
Onnx.ValueInfoProto onnxNode = getOutputNode(outputName, onnxGraph);
OrderedTensorType type = operation.type().orElseThrow(
() -> new IllegalArgumentException("Output of '" + outputName + "' has no type."));
TypeConverter.verifyType(onnxNode.getType(), type);
}
}
private static Onnx.NodeProto getNodeFromGraph(String nodeName, Onnx.GraphProto graph) {
boolean hasPortNumber = nodeName.contains(":");
for (Onnx.NodeProto node : graph.getNodeList()) {
if (hasPortNumber) {
for (String outputName : node.getOutputList()) {
if (outputName.equals(nodeName)) {
return node;
}
}
} else if (node.getName().equals(nodeName)) {
return node;
}
}
throw new IllegalArgumentException("Node '" + nodeName + "' not found in ONNX graph");
}
}
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