diff options
| author | Jon Bratseth <bratseth@oath.com> | 2018-11-21 19:21:22 +0100 |
|---|---|---|
| committer | Jon Bratseth <bratseth@oath.com> | 2018-11-21 19:21:22 +0100 |
| commit | 99ca9b2907ff637fc6e4e0a61860923ac1c9dee5 (patch) | |
| tree | d5a5e408d56e9165cd716e9531ab9bcec6a29e4a /model-integration | |
| parent | 61cae2609740b51c180b2f507b5e4d0eb399fedc (diff) | |
Separate model integration into a separate module
This allows us to access model importers (such as TensorFlow)
in config models without loading one instance per config model
instance, which is not possible with TensorFlow because it depends
on JNI code.
Diffstat (limited to 'model-integration')
49 files changed, 66113 insertions, 0 deletions
diff --git a/model-integration/pom.xml b/model-integration/pom.xml new file mode 100644 index 00000000000..28a00dcbdbc --- /dev/null +++ b/model-integration/pom.xml @@ -0,0 +1,100 @@ +<?xml version="1.0"?> +<!-- Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. --> +<project xmlns="http://maven.apache.org/POM/4.0.0" + xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" + xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 + http://maven.apache.org/xsd/maven-4.0.0.xsd"> + <modelVersion>4.0.0</modelVersion> + <parent> + <groupId>com.yahoo.vespa</groupId> + <artifactId>parent</artifactId> + <version>6-SNAPSHOT</version> + <relativePath>../parent/pom.xml</relativePath> + </parent> + <artifactId>model-integration</artifactId> + <version>6-SNAPSHOT</version> + <packaging>container-plugin</packaging> + <dependencies> + <dependency> + <groupId>junit</groupId> + <artifactId>junit</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>com.yahoo.vespa</groupId> + <artifactId>component</artifactId> + <version>${project.version}</version> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>com.yahoo.vespa</groupId> + <artifactId>vespajlib</artifactId> + <version>${project.version}</version> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>com.yahoo.vespa</groupId> + <artifactId>searchlib</artifactId> + <version>${project.version}</version> + <scope>provided</scope> + </dependency> + <dependency> + <groupId>com.google.guava</groupId> + <artifactId>guava</artifactId> + <scope>provided</scope> + </dependency> + + <dependency> + <groupId>com.google.protobuf</groupId> + <artifactId>protobuf-java</artifactId> + </dependency> + <dependency> + <groupId>org.tensorflow</groupId> + <artifactId>proto</artifactId> + </dependency> + <dependency> + <groupId>org.tensorflow</groupId> + <artifactId>tensorflow</artifactId> + </dependency> + </dependencies> + <build> + <plugins> + <plugin> + <groupId>com.yahoo.vespa</groupId> + <artifactId>bundle-plugin</artifactId> + <extensions>true</extensions> + </plugin> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-compiler-plugin</artifactId> + <configuration> + <compilerArgs> + <arg>-Xlint:rawtypes</arg> + <arg>-Xlint:unchecked</arg> + <arg>-Werror</arg> + </compilerArgs> + </configuration> + </plugin> + <plugin> + <groupId>com.github.os72</groupId> + <artifactId>protoc-jar-maven-plugin</artifactId> + <version>3.5.1.1</version> + <executions> + <execution> + <phase>generate-sources</phase> + <goals> + <goal>run</goal> + </goals> + <configuration> + <addSources>main</addSources> + <outputDirectory>${project.build.directory}/generated-sources/protobuf</outputDirectory> + <inputDirectories> + <include>src/main/protobuf</include> + </inputDirectories> + </configuration> + </execution> + </executions> + </plugin> + </plugins> + </build> +</project> diff --git a/model-integration/src/main/config/model-integration.xml b/model-integration/src/main/config/model-integration.xml new file mode 100644 index 00000000000..da45ce23575 --- /dev/null +++ b/model-integration/src/main/config/model-integration.xml @@ -0,0 +1,10 @@ +<!-- Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. --> +<!-- Component which can import some ml model. + This is included into the config server services.xml to enable it to translate + model pseudofeatures in ranking expressions during config mddel building. + It is provided as separate bundles instead of being included in the config model + because some of these (TensorFlow) includes + JNI code, and so can only exist in one instance in the server. --> +<component id="ai.vespa.rankingexpression.importer.onnx.OnnxImporter" bundle="model-integration" /> +<component id="ai.vespa.rankingexpression.importer.tensorflow.TensorFlowImporter" bundle="model-integration" /> +<component id="ai.vespa.rankingexpression.importer.xgboost.XGBoostImporter" bundle="model-integration" /> diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/GraphImporter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/GraphImporter.java new file mode 100644 index 00000000000..81d0753ea4b --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/GraphImporter.java @@ -0,0 +1,216 @@ +// 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 com.yahoo.searchlib.rankingexpression.integration.ml.importer.IntermediateGraph; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Argument; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.ConcatV2; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Constant; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Identity; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.IntermediateOperation; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Join; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Map; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.MatMul; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.NoOp; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Reshape; +import com.yahoo.searchlib.rankingexpression.integration.ml.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"); + } +} diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/OnnxImporter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/OnnxImporter.java new file mode 100644 index 00000000000..0418581d7b2 --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/OnnxImporter.java @@ -0,0 +1,40 @@ +// 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.integration.ml.importer.ImportedModel; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.IntermediateGraph; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.ModelImporter; +import onnx.Onnx; + +import java.io.File; +import java.io.FileInputStream; +import java.io.IOException; + +/** + * Converts a ONNX model into a ranking expression and set of constants. + * + * @author lesters + */ +public class OnnxImporter extends ModelImporter { + + @Override + public boolean canImport(String modelPath) { + File modelFile = new File(modelPath); + if ( ! modelFile.isFile()) return false; + + return modelFile.toString().endsWith(".onnx"); + } + + @Override + public ImportedModel importModel(String modelName, String modelPath) { + try (FileInputStream inputStream = new FileInputStream(modelPath)) { + Onnx.ModelProto model = Onnx.ModelProto.parseFrom(inputStream); + IntermediateGraph graph = GraphImporter.importGraph(modelName, model); + return convertIntermediateGraphToModel(graph, modelPath); + } catch (IOException e) { + throw new IllegalArgumentException("Could not import ONNX model from '" + modelPath + "'", e); + } + } + +} diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TensorConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TensorConverter.java new file mode 100644 index 00000000000..6dd33c79852 --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TensorConverter.java @@ -0,0 +1,79 @@ +// 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.google.protobuf.ByteString; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType; +import com.yahoo.tensor.IndexedTensor; +import com.yahoo.tensor.Tensor; +import onnx.Onnx; + +import java.nio.ByteBuffer; +import java.nio.ByteOrder; +import java.nio.FloatBuffer; + +/** + * Converts Onnx tensors into Vespa tensors. + * + * @author lesters + */ +class TensorConverter { + + static Tensor toVespaTensor(Onnx.TensorProto tensorProto, OrderedTensorType type) { + Values values = readValuesOf(tensorProto); + IndexedTensor.BoundBuilder builder = (IndexedTensor.BoundBuilder) Tensor.Builder.of(type.type()); + for (int i = 0; i < values.size(); i++) { + builder.cellByDirectIndex(type.toDirectIndex(i), values.get(i)); + } + return builder.build(); + } + + private static Values readValuesOf(Onnx.TensorProto tensorProto) { + if (tensorProto.hasRawData()) { + switch (tensorProto.getDataType()) { + case FLOAT: return new RawFloatValues(tensorProto); + } + } else { + switch (tensorProto.getDataType()) { + case FLOAT: return new FloatValues(tensorProto); + } + } + throw new IllegalArgumentException("Cannot convert a tensor with elements of type " + + tensorProto.getDataType() + " to a Vespa tensor"); + } + + /** Allows reading values from buffers of various numeric types as bytes */ + private static abstract class Values { + abstract double get(int i); + abstract int size(); + } + + private static abstract class RawValues extends Values { + ByteBuffer bytes(Onnx.TensorProto tensorProto) { + ByteString byteString = tensorProto.getRawData(); + return byteString.asReadOnlyByteBuffer().order(ByteOrder.LITTLE_ENDIAN); + } + } + + private static class RawFloatValues extends RawValues { + private final FloatBuffer values; + private final int size; + RawFloatValues(Onnx.TensorProto tensorProto) { + values = bytes(tensorProto).asFloatBuffer(); + size = values.remaining(); + } + @Override double get(int i) { return values.get(i); } + @Override int size() { return size; } + } + + private static class FloatValues extends Values { + private final Onnx.TensorProto tensorProto; + FloatValues(Onnx.TensorProto tensorProto) { + this.tensorProto = tensorProto; + } + @Override double get(int i) { return tensorProto.getFloatData(i); } + @Override int size() { return tensorProto.getFloatDataCount(); } + } + + +} diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TypeConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TypeConverter.java new file mode 100644 index 00000000000..43ceaa747b7 --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TypeConverter.java @@ -0,0 +1,52 @@ +// 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.integration.ml.importer.OrderedTensorType; +import com.yahoo.tensor.TensorType; +import onnx.Onnx; + +/** + * Converts and verifies ONNX tensor types into Vespa tensor types. + * + * @author lesters + */ +class TypeConverter { + + static void verifyType(Onnx.TypeProto typeProto, OrderedTensorType type) { + Onnx.TensorShapeProto shape = typeProto.getTensorType().getShape(); + if (shape != null) { + if (shape.getDimCount() != type.rank()) { + throw new IllegalArgumentException("Onnx shape of does not match Vespa shape"); + } + for (int onnxIndex = 0; onnxIndex < type.dimensions().size(); ++onnxIndex) { + int vespaIndex = type.dimensionMap(onnxIndex); + Onnx.TensorShapeProto.Dimension onnxDimension = shape.getDim(onnxIndex); + TensorType.Dimension vespaDimension = type.type().dimensions().get(vespaIndex); + if (onnxDimension.getDimValue() != vespaDimension.size().orElse(-1L)) { + throw new IllegalArgumentException("Onnx dimensions of does not match Vespa dimensions"); + } + } + } + } + + static OrderedTensorType fromOnnxType(Onnx.TypeProto type) { + return fromOnnxType(type, "d"); // standard naming convention: d0, d1, ... + } + + private static OrderedTensorType fromOnnxType(Onnx.TypeProto type, String dimensionPrefix) { + Onnx.TensorShapeProto shape = type.getTensorType().getShape(); + OrderedTensorType.Builder builder = new OrderedTensorType.Builder(); + for (int i = 0; i < shape.getDimCount(); ++ i) { + String dimensionName = dimensionPrefix + i; + Onnx.TensorShapeProto.Dimension onnxDimension = shape.getDim(i); + if (onnxDimension.getDimValue() >= 0) { + builder.add(TensorType.Dimension.indexed(dimensionName, onnxDimension.getDimValue())); + } else { + builder.add(TensorType.Dimension.indexed(dimensionName)); + } + } + return builder.build(); + } + +} diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/package-info.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/package-info.java new file mode 100644 index 00000000000..9599cf8627c --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/package-info.java @@ -0,0 +1,5 @@ +@ExportPackage + +package ai.vespa.rankingexpression.importer.onnx; + +import com.yahoo.osgi.annotation.ExportPackage;
\ No newline at end of file diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/AttributeConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/AttributeConverter.java new file mode 100644 index 00000000000..73310c78cab --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/AttributeConverter.java @@ -0,0 +1,87 @@ +// 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.tensorflow; + +import com.yahoo.searchlib.rankingexpression.evaluation.BooleanValue; +import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue; +import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue; +import com.yahoo.searchlib.rankingexpression.evaluation.Value; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.IntermediateOperation; +import org.tensorflow.framework.AttrValue; +import org.tensorflow.framework.NodeDef; + +import java.util.List; +import java.util.Map; +import java.util.Optional; +import java.util.stream.Collectors; + +/** + * Converts TensorFlow node attributes to Vespa attribute values. + * + * @author lesters + */ +class AttributeConverter implements IntermediateOperation.AttributeMap { + + private final Map<String, AttrValue> attributeMap; + + private AttributeConverter(NodeDef node) { + attributeMap = node.getAttrMap(); + } + + static AttributeConverter convert(NodeDef node) { + return new AttributeConverter(node); + } + + @Override + public Optional<Value> get(String key) { + if (attributeMap.containsKey(key)) { + AttrValue attrValue = attributeMap.get(key); + if (attrValue.getValueCase() == AttrValue.ValueCase.TENSOR) { + return Optional.empty(); // requires type + } + if (attrValue.getValueCase() == AttrValue.ValueCase.B) { + return Optional.of(new BooleanValue(attrValue.getB())); + } + if (attrValue.getValueCase() == AttrValue.ValueCase.I) { + return Optional.of(new DoubleValue(attrValue.getI())); + } + if (attrValue.getValueCase() == AttrValue.ValueCase.F) { + return Optional.of(new DoubleValue(attrValue.getF())); + } + } + return Optional.empty(); + } + + @Override + public Optional<Value> get(String key, OrderedTensorType type) { + if (attributeMap.containsKey(key)) { + AttrValue attrValue = attributeMap.get(key); + if (attrValue.getValueCase() == AttrValue.ValueCase.TENSOR) { + return Optional.of(new TensorValue(TensorConverter.toVespaTensor(attrValue.getTensor(), type.type()))); + } + } + return get(key); + } + + @Override + public Optional<List<Value>> getList(String key) { + if (attributeMap.containsKey(key)) { + AttrValue attrValue = attributeMap.get(key); + if (attrValue.getValueCase() == AttrValue.ValueCase.LIST) { + AttrValue.ListValue listValue = attrValue.getList(); + if ( ! listValue.getBList().isEmpty()) { + return Optional.of(listValue.getBList().stream().map(BooleanValue::new).collect(Collectors.toList())); + } + if ( ! listValue.getIList().isEmpty()) { + return Optional.of(listValue.getIList().stream().map(DoubleValue::new).collect(Collectors.toList())); + } + if ( ! listValue.getFList().isEmpty()) { + return Optional.of(listValue.getFList().stream().map(DoubleValue::new).collect(Collectors.toList())); + } + // add the rest + } + } + return Optional.empty(); + } + +} diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/GraphImporter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/GraphImporter.java new file mode 100644 index 00000000000..c012bc3c54f --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/GraphImporter.java @@ -0,0 +1,234 @@ +// 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.tensorflow; + +import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.IntermediateGraph; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Argument; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.ConcatV2; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Const; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Constant; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.ExpandDims; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Identity; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.IntermediateOperation; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Join; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Map; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.MatMul; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Mean; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Merge; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.NoOp; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.PlaceholderWithDefault; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Reshape; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Select; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Shape; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Squeeze; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.Switch; +import com.yahoo.tensor.functions.ScalarFunctions; +import org.tensorflow.SavedModelBundle; +import org.tensorflow.Session; +import org.tensorflow.framework.GraphDef; +import org.tensorflow.framework.MetaGraphDef; +import org.tensorflow.framework.NodeDef; +import org.tensorflow.framework.SignatureDef; +import org.tensorflow.framework.TensorInfo; + +import java.io.IOException; +import java.util.List; +import java.util.stream.Collectors; + +/** + * Converts a TensorFlow graph to a Vespa IntermediateGraph which is the basis + * for generating Vespa ranking expressions. + * + * @author lesters + */ +class GraphImporter { + + private static IntermediateOperation mapOperation(NodeDef node, + List<IntermediateOperation> inputs, + IntermediateGraph graph) { + String nodeName = node.getName(); + String modelName = graph.name(); + int nodePort = IntermediateOperation.indexPartOf(nodeName); + OrderedTensorType nodeType = TypeConverter.fromTensorFlowType(node); + AttributeConverter attributes = AttributeConverter.convert(node); + + switch (node.getOp().toLowerCase()) { + // array ops + case "concatv2": return new ConcatV2(modelName, nodeName, inputs); + case "const": return new Const(modelName, nodeName, inputs, attributes, nodeType); + case "expanddims": return new ExpandDims(modelName, nodeName, inputs); + case "identity": return new Identity(modelName, nodeName, inputs); + case "placeholder": return new Argument(modelName, nodeName, nodeType); + case "placeholderwithdefault": return new PlaceholderWithDefault(modelName, nodeName, inputs); + case "reshape": return new Reshape(modelName, nodeName, inputs); + case "shape": return new Shape(modelName, nodeName, inputs); + case "squeeze": return new Squeeze(modelName, nodeName, inputs, attributes); + + // control flow + case "merge": return new Merge(modelName, nodeName, inputs); + case "switch": return new Switch(modelName, nodeName, inputs, nodePort); + + // math ops + case "add": return new Join(modelName, nodeName, inputs, ScalarFunctions.add()); + case "add_n": return new Join(modelName, nodeName, inputs, ScalarFunctions.add()); + case "acos": return new Map(modelName, nodeName, inputs, ScalarFunctions.acos()); + case "div": return new Join(modelName, nodeName, inputs, ScalarFunctions.divide()); + case "realdiv": return new Join(modelName, nodeName, inputs, ScalarFunctions.divide()); + case "floor": return new Map(modelName, nodeName, inputs, ScalarFunctions.floor()); + case "matmul": return new MatMul(modelName, nodeName, inputs); + case "maximum": return new Join(modelName, nodeName, inputs, ScalarFunctions.max()); + case "mean": return new Mean(modelName, nodeName, inputs, attributes); + case "reducemean": return new Mean(modelName, nodeName, inputs, attributes); + case "mul": return new Join(modelName, nodeName, inputs, ScalarFunctions.multiply()); + case "multiply": return new Join(modelName, nodeName, inputs, ScalarFunctions.multiply()); + case "rsqrt": return new Map(modelName, nodeName, inputs, ScalarFunctions.rsqrt()); + case "select": return new Select(modelName, nodeName, inputs); + case "where3": return new Select(modelName, nodeName, inputs); + case "sigmoid": return new Map(modelName, nodeName, inputs, ScalarFunctions.sigmoid()); + case "squareddifference": return new Join(modelName, nodeName, inputs, ScalarFunctions.squareddifference()); + case "sub": return new Join(modelName, nodeName, inputs, ScalarFunctions.subtract()); + case "subtract": return new Join(modelName, nodeName, inputs, ScalarFunctions.subtract()); + + // nn ops + case "biasadd": return new Join(modelName, nodeName, inputs, ScalarFunctions.add()); + case "elu": return new Map(modelName, nodeName, inputs, ScalarFunctions.elu()); + case "relu": return new Map(modelName, nodeName, inputs, ScalarFunctions.relu()); + case "selu": return new Map(modelName, nodeName, inputs, ScalarFunctions.selu()); + + // state ops + case "variable": return new Constant(modelName, nodeName, nodeType); + case "variablev2": return new Constant(modelName, nodeName, nodeType); + + // evaluation no-ops + case "stopgradient":return new Identity(modelName, nodeName, inputs); + case "noop": return new NoOp(modelName, nodeName, inputs); + + } + + IntermediateOperation op = new NoOp(modelName, node.getName(), inputs); + op.warning("Operation '" + node.getOp() + "' is currently not implemented"); + return op; + } + + static IntermediateGraph importGraph(String modelName, SavedModelBundle bundle) throws IOException { + MetaGraphDef tfGraph = MetaGraphDef.parseFrom(bundle.metaGraphDef()); + + IntermediateGraph intermediateGraph = new IntermediateGraph(modelName); + importSignatures(tfGraph, intermediateGraph); + importOperations(tfGraph, intermediateGraph, bundle); + verifyOutputTypes(tfGraph, intermediateGraph); + + return intermediateGraph; + } + + private static void importSignatures(MetaGraphDef tfGraph, IntermediateGraph intermediateGraph) { + for (java.util.Map.Entry<String, SignatureDef> signatureEntry : tfGraph.getSignatureDefMap().entrySet()) { + String signatureName = signatureEntry.getKey(); + java.util.Map<String, TensorInfo> inputInfoMap = signatureEntry.getValue().getInputsMap(); + for (java.util.Map.Entry<String, TensorInfo> input : inputInfoMap.entrySet()) { + String inputName = input.getKey(); + String nodeName = input.getValue().getName(); + intermediateGraph.inputs(signatureName).put(inputName, IntermediateOperation.namePartOf(nodeName)); + } + java.util.Map<String, TensorInfo> outputInfoMap = signatureEntry.getValue().getOutputsMap(); + for (java.util.Map.Entry<String, TensorInfo> output : outputInfoMap.entrySet()) { + String outputName = output.getKey(); + String nodeName = output.getValue().getName(); + intermediateGraph.outputs(signatureName).put(outputName, IntermediateOperation.namePartOf(nodeName)); + } + } + } + + private static void importOperations(MetaGraphDef tfGraph, + IntermediateGraph intermediateGraph, + SavedModelBundle bundle) { + for (String signatureName : intermediateGraph.signatures()) { + for (String outputName : intermediateGraph.outputs(signatureName).values()) { + importOperation(outputName, tfGraph.getGraphDef(), intermediateGraph, bundle); + } + } + } + + private static IntermediateOperation importOperation(String nodeName, + GraphDef tfGraph, + IntermediateGraph intermediateGraph, + SavedModelBundle bundle) { + if (intermediateGraph.alreadyImported(nodeName)) { + return intermediateGraph.get(nodeName); + } + NodeDef node = getTensorFlowNodeFromGraph(IntermediateOperation.namePartOf(nodeName), tfGraph); + List<IntermediateOperation> inputs = importOperationInputs(node, tfGraph, intermediateGraph, bundle); + IntermediateOperation operation = mapOperation(node, inputs, intermediateGraph); + intermediateGraph.put(nodeName, operation); + + List<IntermediateOperation> controlInputs = importControlInputs(node, tfGraph, intermediateGraph, bundle); + if (controlInputs.size() > 0) { + operation.setControlInputs(controlInputs); + } + + if (operation.isConstant()) { + operation.setConstantValueFunction( + type -> new TensorValue(TensorConverter.toVespaTensor(readVariable(nodeName, bundle), type))); + } + + return operation; + } + + private static List<IntermediateOperation> importOperationInputs(NodeDef node, + GraphDef tfGraph, + IntermediateGraph intermediateGraph, + SavedModelBundle bundle) { + return node.getInputList().stream() + .filter(name -> ! isControlDependency(name)) + .map(nodeName -> importOperation(nodeName, tfGraph, intermediateGraph, bundle)) + .collect(Collectors.toList()); + } + + private static List<IntermediateOperation> importControlInputs(NodeDef node, + GraphDef tfGraph, + IntermediateGraph intermediateGraph, + SavedModelBundle bundle) { + return node.getInputList().stream() + .filter(nodeName -> isControlDependency(nodeName)) + .map(nodeName -> importOperation(nodeName, tfGraph, intermediateGraph, bundle)) + .collect(Collectors.toList()); + } + + private static boolean isControlDependency(String name) { + return name.startsWith("^"); + } + + private static NodeDef getTensorFlowNodeFromGraph(String name, GraphDef tfGraph) { + for (NodeDef node : tfGraph.getNodeList()) { + if (node.getName().equals(name)) { + return node; + } + } + throw new IllegalArgumentException("Could not find node '" + name + "'"); + } + + static org.tensorflow.Tensor<?> readVariable(String name, SavedModelBundle bundle) { + Session.Runner fetched = bundle.session().runner().fetch(name); + List<org.tensorflow.Tensor<?>> importedTensors = fetched.run(); + if (importedTensors.size() != 1) + throw new IllegalStateException("Expected 1 tensor from fetching " + name + + ", but got " + importedTensors.size()); + return importedTensors.get(0); + } + + private static void verifyOutputTypes(MetaGraphDef tfGraph, IntermediateGraph intermediateGraph) { + for (String signatureName : intermediateGraph.signatures()) { + for (String outputName : intermediateGraph.outputs(signatureName).values()) { + IntermediateOperation operation = intermediateGraph.get(outputName); + NodeDef node = getTensorFlowNodeFromGraph(IntermediateOperation.namePartOf(operation.name()), tfGraph.getGraphDef()); + OrderedTensorType type = operation.type().orElseThrow( + () -> new IllegalArgumentException("Output of '" + outputName + "' has no type.")); + TypeConverter.verifyType(node, type); + } + } + + } + +} diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorConverter.java new file mode 100644 index 00000000000..4e67286ef09 --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorConverter.java @@ -0,0 +1,225 @@ +// 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.tensorflow; + +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType; +import com.yahoo.tensor.IndexedTensor; +import com.yahoo.tensor.Tensor; +import com.yahoo.tensor.TensorType; +import org.tensorflow.framework.TensorProto; + +import java.nio.ByteBuffer; +import java.nio.DoubleBuffer; +import java.nio.FloatBuffer; +import java.nio.IntBuffer; +import java.nio.LongBuffer; + + +/** + * Converts TensorFlow tensors into Vespa tensors. + * + * @author bratseth + * @author lesters + */ +public class TensorConverter { + + public static Tensor toVespaTensor(org.tensorflow.Tensor<?> tfTensor) { + return toVespaTensor(tfTensor, "d"); + } + + private static Tensor toVespaTensor(org.tensorflow.Tensor<?> tfTensor, String dimensionPrefix) { + TensorType type = toVespaTensorType(tfTensor.shape(), dimensionPrefix); + Values values = readValuesOf(tfTensor); + IndexedTensor.BoundBuilder builder = (IndexedTensor.BoundBuilder)Tensor.Builder.of(type); + for (int i = 0; i < values.size(); i++) + builder.cellByDirectIndex(i, values.get(i)); + return builder.build(); + } + + static Tensor toVespaTensor(org.tensorflow.Tensor<?> tfTensor, OrderedTensorType type) { + Values values = readValuesOf(tfTensor); + IndexedTensor.BoundBuilder builder = (IndexedTensor.BoundBuilder) Tensor.Builder.of(type.type()); + for (int i = 0; i < values.size(); i++) { + builder.cellByDirectIndex(type.toDirectIndex(i), values.get(i)); + } + return builder.build(); + } + + static Tensor toVespaTensor(TensorProto tensorProto, TensorType type) { + IndexedTensor.BoundBuilder builder = (IndexedTensor.BoundBuilder)Tensor.Builder.of(type); + Values values = readValuesOf(tensorProto); + for (int i = 0; i < values.size(); ++i) { + builder.cellByDirectIndex(i, values.get(i)); + } + return builder.build(); + } + + private static TensorType toVespaTensorType(long[] shape, String dimensionPrefix) { + TensorType.Builder b = new TensorType.Builder(); + int dimensionIndex = 0; + for (long dimensionSize : shape) { + if (dimensionSize == 0) dimensionSize = 1; // TensorFlow ... + b.indexed(dimensionPrefix + (dimensionIndex++), dimensionSize); + } + return b.build(); + } + + public static Long tensorSize(TensorType type) { + Long size = 1L; + for (TensorType.Dimension dimension : type.dimensions()) { + size *= dimensionSize(dimension); + } + return size; + } + + private static Long dimensionSize(TensorType.Dimension dim) { + return dim.size().orElseThrow(() -> new IllegalArgumentException("Dimension has no size")); + } + + private static Values readValuesOf(org.tensorflow.Tensor<?> tfTensor) { + switch (tfTensor.dataType()) { + case DOUBLE: return new DoubleValues(tfTensor); + case FLOAT: return new FloatValues(tfTensor); + case BOOL: return new BoolValues(tfTensor); + case UINT8: return new IntValues(tfTensor); + case INT32: return new IntValues(tfTensor); + case INT64: return new LongValues(tfTensor); + } + throw new IllegalArgumentException("Cannot convert a tensor with elements of type " + + tfTensor.dataType() + " to a Vespa tensor"); + } + + private static Values readValuesOf(TensorProto tensorProto) { + switch (tensorProto.getDtype()) { + case DT_BOOL: + return new ProtoBoolValues(tensorProto); + case DT_HALF: + return new ProtoHalfValues(tensorProto); + case DT_INT16: + case DT_INT32: + return new ProtoIntValues(tensorProto); + case DT_INT64: + return new ProtoInt64Values(tensorProto); + case DT_FLOAT: + return new ProtoFloatValues(tensorProto); + case DT_DOUBLE: + return new ProtoDoubleValues(tensorProto); + } + throw new IllegalArgumentException("Unsupported data type in attribute tensor import"); + } + + /** Allows reading values from buffers of various numeric types as bytes */ + private static abstract class Values { + abstract double get(int i); + abstract int size(); + } + + private static abstract class TensorFlowValues extends Values { + private final int size; + TensorFlowValues(int size) { + this.size = size; + } + @Override int size() { return this.size; } + } + + private static class DoubleValues extends TensorFlowValues { + private final DoubleBuffer values; + DoubleValues(org.tensorflow.Tensor<?> tfTensor) { + super(tfTensor.numElements()); + values = DoubleBuffer.allocate(tfTensor.numElements()); + tfTensor.writeTo(values); + } + @Override double get(int i) { + return values.get(i); + } + } + + private static class FloatValues extends TensorFlowValues { + private final FloatBuffer values; + FloatValues(org.tensorflow.Tensor<?> tfTensor) { + super(tfTensor.numElements()); + values = FloatBuffer.allocate(tfTensor.numElements()); + tfTensor.writeTo(values); + } + @Override double get(int i) { + return values.get(i); + } + } + + private static class BoolValues extends TensorFlowValues { + private final ByteBuffer values; + BoolValues(org.tensorflow.Tensor<?> tfTensor) { + super(tfTensor.numElements()); + values = ByteBuffer.allocate(tfTensor.numElements()); + tfTensor.writeTo(values); + } + @Override double get(int i) { + return values.get(i); + } + } + + private static class IntValues extends TensorFlowValues { + private final IntBuffer values; + IntValues(org.tensorflow.Tensor<?> tfTensor) { + super(tfTensor.numElements()); + values = IntBuffer.allocate(tfTensor.numElements()); + tfTensor.writeTo(values); + } + @Override double get(int i) { + return values.get(i); + } + } + + private static class LongValues extends TensorFlowValues { + private final LongBuffer values; + LongValues(org.tensorflow.Tensor<?> tfTensor) { + super(tfTensor.numElements()); + values = LongBuffer.allocate(tfTensor.numElements()); + tfTensor.writeTo(values); + } + @Override double get(int i) { + return values.get(i); + } + } + + private static abstract class ProtoValues extends Values { + final TensorProto tensorProto; + ProtoValues(TensorProto tensorProto) { this.tensorProto = tensorProto; } + } + + private static class ProtoBoolValues extends ProtoValues { + ProtoBoolValues(TensorProto tensorProto) { super(tensorProto); } + @Override double get(int i) { return tensorProto.getBoolVal(i) ? 1.0 : 0.0; } + @Override int size() { return tensorProto.getBoolValCount(); } + } + + private static class ProtoHalfValues extends ProtoValues { + ProtoHalfValues(TensorProto tensorProto) { super(tensorProto); } + @Override double get(int i) { return tensorProto.getHalfVal(i); } + @Override int size() { return tensorProto.getHalfValCount(); } + } + + private static class ProtoIntValues extends ProtoValues { + ProtoIntValues(TensorProto tensorProto) { super(tensorProto); } + @Override double get(int i) { return tensorProto.getIntVal(i); } + @Override int size() { return tensorProto.getIntValCount(); } + } + + private static class ProtoInt64Values extends ProtoValues { + ProtoInt64Values(TensorProto tensorProto) { super(tensorProto); } + @Override double get(int i) { return tensorProto.getInt64Val(i); } + @Override int size() { return tensorProto.getInt64ValCount(); } + } + + private static class ProtoFloatValues extends ProtoValues { + ProtoFloatValues(TensorProto tensorProto) { super(tensorProto); } + @Override double get(int i) { return tensorProto.getFloatVal(i); } + @Override int size() { return tensorProto.getFloatValCount(); } + } + + private static class ProtoDoubleValues extends ProtoValues { + ProtoDoubleValues(TensorProto tensorProto) { super(tensorProto); } + @Override double get(int i) { return tensorProto.getDoubleVal(i); } + @Override int size() { return tensorProto.getDoubleValCount(); } + } + +} diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowImporter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowImporter.java new file mode 100644 index 00000000000..f8a25e4d94c --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowImporter.java @@ -0,0 +1,62 @@ +// 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.tensorflow; + +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.ImportedModel; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.IntermediateGraph; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.ModelImporter; +import org.tensorflow.SavedModelBundle; + +import java.io.File; +import java.io.IOException; + +/** + * Converts a saved TensorFlow model into a ranking expression and set of constants. + * + * @author bratseth + * @author lesters + */ +public class TensorFlowImporter extends ModelImporter { + + @Override + public boolean canImport(String modelPath) { + File modelDir = new File(modelPath); + if ( ! modelDir.isDirectory()) return false; + + // No other model types are stored in protobuf files thus far + for (File file : modelDir.listFiles()) { + if (file.toString().endsWith(".pbtxt")) return true; + if (file.toString().endsWith(".pb")) return true; + } + return false; + } + + /** + * Imports a saved TensorFlow model from a directory. + * The model should be saved as a .pbtxt or .pb file. + * + * @param modelName the name of the model to import, consisting of characters in [A-Za-z0-9_] + * @param modelDir the directory containing the TensorFlow model files to import + */ + @Override + public ImportedModel importModel(String modelName, String modelDir) { + try (SavedModelBundle model = SavedModelBundle.load(modelDir, "serve")) { + return importModel(modelName, modelDir, model); + } + catch (IllegalArgumentException e) { + throw new IllegalArgumentException("Could not import TensorFlow model from directory '" + modelDir + "'", e); + } + } + + /** Imports a TensorFlow model */ + public ImportedModel importModel(String modelName, String modelDir, SavedModelBundle model) { + try { + IntermediateGraph graph = GraphImporter.importGraph(modelName, model); + return convertIntermediateGraphToModel(graph, modelDir); + } + catch (IOException e) { + throw new IllegalArgumentException("Could not import TensorFlow model '" + model + "'", e); + } + } + + +} diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TypeConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TypeConverter.java new file mode 100644 index 00000000000..a5a506fdb6d --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TypeConverter.java @@ -0,0 +1,72 @@ +// 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.tensorflow; + +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType; +import com.yahoo.tensor.TensorType; +import org.tensorflow.framework.AttrValue; +import org.tensorflow.framework.NodeDef; +import org.tensorflow.framework.TensorShapeProto; + +import java.util.List; + +/** + * Converts and verifies TensorFlow tensor types into Vespa tensor types. + * + * @author lesters + */ +class TypeConverter { + + static void verifyType(NodeDef node, OrderedTensorType type) { + TensorShapeProto shape = tensorFlowShape(node); + if (shape != null) { + if (shape.getDimCount() != type.rank()) { + throw new IllegalArgumentException("TensorFlow shape of '" + node.getName() + "' " + + "does not match Vespa shape"); + } + for (int tensorFlowIndex = 0; tensorFlowIndex < type.dimensions().size(); ++tensorFlowIndex) { + int vespaIndex = type.dimensionMap(tensorFlowIndex); + TensorShapeProto.Dim tensorFlowDimension = shape.getDim(tensorFlowIndex); + TensorType.Dimension vespaDimension = type.type().dimensions().get(vespaIndex); + if (tensorFlowDimension.getSize() != vespaDimension.size().orElse(-1L)) { + throw new IllegalArgumentException("TensorFlow dimensions of '" + node.getName() + "' " + + "does not match Vespa dimensions"); + } + } + } + } + + private static TensorShapeProto tensorFlowShape(NodeDef node) { + AttrValue attrValueList = node.getAttrMap().get("_output_shapes"); + if (attrValueList == null) { + throw new IllegalArgumentException("_output_shapes attribute of '" + node.getName() + "' " + + "does not exist"); + } + if (attrValueList.getValueCase() != AttrValue.ValueCase.LIST) { + throw new IllegalArgumentException("_output_shapes attribute of '" + node.getName() + "' " + + "is not of expected type"); + } + List<TensorShapeProto> shapeList = attrValueList.getList().getShapeList(); + return shapeList.get(0); // support multiple outputs? + } + + static OrderedTensorType fromTensorFlowType(NodeDef node) { + return fromTensorFlowType(node, "d"); // standard naming convention: d0, d1, ... + } + + private static OrderedTensorType fromTensorFlowType(NodeDef node, String dimensionPrefix) { + OrderedTensorType.Builder builder = new OrderedTensorType.Builder(); + TensorShapeProto shape = tensorFlowShape(node); + for (int i = 0; i < shape.getDimCount(); ++ i) { + String dimensionName = dimensionPrefix + i; + TensorShapeProto.Dim tensorFlowDimension = shape.getDim(i); + if (tensorFlowDimension.getSize() >= 0) { + builder.add(TensorType.Dimension.indexed(dimensionName, tensorFlowDimension.getSize())); + } else { + builder.add(TensorType.Dimension.indexed(dimensionName)); + } + } + return builder.build(); + } + +} diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/VariableConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/VariableConverter.java new file mode 100644 index 00000000000..b777ee07e58 --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/VariableConverter.java @@ -0,0 +1,59 @@ +// 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.tensorflow; + +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType; +import com.yahoo.tensor.serialization.JsonFormat; +import com.yahoo.yolean.Exceptions; +import org.tensorflow.SavedModelBundle; + +import java.nio.charset.StandardCharsets; + +/** + * Converts TensorFlow Variables to the Vespa document format. + * Intended to be used from the command line to convert trained tensors to document form. + * + * @author bratseth + */ +class VariableConverter { + + /** + * Reads the tensor with the given TensorFlow name at the given model location, + * and encodes it as UTF-8 Vespa document tensor JSON having the given ordered tensor type. + * Note that order of dimensions in the tensor type does matter as the TensorFlow tensor + * tensor dimensions are implicitly ordered. + */ + static byte[] importVariable(String modelDir, String tensorFlowVariableName, String orderedTypeSpec) { + try (SavedModelBundle bundle = SavedModelBundle.load(modelDir, "serve")) { + return JsonFormat.encode(TensorConverter.toVespaTensor(GraphImporter.readVariable(tensorFlowVariableName, + bundle), + OrderedTensorType.fromSpec(orderedTypeSpec))); + } + catch (IllegalArgumentException e) { + throw new IllegalArgumentException("Could not import TensorFlow model from directory '" + modelDir + "'", e); + } + } + + public static void main(String[] args) { + if ( args.length != 3) { + System.out.println("Converts a TensorFlow variable into Vespa tensor document field value JSON:"); + System.out.println("A JSON map containing a 'cells' array, see"); + System.out.println("http://docs.vespa.ai/documentation/reference/document-json-put-format.html#tensor)"); + System.out.println(""); + System.out.println("Arguments: modelDirectory tensorFlowVariableName orderedTypeSpec"); + System.out.println(" - modelDirectory: The directory of the TensorFlow SavedModel"); + System.out.println(" - tensorFlowVariableName: The name of the TensorFlow variable to convert"); + System.out.println(" - orderedTypeSpec: The tensor type, e.g tensor(b[],a[10]), where dimensions are "); + System.out.println(" ordered as given in the deployment log message starting by "); + System.out.println(" 'Importing TensorFlow variable'"); + return; + } + + try { + System.out.println(new String(importVariable(args[0], args[1], args[2]), StandardCharsets.UTF_8)); + } + catch (Exception e) { + System.err.println("Import failed: " + Exceptions.toMessageString(e)); + } + } + +} diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/package-info.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/package-info.java new file mode 100644 index 00000000000..0840e584d25 --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/package-info.java @@ -0,0 +1,4 @@ +@ExportPackage +package ai.vespa.rankingexpression.importer.tensorflow; + +import com.yahoo.osgi.annotation.ExportPackage;
\ No newline at end of file diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/xgboost/XGBoostImporter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/xgboost/XGBoostImporter.java new file mode 100644 index 00000000000..e87dd265d50 --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/xgboost/XGBoostImporter.java @@ -0,0 +1,43 @@ +// 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.xgboost; + +import com.yahoo.searchlib.rankingexpression.RankingExpression; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.ImportedModel; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.ModelImporter; +import com.yahoo.searchlib.rankingexpression.parser.ParseException; + +import java.io.File; +import java.io.IOException; + +/** + * Converts a saved XGBoost model into a ranking expression. + * + * @author grace-lam + * @author bratseth + */ +public class XGBoostImporter extends ModelImporter { + + @Override + public boolean canImport(String modelPath) { + File modelFile = new File(modelPath); + if ( ! modelFile.isFile()) return false; + + return modelFile.toString().endsWith(".json"); // No other models ends by json yet + } + + @Override + public ImportedModel importModel(String modelName, String modelPath) { + try { + ImportedModel model = new ImportedModel(modelName, modelPath); + XGBoostParser parser = new XGBoostParser(modelPath); + RankingExpression expression = new RankingExpression(parser.toRankingExpression()); + model.expression(modelName, expression); + return model; + } catch (IOException e) { + throw new IllegalArgumentException("Could not import XGBoost model from '" + modelPath + "'", e); + } catch (ParseException e) { + throw new IllegalArgumentException("Could not parse ranking expression resulting from '" + modelPath + "'", e); + } + } + +} diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/xgboost/XGBoostParser.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/xgboost/XGBoostParser.java new file mode 100644 index 00000000000..2b215f816f5 --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/xgboost/XGBoostParser.java @@ -0,0 +1,77 @@ +// 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.xgboost; + +import java.io.File; +import java.io.IOException; +import java.util.ArrayList; +import java.util.List; + +import com.fasterxml.jackson.core.JsonProcessingException; +import com.fasterxml.jackson.databind.JsonNode; +import com.fasterxml.jackson.databind.ObjectMapper; + +/** + * @author grace-lam + */ +class XGBoostParser { + + private List<XGBoostTree> xgboostTrees; + + /** + * Constructor stores parsed JSON trees. + * + * @param filePath XGBoost JSON output file. + * @throws JsonProcessingException Fails JSON parsing. + * @throws IOException Fails file reading. + */ + XGBoostParser(String filePath) throws JsonProcessingException, IOException { + this.xgboostTrees = new ArrayList<>(); + ObjectMapper mapper = new ObjectMapper(); + JsonNode forestNode = mapper.readTree(new File(filePath)); + for (JsonNode treeNode : forestNode) { + this.xgboostTrees.add(mapper.treeToValue(treeNode, XGBoostTree.class)); + } + } + + /** + * Converts parsed JSON trees to Vespa ranking expressions. + * + * @return Vespa ranking expressions. + */ + String toRankingExpression() { + StringBuilder ret = new StringBuilder(); + for (int i = 0; i < xgboostTrees.size(); i++) { + ret.append(treeToRankExp(xgboostTrees.get(i))); + if (i != xgboostTrees.size() - 1) { + ret.append(" + \n"); + } + } + return ret.toString(); + } + + /** + * Recursive helper function for toRankingExpression(). + * + * @param node XGBoost tree node to convert. + * @return Vespa ranking expression for input node. + */ + private String treeToRankExp(XGBoostTree node) { + if (node.isLeaf()) { + return Double.toString(node.getLeaf()); + } else { + assert node.getChildren().size() == 2; + String trueExp; + String falseExp; + if (node.getYes() == node.getChildren().get(0).getNodeid()) { + trueExp = treeToRankExp(node.getChildren().get(0)); + falseExp = treeToRankExp(node.getChildren().get(1)); + } else { + trueExp = treeToRankExp(node.getChildren().get(1)); + falseExp = treeToRankExp(node.getChildren().get(0)); + } + return "if (" + node.getSplit() + " < " + Double.toString(node.getSplit_condition()) + ", " + trueExp + ", " + + falseExp + ")"; + } + } + +}
\ No newline at end of file diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/xgboost/XGBoostTree.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/xgboost/XGBoostTree.java new file mode 100644 index 00000000000..e32e0f1eab5 --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/xgboost/XGBoostTree.java @@ -0,0 +1,77 @@ +// 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.xgboost; + +import java.util.List; + +/** + * Outlines the JSON representation used for parsing the XGBoost output file. + * + * @author grace-lam + */ +public class XGBoostTree { + + // ID of current node. + private int nodeid; + // Depth of current node w.r.t. the tree's root. + private int depth; + // Feature name used for split. + private String split; + // Feature value threshold to split on. + private double split_condition; + // Next node if feature value < split_condition. + private int yes; + // Next node if feature value >= split_condition. + private int no; + // Next node if feature value is missing. + private int missing; + // Response value for leaf node. + private double leaf; + // List of child nodes. + private List<XGBoostTree> children; + + public int getNodeid() { + return nodeid; + } + + public int getDepth() { + return depth; + } + + public String getSplit() { + return split; + } + + public double getSplit_condition() { + return split_condition; + } + + public int getYes() { + return yes; + } + + public int getNo() { + return no; + } + + public int getMissing() { + return missing; + } + + public double getLeaf() { + return leaf; + } + + public List<XGBoostTree> getChildren() { + return children; + } + + /** + * Check if current node is a leaf node. + * + * @return True if leaf, false otherwise. + */ + public boolean isLeaf() { + return children == null; + } + +}
\ No newline at end of file diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/xgboost/package-info.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/xgboost/package-info.java new file mode 100644 index 00000000000..d310de9041b --- /dev/null +++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/xgboost/package-info.java @@ -0,0 +1,4 @@ +@ExportPackage +package ai.vespa.rankingexpression.importer.xgboost; + +import com.yahoo.osgi.annotation.ExportPackage;
\ No newline at end of file diff --git a/model-integration/src/main/protobuf/onnx.proto b/model-integration/src/main/protobuf/onnx.proto new file mode 100644 index 00000000000..dc6542867e0 --- /dev/null +++ b/model-integration/src/main/protobuf/onnx.proto @@ -0,0 +1,464 @@ +// +// WARNING: This file is automatically generated! Please edit onnx.in.proto. +// + + +// Copyright (c) Facebook Inc. and Microsoft Corporation. +// Licensed under the MIT license. + +syntax = "proto2"; + +package onnx; + +// Overview +// +// ONNX is an open specification that is comprised of the following components: +// +// 1) A definition of an extensible computation graph model. +// 2) Definitions of standard data types. +// 3) Definitions of built-in operators. +// +// This document describes the syntax of models and their computation graphs, +// as well as the standard data types. Together, they are referred to as the ONNX +// Intermediate Representation, or 'IR' for short. +// +// The normative semantic specification of the ONNX IR is found in docs/IR.md. +// Definitions of the built-in neural network operators may be found in docs/Operators.md. + +// Notes +// +// Release +// +// We are still in the very early stage of defining ONNX. The current +// version of ONNX is a starting point. While we are actively working +// towards a complete spec, we would like to get the community involved +// by sharing our working version of ONNX. +// +// Protobuf compatibility +// +// To simplify framework compatibility, ONNX is defined using the subset of protobuf +// that is compatible with both protobuf v2 and v3. This means that we do not use any +// protobuf features that are only available in one of the two versions. +// +// Here are the most notable contortions we have to carry out to work around +// these limitations: +// +// - No 'map' (added protobuf 3.0). We instead represent mappings as lists +// of key-value pairs, where order does not matter and duplicates +// are not allowed. + + +// Versioning +// +// ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md +// +// To be compatible with both proto2 and proto3, we will use a version number +// that is not defined by the default value but an explicit enum number. +enum Version { + // proto3 requires the first enum value to be zero. + // We add this just to appease the compiler. + _START_VERSION = 0; + // The version field is always serialized and we will use it to store the + // version that the graph is generated from. This helps us set up version + // control. We should use version as + // xx(major) - xx(minor) - xxxx(bugfix) + // and we are starting with 0x00000001 (0.0.1), which was the + // version we published on Oct 10, 2017. + IR_VERSION_2017_10_10 = 0x00000001; + + // IR_VERSION 0.0.2 published on Oct 30, 2017 + // - Added type discriminator to AttributeProto to support proto3 users + IR_VERSION_2017_10_30 = 0x00000002; + + // IR VERSION 0.0.3 published on Nov 3, 2017 + // - For operator versioning: + // - Added new message OperatorSetIdProto + // - Added opset_import in ModelProto + // - For vendor extensions, added domain in NodeProto + IR_VERSION = 0x00000003; +} + +// Attributes +// +// A named attribute containing either singular float, integer, string, graph, +// and tensor values, or repeated float, integer, string, graph, and tensor values. +// An AttributeProto MUST contain the name field, and *only one* of the +// following content fields, effectively enforcing a C/C++ union equivalent. +message AttributeProto { + + // Note: this enum is structurally identical to the OpSchema::AttrType + // enum defined in schema.h. If you rev one, you likely need to rev the other. + enum AttributeType { + UNDEFINED = 0; + FLOAT = 1; + INT = 2; + STRING = 3; + TENSOR = 4; + GRAPH = 5; + + FLOATS = 6; + INTS = 7; + STRINGS = 8; + TENSORS = 9; + GRAPHS = 10; + } + + // The name field MUST be present for this version of the IR. + optional string name = 1; // namespace Attribute + + // if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function. + // In this case, this AttributeProto does not contain data, and it's a reference of attribute + // in parent scope. + // NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph. + optional string ref_attr_name = 21; + + // A human-readable documentation for this attribute. Markdown is allowed. + optional string doc_string = 13; + + // The type field MUST be present for this version of the IR. + // For 0.0.1 versions of the IR, this field was not defined, and + // implementations needed to use has_field hueristics to determine + // which value field was in use. For IR_VERSION 0.0.2 or later, this + // field MUST be set and match the f|i|s|t|... field in use. This + // change was made to accomodate proto3 implementations. + optional AttributeType type = 20; // discriminator that indicates which field below is in use + + // Exactly ONE of the following fields must be present for this version of the IR + optional float f = 2; // float + optional int64 i = 3; // int + optional bytes s = 4; // UTF-8 string + optional TensorProto t = 5; // tensor value + optional GraphProto g = 6; // graph + // Do not use field below, it's deprecated. + // optional ValueProto v = 12; // value - subsumes everything but graph + + repeated float floats = 7; // list of floats + repeated int64 ints = 8; // list of ints + repeated bytes strings = 9; // list of UTF-8 strings + repeated TensorProto tensors = 10; // list of tensors + repeated GraphProto graphs = 11; // list of graph +} + +// Defines information on value, including the name, the type, and +// the shape of the value. +message ValueInfoProto { + // This field MUST be present in this version of the IR. + optional string name = 1; // namespace Value + // This field MUST be present in this version of the IR. + optional TypeProto type = 2; + // A human-readable documentation for this value. Markdown is allowed. + optional string doc_string = 3; +} + +// Nodes +// +// Computation graphs are made up of a DAG of nodes, which represent what is +// commonly called a "layer" or "pipeline stage" in machine learning frameworks. +// +// For example, it can be a node of type "Conv" that takes in an image, a filter +// tensor and a bias tensor, and produces the convolved output. +message NodeProto { + repeated string input = 1; // namespace Value + repeated string output = 2; // namespace Value + + // An optional identifier for this node in a graph. + // This field MAY be absent in ths version of the IR. + optional string name = 3; // namespace Node + + // The symbolic identifier of the Operator to execute. + optional string op_type = 4; // namespace Operator + // The domain of the OperatorSet that specifies the operator named by op_type. + optional string domain = 7; // namespace Domain + + // Additional named attributes. + repeated AttributeProto attribute = 5; + + // A human-readable documentation for this node. Markdown is allowed. + optional string doc_string = 6; +} + +// Models +// +// ModelProto is a top-level file/container format for bundling a ML model and +// associating its computation graph with metadata. +// +// The semantics of the model are described by the associated GraphProto. +message ModelProto { + // The version of the IR this model targets. See Version enum above. + // This field MUST be present. + optional int64 ir_version = 1; + + // The OperatorSets this model relies on. + // All ModelProtos MUST have at least one entry that + // specifies which version of the ONNX OperatorSet is + // being imported. + // + // All nodes in the ModelProto's graph will bind against the operator + // with the same-domain/same-op_type operator with the HIGHEST version + // in the referenced operator sets. + repeated OperatorSetIdProto opset_import = 8; + + // The name of the framework or tool used to generate this model. + // This field SHOULD be present to indicate which implementation/tool/framework + // emitted the model. + optional string producer_name = 2; + + // The version of the framework or tool used to generate this model. + // This field SHOULD be present to indicate which implementation/tool/framework + // emitted the model. + optional string producer_version = 3; + + // Domain name of the model. + // We use reverse domain names as name space indicators. For example: + // `com.facebook.fair` or `com.microsoft.cognitiveservices` + // + // Together with `model_version` and GraphProto.name, this forms the unique identity of + // the graph. + optional string domain = 4; + + // The version of the graph encoded. See Version enum below. + optional int64 model_version = 5; + + // A human-readable documentation for this model. Markdown is allowed. + optional string doc_string = 6; + + // The parameterized graph that is evaluated to execute the model. + optional GraphProto graph = 7; + + // Named metadata values; keys should be distinct. + repeated StringStringEntryProto metadata_props = 14; +}; + +// StringStringEntryProto follows the pattern for cross-proto-version maps. +// See https://developers.google.com/protocol-buffers/docs/proto3#maps +message StringStringEntryProto { + optional string key = 1; + optional string value= 2; +}; + +// Graphs +// +// A graph defines the computational logic of a model and is comprised of a parameterized +// list of nodes that form a directed acyclic graph based on their inputs and outputs. +// This is the equivalent of the "network" or "graph" in many deep learning +// frameworks. +message GraphProto { + // The nodes in the graph, sorted topologically. + repeated NodeProto node = 1; + + // The name of the graph. + optional string name = 2; // namespace Graph + + // A list of named tensor values, used to specify constant inputs of the graph. + // Each TensorProto entry must have a distinct name (within the list) that + // also appears in the input list. + repeated TensorProto initializer = 5; + + // A human-readable documentation for this graph. Markdown is allowed. + optional string doc_string = 10; + + // The inputs and outputs of the graph. + repeated ValueInfoProto input = 11; + repeated ValueInfoProto output = 12; + + // Information for the values in the graph. The ValueInfoProto.name's + // must be distinct. It is optional for a value to appear in value_info list. + repeated ValueInfoProto value_info = 13; + + // DO NOT USE the following fields, they were deprecated from earlier versions. + // repeated string input = 3; + // repeated string output = 4; + // optional int64 ir_version = 6; + // optional int64 producer_version = 7; + // optional string producer_tag = 8; + // optional string domain = 9; +} + +// Tensors +// +// A serialized tensor value. +message TensorProto { + enum DataType { + UNDEFINED = 0; + // Basic types. + FLOAT = 1; // float + UINT8 = 2; // uint8_t + INT8 = 3; // int8_t + UINT16 = 4; // uint16_t + INT16 = 5; // int16_t + INT32 = 6; // int32_t + INT64 = 7; // int64_t + STRING = 8; // string + BOOL = 9; // bool + + // Advanced types + FLOAT16 = 10; + DOUBLE = 11; + UINT32 = 12; + UINT64 = 13; + COMPLEX64 = 14; // complex with float32 real and imaginary components + COMPLEX128 = 15; // complex with float64 real and imaginary components + // Future extensions go here. + } + + // The shape of the tensor. + repeated int64 dims = 1; + + // The data type of the tensor. + optional DataType data_type = 2; + + // For very large tensors, we may want to store them in chunks, in which + // case the following fields will specify the segment that is stored in + // the current TensorProto. + message Segment { + optional int64 begin = 1; + optional int64 end = 2; + } + optional Segment segment = 3; + + // Tensor content must be organized in row-major order. + // + // Depending on the data_type field, exactly one of the fields below with + // name ending in _data is used to store the elements of the tensor. + + // For float and complex64 values + // Complex64 tensors are encoded as a single array of floats, + // with the real components appearing in odd numbered positions, + // and the corresponding imaginary component apparing in the + // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] + // is encoded as [1.0, 2.0 ,3.0 ,4.0] + // When this field is present, the data_type field MUST be FLOAT or COMPLEX64. + repeated float float_data = 4 [packed = true]; + + // For int32, uint8, int8, uint16, int16, bool, and float16 values + // float16 values must be bit-wise converted to an uint16_t prior + // to writing to the buffer. + // When this field is present, the data_type field MUST be + // INT32, INT16, INT8, UINT16, INT8, BOOL, or FLOAT32 + repeated int32 int32_data = 5 [packed = true]; + + // For strings. + // Each element of string_data is a UTF-8 encoded Unicode + // string. No trailing null, no leading BOM. The protobuf "string" + // scalar type is not used to match ML community conventions. + // When this field is present, the data_type field MUST be STRING + repeated bytes string_data = 6; + + // For int64. + // When this field is present, the data_type field MUST be INT64 + repeated int64 int64_data = 7 [packed = true]; + + // Optionally, a name for the tensor. + optional string name = 8; // namespace Value + + // A human-readable documentation for this tensor. Markdown is allowed. + optional string doc_string = 12; + + // Serializations can either use one of the fields above, or use this + // raw bytes field. The only exception is the string case, where one is + // required to store the content in the repeated bytes string_data field. + // + // When this raw_data field is used to store tensor value, elements MUST + // be stored in as fixed-width, little-endian order. + // Floating-point data types MUST be stored in IEEE 754 format. + // Complex64 elements must be written as two consecutive FLOAT values, real component first. + // Complex128 elements must be written as two consecutive DOUBLE values, real component first. + // Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false). + // + // Note: the advantage of specific field rather than the raw_data field is + // that in some cases (e.g. int data), protobuf does a better packing via + // variable length storage, and may lead to smaller binary footprint. + // When this field is present, the data_type field MUST NOT be STRING or UNDEFINED + optional bytes raw_data = 9; + + // For double + // Complex64 tensors are encoded as a single array of doubles, + // with the real components appearing in odd numbered positions, + // and the corresponding imaginary component apparing in the + // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] + // is encoded as [1.0, 2.0 ,3.0 ,4.0] + // When this field is present, the data_type field MUST be DOUBLE or COMPLEX128 + repeated double double_data = 10 [packed = true]; + + // For uint64 and uint32 values + // When this field is present, the data_type field MUST be + // UINT32 or UINT64 + repeated uint64 uint64_data = 11 [packed = true]; +} + +// Defines a tensor shape. A dimension can be either an integer value +// or a symbolic variable. A symbolic variable represents an unknown +// dimension. +message TensorShapeProto { + message Dimension { + oneof value { + int64 dim_value = 1; + string dim_param = 2; // namespace Shape + }; + // Standard denotation can optionally be used to denote tensor + // dimensions with standard semantic descriptions to ensure + // that operations are applied to the correct axis of a tensor. + optional string denotation = 3; + }; + repeated Dimension dim = 1; +} + +// A set of pre-defined constants to be used as values for +// the standard denotation field in TensorShapeProto.Dimension +// for semantic description of the tensor dimension. +message DenotationConstProto { + // Describe a batch number dimension. + optional string DATA_BATCH = 1 [default = "DATA_BATCH"]; + // Describe a channel dimension. + optional string DATA_CHANNEL = 2 [default = "DATA_CHANNEL"]; + // Describe a time dimension. + optional string DATA_TIME = 3 [default = "DATA_TIME"]; + // Describe a feature dimension. This is typically a feature + // dimension in RNN and/or spatial dimension in CNN. + optional string DATA_FEATURE = 4 [default = "DATA_FEATURE"]; + // Describe a filter in-channel dimension. This is the dimension + // that is identical (in size) to the channel dimension of the input + // image feature maps. + optional string FILTER_IN_CHANNEL = 5 [default = "FILTER_IN_CHANNEL"]; + // Describe a filter out channel dimension. This is the dimension + // that is identical (int size) to the channel dimension of the output + // image feature maps. + optional string FILTER_OUT_CHANNEL = 6 [default = "FILTER_OUT_CHANNEL"]; + // Describe a filter spatial dimension. + optional string FILTER_SPATIAL = 7 [default = "FILTER_SPATIAL"]; +} + +// Types +// +// The standard ONNX data types. +message TypeProto { + + message Tensor { + // This field MUST NOT have the value of UNDEFINED + // This field MUST be present for this version of the IR. + optional TensorProto.DataType elem_type = 1; + optional TensorShapeProto shape = 2; + } + + + oneof value { + // The type of a tensor. + Tensor tensor_type = 1; + + } +} + +// Operator Sets +// +// OperatorSets are uniquely identified by a (domain, opset_version) pair. +message OperatorSetIdProto { + // The domain of the operator set being identified. + // The empty string ("") or absence of this field implies the operator + // set that is defined as part of the ONNX specification. + // This field MUST be present in this version of the IR when referring to any other operator set. + optional string domain = 1; + + // The version of the operator set being identified. + // This field MUST be present in this version of the IR. + optional int64 version = 2; +}
\ No newline at end of file diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java new file mode 100644 index 00000000000..a71d7a42551 --- /dev/null +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java @@ -0,0 +1,101 @@ +// 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.ExpressionFunction; +import com.yahoo.searchlib.rankingexpression.RankingExpression; +import com.yahoo.searchlib.rankingexpression.evaluation.Context; +import com.yahoo.searchlib.rankingexpression.evaluation.MapContext; +import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.ImportedModel; +import ai.vespa.rankingexpression.importer.tensorflow.TensorFlowImporter; +import com.yahoo.tensor.Tensor; +import com.yahoo.tensor.TensorType; +import org.junit.Test; + +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; +import static org.junit.Assert.assertTrue; + +/** + * @author lesters + */ +public class OnnxMnistSoftmaxImportTestCase { + + @Test + public void testMnistSoftmaxImport() { + ImportedModel model = new OnnxImporter().importModel("test", "src/test/models/onnx/mnist_softmax/mnist_softmax.onnx"); + + // Check constants + assertEquals(2, model.largeConstants().size()); + + Tensor constant0 = model.largeConstants().get("test_Variable"); + assertNotNull(constant0); + assertEquals(new TensorType.Builder().indexed("d2", 784).indexed("d1", 10).build(), + constant0.type()); + assertEquals(7840, constant0.size()); + + Tensor constant1 = model.largeConstants().get("test_Variable_1"); + assertNotNull(constant1); + assertEquals(new TensorType.Builder().indexed("d1", 10).build(), constant1.type()); + assertEquals(10, constant1.size()); + + // Check inputs + assertEquals(1, model.inputs().size()); + assertTrue(model.inputs().containsKey("Placeholder")); + assertEquals(TensorType.fromSpec("tensor(d0[],d1[784])"), model.inputs().get("Placeholder")); + + // Check signature + ExpressionFunction output = model.defaultSignature().outputExpression("add"); + assertNotNull(output); + assertEquals("add", output.getBody().getName()); + assertEquals("join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(test_Variable), f(a,b)(a * b)), sum, d2), constant(test_Variable_1), f(a,b)(a + b))", + output.getBody().getRoot().toString()); + assertEquals(TensorType.fromSpec("tensor(d0[],d1[784])"), + model.inputs().get(model.defaultSignature().inputs().get("Placeholder"))); + assertEquals("{Placeholder=tensor(d0[],d1[784])}", output.argumentTypes().toString()); + } + + @Test + public void testComparisonBetweenOnnxAndTensorflow() { + String tfModelPath = "src/test/models/tensorflow/mnist_softmax/saved"; + String onnxModelPath = "src/test/models/onnx/mnist_softmax/mnist_softmax.onnx"; + + Tensor argument = placeholderArgument(); + Tensor tensorFlowResult = evaluateTensorFlowModel(tfModelPath, argument, "Placeholder", "add"); + Tensor onnxResult = evaluateOnnxModel(onnxModelPath, argument, "Placeholder", "add"); + + assertEquals("Operation 'add' produces equal results", tensorFlowResult, onnxResult); + } + + private Tensor evaluateTensorFlowModel(String path, Tensor argument, String input, String output) { + ImportedModel model = new TensorFlowImporter().importModel("test", path); + return evaluateExpression(model.expressions().get(output), contextFrom(model), argument, input); + } + + private Tensor evaluateOnnxModel(String path, Tensor argument, String input, String output) { + ImportedModel model = new OnnxImporter().importModel("test", path); + return evaluateExpression(model.expressions().get(output), contextFrom(model), argument, input); + } + + private Tensor evaluateExpression(RankingExpression expression, Context context, Tensor argument, String input) { + context.put(input, new TensorValue(argument)); + return expression.evaluate(context).asTensor(); + } + + private Context contextFrom(ImportedModel result) { + MapContext context = new MapContext(); + result.largeConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(tensor))); + result.smallConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(tensor))); + return context; + } + + private Tensor placeholderArgument() { + Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", 1).indexed("d1", 784).build()); + for (int d0 = 0; d0 < 1; d0++) + for (int d1 = 0; d1 < 784; d1++) + b.cell(d1 * 1.0 / 784, d0, d1); + return b.build(); + } + + +} diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BatchNormImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BatchNormImportTestCase.java new file mode 100644 index 00000000000..acd649d985b --- /dev/null +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BatchNormImportTestCase.java @@ -0,0 +1,32 @@ +// 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.tensorflow; + +import com.yahoo.searchlib.rankingexpression.ExpressionFunction; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.ImportedModel; +import org.junit.Test; + +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; + +/** + * @author lesters + */ +public class BatchNormImportTestCase { + + @Test + public void testBatchNormImport() { + TestableTensorFlowModel model = new TestableTensorFlowModel("test", + "src/test/models/tensorflow/batch_norm/saved"); + ImportedModel.Signature signature = model.get().signature("serving_default"); + + assertEquals("Has skipped outputs", + 0, model.get().signature("serving_default").skippedOutputs().size()); + + ExpressionFunction function = signature.outputExpression("y"); + assertNotNull(function); + assertEquals("dnn/batch_normalization_3/batchnorm/add_1", function.getBody().getName()); + model.assertEqualResult("X", function.getBody().getName()); + assertEquals("{X=tensor(d0[],d1[784])}", function.argumentTypes().toString()); + } + +} diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BlogEvaluationBenchmark.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BlogEvaluationBenchmark.java new file mode 100644 index 00000000000..a878b284b2c --- /dev/null +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BlogEvaluationBenchmark.java @@ -0,0 +1,116 @@ +// 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.tensorflow; + +import com.yahoo.searchlib.rankingexpression.RankingExpression; +import com.yahoo.searchlib.rankingexpression.evaluation.Context; +import com.yahoo.searchlib.rankingexpression.evaluation.ContextIndex; +import com.yahoo.searchlib.rankingexpression.evaluation.ExpressionOptimizer; +import com.yahoo.searchlib.rankingexpression.evaluation.OptimizationReport; +import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.ImportedModel; +import com.yahoo.searchlib.rankingexpression.parser.ParseException; +import com.yahoo.tensor.Tensor; +import com.yahoo.tensor.TensorType; +import org.tensorflow.SavedModelBundle; +import org.tensorflow.Session; + +import java.nio.FloatBuffer; +import java.util.List; + +/** + * Microbenchmark of imported ML model evaluation. + * + * @author lesters + */ +public class BlogEvaluationBenchmark { + + static final String modelDir = "src/test/models/tensorflow/blog/saved"; + + public static void main(String[] args) throws ParseException { + SavedModelBundle tensorFlowModel = SavedModelBundle.load(modelDir, "serve"); + ImportedModel model = new TensorFlowImporter().importModel("blog", modelDir, tensorFlowModel); + + Context context = TestableTensorFlowModel.contextFrom(model); + Tensor u = generateInputTensor(); + Tensor d = generateInputTensor(); + context.put("input_u", new TensorValue(u)); + context.put("input_d", new TensorValue(d)); + + // Parse the ranking expression from imported string to force primitive tensor functions. + RankingExpression expression = new RankingExpression(model.expressions().get("y").getRoot().toString()); + benchmarkJava(expression, context, 20, 200); + + System.out.println("*** Optimizing expression ***"); + ExpressionOptimizer optimizer = new ExpressionOptimizer(); + OptimizationReport report = optimizer.optimize(expression, (ContextIndex)context); + System.out.println(report.toString()); + + benchmarkJava(expression, context, 2000, 20000); + benchmarkTensorFlow(tensorFlowModel, 2000, 20000); + } + + private static void benchmarkJava(RankingExpression expression, Context context, int warmup, int iterations) { + System.out.println("*** Java evaluation - warmup ***"); + evaluate(expression, context, warmup); + System.gc(); + System.out.println("*** Java evaluation - " + iterations + " iterations ***"); + double startTime = System.nanoTime(); + evaluate(expression, context, iterations); + double endTime = System.nanoTime(); + System.out.println("Model evaluation time is " + ((endTime-startTime) / (1000*1000)) + " ms"); + System.out.println("Average model evaluation time is " + ((endTime-startTime) / (1000*1000)) / iterations + " ms"); + } + + private static double evaluate(RankingExpression expression, Context context, int iterations) { + double result = 0; + for (int i = 0 ; i < iterations; i++) { + result = expression.evaluate(context).asTensor().sum().asDouble(); + } + return result; + } + + private static Tensor generateInputTensor() { + Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", 1).indexed("d1", 128).build()); + for (int d0 = 0; d0 < 1; d0++) + for (int d1 = 0; d1 < 128; d1++) + b.cell(d1 * 1.0 / 128, d0, d1); + return b.build(); + } + + private static void benchmarkTensorFlow(SavedModelBundle tensorFlowModel, int warmup, int iterations) { + org.tensorflow.Tensor<?> u = generateInputTensorFlow(); + org.tensorflow.Tensor<?> d = generateInputTensorFlow(); + + System.out.println("*** TensorFlow evaluation - warmup ***"); + evaluateTensorflow(tensorFlowModel, u, d, warmup); + + System.gc(); + System.out.println("*** TensorFlow evaluation - " + iterations + " iterations ***"); + double startTime = System.nanoTime(); + evaluateTensorflow(tensorFlowModel, u, d, iterations); + double endTime = System.nanoTime(); + System.out.println("Model evaluation time is " + ((endTime-startTime) / (1000*1000) + " ms")); + System.out.println("Average model evaluation time is " + ((endTime-startTime) / (1000*1000)) / iterations + " ms"); + } + + private static double evaluateTensorflow(SavedModelBundle tensorFlowModel, org.tensorflow.Tensor<?> u, org.tensorflow.Tensor<?> d, int iterations) { + double result = 0; + for (int i = 0 ; i < iterations; i++) { + Session.Runner runner = tensorFlowModel.session().runner(); + runner.feed("input_u", u); + runner.feed("input_d", d); + List<org.tensorflow.Tensor<?>> results = runner.fetch("y").run(); + result = TensorConverter.toVespaTensor(results.get(0)).sum().asDouble(); + } + return result; + } + + private static org.tensorflow.Tensor<?> generateInputTensorFlow() { + FloatBuffer fb = FloatBuffer.allocate(1 * 128); + for (int i = 0; i < 128; ++i) { + fb.put(i, (float)(i * 1.0 / 128)); + } + return org.tensorflow.Tensor.create(new long[]{ 1, 128 }, fb); + } + +} diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/DropoutImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/DropoutImportTestCase.java new file mode 100644 index 00000000000..6e58761e5ce --- /dev/null +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/DropoutImportTestCase.java @@ -0,0 +1,43 @@ +// 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.tensorflow; + +import com.yahoo.searchlib.rankingexpression.ExpressionFunction; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.ImportedModel; +import com.yahoo.tensor.TensorType; +import org.junit.Assert; +import org.junit.Test; + +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; +import static org.junit.Assert.assertTrue; + +/** + * @author lesters + */ +public class DropoutImportTestCase { + + @Test + public void testDropoutImport() { + TestableTensorFlowModel model = new TestableTensorFlowModel("test", "src/test/models/tensorflow/dropout/saved"); + + // Check required functions + Assert.assertEquals(1, model.get().inputs().size()); + assertTrue(model.get().inputs().containsKey("X")); + Assert.assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(), + model.get().inputs().get("X")); + + ImportedModel.Signature signature = model.get().signature("serving_default"); + + Assert.assertEquals("Has skipped outputs", + 0, model.get().signature("serving_default").skippedOutputs().size()); + + ExpressionFunction function = signature.outputExpression("y"); + assertNotNull(function); + assertEquals("outputs/Maximum", function.getBody().getName()); + assertEquals("join(join(imported_ml_function_test_outputs_BiasAdd, reduce(constant(test_outputs_Const), sum, d1), f(a,b)(a * b)), imported_ml_function_test_outputs_BiasAdd, f(a,b)(max(a,b)))", + function.getBody().getRoot().toString()); + model.assertEqualResult("X", function.getBody().getName()); + assertEquals("{X=tensor(d0[],d1[784])}", function.argumentTypes().toString()); + } + +} diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/MnistImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/MnistImportTestCase.java new file mode 100644 index 00000000000..b338f46fb4d --- /dev/null +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/MnistImportTestCase.java @@ -0,0 +1,31 @@ +// 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.tensorflow; + +import com.yahoo.searchlib.rankingexpression.ExpressionFunction; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.ImportedModel; +import org.junit.Assert; +import org.junit.Test; + +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; + +/** + * @author bratseth + */ +public class MnistImportTestCase { + + @Test + public void testMnistImport() { + TestableTensorFlowModel model = new TestableTensorFlowModel("test", "src/test/models/tensorflow/mnist/saved"); + ImportedModel.Signature signature = model.get().signature("serving_default"); + + Assert.assertEquals("Has skipped outputs", + 0, model.get().signature("serving_default").skippedOutputs().size()); + + ExpressionFunction output = signature.outputExpression("y"); + assertNotNull(output); + assertEquals("dnn/outputs/add", output.getBody().getName()); + model.assertEqualResultSum("input", output.getBody().getName(), 0.00001); + } + +} diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowMnistSoftmaxImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowMnistSoftmaxImportTestCase.java new file mode 100644 index 00000000000..7e8cbef8ada --- /dev/null +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowMnistSoftmaxImportTestCase.java @@ -0,0 +1,73 @@ +// 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.tensorflow; + +import com.yahoo.searchlib.rankingexpression.ExpressionFunction; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.ImportedModel; +import com.yahoo.tensor.Tensor; +import com.yahoo.tensor.TensorType; +import org.junit.Assert; +import org.junit.Test; + +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; +import static org.junit.Assert.assertTrue; + +/** + * @author bratseth + */ +public class TensorFlowMnistSoftmaxImportTestCase { + + @Test + public void testMnistSoftmaxImport() { + TestableTensorFlowModel model = new TestableTensorFlowModel("test", "src/test/models/tensorflow/mnist_softmax/saved"); + + // Check constants + Assert.assertEquals(2, model.get().largeConstants().size()); + + Tensor constant0 = model.get().largeConstants().get("test_Variable_read"); + assertNotNull(constant0); + assertEquals(new TensorType.Builder().indexed("d2", 784).indexed("d1", 10).build(), + constant0.type()); + assertEquals(7840, constant0.size()); + + Tensor constant1 = model.get().largeConstants().get("test_Variable_1_read"); + assertNotNull(constant1); + assertEquals(new TensorType.Builder().indexed("d1", 10).build(), + constant1.type()); + assertEquals(10, constant1.size()); + + // Check (provided) functions + Assert.assertEquals(0, model.get().functions().size()); + + // Check required functions + Assert.assertEquals(1, model.get().inputs().size()); + assertTrue(model.get().inputs().containsKey("Placeholder")); + Assert.assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(), + model.get().inputs().get("Placeholder")); + + // Check signatures + Assert.assertEquals(1, model.get().signatures().size()); + ImportedModel.Signature signature = model.get().signatures().get("serving_default"); + assertNotNull(signature); + + // ... signature inputs + assertEquals(1, signature.inputs().size()); + TensorType argument0 = signature.inputArgument("x"); + assertNotNull(argument0); + assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(), argument0); + + // ... signature outputs + assertEquals(1, signature.outputs().size()); + ExpressionFunction output = signature.outputExpression("y"); + assertNotNull(output); + assertEquals("add", output.getBody().getName()); + assertEquals("join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(test_Variable_read), f(a,b)(a * b)), sum, d2), constant(test_Variable_1_read), f(a,b)(a + b))", + output.getBody().getRoot().toString()); + assertEquals("{Placeholder=tensor(d0[],d1[784])}", output.argumentTypes().toString()); + + // Test execution + model.assertEqualResult("Placeholder", "MatMul"); + model.assertEqualResult("Placeholder", "add"); + } + +} diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/TestableTensorFlowModel.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/TestableTensorFlowModel.java new file mode 100644 index 00000000000..dbed537885e --- /dev/null +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/TestableTensorFlowModel.java @@ -0,0 +1,142 @@ +// 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.tensorflow; + +import com.yahoo.searchlib.rankingexpression.RankingExpression; +import com.yahoo.searchlib.rankingexpression.evaluation.Context; +import com.yahoo.searchlib.rankingexpression.evaluation.ContextIndex; +import com.yahoo.searchlib.rankingexpression.evaluation.ExpressionOptimizer; +import com.yahoo.searchlib.rankingexpression.evaluation.MapContext; +import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.ImportedModel; +import com.yahoo.searchlib.rankingexpression.rule.CompositeNode; +import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode; +import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode; +import com.yahoo.tensor.Tensor; +import com.yahoo.tensor.TensorType; +import org.tensorflow.SavedModelBundle; +import org.tensorflow.Session; + +import java.nio.FloatBuffer; +import java.util.List; + +import static org.junit.Assert.assertEquals; + +/** + * Helper for TensorFlow import tests: Imports a model and provides asserts on it. + * This currently assumes the TensorFlow model takes a single input of type tensor(d0[1],d1[784]) + * + * @author bratseth + */ +public class TestableTensorFlowModel { + + private SavedModelBundle tensorFlowModel; + private ImportedModel model; + + // Sizes of the input vector + private final int d0Size = 1; + private final int d1Size = 784; + + public TestableTensorFlowModel(String modelName, String modelDir) { + tensorFlowModel = SavedModelBundle.load(modelDir, "serve"); + model = new TensorFlowImporter().importModel(modelName, modelDir, tensorFlowModel); + } + + public ImportedModel get() { return model; } + + /** Compare that summing the tensors produce the same result to within some tolerance delta */ + public void assertEqualResultSum(String inputName, String operationName, double delta) { + Tensor tfResult = tensorFlowExecute(tensorFlowModel, inputName, operationName); + Context context = contextFrom(model); + Tensor placeholder = placeholderArgument(); + context.put(inputName, new TensorValue(placeholder)); + + model.functions().forEach((k, v) -> evaluateFunction(context, model, k)); + + RankingExpression expression = model.expressions().get(operationName); + ExpressionOptimizer optimizer = new ExpressionOptimizer(); + optimizer.optimize(expression, (ContextIndex)context); + + Tensor vespaResult = expression.evaluate(context).asTensor(); + assertEquals("Operation '" + operationName + "' produces equal results", + tfResult.sum().asDouble(), vespaResult.sum().asDouble(), delta); + } + + /** Compare tensors 100% exactly */ + public void assertEqualResult(String inputName, String operationName) { + Tensor tfResult = tensorFlowExecute(tensorFlowModel, inputName, operationName); + Context context = contextFrom(model); + Tensor placeholder = placeholderArgument(); + context.put(inputName, new TensorValue(placeholder)); + + model.functions().forEach((k, v) -> evaluateFunction(context, model, k)); + + RankingExpression expression = model.expressions().get(operationName); + ExpressionOptimizer optimizer = new ExpressionOptimizer(); + optimizer.optimize(expression, (ContextIndex)context); + + Tensor vespaResult = expression.evaluate(context).asTensor(); + assertEquals("Operation '" + operationName + "' produces equal results", tfResult, vespaResult); + } + + private Tensor tensorFlowExecute(SavedModelBundle model, String inputName, String operationName) { + Session.Runner runner = model.session().runner(); + FloatBuffer fb = FloatBuffer.allocate(d0Size * d1Size); + for (int i = 0; i < d1Size; ++i) { + fb.put(i, (float)(i * 1.0 / d1Size)); + } + org.tensorflow.Tensor<?> placeholder = org.tensorflow.Tensor.create(new long[]{ d0Size, d1Size }, fb); + runner.feed(inputName, placeholder); + List<org.tensorflow.Tensor<?>> results = runner.fetch(operationName).run(); + assertEquals(1, results.size()); + return TensorConverter.toVespaTensor(results.get(0)); + } + + static Context contextFrom(ImportedModel result) { + TestableModelContext context = new TestableModelContext(); + result.largeConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(tensor))); + result.smallConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(tensor))); + return context; + } + + private Tensor placeholderArgument() { + Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", d0Size).indexed("d1", d1Size).build()); + for (int d0 = 0; d0 < d0Size; d0++) + for (int d1 = 0; d1 < d1Size; d1++) + b.cell(d1 * 1.0 / d1Size, d0, d1); + return b.build(); + } + + private void evaluateFunction(Context context, ImportedModel model, String functionName) { + if (!context.names().contains(functionName)) { + RankingExpression e = model.functions().get(functionName); + evaluateFunctionDependencies(context, model, e.getRoot()); + context.put(functionName, new TensorValue(e.evaluate(context).asTensor())); + } + } + + private void evaluateFunctionDependencies(Context context, ImportedModel model, ExpressionNode node) { + if (node instanceof ReferenceNode) { + String name = node.toString(); + if (model.functions().containsKey(name)) { + evaluateFunction(context, model, name); + } + } + else if (node instanceof CompositeNode) { + for (ExpressionNode child : ((CompositeNode)node).children()) { + evaluateFunctionDependencies(context, model, child); + } + } + } + + private static class TestableModelContext extends MapContext implements ContextIndex { + @Override + public int size() { + return bindings().size(); + } + @Override + public int getIndex(String name) { + throw new UnsupportedOperationException(this + " does not support index lookup by name"); + } + } + +} diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/VariableConverterTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/VariableConverterTestCase.java new file mode 100644 index 00000000000..c9fffe143b4 --- /dev/null +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/VariableConverterTestCase.java @@ -0,0 +1,21 @@ +// 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.tensorflow; + +import org.junit.Test; + +import java.nio.charset.StandardCharsets; + +import static org.junit.Assert.assertEquals; + +public class VariableConverterTestCase { + + @Test + public void testConversion() { + byte[] converted = VariableConverter.importVariable("src/test/models/tensorflow/mnist_softmax/saved", + "Variable_1", + "tensor(d0[10],d1[1])"); + assertEquals("{\"cells\":[{\"address\":{\"d0\":\"0\",\"d1\":\"0\"},\"value\":-0.3546536862850189},{\"address\":{\"d0\":\"1\",\"d1\":\"0\"},\"value\":0.3759574592113495},{\"address\":{\"d0\":\"2\",\"d1\":\"0\"},\"value\":0.06054411828517914},{\"address\":{\"d0\":\"3\",\"d1\":\"0\"},\"value\":-0.251544713973999},{\"address\":{\"d0\":\"4\",\"d1\":\"0\"},\"value\":0.017951013520359993},{\"address\":{\"d0\":\"5\",\"d1\":\"0\"},\"value\":1.2899067401885986},{\"address\":{\"d0\":\"6\",\"d1\":\"0\"},\"value\":-0.10389615595340729},{\"address\":{\"d0\":\"7\",\"d1\":\"0\"},\"value\":0.6367976665496826},{\"address\":{\"d0\":\"8\",\"d1\":\"0\"},\"value\":-1.4136744737625122},{\"address\":{\"d0\":\"9\",\"d1\":\"0\"},\"value\":-0.2573896050453186}]}", + new String(converted, StandardCharsets.UTF_8)); + } + +} diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/xgboost/XGBoostImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/xgboost/XGBoostImportTestCase.java new file mode 100644 index 00000000000..48c7f5bee19 --- /dev/null +++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/xgboost/XGBoostImportTestCase.java @@ -0,0 +1,28 @@ +package ai.vespa.rankingexpression.importer.xgboost; + +import com.yahoo.searchlib.rankingexpression.RankingExpression; +import com.yahoo.searchlib.rankingexpression.integration.ml.importer.ImportedModel; +import org.junit.Test; + +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; +import static org.junit.Assert.assertTrue; + +/** + * @author bratseth + */ +public class XGBoostImportTestCase { + + @Test + public void testXGBoost() { + ImportedModel model = new XGBoostImporter().importModel("test", "src/test/models/xgboost/xgboost.2.2.json"); + assertTrue("All inputs are scalar", model.inputs().isEmpty()); + assertEquals(1, model.expressions().size()); + System.out.println(model.expressions().keySet()); + RankingExpression expression = model.expressions().get("test"); + assertNotNull(expression); + assertEquals("if (f29 < -0.1234567, if (f56 < -0.242398, 1.71218, -1.70044), if (f109 < 0.8723473, -1.94071, 1.85965)) + if (f60 < -0.482947, if (f29 < -4.2387498, 0.784718, -0.96853), -6.23624)", + expression.getRoot().toString()); + } + +} diff --git a/model-integration/src/test/models/onnx/mnist_softmax/mnist_softmax.onnx b/model-integration/src/test/models/onnx/mnist_softmax/mnist_softmax.onnx Binary files differnew file mode 100644 index 00000000000..a86019bf53a --- /dev/null +++ b/model-integration/src/test/models/onnx/mnist_softmax/mnist_softmax.onnx diff --git a/model-integration/src/test/models/tensorflow/batch_norm/batch_normalization_mnist.py b/model-integration/src/test/models/tensorflow/batch_norm/batch_normalization_mnist.py new file mode 100644 index 00000000000..bc6ea13ebc1 --- /dev/null +++ b/model-integration/src/test/models/tensorflow/batch_norm/batch_normalization_mnist.py @@ -0,0 +1,95 @@ +# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. + +import tensorflow as tf + +from functools import partial +from tensorflow.examples.tutorials.mnist import input_data +from datetime import datetime + +now = datetime.utcnow().strftime("%Y%m%d%H%M%S") +root_logdir = "tf_logs" +logdir = "{}/run-{}/".format(root_logdir, now) + +mnist = input_data.read_data_sets("/tmp/data/") +X_train = mnist.train.images +X_test = mnist.test.images +y_train = mnist.train.labels.astype("int") +y_test = mnist.test.labels.astype("int") + +n_inputs = 28*28 # MNIST +n_hidden1 = 300 +n_hidden2 = 100 +n_hidden3 = 40 +n_outputs = 10 + +learning_rate = 0.01 +n_epochs = 20 +batch_size = 200 +batch_norm_momentum = 0.9 + +X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X") +y = tf.placeholder(tf.int64, shape=(None), name="y") +training = tf.placeholder_with_default(False, shape=(), name='training') + +def leaky_relu(z, name=None): + return tf.maximum(0.01 * z, z, name=name) + +with tf.name_scope("dnn"): + he_init = tf.contrib.layers.variance_scaling_initializer() + + batch_norm_layer = partial(tf.layers.batch_normalization, training=training, momentum=batch_norm_momentum) + dense_layer = partial(tf.layers.dense, kernel_initializer=he_init) + + hidden1 = dense_layer(X, n_hidden1, name="hidden1", activation=leaky_relu) + bn1 = tf.nn.elu(batch_norm_layer(hidden1)) + hidden2 = dense_layer(bn1, n_hidden2, name="hidden2", activation=tf.nn.elu) + bn2 = tf.nn.elu(batch_norm_layer(hidden2)) + logits_before_bn = dense_layer(bn2, n_outputs, name="outputs", activation=tf.nn.selu) + logits = batch_norm_layer(logits_before_bn) + +with tf.name_scope("loss"): + xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits) + loss = tf.reduce_mean(xentropy, name="loss") + +with tf.name_scope("train"): + optimizer = tf.train.GradientDescentOptimizer(learning_rate) + training_op = optimizer.minimize(loss) + +with tf.name_scope("eval"): + correct = tf.nn.in_top_k(logits, y, 1) + accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) + +init = tf.global_variables_initializer() +accuracy_summary = tf.summary.scalar('Accuracy', accuracy) +file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph()) +extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + +with tf.Session() as sess: + init.run() + for epoch in range(n_epochs): + for iteration in range(mnist.train.num_examples // batch_size): + X_batch, y_batch = mnist.train.next_batch(batch_size) + sess.run([training_op, extra_update_ops], + feed_dict={training: True, X: X_batch, y: y_batch}) + + accuracy_val = accuracy.eval(feed_dict={X: mnist.test.images, + y: mnist.test.labels}) + print(epoch, "Test accuracy:", accuracy_val) + + # Save summary for tensorboard + summary_str = accuracy_summary.eval(feed_dict={X: mnist.validation.images, + y: mnist.validation.labels}) + file_writer.add_summary(summary_str, epoch) + + export_path = "saved" + print('Exporting trained model to ', export_path) + builder = 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a/model-integration/src/test/models/tensorflow/dropout/dropout.py b/model-integration/src/test/models/tensorflow/dropout/dropout.py new file mode 100644 index 00000000000..42c15cd2812 --- /dev/null +++ b/model-integration/src/test/models/tensorflow/dropout/dropout.py @@ -0,0 +1,44 @@ +# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. + +# Common imports +import numpy as np +import tensorflow as tf +import datetime + +now = datetime.datetime.utcnow().strftime("%Y%m%d%H%M%S") +root_logdir = "tf_logs" +logdir = "{}/run-{}/".format(root_logdir, now) + +n_inputs = 784 +n_outputs = 10 +dropout_rate = 0.5 # == 1 - keep_prob + +X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X") +y = tf.placeholder(tf.int64, shape=(None), name="y") +training = tf.placeholder_with_default(False, shape=(), name='training') + +def leaky_relu_with_small_constant(z, name=None): + return tf.maximum(tf.constant(0.01, shape=[1]) * z, z, name=name) + +X_drop = tf.layers.dropout(X, dropout_rate, training=training, name="xdrop") +output = tf.layers.dense(X_drop, n_outputs, activation=leaky_relu_with_small_constant, name="outputs") + +init = tf.global_variables_initializer() +file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph()) + +with tf.Session() as sess: + init.run() + sess.run(output, feed_dict={training: False, X: np.random.random((1,784))}) + + export_path = "saved" + print('Exporting trained model to ', export_path) + builder = tf.saved_model.builder.SavedModelBuilder(export_path) + signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':X}, outputs = {'y':output}) + builder.add_meta_graph_and_variables(sess, + [tf.saved_model.tag_constants.SERVING], + signature_def_map={'serving_default':signature}) + builder.save(as_text=True) + +file_writer.close() + + diff --git a/model-integration/src/test/models/tensorflow/dropout/saved/saved_model.pbtxt b/model-integration/src/test/models/tensorflow/dropout/saved/saved_model.pbtxt new file mode 100644 index 00000000000..ad431f0460d --- /dev/null +++ b/model-integration/src/test/models/tensorflow/dropout/saved/saved_model.pbtxt @@ -0,0 +1,2875 @@ +saved_model_schema_version: 1 +meta_graphs { + meta_info_def { + stripped_op_list { + op { + name: "Add" + input_arg { + name: "x" + type_attr: "T" + } 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a/model-integration/src/test/models/tensorflow/mnist/saved/variables/variables.data-00000-of-00001 b/model-integration/src/test/models/tensorflow/mnist/saved/variables/variables.data-00000-of-00001 Binary files differnew file mode 100644 index 00000000000..a7ca01888c7 --- /dev/null +++ b/model-integration/src/test/models/tensorflow/mnist/saved/variables/variables.data-00000-of-00001 diff --git a/model-integration/src/test/models/tensorflow/mnist/saved/variables/variables.index b/model-integration/src/test/models/tensorflow/mnist/saved/variables/variables.index Binary files differnew file mode 100644 index 00000000000..7989c109a3a --- /dev/null +++ b/model-integration/src/test/models/tensorflow/mnist/saved/variables/variables.index diff --git a/model-integration/src/test/models/tensorflow/mnist/simple_mnist.py b/model-integration/src/test/models/tensorflow/mnist/simple_mnist.py new file mode 100644 index 00000000000..86a17e81f8f --- /dev/null +++ b/model-integration/src/test/models/tensorflow/mnist/simple_mnist.py @@ -0,0 +1,98 @@ +# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. + +# Common imports +import numpy as np +import tensorflow as tf + +from tensorflow.examples.tutorials.mnist import input_data +from datetime import datetime + +now = datetime.utcnow().strftime("%Y%m%d%H%M%S") +root_logdir = "tf_logs" +logdir = "{}/run-{}/".format(root_logdir, now) + +mnist = input_data.read_data_sets("/tmp/data/") +X_train = mnist.train.images +X_test = mnist.test.images +y_train = mnist.train.labels.astype("int") +y_test = mnist.test.labels.astype("int") + +n_inputs = 28*28 # MNIST +n_hidden1 = 300 +n_hidden2 = 100 +n_hidden3 = 40 +n_outputs = 10 + +learning_rate = 0.01 +n_epochs = 20 +batch_size = 50 + +input = tf.placeholder(tf.float32, shape=(None, n_inputs), name="input") +y = tf.placeholder(tf.int64, shape=(None), name="y") + + +def neuron_layer(X, n_neurons, name, activation=None): + with tf.name_scope(name): + n_inputs = int(X.get_shape()[1]) + stddev = 2 / np.sqrt(n_inputs) + init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev) + W = tf.Variable(init, name="weights") + b = tf.Variable(tf.zeros([n_neurons]), name="bias") + Z = tf.matmul(X, W) + b + if activation is not None: + return activation(Z) + else: + return Z + + +def leaky_relu(z, name=None): + return tf.maximum(0.01 * z, z, name=name) + + +with tf.name_scope("dnn"): + hidden1 = neuron_layer(input, n_hidden1, name="hidden1", activation=leaky_relu) + hidden2 = neuron_layer(hidden1, n_hidden2, name="hidden2", activation=tf.nn.selu) + logits = neuron_layer(hidden2, n_outputs, name="outputs") #, activation=tf.nn.sigmoid) + +with tf.name_scope("loss"): + xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits) + loss = tf.reduce_mean(xentropy, name="loss") + +with tf.name_scope("train"): + optimizer = tf.train.GradientDescentOptimizer(learning_rate) + training_op = optimizer.minimize(loss) + +with tf.name_scope("eval"): + correct = tf.nn.in_top_k(logits, y, 1) + accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) + +init = tf.global_variables_initializer() +accuracy_summary = tf.summary.scalar('Accuracy', accuracy) +file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph()) + +with tf.Session() as sess: + init.run() + for epoch in range(n_epochs): + for iteration in range(mnist.train.num_examples // batch_size): + X_batch, y_batch = mnist.train.next_batch(batch_size) + sess.run(training_op, feed_dict={input: X_batch, y: y_batch}) + acc_train = accuracy.eval(feed_dict={input: X_batch, y: y_batch}) + acc_val = accuracy.eval(feed_dict={input: mnist.validation.images, + y: mnist.validation.labels}) + print(epoch, "Train accuracy:", acc_train, "Val accuracy:", acc_val) + + # Save summary for tensorboard + summary_str = accuracy_summary.eval(feed_dict={input: mnist.validation.images, + y: mnist.validation.labels}) + file_writer.add_summary(summary_str, epoch) + + export_path = "saved" + print('Exporting trained model to ', export_path) + builder = tf.saved_model.builder.SavedModelBuilder(export_path) + signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':input}, outputs = {'y':logits}) + builder.add_meta_graph_and_variables(sess, + [tf.saved_model.tag_constants.SERVING], + signature_def_map={'serving_default':signature}) + builder.save(as_text=True) + +file_writer.close() diff --git a/model-integration/src/test/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py b/model-integration/src/test/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py new file mode 100644 index 00000000000..07a9fa4a213 --- /dev/null +++ b/model-integration/src/test/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py @@ -0,0 +1,90 @@ +# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""A very simple MNIST classifier. + +See extensive documentation at +https://www.tensorflow.org/get_started/mnist/beginners +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import sys + +from tensorflow.examples.tutorials.mnist import input_data + +import tensorflow as tf + +FLAGS = None + + +def main(_): + # Import data + mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) + + # Create the model + x = tf.placeholder(tf.float32, [None, 784]) + W = tf.Variable(tf.zeros([784, 10])) + b = tf.Variable(tf.zeros([10])) + y = tf.matmul(x, W) + b + + # Define loss and optimizer + y_ = tf.placeholder(tf.float32, [None, 10]) + + # The raw formulation of cross-entropy, + # + # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)), + # reduction_indices=[1])) + # + # can be numerically unstable. + # + # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw + # outputs of 'y', and then average across the batch. + cross_entropy = tf.reduce_mean( + tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) + train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) + + sess = tf.InteractiveSession() + tf.global_variables_initializer().run() + # Train + for _ in range(1000): + batch_xs, batch_ys = mnist.train.next_batch(100) + sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) + + # Test trained model + correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print(sess.run(accuracy, feed_dict={x: mnist.test.images, + y_: mnist.test.labels})) + + # Save the model + export_path = "saved" + print('Exporting trained model to ', export_path) + builder = tf.saved_model.builder.SavedModelBuilder(export_path) + signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':x}, outputs = {'y':y}) + builder.add_meta_graph_and_variables(sess, + [tf.saved_model.tag_constants.SERVING], + signature_def_map={'serving_default':signature}) + builder.save(as_text=True) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data', + help='Directory for storing input data') + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/model-integration/src/test/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt b/model-integration/src/test/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt new file mode 100644 index 00000000000..8100dfd594d --- /dev/null +++ b/model-integration/src/test/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt @@ -0,0 +1,5039 @@ +saved_model_schema_version: 1 +meta_graphs { + meta_info_def { + stripped_op_list { + op { + name: "Add" + input_arg { + name: "x" + type_attr: "T" + } + input_arg { + name: "y" + type_attr: "T" + } + output_arg { + name: "z" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_HALF + type: DT_FLOAT + type: DT_DOUBLE + type: DT_UINT8 + type: DT_INT8 + type: DT_INT16 + type: DT_INT32 + type: DT_INT64 + type: DT_COMPLEX64 + type: DT_COMPLEX128 + type: DT_STRING + } + } + } + } + op { + name: 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