Merge pull request #6106 from vespa-engine/revert-6046-lesters/refactor-onnx-tensorflow-import

Revert "Refactor ONNX and TF import to use same code base"

64 files changed, 3726 insertions, 2365 deletions

diff --git a/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/MLImportFeatureConverter.java b/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/MLImportFeatureConverter.java
deleted file mode 100644
index effa261be3b..00000000000
--- a/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/MLImportFeatureConverter.java
+++ /dev/null
@@ -1,674 +0,0 @@
-package com.yahoo.searchdefinition.expressiontransforms;
-
-import com.google.common.base.Joiner;
-import com.yahoo.collections.Pair;
-import com.yahoo.config.application.api.ApplicationFile;
-import com.yahoo.config.application.api.ApplicationPackage;
-import com.yahoo.config.model.application.provider.FilesApplicationPackage;
-import com.yahoo.io.IOUtils;
-import com.yahoo.path.Path;
-import com.yahoo.search.query.profile.QueryProfileRegistry;
-import com.yahoo.searchdefinition.FeatureNames;
-import com.yahoo.searchdefinition.RankProfile;
-import com.yahoo.searchdefinition.RankingConstant;
-import com.yahoo.searchlib.rankingexpression.RankingExpression;
-import com.yahoo.searchlib.rankingexpression.Reference;
-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.ImportedModel;
-import com.yahoo.searchlib.rankingexpression.parser.ParseException;
-import com.yahoo.searchlib.rankingexpression.rule.Arguments;
-import com.yahoo.searchlib.rankingexpression.rule.CompositeNode;
-import com.yahoo.searchlib.rankingexpression.rule.ConstantNode;
-import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
-import com.yahoo.searchlib.rankingexpression.rule.GeneratorLambdaFunctionNode;
-import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
-import com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode;
-import com.yahoo.searchlib.rankingexpression.transform.ExpressionTransformer;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-import com.yahoo.tensor.evaluation.TypeContext;
-import com.yahoo.tensor.functions.Generate;
-import com.yahoo.tensor.functions.Join;
-import com.yahoo.tensor.functions.Reduce;
-import com.yahoo.tensor.functions.Rename;
-import com.yahoo.tensor.functions.ScalarFunctions;
-import com.yahoo.tensor.functions.TensorFunction;
-import com.yahoo.tensor.serialization.TypedBinaryFormat;
-
-import java.io.BufferedReader;
-import java.io.File;
-import java.io.IOException;
-import java.io.StringReader;
-import java.io.UncheckedIOException;
-import java.util.ArrayList;
-import java.util.Collections;
-import java.util.HashSet;
-import java.util.List;
-import java.util.Optional;
-import java.util.Set;
-import java.util.stream.Collectors;
-
-/**
- * Base class for replacing instances of a pseudofeature for imported ML
- * ranking models with native Vespa ranking expressions.
- *
- * @author bratseth
- * @author lesters
- */
-abstract class MLImportFeatureConverter extends ExpressionTransformer<RankProfileTransformContext> {
-
-    ExpressionNode transformFromImportedModel(ImportedModel model,
-                                                          ModelStore store,
-                                                          RankProfile profile,
-                                                          QueryProfileRegistry queryProfiles) {
-        // Add constants
-        Set<String> constantsReplacedByMacros = new HashSet<>();
-        model.smallConstants().forEach((k, v) -> transformSmallConstant(store, profile, k, v));
-        model.largeConstants().forEach((k, v) -> transformLargeConstant(store, profile, queryProfiles,
-                constantsReplacedByMacros, k, v));
-
-        // Find the specified expression
-        ImportedModel.Signature signature = chooseSignature(model, store.arguments().signature());
-        String output = chooseOutput(signature, store.arguments().output());
-        if (signature.skippedOutputs().containsKey(output)) {
-            String message = "Could not import model output '" + output + "'";
-            if (!signature.skippedOutputs().get(output).isEmpty()) {
-                message += ": " + signature.skippedOutputs().get(output);
-            }
-            if (!signature.importWarnings().isEmpty()) {
-                message += ": "  + String.join(", ", signature.importWarnings());
-            }
-            throw new IllegalArgumentException(message);
-        }
-
-        RankingExpression expression = model.expressions().get(output);
-        expression = replaceConstantsByMacros(expression, constantsReplacedByMacros);
-        verifyRequiredMacros(expression, model, profile, queryProfiles);
-        addGeneratedMacros(model, profile);
-        reduceBatchDimensions(expression, model, profile, queryProfiles);
-
-        model.macros().forEach((k, v) -> transformGeneratedMacro(store, constantsReplacedByMacros, k, v));
-
-        store.writeConverted(expression);
-        return expression.getRoot();
-    }
-
-    ExpressionNode transformFromStoredModel(ModelStore store, RankProfile profile) {
-        for (Pair<String, Tensor> constant : store.readSmallConstants())
-            profile.addConstant(constant.getFirst(), asValue(constant.getSecond()));
-
-        for (RankingConstant constant : store.readLargeConstants()) {
-            if ( ! profile.getSearch().getRankingConstants().containsKey(constant.getName()))
-                profile.getSearch().addRankingConstant(constant);
-        }
-
-        for (Pair<String, RankingExpression> macro : store.readMacros()) {
-            addGeneratedMacroToProfile(profile, macro.getFirst(), macro.getSecond());
-        }
-
-        return store.readConverted().getRoot();
-    }
-
-    /**
-     * Returns the specified, existing signature, or the only signature if none is specified.
-     * Throws IllegalArgumentException in all other cases.
-     */
-    private ImportedModel.Signature chooseSignature(ImportedModel importResult, Optional<String> signatureName) {
-        if ( ! signatureName.isPresent()) {
-            if (importResult.signatures().size() == 0)
-                throw new IllegalArgumentException("No signatures are available");
-            if (importResult.signatures().size() > 1)
-                throw new IllegalArgumentException("Model has multiple signatures (" +
-                        Joiner.on(", ").join(importResult.signatures().keySet()) +
-                        "), one must be specified " +
-                        "as a second argument to tensorflow()");
-            return importResult.signatures().values().stream().findFirst().get();
-        }
-        else {
-            ImportedModel.Signature signature = importResult.signatures().get(signatureName.get());
-            if (signature == null)
-                throw new IllegalArgumentException("Model does not have the specified signature '" +
-                        signatureName.get() + "'");
-            return signature;
-        }
-    }
-
-    /**
-     * Returns the specified, existing output expression, or the only output expression if no output name is specified.
-     * Throws IllegalArgumentException in all other cases.
-     */
-    private String chooseOutput(ImportedModel.Signature signature, Optional<String> outputName) {
-        if ( ! outputName.isPresent()) {
-            if (signature.outputs().size() == 0)
-                throw new IllegalArgumentException("No outputs are available" + skippedOutputsDescription(signature));
-            if (signature.outputs().size() > 1)
-                throw new IllegalArgumentException(signature + " has multiple outputs (" +
-                        Joiner.on(", ").join(signature.outputs().keySet()) +
-                        "), one must be specified " +
-                        "as a third argument to tensorflow()");
-            return signature.outputs().get(signature.outputs().keySet().stream().findFirst().get());
-        }
-        else {
-            String output = signature.outputs().get(outputName.get());
-            if (output == null) {
-                if (signature.skippedOutputs().containsKey(outputName.get()))
-                    throw new IllegalArgumentException("Could not use output '" + outputName.get() + "': " +
-                            signature.skippedOutputs().get(outputName.get()));
-                else
-                    throw new IllegalArgumentException("Model does not have the specified output '" +
-                            outputName.get() + "'");
-            }
-            return output;
-        }
-    }
-
-    private void transformSmallConstant(ModelStore store, RankProfile profile, String constantName, Tensor constantValue) {
-        store.writeSmallConstant(constantName, constantValue);
-        profile.addConstant(constantName, asValue(constantValue));
-    }
-
-    private void transformLargeConstant(ModelStore store, RankProfile profile, QueryProfileRegistry queryProfiles,
-                                        Set<String> constantsReplacedByMacros,
-                                        String constantName, Tensor constantValue) {
-        RankProfile.Macro macroOverridingConstant = profile.getMacros().get(constantName);
-        if (macroOverridingConstant != null) {
-            TensorType macroType = macroOverridingConstant.getRankingExpression().type(profile.typeContext(queryProfiles));
-            if ( ! macroType.equals(constantValue.type()))
-                throw new IllegalArgumentException("Macro '" + constantName + "' replaces the constant with this name. " +
-                        typeMismatchExplanation(constantValue.type(), macroType));
-            constantsReplacedByMacros.add(constantName); // will replace constant(constantName) by constantName later
-        }
-        else {
-            Path constantPath = store.writeLargeConstant(constantName, constantValue);
-            if ( ! profile.getSearch().getRankingConstants().containsKey(constantName)) {
-                profile.getSearch().addRankingConstant(new RankingConstant(constantName, constantValue.type(),
-                        constantPath.toString()));
-            }
-        }
-    }
-
-    private void transformGeneratedMacro(ModelStore store,
-                                         Set<String> constantsReplacedByMacros,
-                                         String macroName, RankingExpression expression) {
-
-        expression = replaceConstantsByMacros(expression, constantsReplacedByMacros);
-        store.writeMacro(macroName, expression);
-    }
-
-    private void addGeneratedMacroToProfile(RankProfile profile, String macroName, RankingExpression expression) {
-        if (profile.getMacros().containsKey(macroName)) {
-            throw new IllegalArgumentException("Generated macro '" + macroName + "' already exists.");
-        }
-        profile.addMacro(macroName, false);  // todo: inline if only used once
-        RankProfile.Macro macro = profile.getMacros().get(macroName);
-        macro.setRankingExpression(expression);
-        macro.setTextualExpression(expression.getRoot().toString());
-    }
-
-    private String skippedOutputsDescription(ImportedModel.Signature signature) {
-        if (signature.skippedOutputs().isEmpty()) return "";
-        StringBuilder b = new StringBuilder(": ");
-        signature.skippedOutputs().forEach((k, v) -> b.append("Skipping output '").append(k).append("': ").append(v));
-        return b.toString();
-    }
-
-    /**
-     * Verify that the macros referred in the given expression exists in the given rank profile,
-     * and return tensors of the types specified in requiredMacros.
-     */
-    private void verifyRequiredMacros(RankingExpression expression, ImportedModel model,
-                                      RankProfile profile, QueryProfileRegistry queryProfiles) {
-        Set<String> macroNames = new HashSet<>();
-        addMacroNamesIn(expression.getRoot(), macroNames, model);
-        for (String macroName : macroNames) {
-            TensorType requiredType = model.requiredMacros().get(macroName);
-            if (requiredType == null) continue; // Not a required macro
-
-            RankProfile.Macro macro = profile.getMacros().get(macroName);
-            if (macro == null)
-                throw new IllegalArgumentException("Model refers input '" + macroName +
-                        "' of type " + requiredType + " but this macro is not present in " +
-                        profile);
-            // TODO: We should verify this in the (function reference(s) this is invoked (starting from first/second
-            // phase and summary features), as it may only resolve correctly given those bindings
-            // Or, probably better, annotate the macros with type constraints here and verify during general
-            // type verification
-            TensorType actualType = macro.getRankingExpression().getRoot().type(profile.typeContext(queryProfiles));
-            if ( actualType == null)
-                throw new IllegalArgumentException("Model refers input '" + macroName +
-                        "' of type " + requiredType +
-                        " which must be produced by a macro in the rank profile, but " +
-                        "this macro references a feature which is not declared");
-            if ( ! actualType.isAssignableTo(requiredType))
-                throw new IllegalArgumentException("Model refers input '" + macroName + "'. " +
-                        typeMismatchExplanation(requiredType, actualType));
-        }
-    }
-
-    private String typeMismatchExplanation(TensorType requiredType, TensorType actualType) {
-        return "The required type of this is " + requiredType + ", but this macro returns " + actualType +
-                (actualType.rank() == 0 ? ". This is often due to missing declaration of query tensor features " +
-                        "in query profile types - see the documentation."
-                        : "");
-    }
-
-    /**
-     * Add the generated macros to the rank profile
-     */
-    private void addGeneratedMacros(ImportedModel model, RankProfile profile) {
-        model.macros().forEach((k, v) -> addGeneratedMacroToProfile(profile, k, v));
-    }
-
-    /**
-     * Check if batch dimensions of inputs can be reduced out. If the input
-     * macro specifies that a single exemplar should be evaluated, we can
-     * reduce the batch dimension out.
-     */
-    private void reduceBatchDimensions(RankingExpression expression, ImportedModel model,
-                                       RankProfile profile, QueryProfileRegistry queryProfiles) {
-        TypeContext<Reference> typeContext = profile.typeContext(queryProfiles);
-        TensorType typeBeforeReducing = expression.getRoot().type(typeContext);
-
-        // Check generated macros for inputs to reduce
-        Set<String> macroNames = new HashSet<>();
-        addMacroNamesIn(expression.getRoot(), macroNames, model);
-        for (String macroName : macroNames) {
-            if ( ! model.macros().containsKey(macroName)) {
-                continue;
-            }
-            RankProfile.Macro macro = profile.getMacros().get(macroName);
-            if (macro == null) {
-                throw new IllegalArgumentException("Model refers to generated macro '" + macroName +
-                        "but this macro is not present in " + profile);
-            }
-            RankingExpression macroExpression = macro.getRankingExpression();
-            macroExpression.setRoot(reduceBatchDimensionsAtInput(macroExpression.getRoot(), model, typeContext));
-        }
-
-        // Check expression for inputs to reduce
-        ExpressionNode root = expression.getRoot();
-        root = reduceBatchDimensionsAtInput(root, model, typeContext);
-        TensorType typeAfterReducing = root.type(typeContext);
-        root = expandBatchDimensionsAtOutput(root, typeBeforeReducing, typeAfterReducing);
-        expression.setRoot(root);
-    }
-
-    private ExpressionNode reduceBatchDimensionsAtInput(ExpressionNode node, ImportedModel model,
-                                                        TypeContext<Reference> typeContext) {
-        if (node instanceof TensorFunctionNode) {
-            TensorFunction tensorFunction = ((TensorFunctionNode) node).function();
-            if (tensorFunction instanceof Rename) {
-                List<ExpressionNode> children = ((TensorFunctionNode)node).children();
-                if (children.size() == 1 && children.get(0) instanceof ReferenceNode) {
-                    ReferenceNode referenceNode = (ReferenceNode) children.get(0);
-                    if (model.requiredMacros().containsKey(referenceNode.getName())) {
-                        return reduceBatchDimensionExpression(tensorFunction, typeContext);
-                    }
-                }
-            }
-        }
-        if (node instanceof ReferenceNode) {
-            ReferenceNode referenceNode = (ReferenceNode) node;
-            if (model.requiredMacros().containsKey(referenceNode.getName())) {
-                return reduceBatchDimensionExpression(TensorFunctionNode.wrapArgument(node), typeContext);
-            }
-        }
-        if (node instanceof CompositeNode) {
-            List<ExpressionNode> children = ((CompositeNode)node).children();
-            List<ExpressionNode> transformedChildren = new ArrayList<>(children.size());
-            for (ExpressionNode child : children) {
-                transformedChildren.add(reduceBatchDimensionsAtInput(child, model, typeContext));
-            }
-            return ((CompositeNode)node).setChildren(transformedChildren);
-        }
-        return node;
-    }
-
-    private ExpressionNode reduceBatchDimensionExpression(TensorFunction function, TypeContext<Reference> context) {
-        TensorFunction result = function;
-        TensorType type = function.type(context);
-        if (type.dimensions().size() > 1) {
-            List<String> reduceDimensions = new ArrayList<>();
-            for (TensorType.Dimension dimension : type.dimensions()) {
-                if (dimension.size().orElse(-1L) == 1) {
-                    reduceDimensions.add(dimension.name());
-                }
-            }
-            if (reduceDimensions.size() > 0) {
-                result = new Reduce(function, Reduce.Aggregator.sum, reduceDimensions);
-            }
-        }
-        return new TensorFunctionNode(result);
-    }
-
-    /**
-     * If batch dimensions have been reduced away above, bring them back here
-     * for any following computation of the tensor.
-     * Todo: determine when this is not necessary!
-     */
-    private ExpressionNode expandBatchDimensionsAtOutput(ExpressionNode node, TensorType before, TensorType after) {
-        if (after.equals(before)) {
-            return node;
-        }
-        TensorType.Builder typeBuilder = new TensorType.Builder();
-        for (TensorType.Dimension dimension : before.dimensions()) {
-            if (dimension.size().orElse(-1L) == 1 && !after.dimensionNames().contains(dimension.name())) {
-                typeBuilder.indexed(dimension.name(), 1);
-            }
-        }
-        TensorType expandDimensionsType = typeBuilder.build();
-        if (expandDimensionsType.dimensions().size() > 0) {
-            ExpressionNode generatedExpression = new ConstantNode(new DoubleValue(1.0));
-            Generate generatedFunction = new Generate(expandDimensionsType,
-                    new GeneratorLambdaFunctionNode(expandDimensionsType,
-                            generatedExpression)
-                            .asLongListToDoubleOperator());
-            Join expand = new Join(TensorFunctionNode.wrapArgument(node), generatedFunction, ScalarFunctions.multiply());
-            return new TensorFunctionNode(expand);
-        }
-        return node;
-    }
-
-    /**
-     * If a constant c is overridden by a macro, we need to replace instances of "constant(c)" by "c" in expressions.
-     * This method does that for the given expression and returns the result.
-     */
-    private RankingExpression replaceConstantsByMacros(RankingExpression expression,
-                                                       Set<String> constantsReplacedByMacros) {
-        if (constantsReplacedByMacros.isEmpty()) return expression;
-        return new RankingExpression(expression.getName(),
-                replaceConstantsByMacros(expression.getRoot(), constantsReplacedByMacros));
-    }
-
-    private ExpressionNode replaceConstantsByMacros(ExpressionNode node, Set<String> constantsReplacedByMacros) {
-        if (node instanceof ReferenceNode) {
-            Reference reference = ((ReferenceNode)node).reference();
-            if (FeatureNames.isSimpleFeature(reference) && reference.name().equals("constant")) {
-                String argument = reference.simpleArgument().get();
-                if (constantsReplacedByMacros.contains(argument))
-                    return new ReferenceNode(argument);
-            }
-        }
-        if (node instanceof CompositeNode) { // not else: this matches some of the same nodes as the outer if above
-            CompositeNode composite = (CompositeNode)node;
-            return composite.setChildren(composite.children().stream()
-                    .map(child -> replaceConstantsByMacros(child, constantsReplacedByMacros))
-                    .collect(Collectors.toList()));
-        }
-        return node;
-    }
-
-    private void addMacroNamesIn(ExpressionNode node, Set<String> names, ImportedModel model) {
-        if (node instanceof ReferenceNode) {
-            ReferenceNode referenceNode = (ReferenceNode)node;
-            if (referenceNode.getOutput() == null) { // macro references cannot specify outputs
-                names.add(referenceNode.getName());
-                if (model.macros().containsKey(referenceNode.getName())) {
-                    addMacroNamesIn(model.macros().get(referenceNode.getName()).getRoot(), names, model);
-                }
-            }
-        }
-        else if (node instanceof CompositeNode) {
-            for (ExpressionNode child : ((CompositeNode)node).children())
-                addMacroNamesIn(child, names, model);
-        }
-    }
-
-    private Value asValue(Tensor tensor) {
-        if (tensor.type().rank() == 0)
-            return new DoubleValue(tensor.asDouble()); // the backend gets offended by dimensionless tensors
-        else
-            return new TensorValue(tensor);
-    }
-
-    /**
-     * Provides read/write access to the correct directories of the application package given by the feature arguments
-     */
-    static class ModelStore {
-
-        private final ApplicationPackage application;
-        private final FeatureArguments arguments;
-
-        ModelStore(ApplicationPackage application, FeatureArguments arguments) {
-            this.application = application;
-            this.arguments = arguments;
-        }
-
-        public FeatureArguments arguments() { return arguments; }
-
-        public boolean hasStoredModel() {
-            try {
-                return application.getFile(arguments.expressionPath()).exists();
-            }
-            catch (UnsupportedOperationException e) {
-                return false;
-            }
-        }
-
-        /**
-         * Returns the directory which contains the source model to use for these arguments
-         */
-        public File modelDir() {
-            return application.getFileReference(ApplicationPackage.MODELS_DIR.append(arguments.modelPath()));
-        }
-
-        /**
-         * Adds this expression to the application package, such that it can be read later.
-         */
-        void writeConverted(RankingExpression expression) {
-            application.getFile(arguments.expressionPath())
-                    .writeFile(new StringReader(expression.getRoot().toString()));
-        }
-
-        /** Reads the previously stored ranking expression for these arguments */
-        RankingExpression readConverted() {
-            try {
-                return new RankingExpression(application.getFile(arguments.expressionPath()).createReader());
-            }
-            catch (IOException e) {
-                throw new UncheckedIOException("Could not read " + arguments.expressionPath(), e);
-            }
-            catch (ParseException e) {
-                throw new IllegalStateException("Could not parse " + arguments.expressionPath(), e);
-            }
-        }
-
-        /** Adds this macro expression to the application package to it can be read later. */
-        void writeMacro(String name, RankingExpression expression) {
-            application.getFile(arguments.macrosPath()).appendFile(name + "\t" +
-                    expression.getRoot().toString() + "\n");
-        }
-
-        /** Reads the previously stored macro expressions for these arguments */
-        List<Pair<String, RankingExpression>> readMacros() {
-            try {
-                ApplicationFile file = application.getFile(arguments.macrosPath());
-                if (!file.exists()) return Collections.emptyList();
-
-                List<Pair<String, RankingExpression>> macros = new ArrayList<>();
-                BufferedReader reader = new BufferedReader(file.createReader());
-                String line;
-                while (null != (line = reader.readLine())) {
-                    String[] parts = line.split("\t");
-                    String name = parts[0];
-                    try {
-                        RankingExpression expression = new RankingExpression(parts[1]);
-                        macros.add(new Pair<>(name, expression));
-                    }
-                    catch (ParseException e) {
-                        throw new IllegalStateException("Could not parse " + arguments.expressionPath(), e);
-                    }
-                }
-                return macros;
-            }
-            catch (IOException e) {
-                throw new UncheckedIOException(e);
-            }
-        }
-
-        /**
-         * Reads the information about all the large (aka ranking) constants stored in the application package
-         * (the constant value itself is replicated with file distribution).
-         */
-        List<RankingConstant> readLargeConstants() {
-            try {
-                List<RankingConstant> constants = new ArrayList<>();
-                for (ApplicationFile constantFile : application.getFile(arguments.largeConstantsPath()).listFiles()) {
-                    String[] parts = IOUtils.readAll(constantFile.createReader()).split(":");
-                    constants.add(new RankingConstant(parts[0], TensorType.fromSpec(parts[1]), parts[2]));
-                }
-                return constants;
-            }
-            catch (IOException e) {
-                throw new UncheckedIOException(e);
-            }
-        }
-
-        /**
-         * Adds this constant to the application package as a file,
-         * such that it can be distributed using file distribution.
-         *
-         * @return the path to the stored constant, relative to the application package root
-         */
-        Path writeLargeConstant(String name, Tensor constant) {
-            Path constantsPath = ApplicationPackage.MODELS_GENERATED_DIR.append(arguments.modelPath).append("constants");
-
-            // "tbf" ending for "typed binary format" - recognized by the nodes receiving the file:
-            Path constantPath = constantsPath.append(name + ".tbf");
-
-            // Remember the constant in a file we replicate in ZooKeeper
-            application.getFile(arguments.largeConstantsPath().append(name + ".constant"))
-                    .writeFile(new StringReader(name + ":" + constant.type() + ":" + correct(constantPath)));
-
-            // Write content explicitly as a file on the file system as this is distributed using file distribution
-            createIfNeeded(constantsPath);
-            IOUtils.writeFile(application.getFileReference(constantPath), TypedBinaryFormat.encode(constant));
-            return correct(constantPath);
-        }
-
-        private List<Pair<String, Tensor>> readSmallConstants() {
-            try {
-                ApplicationFile file = application.getFile(arguments.smallConstantsPath());
-                if (!file.exists()) return Collections.emptyList();
-
-                List<Pair<String, Tensor>> constants = new ArrayList<>();
-                BufferedReader reader = new BufferedReader(file.createReader());
-                String line;
-                while (null != (line = reader.readLine())) {
-                    String[] parts = line.split("\t");
-                    String name = parts[0];
-                    TensorType type = TensorType.fromSpec(parts[1]);
-                    Tensor tensor = Tensor.from(type, parts[2]);
-                    constants.add(new Pair<>(name, tensor));
-                }
-                return constants;
-            }
-            catch (IOException e) {
-                throw new UncheckedIOException(e);
-            }
-        }
-
-        /**
-         * Append this constant to the single file used for small constants distributed as config
-         */
-        public void writeSmallConstant(String name, Tensor constant) {
-            // Secret file format for remembering constants:
-            application.getFile(arguments.smallConstantsPath()).appendFile(name + "\t" +
-                    constant.type().toString() + "\t" +
-                    constant.toString() + "\n");
-        }
-
-        /** Workaround for being constructed with the .preprocessed dir as root while later being used outside it */
-        private Path correct(Path path) {
-            if (application.getFileReference(Path.fromString("")).getAbsolutePath().endsWith(FilesApplicationPackage.preprocessed)
-                    && ! path.elements().contains(FilesApplicationPackage.preprocessed)) {
-                return Path.fromString(FilesApplicationPackage.preprocessed).append(path);
-            }
-            else {
-                return path;
-            }
-        }
-
-        private void createIfNeeded(Path path) {
-            File dir = application.getFileReference(path);
-            if ( ! dir.exists()) {
-                if (!dir.mkdirs())
-                    throw new IllegalStateException("Could not create " + dir);
-            }
-        }
-
-    }
-
-    /** Encapsulates the arguments to the import feature */
-    static abstract class FeatureArguments {
-
-        Path modelPath;
-
-        /** Optional arguments */
-        Optional<String> signature, output;
-
-        /** Returns modelPath with slashes replaced by underscores */
-        public String modelName() { return modelPath.toString().replace('/', '_').replace('.', '_'); }
-
-        /** Returns relative path to this model below the "models/" dir in the application package */
-        public Path modelPath() { return modelPath; }
-        public Optional<String> signature() { return signature; }
-        public Optional<String> output() { return output; }
-
-        /** Path to the small constants file */
-        public Path smallConstantsPath() {
-            return ApplicationPackage.MODELS_GENERATED_DIR.append(modelPath).append("constants.txt");
-        }
-
-        /** Path to the large (ranking) constants directory */
-        public Path largeConstantsPath() {
-            return ApplicationPackage.MODELS_GENERATED_REPLICATED_DIR.append(modelPath).append("constants");
-        }
-
-        /** Path to the macros file */
-        public Path macrosPath() {
-            return ApplicationPackage.MODELS_GENERATED_REPLICATED_DIR.append(modelPath).append("macros.txt");
-        }
-
-        public Path expressionPath() {
-            return ApplicationPackage.MODELS_GENERATED_REPLICATED_DIR
-                    .append(modelPath).append("expressions").append(expressionFileName());
-        }
-
-        private String expressionFileName() {
-            StringBuilder fileName = new StringBuilder();
-            signature.ifPresent(s -> fileName.append(s).append("."));
-            output.ifPresent(s -> fileName.append(s).append("."));
-            if (fileName.length() == 0) // single signature and output
-                fileName.append("single.");
-            fileName.append("expression");
-            return fileName.toString();
-        }
-
-        Optional<String> optionalArgument(int argumentIndex, Arguments arguments) {
-            if (argumentIndex >= arguments.expressions().size())
-                return Optional.empty();
-            return Optional.of(asString(arguments.expressions().get(argumentIndex)));
-        }
-
-        String asString(ExpressionNode node) {
-            if ( ! (node instanceof ConstantNode))
-                throw new IllegalArgumentException("Expected a constant string as argument, but got '" + node);
-            return stripQuotes(((ConstantNode)node).sourceString());
-        }
-
-        private String stripQuotes(String s) {
-            if ( ! isQuoteSign(s.codePointAt(0))) return s;
-            if ( ! isQuoteSign(s.codePointAt(s.length() - 1 )))
-                throw new IllegalArgumentException("argument [" + s + "] is missing endquote");
-            return s.substring(1, s.length()-1);
-        }
-
-        private boolean isQuoteSign(int c) {
-            return c == '\'' || c == '"';
-        }
-
-    }
-}
diff --git a/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/OnnxFeatureConverter.java b/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/OnnxFeatureConverter.java
index 44eeb364603..1c41ad8284e 100644
--- a/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/OnnxFeatureConverter.java
+++ b/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/OnnxFeatureConverter.java
@@ -2,20 +2,58 @@
 
 package com.yahoo.searchdefinition.expressiontransforms;
 
+import com.google.common.base.Joiner;
+import com.yahoo.collections.Pair;
+import com.yahoo.config.application.api.ApplicationFile;
+import com.yahoo.config.application.api.ApplicationPackage;
+import com.yahoo.config.model.application.provider.FilesApplicationPackage;
+import com.yahoo.io.IOUtils;
 import com.yahoo.path.Path;
 import com.yahoo.search.query.profile.QueryProfileRegistry;
+import com.yahoo.searchdefinition.FeatureNames;
 import com.yahoo.searchdefinition.RankProfile;
-import com.yahoo.searchlib.rankingexpression.integration.ml.ImportedModel;
-import com.yahoo.searchlib.rankingexpression.integration.ml.OnnxImporter;
+import com.yahoo.searchdefinition.RankingConstant;
+import com.yahoo.searchlib.rankingexpression.RankingExpression;
+import com.yahoo.searchlib.rankingexpression.Reference;
+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.onnx.OnnxImporter;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.OnnxModel;
+import com.yahoo.searchlib.rankingexpression.parser.ParseException;
 import com.yahoo.searchlib.rankingexpression.rule.Arguments;
 import com.yahoo.searchlib.rankingexpression.rule.CompositeNode;
+import com.yahoo.searchlib.rankingexpression.rule.ConstantNode;
 import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
+import com.yahoo.searchlib.rankingexpression.rule.GeneratorLambdaFunctionNode;
 import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
+import com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode;
+import com.yahoo.searchlib.rankingexpression.transform.ExpressionTransformer;
+import com.yahoo.tensor.Tensor;
+import com.yahoo.tensor.TensorType;
+import com.yahoo.tensor.evaluation.TypeContext;
+import com.yahoo.tensor.functions.Generate;
+import com.yahoo.tensor.functions.Join;
+import com.yahoo.tensor.functions.Reduce;
+import com.yahoo.tensor.functions.Rename;
+import com.yahoo.tensor.functions.ScalarFunctions;
+import com.yahoo.tensor.functions.TensorFunction;
+import com.yahoo.tensor.serialization.TypedBinaryFormat;
 
+import java.io.BufferedReader;
+import java.io.File;
+import java.io.IOException;
+import java.io.StringReader;
 import java.io.UncheckedIOException;
+import java.util.ArrayList;
+import java.util.Collections;
 import java.util.HashMap;
+import java.util.HashSet;
+import java.util.List;
 import java.util.Map;
 import java.util.Optional;
+import java.util.Set;
+import java.util.stream.Collectors;
 
 /**
  * Replaces instances of the onnx(model-path, output)
@@ -25,12 +63,12 @@ import java.util.Optional;
  * @author bratseth
  * @author lesters
  */
-public class OnnxFeatureConverter extends MLImportFeatureConverter {
+public class OnnxFeatureConverter extends ExpressionTransformer<RankProfileTransformContext> {
 
     private final OnnxImporter onnxImporter = new OnnxImporter();
 
     /** A cache of imported models indexed by model path. This avoids importing the same model multiple times. */
-    private final Map<Path, ImportedModel> importedModels = new HashMap<>();
+    private final Map<Path, OnnxModel> importedModels = new HashMap<>();
 
     @Override
     public ExpressionNode transform(ExpressionNode node, RankProfileTransformContext context) {
@@ -46,8 +84,7 @@ public class OnnxFeatureConverter extends MLImportFeatureConverter {
         if ( ! feature.getName().equals("onnx")) return feature;
 
         try {
-            FeatureArguments arguments = new OnnxFeatureArguments(feature.getArguments());
-            ModelStore store = new ModelStore(context.rankProfile().getSearch().sourceApplication(), arguments);
+            ModelStore store = new ModelStore(context.rankProfile().getSearch().sourceApplication(), feature.getArguments());
             if ( ! store.hasStoredModel()) // not converted yet - access Onnx model files
                 return transformFromOnnxModel(store, context.rankProfile(), context.queryProfiles());
             else
@@ -61,24 +98,597 @@ public class OnnxFeatureConverter extends MLImportFeatureConverter {
     private ExpressionNode transformFromOnnxModel(ModelStore store,
                                                   RankProfile profile,
                                                   QueryProfileRegistry queryProfiles) {
-        ImportedModel model = importedModels.computeIfAbsent(store.arguments().modelPath(),
+        OnnxModel model = importedModels.computeIfAbsent(store.arguments().modelPath(),
                                                          k -> onnxImporter.importModel(store.arguments().modelName(),
-                                                                                       store.modelDir()));
-        return transformFromImportedModel(model, store, profile, queryProfiles);
+                                                                                       store.onnxModelDir()));
+
+        // Add constants
+        Set<String> constantsReplacedByMacros = new HashSet<>();
+        model.smallConstants().forEach((k, v) -> transformSmallConstant(store, profile, k, v));
+        model.largeConstants().forEach((k, v) -> transformLargeConstant(store, profile, queryProfiles,
+                                                                        constantsReplacedByMacros, k, v));
+
+        // Find the specified expression
+        String output = chooseOutput(model, store.arguments().output());
+        if (model.skippedOutputs().containsKey(output)) {
+            String message = "Could not import Onnx model output '" + output + "'";
+            if (!model.skippedOutputs().get(output).isEmpty()) {
+                message += ": " + model.skippedOutputs().get(output);
+            }
+            if (!model.importWarnings().isEmpty()) {
+                message += ": "  + String.join(", ", model.importWarnings());
+            }
+            throw new IllegalArgumentException(message);
+        }
+
+        RankingExpression expression = model.expressions().get(output);
+        expression = replaceConstantsByMacros(expression, constantsReplacedByMacros);
+        verifyRequiredMacros(expression, model, profile, queryProfiles);
+        addGeneratedMacros(model, profile);
+        reduceBatchDimensions(expression, model, profile, queryProfiles);
+
+        model.macros().forEach((k, v) -> transformGeneratedMacro(store, profile, constantsReplacedByMacros, k, v));
+
+        store.writeConverted(expression);
+        return expression.getRoot();
+    }
+
+    private ExpressionNode transformFromStoredModel(ModelStore store, RankProfile profile) {
+        for (Pair<String, Tensor> constant : store.readSmallConstants())
+            profile.addConstant(constant.getFirst(), asValue(constant.getSecond()));
+
+        for (RankingConstant constant : store.readLargeConstants()) {
+            if ( ! profile.getSearch().getRankingConstants().containsKey(constant.getName()))
+                profile.getSearch().addRankingConstant(constant);
+        }
+
+        for (Pair<String, RankingExpression> macro : store.readMacros()) {
+            addGeneratedMacroToProfile(profile, macro.getFirst(), macro.getSecond());
+        }
+
+        return store.readConverted().getRoot();
+    }
+
+    /**
+     * Returns the specified, existing output expression, or the only output expression if no output name is specified.
+     * Throws IllegalArgumentException in all other cases.
+     */
+    private String chooseOutput(OnnxModel model, Optional<String> outputName) {
+        if ( ! outputName.isPresent()) {
+            if (model.outputs().size() == 0)
+                throw new IllegalArgumentException("No outputs are available" + skippedOutputsDescription(model));
+            if (model.outputs().size() > 1)
+                throw new IllegalArgumentException("Onnx model has multiple outputs (" +
+                                                   Joiner.on(", ").join(model.outputs().keySet()) +
+                                                   "), one must be specified " +
+                                                   "as a second argument to onnx()");
+            return model.outputs().get(model.outputs().keySet().stream().findFirst().get());
+        }
+        else {
+            String output = model.outputs().get(outputName.get());
+            if (output == null) {
+                if (model.skippedOutputs().containsKey(outputName.get()))
+                    throw new IllegalArgumentException("Could not use output '" + outputName.get() + "': " +
+                                                       model.skippedOutputs().get(outputName.get()));
+                else
+                    throw new IllegalArgumentException("Model does not have the specified output '" +
+                                                       outputName.get() + "'");
+            }
+            return output;
+        }
     }
 
-    static class OnnxFeatureArguments extends FeatureArguments {
-        public OnnxFeatureArguments(Arguments arguments) {
+    private void transformSmallConstant(ModelStore store, RankProfile profile, String constantName, Tensor constantValue) {
+        store.writeSmallConstant(constantName, constantValue);
+        profile.addConstant(constantName, asValue(constantValue));
+    }
+
+    private void transformLargeConstant(ModelStore store, RankProfile profile, QueryProfileRegistry queryProfiles,
+                                        Set<String> constantsReplacedByMacros,
+                                        String constantName, Tensor constantValue) {
+        RankProfile.Macro macroOverridingConstant = profile.getMacros().get(constantName);
+        if (macroOverridingConstant != null) {
+            TensorType macroType = macroOverridingConstant.getRankingExpression().type(profile.typeContext(queryProfiles));
+            if ( ! macroType.equals(constantValue.type()))
+                throw new IllegalArgumentException("Macro '" + constantName + "' replaces the constant with this name. " +
+                                                   "The required type of this is " + constantValue.type() +
+                                                   ", but the macro returns " + macroType);
+            constantsReplacedByMacros.add(constantName); // will replace constant(constantName) by constantName later
+        }
+        else {
+            Path constantPath = store.writeLargeConstant(constantName, constantValue);
+            if ( ! profile.getSearch().getRankingConstants().containsKey(constantName)) {
+                profile.getSearch().addRankingConstant(new RankingConstant(constantName, constantValue.type(),
+                                                                           constantPath.toString()));
+            }
+        }
+    }
+
+    private void transformGeneratedMacro(ModelStore store, RankProfile profile,
+                                         Set<String> constantsReplacedByMacros,
+                                         String macroName, RankingExpression expression) {
+
+        expression = replaceConstantsByMacros(expression, constantsReplacedByMacros);
+        store.writeMacro(macroName, expression);
+    }
+
+    private void addGeneratedMacroToProfile(RankProfile profile, String macroName, RankingExpression expression) {
+        if (profile.getMacros().containsKey(macroName)) {
+            throw new IllegalArgumentException("Generated Onnx macro '" + macroName + "' already exists.");
+        }
+        profile.addMacro(macroName, false);  // todo: inline if only used once
+        RankProfile.Macro macro = profile.getMacros().get(macroName);
+        macro.setRankingExpression(expression);
+        macro.setTextualExpression(expression.getRoot().toString());
+    }
+
+    private String skippedOutputsDescription(OnnxModel model) {
+        if (model.skippedOutputs().isEmpty()) return "";
+        StringBuilder b = new StringBuilder(": ");
+        model.skippedOutputs().forEach((k, v) -> b.append("Skipping output '").append(k).append("': ").append(v));
+        return b.toString();
+    }
+
+    /**
+     * Verify that the macros referred in the given expression exists in the given rank profile,
+     * and return tensors of the types specified in requiredMacros.
+     */
+    private void verifyRequiredMacros(RankingExpression expression, OnnxModel model,
+                                      RankProfile profile, QueryProfileRegistry queryProfiles) {
+        Set<String> macroNames = new HashSet<>();
+        addMacroNamesIn(expression.getRoot(), macroNames, model);
+        for (String macroName : macroNames) {
+            TensorType requiredType = model.requiredMacros().get(macroName);
+            if (requiredType == null) continue; // Not a required macro
+
+            RankProfile.Macro macro = profile.getMacros().get(macroName);
+            if (macro == null)
+                throw new IllegalArgumentException("Model refers Placeholder '" + macroName +
+                                                   "' of type " + requiredType + " but this macro is not present in " +
+                                                   profile);
+            // TODO: We should verify this in the (function reference(s) this is invoked (starting from first/second
+            // phase and summary features), as it may only resolve correctly given those bindings
+            // Or, probably better, annotate the macros with type constraints here and verify during general
+            // type verification
+            TensorType actualType = macro.getRankingExpression().getRoot().type(profile.typeContext(queryProfiles));
+            if ( actualType == null)
+                throw new IllegalArgumentException("Model refers input '" + macroName +
+                                                   "' of type " + requiredType +
+                                                   " which must be produced by a macro in the rank profile, but " +
+                                                   "this macro references a feature which is not declared");
+            if ( ! actualType.isAssignableTo(requiredType))
+                throw new IllegalArgumentException("Model refers input '" + macroName +
+                                                   "' of type " + requiredType +
+                                                   " which must be produced by a macro in the rank profile, but " +
+                                                   "this macro produces type " + actualType);
+        }
+    }
+
+    /**
+     * Add the generated macros to the rank profile
+     */
+    private void addGeneratedMacros(OnnxModel model, RankProfile profile) {
+        model.macros().forEach((k, v) -> addGeneratedMacroToProfile(profile, k, v));
+    }
+
+    /**
+     * Check if batch dimensions of inputs can be reduced out. If the input
+     * macro specifies that a single exemplar should be evaluated, we can
+     * reduce the batch dimension out.
+     */
+    private void reduceBatchDimensions(RankingExpression expression, OnnxModel model,
+                                       RankProfile profile, QueryProfileRegistry queryProfiles) {
+        TypeContext<Reference> typeContext = profile.typeContext(queryProfiles);
+        TensorType typeBeforeReducing = expression.getRoot().type(typeContext);
+
+        // Check generated macros for inputs to reduce
+        Set<String> macroNames = new HashSet<>();
+        addMacroNamesIn(expression.getRoot(), macroNames, model);
+        for (String macroName : macroNames) {
+            if ( ! model.macros().containsKey(macroName)) {
+                continue;
+            }
+            RankProfile.Macro macro = profile.getMacros().get(macroName);
+            if (macro == null) {
+                throw new IllegalArgumentException("Model refers to generated macro '" + macroName +
+                                                   "but this macro is not present in " + profile);
+            }
+            RankingExpression macroExpression = macro.getRankingExpression();
+            macroExpression.setRoot(reduceBatchDimensionsAtInput(macroExpression.getRoot(), model, typeContext));
+        }
+
+        // Check expression for inputs to reduce
+        ExpressionNode root = expression.getRoot();
+        root = reduceBatchDimensionsAtInput(root, model, typeContext);
+        TensorType typeAfterReducing = root.type(typeContext);
+        root = expandBatchDimensionsAtOutput(root, typeBeforeReducing, typeAfterReducing);
+        expression.setRoot(root);
+    }
+
+    private ExpressionNode reduceBatchDimensionsAtInput(ExpressionNode node, OnnxModel model,
+                                                        TypeContext<Reference> typeContext) {
+        if (node instanceof TensorFunctionNode) {
+            TensorFunction tensorFunction = ((TensorFunctionNode) node).function();
+            if (tensorFunction instanceof Rename) {
+                List<ExpressionNode> children = ((TensorFunctionNode)node).children();
+                if (children.size() == 1 && children.get(0) instanceof ReferenceNode) {
+                    ReferenceNode referenceNode = (ReferenceNode) children.get(0);
+                    if (model.requiredMacros().containsKey(referenceNode.getName())) {
+                        return reduceBatchDimensionExpression(tensorFunction, typeContext);
+                    }
+                }
+            }
+        }
+        if (node instanceof ReferenceNode) {
+            ReferenceNode referenceNode = (ReferenceNode) node;
+            if (model.requiredMacros().containsKey(referenceNode.getName())) {
+                return reduceBatchDimensionExpression(TensorFunctionNode.wrapArgument(node), typeContext);
+            }
+        }
+        if (node instanceof CompositeNode) {
+            List<ExpressionNode> children = ((CompositeNode)node).children();
+            List<ExpressionNode> transformedChildren = new ArrayList<>(children.size());
+            for (ExpressionNode child : children) {
+                transformedChildren.add(reduceBatchDimensionsAtInput(child, model, typeContext));
+            }
+            return ((CompositeNode)node).setChildren(transformedChildren);
+        }
+        return node;
+    }
+
+    private ExpressionNode reduceBatchDimensionExpression(TensorFunction function, TypeContext<Reference> context) {
+        TensorFunction result = function;
+        TensorType type = function.type(context);
+        if (type.dimensions().size() > 1) {
+            List<String> reduceDimensions = new ArrayList<>();
+            for (TensorType.Dimension dimension : type.dimensions()) {
+                if (dimension.size().orElse(-1L) == 1) {
+                    reduceDimensions.add(dimension.name());
+                }
+            }
+            if (reduceDimensions.size() > 0) {
+                result = new Reduce(function, Reduce.Aggregator.sum, reduceDimensions);
+            }
+        }
+        return new TensorFunctionNode(result);
+    }
+
+    /**
+     * If batch dimensions have been reduced away above, bring them back here
+     * for any following computation of the tensor.
+     * Todo: determine when this is not necessary!
+     */
+    private ExpressionNode expandBatchDimensionsAtOutput(ExpressionNode node, TensorType before, TensorType after) {
+        if (after.equals(before)) {
+            return node;
+        }
+        TensorType.Builder typeBuilder = new TensorType.Builder();
+        for (TensorType.Dimension dimension : before.dimensions()) {
+            if (dimension.size().orElse(-1L) == 1 && !after.dimensionNames().contains(dimension.name())) {
+                typeBuilder.indexed(dimension.name(), 1);
+            }
+        }
+        TensorType expandDimensionsType = typeBuilder.build();
+        if (expandDimensionsType.dimensions().size() > 0) {
+            ExpressionNode generatedExpression = new ConstantNode(new DoubleValue(1.0));
+            Generate generatedFunction = new Generate(expandDimensionsType,
+                                                      new GeneratorLambdaFunctionNode(expandDimensionsType,
+                                                                                      generatedExpression)
+                                                              .asLongListToDoubleOperator());
+            Join expand = new Join(TensorFunctionNode.wrapArgument(node), generatedFunction, ScalarFunctions.multiply());
+            return new TensorFunctionNode(expand);
+        }
+        return node;
+    }
+
+    /**
+     * If a constant c is overridden by a macro, we need to replace instances of "constant(c)" by "c" in expressions.
+     * This method does that for the given expression and returns the result.
+     */
+    private RankingExpression replaceConstantsByMacros(RankingExpression expression,
+                                                       Set<String> constantsReplacedByMacros) {
+        if (constantsReplacedByMacros.isEmpty()) return expression;
+        return new RankingExpression(expression.getName(),
+                                     replaceConstantsByMacros(expression.getRoot(), constantsReplacedByMacros));
+    }
+
+    private ExpressionNode replaceConstantsByMacros(ExpressionNode node, Set<String> constantsReplacedByMacros) {
+        if (node instanceof ReferenceNode) {
+            Reference reference = ((ReferenceNode)node).reference();
+            if (FeatureNames.isSimpleFeature(reference) && reference.name().equals("constant")) {
+                String argument = reference.simpleArgument().get();
+                if (constantsReplacedByMacros.contains(argument))
+                    return new ReferenceNode(argument);
+            }
+        }
+        if (node instanceof CompositeNode) { // not else: this matches some of the same nodes as the outer if above
+            CompositeNode composite = (CompositeNode)node;
+            return composite.setChildren(composite.children().stream()
+                                                             .map(child -> replaceConstantsByMacros(child, constantsReplacedByMacros))
+                                                             .collect(Collectors.toList()));
+        }
+        return node;
+    }
+
+    private void addMacroNamesIn(ExpressionNode node, Set<String> names, OnnxModel model) {
+        if (node instanceof ReferenceNode) {
+            ReferenceNode referenceNode = (ReferenceNode)node;
+            if (referenceNode.getOutput() == null) { // macro references cannot specify outputs
+                names.add(referenceNode.getName());
+                if (model.macros().containsKey(referenceNode.getName())) {
+                    addMacroNamesIn(model.macros().get(referenceNode.getName()).getRoot(), names, model);
+                }
+            }
+        }
+        else if (node instanceof CompositeNode) {
+            for (ExpressionNode child : ((CompositeNode)node).children())
+                addMacroNamesIn(child, names, model);
+        }
+    }
+
+    private Value asValue(Tensor tensor) {
+        if (tensor.type().rank() == 0)
+            return new DoubleValue(tensor.asDouble()); // the backend gets offended by dimensionless tensors
+        else
+            return new TensorValue(tensor);
+    }
+
+    /**
+     * Provides read/write access to the correct directories of the application package given by the feature arguments
+     */
+    private static class ModelStore {
+
+        private final ApplicationPackage application;
+        private final FeatureArguments arguments;
+
+        public ModelStore(ApplicationPackage application, Arguments arguments) {
+            this.application = application;
+            this.arguments = new FeatureArguments(arguments);
+        }
+
+        public FeatureArguments arguments() { return arguments; }
+
+        public boolean hasStoredModel() {
+            try {
+                return application.getFile(arguments.expressionPath()).exists();
+            }
+            catch (UnsupportedOperationException e) {
+                return false;
+            }
+        }
+
+        /**
+         * Returns the directory which contains the source model to use for these arguments
+         */
+        public File onnxModelDir() {
+            return application.getFileReference(ApplicationPackage.MODELS_DIR.append(arguments.modelPath()));
+        }
+
+        /**
+         * Adds this expression to the application package, such that it can be read later.
+         */
+        public void writeConverted(RankingExpression expression) {
+            application.getFile(arguments.expressionPath())
+                       .writeFile(new StringReader(expression.getRoot().toString()));
+        }
+
+        /** Reads the previously stored ranking expression for these arguments */
+        public RankingExpression readConverted() {
+            try {
+                return new RankingExpression(application.getFile(arguments.expressionPath()).createReader());
+            }
+            catch (IOException e) {
+                throw new UncheckedIOException("Could not read " + arguments.expressionPath(), e);
+            }
+            catch (ParseException e) {
+                throw new IllegalStateException("Could not parse " + arguments.expressionPath(), e);
+            }
+        }
+
+        /** Adds this macro expression to the application package to it can be read later. */
+        public void writeMacro(String name, RankingExpression expression) {
+            application.getFile(arguments.macrosPath()).appendFile(name + "\t" +
+                    expression.getRoot().toString() + "\n");
+        }
+
+        /** Reads the previously stored macro expressions for these arguments */
+        public List<Pair<String, RankingExpression>> readMacros() {
+            try {
+                ApplicationFile file = application.getFile(arguments.macrosPath());
+                if (!file.exists()) return Collections.emptyList();
+
+                List<Pair<String, RankingExpression>> macros = new ArrayList<>();
+                BufferedReader reader = new BufferedReader(file.createReader());
+                String line;
+                while (null != (line = reader.readLine())) {
+                    String[] parts = line.split("\t");
+                    String name = parts[0];
+                    try {
+                        RankingExpression expression = new RankingExpression(parts[1]);
+                        macros.add(new Pair<>(name, expression));
+                    }
+                    catch (ParseException e) {
+                        throw new IllegalStateException("Could not parse " + arguments.expressionPath(), e);
+                    }
+                }
+                return macros;
+            }
+            catch (IOException e) {
+                throw new UncheckedIOException(e);
+            }
+        }
+
+        /**
+         * Reads the information about all the large (aka ranking) constants stored in the application package
+         * (the constant value itself is replicated with file distribution).
+         */
+        public List<RankingConstant> readLargeConstants() {
+            try {
+                List<RankingConstant> constants = new ArrayList<>();
+                for (ApplicationFile constantFile : application.getFile(arguments.largeConstantsPath()).listFiles()) {
+                    String[] parts = IOUtils.readAll(constantFile.createReader()).split(":");
+                    constants.add(new RankingConstant(parts[0], TensorType.fromSpec(parts[1]), parts[2]));
+                }
+                return constants;
+            }
+            catch (IOException e) {
+                throw new UncheckedIOException(e);
+            }
+        }
+
+        /**
+         * Adds this constant to the application package as a file,
+         * such that it can be distributed using file distribution.
+         *
+         * @return the path to the stored constant, relative to the application package root
+         */
+        public Path writeLargeConstant(String name, Tensor constant) {
+            Path constantsPath = ApplicationPackage.MODELS_GENERATED_DIR.append(arguments.modelPath).append("constants");
+
+            // "tbf" ending for "typed binary format" - recognized by the nodes receiving the file:
+            Path constantPath = constantsPath.append(name + ".tbf");
+
+            // Remember the constant in a file we replicate in ZooKeeper
+            application.getFile(arguments.largeConstantsPath().append(name + ".constant"))
+                       .writeFile(new StringReader(name + ":" + constant.type() + ":" + correct(constantPath)));
+
+            // Write content explicitly as a file on the file system as this is distributed using file distribution
+            createIfNeeded(constantsPath);
+            IOUtils.writeFile(application.getFileReference(constantPath), TypedBinaryFormat.encode(constant));
+            return correct(constantPath);
+        }
+
+        private List<Pair<String, Tensor>> readSmallConstants() {
+            try {
+                ApplicationFile file = application.getFile(arguments.smallConstantsPath());
+                if (!file.exists()) return Collections.emptyList();
+
+                List<Pair<String, Tensor>> constants = new ArrayList<>();
+                BufferedReader reader = new BufferedReader(file.createReader());
+                String line;
+                while (null != (line = reader.readLine())) {
+                    String[] parts = line.split("\t");
+                    String name = parts[0];
+                    TensorType type = TensorType.fromSpec(parts[1]);
+                    Tensor tensor = Tensor.from(type, parts[2]);
+                    constants.add(new Pair<>(name, tensor));
+                }
+                return constants;
+            }
+            catch (IOException e) {
+                throw new UncheckedIOException(e);
+            }
+        }
+
+        /**
+         * Append this constant to the single file used for small constants distributed as config
+         */
+        public void writeSmallConstant(String name, Tensor constant) {
+            // Secret file format for remembering constants:
+            application.getFile(arguments.smallConstantsPath()).appendFile(name + "\t" +
+                                                                           constant.type().toString() + "\t" +
+                                                                           constant.toString() + "\n");
+        }
+
+        /** Workaround for being constructed with the .preprocessed dir as root while later being used outside it */
+        private Path correct(Path path) {
+            if (application.getFileReference(Path.fromString("")).getAbsolutePath().endsWith(FilesApplicationPackage.preprocessed)
+                && ! path.elements().contains(FilesApplicationPackage.preprocessed)) {
+                return Path.fromString(FilesApplicationPackage.preprocessed).append(path);
+            }
+            else {
+                return path;
+            }
+        }
+
+        private void createIfNeeded(Path path) {
+            File dir = application.getFileReference(path);
+            if ( ! dir.exists()) {
+                if (!dir.mkdirs())
+                    throw new IllegalStateException("Could not create " + dir);
+            }
+        }
+
+    }
+
+    /** Encapsulates the 1, 2 or 3 arguments to a onnx feature */
+    private static class FeatureArguments {
+
+        private final Path modelPath;
+
+        /** Optional arguments */
+        private final Optional<String> output;
+
+        public FeatureArguments(Arguments arguments) {
             if (arguments.isEmpty())
                 throw new IllegalArgumentException("An onnx node must take an argument pointing to " +
-                        "the tensorflow model directory under [application]/models");
+                                                   "the onnx model directory under [application]/models");
             if (arguments.expressions().size() > 3)
                 throw new IllegalArgumentException("An onnx feature can have at most 2 arguments");
 
             modelPath = Path.fromString(asString(arguments.expressions().get(0)));
             output = optionalArgument(1, arguments);
-            signature = Optional.of("default");
         }
+
+        /** Returns modelPath with slashes replaced by underscores */
+        public String modelName() { return modelPath.toString().replace('/', '_').replace('.', '_'); }
+
+        /** Returns relative path to this model below the "models/" dir in the application package */
+        public Path modelPath() { return modelPath; }
+        public Optional<String> output() { return output; }
+
+        /** Path to the small constants file */
+        public Path smallConstantsPath() {
+            return ApplicationPackage.MODELS_GENERATED_DIR.append(modelPath).append("constants.txt");
+        }
+
+        /** Path to the large (ranking) constants directory */
+        public Path largeConstantsPath() {
+            return ApplicationPackage.MODELS_GENERATED_REPLICATED_DIR.append(modelPath).append("constants");
+        }
+
+        /** Path to the macros file */
+        public Path macrosPath() {
+            return ApplicationPackage.MODELS_GENERATED_REPLICATED_DIR.append(modelPath).append("macros.txt");
+        }
+
+        public Path expressionPath() {
+            return ApplicationPackage.MODELS_GENERATED_REPLICATED_DIR
+                    .append(modelPath).append("expressions").append(expressionFileName());
+        }
+
+        private String expressionFileName() {
+            StringBuilder fileName = new StringBuilder();
+            output.ifPresent(s -> fileName.append(s).append("."));
+            if (fileName.length() == 0) // single signature and output
+                fileName.append("single.");
+            fileName.append("expression");
+            return fileName.toString();
+        }
+
+        private Optional<String> optionalArgument(int argumentIndex, Arguments arguments) {
+            if (argumentIndex >= arguments.expressions().size())
+                return Optional.empty();
+            return Optional.of(asString(arguments.expressions().get(argumentIndex)));
+        }
+
+        private String asString(ExpressionNode node) {
+            if ( ! (node instanceof ConstantNode))
+                throw new IllegalArgumentException("Expected a constant string as onnx argument, but got '" + node);
+            return stripQuotes(((ConstantNode)node).sourceString());
+        }
+
+        private String stripQuotes(String s) {
+            if ( ! isQuoteSign(s.codePointAt(0))) return s;
+            if ( ! isQuoteSign(s.codePointAt(s.length() - 1 )))
+                throw new IllegalArgumentException("onnx argument [" + s + "] is missing endquote");
+            return s.substring(1, s.length()-1);
+        }
+
+        private boolean isQuoteSign(int c) {
+            return c == '\'' || c == '"';
+        }
+
     }
 
 }
diff --git a/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/TensorFlowFeatureConverter.java b/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/TensorFlowFeatureConverter.java
index 27e1ad51b33..41da32f64c3 100644
--- a/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/TensorFlowFeatureConverter.java
+++ b/config-model/src/main/java/com/yahoo/searchdefinition/expressiontransforms/TensorFlowFeatureConverter.java
@@ -1,19 +1,59 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
 package com.yahoo.searchdefinition.expressiontransforms;
 
+import com.google.common.base.Joiner;
+import com.yahoo.collections.Pair;
+import com.yahoo.config.application.api.ApplicationFile;
+import com.yahoo.config.application.api.ApplicationPackage;
+import com.yahoo.config.model.application.provider.FilesApplicationPackage;
+import com.yahoo.io.IOUtils;
 import com.yahoo.path.Path;
 import com.yahoo.search.query.profile.QueryProfileRegistry;
+import com.yahoo.searchdefinition.FeatureNames;
 import com.yahoo.searchdefinition.RankProfile;
-import com.yahoo.searchlib.rankingexpression.integration.ml.ImportedModel;
-import com.yahoo.searchlib.rankingexpression.integration.ml.TensorFlowImporter;
+import com.yahoo.searchdefinition.RankingConstant;
+import com.yahoo.searchlib.rankingexpression.RankingExpression;
+import com.yahoo.searchlib.rankingexpression.Reference;
+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.tensorflow.TensorFlowImporter;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.TensorFlowModel;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.TensorFlowModel.Signature;
+import com.yahoo.searchlib.rankingexpression.parser.ParseException;
 import com.yahoo.searchlib.rankingexpression.rule.Arguments;
 import com.yahoo.searchlib.rankingexpression.rule.CompositeNode;
+import com.yahoo.searchlib.rankingexpression.rule.ConstantNode;
 import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
+import com.yahoo.searchlib.rankingexpression.rule.GeneratorLambdaFunctionNode;
 import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
+import com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode;
+import com.yahoo.searchlib.rankingexpression.transform.ExpressionTransformer;
+import com.yahoo.tensor.Tensor;
+import com.yahoo.tensor.TensorType;
+import com.yahoo.tensor.evaluation.TypeContext;
+import com.yahoo.tensor.functions.Generate;
+import com.yahoo.tensor.functions.Join;
+import com.yahoo.tensor.functions.Reduce;
+import com.yahoo.tensor.functions.Rename;
+import com.yahoo.tensor.functions.ScalarFunctions;
+import com.yahoo.tensor.functions.TensorFunction;
+import com.yahoo.tensor.serialization.TypedBinaryFormat;
 
+import java.io.BufferedReader;
+import java.io.File;
+import java.io.IOException;
+import java.io.StringReader;
 import java.io.UncheckedIOException;
+import java.util.ArrayList;
+import java.util.Collections;
 import java.util.HashMap;
+import java.util.HashSet;
+import java.util.List;
 import java.util.Map;
+import java.util.Optional;
+import java.util.Set;
+import java.util.stream.Collectors;
 
 /**
  * Replaces instances of the tensorflow(model-path, signature, output)
@@ -22,12 +62,12 @@ import java.util.Map;
  *
  * @author bratseth
  */
-public class TensorFlowFeatureConverter extends MLImportFeatureConverter {
+public class TensorFlowFeatureConverter extends ExpressionTransformer<RankProfileTransformContext> {
 
     private final TensorFlowImporter tensorFlowImporter = new TensorFlowImporter();
 
     /** A cache of imported models indexed by model path. This avoids importing the same model multiple times. */
-    private final Map<Path, ImportedModel> importedModels = new HashMap<>();
+    private final Map<Path, TensorFlowModel> importedModels = new HashMap<>();
 
     @Override
     public ExpressionNode transform(ExpressionNode node, RankProfileTransformContext context) {
@@ -43,8 +83,7 @@ public class TensorFlowFeatureConverter extends MLImportFeatureConverter {
         if ( ! feature.getName().equals("tensorflow")) return feature;
 
         try {
-            FeatureArguments arguments = new TensorFlowFeatureArguments(feature.getArguments());
-            ModelStore store = new ModelStore(context.rankProfile().getSearch().sourceApplication(), arguments);
+            ModelStore store = new ModelStore(context.rankProfile().getSearch().sourceApplication(), feature.getArguments());
             if ( ! store.hasStoredModel()) // not converted yet - access TensorFlow model files
                 return transformFromTensorFlowModel(store, context.rankProfile(), context.queryProfiles());
             else
@@ -56,19 +95,565 @@ public class TensorFlowFeatureConverter extends MLImportFeatureConverter {
     }
 
     private ExpressionNode transformFromTensorFlowModel(ModelStore store,
-                                                          RankProfile profile,
-                                                          QueryProfileRegistry queryProfiles) {
-        ImportedModel model = importedModels.computeIfAbsent(store.arguments().modelPath(),
-                k -> tensorFlowImporter.importModel(store.arguments().modelName(),
-                        store.modelDir()));
-        return transformFromImportedModel(model, store, profile, queryProfiles);
+                                                        RankProfile profile,
+                                                        QueryProfileRegistry queryProfiles) {
+        TensorFlowModel model = importedModels.computeIfAbsent(store.arguments().modelPath(),
+                                                               k -> tensorFlowImporter.importModel(store.arguments().modelName(),
+                                                                                                   store.tensorFlowModelDir()));
+
+        // Add constants
+        Set<String> constantsReplacedByMacros = new HashSet<>();
+        model.smallConstants().forEach((k, v) -> transformSmallConstant(store, profile, k, v));
+        model.largeConstants().forEach((k, v) -> transformLargeConstant(store, profile, queryProfiles,
+                                                                        constantsReplacedByMacros, k, v));
+
+        // Find the specified expression
+        Signature signature = chooseSignature(model, store.arguments().signature());
+        String output = chooseOutput(signature, store.arguments().output());
+        if (signature.skippedOutputs().containsKey(output)) {
+            String message = "Could not import TensorFlow model output '" + output + "'";
+            if (!signature.skippedOutputs().get(output).isEmpty()) {
+                message += ": " + signature.skippedOutputs().get(output);
+            }
+            if (!signature.importWarnings().isEmpty()) {
+                message += ": "  + String.join(", ", signature.importWarnings());
+            }
+            throw new IllegalArgumentException(message);
+        }
+
+        RankingExpression expression = model.expressions().get(output);
+        expression = replaceConstantsByMacros(expression, constantsReplacedByMacros);
+        verifyRequiredMacros(expression, model, profile, queryProfiles);
+        addGeneratedMacros(model, profile);
+        reduceBatchDimensions(expression, model, profile, queryProfiles);
+
+        model.macros().forEach((k, v) -> transformGeneratedMacro(store, constantsReplacedByMacros, k, v));
+
+        store.writeConverted(expression);
+        return expression.getRoot();
+    }
+
+    private ExpressionNode transformFromStoredModel(ModelStore store, RankProfile profile) {
+        for (Pair<String, Tensor> constant : store.readSmallConstants())
+            profile.addConstant(constant.getFirst(), asValue(constant.getSecond()));
+
+        for (RankingConstant constant : store.readLargeConstants()) {
+            if ( ! profile.getSearch().getRankingConstants().containsKey(constant.getName()))
+                profile.getSearch().addRankingConstant(constant);
+        }
+
+        for (Pair<String, RankingExpression> macro : store.readMacros()) {
+            addGeneratedMacroToProfile(profile, macro.getFirst(), macro.getSecond());
+        }
+
+        return store.readConverted().getRoot();
+    }
+
+    /**
+     * Returns the specified, existing signature, or the only signature if none is specified.
+     * Throws IllegalArgumentException in all other cases.
+     */
+    private Signature chooseSignature(TensorFlowModel importResult, Optional<String> signatureName) {
+        if ( ! signatureName.isPresent()) {
+            if (importResult.signatures().size() == 0)
+                throw new IllegalArgumentException("No signatures are available");
+            if (importResult.signatures().size() > 1)
+                throw new IllegalArgumentException("Model has multiple signatures (" +
+                                                   Joiner.on(", ").join(importResult.signatures().keySet()) +
+                                                   "), one must be specified " +
+                                                   "as a second argument to tensorflow()");
+            return importResult.signatures().values().stream().findFirst().get();
+        }
+        else {
+            Signature signature = importResult.signatures().get(signatureName.get());
+            if (signature == null)
+                throw new IllegalArgumentException("Model does not have the specified signature '" +
+                                                   signatureName.get() + "'");
+            return signature;
+        }
+    }
+
+    /**
+     * Returns the specified, existing output expression, or the only output expression if no output name is specified.
+     * Throws IllegalArgumentException in all other cases.
+     */
+    private String chooseOutput(Signature signature, Optional<String> outputName) {
+        if ( ! outputName.isPresent()) {
+            if (signature.outputs().size() == 0)
+                throw new IllegalArgumentException("No outputs are available" + skippedOutputsDescription(signature));
+            if (signature.outputs().size() > 1)
+                throw new IllegalArgumentException(signature + " has multiple outputs (" +
+                                                   Joiner.on(", ").join(signature.outputs().keySet()) +
+                                                   "), one must be specified " +
+                                                   "as a third argument to tensorflow()");
+            return signature.outputs().get(signature.outputs().keySet().stream().findFirst().get());
+        }
+        else {
+            String output = signature.outputs().get(outputName.get());
+            if (output == null) {
+                if (signature.skippedOutputs().containsKey(outputName.get()))
+                    throw new IllegalArgumentException("Could not use output '" + outputName.get() + "': " +
+                                                       signature.skippedOutputs().get(outputName.get()));
+                else
+                    throw new IllegalArgumentException("Model does not have the specified output '" +
+                                                       outputName.get() + "'");
+            }
+            return output;
+        }
+    }
+
+    private void transformSmallConstant(ModelStore store, RankProfile profile, String constantName, Tensor constantValue) {
+        store.writeSmallConstant(constantName, constantValue);
+        profile.addConstant(constantName, asValue(constantValue));
+    }
+
+    private void transformLargeConstant(ModelStore store, RankProfile profile, QueryProfileRegistry queryProfiles,
+                                        Set<String> constantsReplacedByMacros,
+                                        String constantName, Tensor constantValue) {
+        RankProfile.Macro macroOverridingConstant = profile.getMacros().get(constantName);
+        if (macroOverridingConstant != null) {
+            TensorType macroType = macroOverridingConstant.getRankingExpression().type(profile.typeContext(queryProfiles));
+            if ( ! macroType.equals(constantValue.type()))
+                throw new IllegalArgumentException("Macro '" + constantName + "' replaces the constant with this name. " +
+                                                   typeMismatchExplanation(constantValue.type(), macroType));
+            constantsReplacedByMacros.add(constantName); // will replace constant(constantName) by constantName later
+        }
+        else {
+            Path constantPath = store.writeLargeConstant(constantName, constantValue);
+            if ( ! profile.getSearch().getRankingConstants().containsKey(constantName)) {
+                profile.getSearch().addRankingConstant(new RankingConstant(constantName, constantValue.type(),
+                                                                           constantPath.toString()));
+            }
+        }
+    }
+
+    private void transformGeneratedMacro(ModelStore store,
+                                         Set<String> constantsReplacedByMacros,
+                                         String macroName, RankingExpression expression) {
+
+        expression = replaceConstantsByMacros(expression, constantsReplacedByMacros);
+        store.writeMacro(macroName, expression);
+    }
+
+    private void addGeneratedMacroToProfile(RankProfile profile, String macroName, RankingExpression expression) {
+        if (profile.getMacros().containsKey(macroName)) {
+            throw new IllegalArgumentException("Generated TensorFlow macro '" + macroName + "' already exists.");
+        }
+        profile.addMacro(macroName, false);  // todo: inline if only used once
+        RankProfile.Macro macro = profile.getMacros().get(macroName);
+        macro.setRankingExpression(expression);
+        macro.setTextualExpression(expression.getRoot().toString());
+    }
+
+    private String skippedOutputsDescription(TensorFlowModel.Signature signature) {
+        if (signature.skippedOutputs().isEmpty()) return "";
+        StringBuilder b = new StringBuilder(": ");
+        signature.skippedOutputs().forEach((k, v) -> b.append("Skipping output '").append(k).append("': ").append(v));
+        return b.toString();
     }
 
-    static class TensorFlowFeatureArguments extends FeatureArguments {
-        public TensorFlowFeatureArguments(Arguments arguments) {
+    /**
+     * Verify that the macros referred in the given expression exists in the given rank profile,
+     * and return tensors of the types specified in requiredMacros.
+     */
+    private void verifyRequiredMacros(RankingExpression expression, TensorFlowModel model,
+                                      RankProfile profile, QueryProfileRegistry queryProfiles) {
+        Set<String> macroNames = new HashSet<>();
+        addMacroNamesIn(expression.getRoot(), macroNames, model);
+        for (String macroName : macroNames) {
+            TensorType requiredType = model.requiredMacros().get(macroName);
+            if (requiredType == null) continue; // Not a required macro
+
+            RankProfile.Macro macro = profile.getMacros().get(macroName);
+            if (macro == null)
+                throw new IllegalArgumentException("Model refers placeholder '" + macroName +
+                                                   "' of type " + requiredType + " but this macro is not present in " +
+                                                   profile);
+            // TODO: We should verify this in the (function reference(s) this is invoked (starting from first/second
+            // phase and summary features), as it may only resolve correctly given those bindings
+            // Or, probably better, annotate the macros with type constraints here and verify during general
+            // type verification
+            TensorType actualType = macro.getRankingExpression().getRoot().type(profile.typeContext(queryProfiles));
+            if ( actualType == null)
+                throw new IllegalArgumentException("Model refers placeholder '" + macroName +
+                                                   "' of type " + requiredType +
+                                                   " which must be produced by a macro in the rank profile, but " +
+                                                   "this macro references a feature which is not declared");
+            if ( ! actualType.isAssignableTo(requiredType))
+                throw new IllegalArgumentException("Model refers placeholder '" + macroName + "'. " +
+                                                   typeMismatchExplanation(requiredType, actualType));
+        }
+    }
+
+    private String typeMismatchExplanation(TensorType requiredType, TensorType actualType) {
+        return "The required type of this is " + requiredType + ", but this macro returns " + actualType +
+               (actualType.rank() == 0 ? ". This is often due to missing declaration of query tensor features " +
+                                         "in query profile types - see the documentation."
+                                       : "");
+    }
+
+    /**
+     * Add the generated macros to the rank profile
+     */
+    private void addGeneratedMacros(TensorFlowModel model, RankProfile profile) {
+        model.macros().forEach((k, v) -> addGeneratedMacroToProfile(profile, k, v));
+    }
+
+    /**
+     * Check if batch dimensions of inputs can be reduced out. If the input
+     * macro specifies that a single exemplar should be evaluated, we can
+     * reduce the batch dimension out.
+     */
+    private void reduceBatchDimensions(RankingExpression expression, TensorFlowModel model,
+                                       RankProfile profile, QueryProfileRegistry queryProfiles) {
+        TypeContext<Reference> typeContext = profile.typeContext(queryProfiles);
+        TensorType typeBeforeReducing = expression.getRoot().type(typeContext);
+
+        // Check generated macros for inputs to reduce
+        Set<String> macroNames = new HashSet<>();
+        addMacroNamesIn(expression.getRoot(), macroNames, model);
+        for (String macroName : macroNames) {
+            if ( ! model.macros().containsKey(macroName)) {
+                continue;
+            }
+            RankProfile.Macro macro = profile.getMacros().get(macroName);
+            if (macro == null) {
+                throw new IllegalArgumentException("Model refers to generated macro '" + macroName +
+                                                   "but this macro is not present in " + profile);
+            }
+            RankingExpression macroExpression = macro.getRankingExpression();
+            macroExpression.setRoot(reduceBatchDimensionsAtInput(macroExpression.getRoot(), model, typeContext));
+        }
+
+        // Check expression for inputs to reduce
+        ExpressionNode root = expression.getRoot();
+        root = reduceBatchDimensionsAtInput(root, model, typeContext);
+        TensorType typeAfterReducing = root.type(typeContext);
+        root = expandBatchDimensionsAtOutput(root, typeBeforeReducing, typeAfterReducing);
+        expression.setRoot(root);
+    }
+
+    private ExpressionNode reduceBatchDimensionsAtInput(ExpressionNode node, TensorFlowModel model,
+                                                        TypeContext<Reference> typeContext) {
+        if (node instanceof TensorFunctionNode) {
+            TensorFunction tensorFunction = ((TensorFunctionNode) node).function();
+            if (tensorFunction instanceof Rename) {
+                List<ExpressionNode> children = ((TensorFunctionNode)node).children();
+                if (children.size() == 1 && children.get(0) instanceof ReferenceNode) {
+                    ReferenceNode referenceNode = (ReferenceNode) children.get(0);
+                    if (model.requiredMacros().containsKey(referenceNode.getName())) {
+                        return reduceBatchDimensionExpression(tensorFunction, typeContext);
+                    }
+                }
+            }
+        }
+        if (node instanceof ReferenceNode) {
+            ReferenceNode referenceNode = (ReferenceNode) node;
+            if (model.requiredMacros().containsKey(referenceNode.getName())) {
+                return reduceBatchDimensionExpression(TensorFunctionNode.wrapArgument(node), typeContext);
+            }
+        }
+        if (node instanceof CompositeNode) {
+            List<ExpressionNode> children = ((CompositeNode)node).children();
+            List<ExpressionNode> transformedChildren = new ArrayList<>(children.size());
+            for (ExpressionNode child : children) {
+                transformedChildren.add(reduceBatchDimensionsAtInput(child, model, typeContext));
+            }
+            return ((CompositeNode)node).setChildren(transformedChildren);
+        }
+        return node;
+    }
+
+    private ExpressionNode reduceBatchDimensionExpression(TensorFunction function, TypeContext<Reference> context) {
+        TensorFunction result = function;
+        TensorType type = function.type(context);
+        if (type.dimensions().size() > 1) {
+            List<String> reduceDimensions = new ArrayList<>();
+            for (TensorType.Dimension dimension : type.dimensions()) {
+                if (dimension.size().orElse(-1L) == 1) {
+                    reduceDimensions.add(dimension.name());
+                }
+            }
+            if (reduceDimensions.size() > 0) {
+                result = new Reduce(function, Reduce.Aggregator.sum, reduceDimensions);
+            }
+        }
+        return new TensorFunctionNode(result);
+    }
+
+    /**
+     * If batch dimensions have been reduced away above, bring them back here
+     * for any following computation of the tensor.
+     * Todo: determine when this is not necessary!
+     */
+    private ExpressionNode expandBatchDimensionsAtOutput(ExpressionNode node, TensorType before, TensorType after) {
+        if (after.equals(before)) {
+            return node;
+        }
+        TensorType.Builder typeBuilder = new TensorType.Builder();
+        for (TensorType.Dimension dimension : before.dimensions()) {
+            if (dimension.size().orElse(-1L) == 1 && !after.dimensionNames().contains(dimension.name())) {
+                typeBuilder.indexed(dimension.name(), 1);
+            }
+        }
+        TensorType expandDimensionsType = typeBuilder.build();
+        if (expandDimensionsType.dimensions().size() > 0) {
+            ExpressionNode generatedExpression = new ConstantNode(new DoubleValue(1.0));
+            Generate generatedFunction = new Generate(expandDimensionsType,
+                                                      new GeneratorLambdaFunctionNode(expandDimensionsType,
+                                                                                      generatedExpression)
+                                                              .asLongListToDoubleOperator());
+            Join expand = new Join(TensorFunctionNode.wrapArgument(node), generatedFunction, ScalarFunctions.multiply());
+            return new TensorFunctionNode(expand);
+        }
+        return node;
+    }
+
+    /**
+     * If a constant c is overridden by a macro, we need to replace instances of "constant(c)" by "c" in expressions.
+     * This method does that for the given expression and returns the result.
+     */
+    private RankingExpression replaceConstantsByMacros(RankingExpression expression,
+                                                       Set<String> constantsReplacedByMacros) {
+        if (constantsReplacedByMacros.isEmpty()) return expression;
+        return new RankingExpression(expression.getName(),
+                                     replaceConstantsByMacros(expression.getRoot(), constantsReplacedByMacros));
+    }
+
+    private ExpressionNode replaceConstantsByMacros(ExpressionNode node, Set<String> constantsReplacedByMacros) {
+        if (node instanceof ReferenceNode) {
+            Reference reference = ((ReferenceNode)node).reference();
+            if (FeatureNames.isSimpleFeature(reference) && reference.name().equals("constant")) {
+                String argument = reference.simpleArgument().get();
+                if (constantsReplacedByMacros.contains(argument))
+                    return new ReferenceNode(argument);
+            }
+        }
+        if (node instanceof CompositeNode) { // not else: this matches some of the same nodes as the outer if above
+            CompositeNode composite = (CompositeNode)node;
+            return composite.setChildren(composite.children().stream()
+                                                             .map(child -> replaceConstantsByMacros(child, constantsReplacedByMacros))
+                                                             .collect(Collectors.toList()));
+        }
+        return node;
+    }
+
+    private void addMacroNamesIn(ExpressionNode node, Set<String> names, TensorFlowModel model) {
+        if (node instanceof ReferenceNode) {
+            ReferenceNode referenceNode = (ReferenceNode)node;
+            if (referenceNode.getOutput() == null) { // macro references cannot specify outputs
+                names.add(referenceNode.getName());
+                if (model.macros().containsKey(referenceNode.getName())) {
+                    addMacroNamesIn(model.macros().get(referenceNode.getName()).getRoot(), names, model);
+                }
+            }
+        }
+        else if (node instanceof CompositeNode) {
+            for (ExpressionNode child : ((CompositeNode)node).children())
+                addMacroNamesIn(child, names, model);
+        }
+    }
+
+    private Value asValue(Tensor tensor) {
+        if (tensor.type().rank() == 0)
+            return new DoubleValue(tensor.asDouble()); // the backend gets offended by dimensionless tensors
+        else
+            return new TensorValue(tensor);
+    }
+
+    /**
+     * Provides read/write access to the correct directories of the application package given by the feature arguments
+     */
+    private static class ModelStore {
+
+        private final ApplicationPackage application;
+        private final FeatureArguments arguments;
+
+        public ModelStore(ApplicationPackage application, Arguments arguments) {
+            this.application = application;
+            this.arguments = new FeatureArguments(arguments);
+        }
+
+
+
+        public FeatureArguments arguments() { return arguments; }
+
+        public boolean hasStoredModel() {
+            try {
+                return application.getFile(arguments.expressionPath()).exists();
+            }
+            catch (UnsupportedOperationException e) {
+                return false;
+            }
+        }
+
+        /**
+         * Returns the directory which (if hasTensorFlowModels is true)
+         * contains the source model to use for these arguments
+         */
+        public File tensorFlowModelDir() {
+            return application.getFileReference(ApplicationPackage.MODELS_DIR.append(arguments.modelPath()));
+        }
+
+        /**
+         * Adds this expression to the application package, such that it can be read later.
+         */
+        public void writeConverted(RankingExpression expression) {
+            application.getFile(arguments.expressionPath())
+                       .writeFile(new StringReader(expression.getRoot().toString()));
+        }
+
+        /** Reads the previously stored ranking expression for these arguments */
+        public RankingExpression readConverted() {
+            try {
+                return new RankingExpression(application.getFile(arguments.expressionPath()).createReader());
+            }
+            catch (IOException e) {
+                throw new UncheckedIOException("Could not read " + arguments.expressionPath(), e);
+            }
+            catch (ParseException e) {
+                throw new IllegalStateException("Could not parse " + arguments.expressionPath(), e);
+            }
+        }
+
+        /** Adds this macro expression to the application package to it can be read later. */
+        public void writeMacro(String name, RankingExpression expression) {
+            application.getFile(arguments.macrosPath()).appendFile(name + "\t" +
+                    expression.getRoot().toString() + "\n");
+        }
+
+        /** Reads the previously stored macro expressions for these arguments */
+        public List<Pair<String, RankingExpression>> readMacros() {
+            try {
+                ApplicationFile file = application.getFile(arguments.macrosPath());
+                if (!file.exists()) return Collections.emptyList();
+
+                List<Pair<String, RankingExpression>> macros = new ArrayList<>();
+                BufferedReader reader = new BufferedReader(file.createReader());
+                String line;
+                while (null != (line = reader.readLine())) {
+                    String[] parts = line.split("\t");
+                    String name = parts[0];
+                    try {
+                        RankingExpression expression = new RankingExpression(parts[1]);
+                        macros.add(new Pair<>(name, expression));
+                    }
+                    catch (ParseException e) {
+                        throw new IllegalStateException("Could not parse " + arguments.expressionPath(), e);
+                    }
+                }
+                return macros;
+            }
+            catch (IOException e) {
+                throw new UncheckedIOException(e);
+            }
+        }
+
+        /**
+         * Reads the information about all the large (aka ranking) constants stored in the application package
+         * (the constant value itself is replicated with file distribution).
+         */
+        public List<RankingConstant> readLargeConstants() {
+            try {
+                List<RankingConstant> constants = new ArrayList<>();
+                for (ApplicationFile constantFile : application.getFile(arguments.largeConstantsPath()).listFiles()) {
+                    String[] parts = IOUtils.readAll(constantFile.createReader()).split(":");
+                    constants.add(new RankingConstant(parts[0], TensorType.fromSpec(parts[1]), parts[2]));
+                }
+                return constants;
+            }
+            catch (IOException e) {
+                throw new UncheckedIOException(e);
+            }
+        }
+
+        /**
+         * Adds this constant to the application package as a file,
+         * such that it can be distributed using file distribution.
+         *
+         * @return the path to the stored constant, relative to the application package root
+         */
+        public Path writeLargeConstant(String name, Tensor constant) {
+            Path constantsPath = ApplicationPackage.MODELS_GENERATED_DIR.append(arguments.modelPath).append("constants");
+
+            // "tbf" ending for "typed binary format" - recognized by the nodes receiving the file:
+            Path constantPath = constantsPath.append(name + ".tbf");
+
+            // Remember the constant in a file we replicate in ZooKeeper
+            application.getFile(arguments.largeConstantsPath().append(name + ".constant"))
+                       .writeFile(new StringReader(name + ":" + constant.type() + ":" + correct(constantPath)));
+
+            // Write content explicitly as a file on the file system as this is distributed using file distribution
+            createIfNeeded(constantsPath);
+            IOUtils.writeFile(application.getFileReference(constantPath), TypedBinaryFormat.encode(constant));
+            return correct(constantPath);
+        }
+
+        private List<Pair<String, Tensor>> readSmallConstants() {
+            try {
+                ApplicationFile file = application.getFile(arguments.smallConstantsPath());
+                if (!file.exists()) return Collections.emptyList();
+
+                List<Pair<String, Tensor>> constants = new ArrayList<>();
+                BufferedReader reader = new BufferedReader(file.createReader());
+                String line;
+                while (null != (line = reader.readLine())) {
+                    String[] parts = line.split("\t");
+                    String name = parts[0];
+                    TensorType type = TensorType.fromSpec(parts[1]);
+                    Tensor tensor = Tensor.from(type, parts[2]);
+                    constants.add(new Pair<>(name, tensor));
+                }
+                return constants;
+            }
+            catch (IOException e) {
+                throw new UncheckedIOException(e);
+            }
+        }
+
+        /**
+         * Append this constant to the single file used for small constants distributed as config
+         */
+        public void writeSmallConstant(String name, Tensor constant) {
+            // Secret file format for remembering constants:
+            application.getFile(arguments.smallConstantsPath()).appendFile(name + "\t" +
+                                                                           constant.type().toString() + "\t" +
+                                                                           constant.toString() + "\n");
+        }
+
+        /** Workaround for being constructed with the .preprocessed dir as root while later being used outside it */
+        private Path correct(Path path) {
+            if (application.getFileReference(Path.fromString("")).getAbsolutePath().endsWith(FilesApplicationPackage.preprocessed)
+                && ! path.elements().contains(FilesApplicationPackage.preprocessed)) {
+                return Path.fromString(FilesApplicationPackage.preprocessed).append(path);
+            }
+            else {
+                return path;
+            }
+        }
+
+        private void createIfNeeded(Path path) {
+            File dir = application.getFileReference(path);
+            if ( ! dir.exists()) {
+                if (!dir.mkdirs())
+                    throw new IllegalStateException("Could not create " + dir);
+            }
+        }
+
+    }
+
+    /** Encapsulates the 1, 2 or 3 arguments to a tensorflow feature */
+    private static class FeatureArguments {
+
+        private final Path modelPath;
+
+        /** Optional arguments */
+        private final Optional<String> signature, output;
+
+        public FeatureArguments(Arguments arguments) {
             if (arguments.isEmpty())
                 throw new IllegalArgumentException("A tensorflow node must take an argument pointing to " +
-                        "the tensorflow model directory under [application]/models");
+                                                   "the tensorflow model directory under [application]/models");
             if (arguments.expressions().size() > 3)
                 throw new IllegalArgumentException("A tensorflow feature can have at most 3 arguments");
 
@@ -76,6 +661,68 @@ public class TensorFlowFeatureConverter extends MLImportFeatureConverter {
             signature = optionalArgument(1, arguments);
             output = optionalArgument(2, arguments);
         }
+
+        /** Returns modelPath with slashes replaced by underscores */
+        public String modelName() { return modelPath.toString().replace('/', '_'); }
+
+        /** Returns relative path to this model below the "models/" dir in the application package */
+        public Path modelPath() { return modelPath; }
+        public Optional<String> signature() { return signature; }
+        public Optional<String> output() { return output; }
+
+        /** Path to the small constants file */
+        public Path smallConstantsPath() {
+            return ApplicationPackage.MODELS_GENERATED_DIR.append(modelPath).append("constants.txt");
+        }
+
+        /** Path to the large (ranking) constants directory */
+        public Path largeConstantsPath() {
+            return ApplicationPackage.MODELS_GENERATED_REPLICATED_DIR.append(modelPath).append("constants");
+        }
+
+        /** Path to the macros file */
+        public Path macrosPath() {
+            return ApplicationPackage.MODELS_GENERATED_REPLICATED_DIR.append(modelPath).append("macros.txt");
+        }
+
+        public Path expressionPath() {
+            return ApplicationPackage.MODELS_GENERATED_REPLICATED_DIR
+                    .append(modelPath).append("expressions").append(expressionFileName());
+        }
+
+        private String expressionFileName() {
+            StringBuilder fileName = new StringBuilder();
+            signature.ifPresent(s -> fileName.append(s).append("."));
+            output.ifPresent(s -> fileName.append(s).append("."));
+            if (fileName.length() == 0) // single signature and output
+                fileName.append("single.");
+            fileName.append("expression");
+            return fileName.toString();
+        }
+
+        private Optional<String> optionalArgument(int argumentIndex, Arguments arguments) {
+            if (argumentIndex >= arguments.expressions().size())
+                return Optional.empty();
+            return Optional.of(asString(arguments.expressions().get(argumentIndex)));
+        }
+
+        private String asString(ExpressionNode node) {
+            if ( ! (node instanceof ConstantNode))
+                throw new IllegalArgumentException("Expected a constant string as tensorflow argument, but got '" + node);
+            return stripQuotes(((ConstantNode)node).sourceString());
+        }
+
+        private String stripQuotes(String s) {
+            if ( ! isQuoteSign(s.codePointAt(0))) return s;
+            if ( ! isQuoteSign(s.codePointAt(s.length() - 1 )))
+                throw new IllegalArgumentException("tensorflow argument [" + s + "] is missing endquote");
+            return s.substring(1, s.length()-1);
+        }
+
+        private boolean isQuoteSign(int c) {
+            return c == '\'' || c == '"';
+        }
+
     }
 
 }
diff --git a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithOnnxTestCase.java b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithOnnxTestCase.java
index d9beab6e2f2..1c54d12d8b3 100644
--- a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithOnnxTestCase.java
+++ b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithOnnxTestCase.java
@@ -37,6 +37,15 @@ public class RankingExpressionWithOnnxTestCase {
     }
 
     @Test
+    public void testOnnxReference() throws ParseException {
+        RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)",
+                "onnx('mnist_softmax.onnx')");
+        search.assertFirstPhaseExpression(vespaExpression, "my_profile");
+        assertLargeConstant("mnist_softmax_onnx_Variable_1", search, Optional.of(10L));
+        assertLargeConstant("mnist_softmax_onnx_Variable", search, Optional.of(7840L));
+    }
+
+    @Test
     public void testOnnxReferenceWithConstantFeature() {
         RankProfileSearchFixture search = fixtureWith("constant(mytensor)",
                 "onnx('mnist_softmax.onnx')",
@@ -113,6 +122,13 @@ public class RankingExpressionWithOnnxTestCase {
     }
 
     @Test
+    public void testOnnxReferenceSpecifyingOutput() {
+        RankProfileSearchFixture search = fixtureWith("tensor(d0[2],d1[784])(0.0)",
+                "onnx('mnist_softmax.onnx', 'add')");
+        search.assertFirstPhaseExpression(vespaExpression, "my_profile");
+    }
+
+    @Test
     public void testOnnxReferenceMissingMacro() throws ParseException {
         try {
             RankProfileSearchFixture search = new RankProfileSearchFixture(
@@ -129,7 +145,7 @@ public class RankingExpressionWithOnnxTestCase {
         catch (IllegalArgumentException expected) {
             assertEquals("Rank profile 'my_profile' is invalid: Could not use Onnx model from " +
                             "onnx('mnist_softmax.onnx'): " +
-                            "Model refers input 'Placeholder' of type tensor(d0[],d1[784]) but this macro is " +
+                            "Model refers Placeholder 'Placeholder' of type tensor(d0[],d1[784]) but this macro is " +
                             "not present in rank profile 'my_profile'",
                     Exceptions.toMessageString(expected));
         }
@@ -147,8 +163,8 @@ public class RankingExpressionWithOnnxTestCase {
         catch (IllegalArgumentException expected) {
             assertEquals("Rank profile 'my_profile' is invalid: Could not use Onnx model from " +
                             "onnx('mnist_softmax.onnx'): " +
-                            "Model refers input 'Placeholder'. The required type of this is tensor(d0[],d1[784]), " +
-                            "but this macro returns tensor(d0[2],d5[10])",
+                            "Model refers input 'Placeholder' of type tensor(d0[],d1[784]) which must be produced " +
+                            "by a macro in the rank profile, but this macro produces type tensor(d0[2],d5[10])",
                     Exceptions.toMessageString(expected));
         }
     }
diff --git a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorFlowTestCase.java b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorFlowTestCase.java
index 7228af2b0de..d288a396732 100644
--- a/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorFlowTestCase.java
+++ b/config-model/src/test/java/com/yahoo/searchdefinition/processing/RankingExpressionWithTensorFlowTestCase.java
@@ -162,7 +162,7 @@ public class RankingExpressionWithTensorFlowTestCase {
         catch (IllegalArgumentException expected) {
             assertEquals("Rank profile 'my_profile' is invalid: Could not use tensorflow model from " +
                          "tensorflow('mnist_softmax/saved'): " +
-                         "Model refers input 'Placeholder' of type tensor(d0[],d1[784]) but this macro is " +
+                         "Model refers placeholder 'Placeholder' of type tensor(d0[],d1[784]) but this macro is " +
                          "not present in rank profile 'my_profile'",
                          Exceptions.toMessageString(expected));
         }
@@ -179,7 +179,7 @@ public class RankingExpressionWithTensorFlowTestCase {
         catch (IllegalArgumentException expected) {
             assertEquals("Rank profile 'my_profile' is invalid: Could not use tensorflow model from " +
                          "tensorflow('mnist_softmax/saved'): " +
-                         "Model refers input 'Placeholder'. The required type of this is tensor(d0[],d1[784]), " +
+                         "Model refers placeholder 'Placeholder'. The required type of this is tensor(d0[],d1[784]), " +
                          "but this macro returns tensor(d0[2],d5[10])",
                          Exceptions.toMessageString(expected));
         }
@@ -305,9 +305,9 @@ public class RankingExpressionWithTensorFlowTestCase {
 
     @Test
     public void testMacroGeneration() {
-        final String expression = "join(join(reduce(join(join(join(imported_ml_macro_mnist_saved_dnn_hidden2_add, reduce(constant(mnist_saved_dnn_hidden2_Const), sum, d2), f(a,b)(a * b)), imported_ml_macro_mnist_saved_dnn_hidden2_add, f(a,b)(max(a,b))), constant(mnist_saved_dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(mnist_saved_dnn_outputs_bias_read), f(a,b)(a + b)), tensor(d0[1])(1.0), f(a,b)(a * b))";
+        final String expression = "join(join(reduce(join(join(join(tf_macro_mnist_saved_dnn_hidden2_add, reduce(constant(mnist_saved_dnn_hidden2_Const), sum, d2), f(a,b)(a * b)), tf_macro_mnist_saved_dnn_hidden2_add, f(a,b)(max(a,b))), constant(mnist_saved_dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(mnist_saved_dnn_outputs_bias_read), f(a,b)(a + b)), tensor(d0[1])(1.0), f(a,b)(a * b))";
         final String macroExpression1 = "join(reduce(join(reduce(rename(input, (d0, d1), (d0, d4)), sum, d0), constant(mnist_saved_dnn_hidden1_weights_read), f(a,b)(a * b)), sum, d4), constant(mnist_saved_dnn_hidden1_bias_read), f(a,b)(a + b))";
-        final String macroExpression2 = "join(reduce(join(join(join(imported_ml_macro_mnist_saved_dnn_hidden1_add, 0.009999999776482582, f(a,b)(a * b)), imported_ml_macro_mnist_saved_dnn_hidden1_add, f(a,b)(max(a,b))), constant(mnist_saved_dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(mnist_saved_dnn_hidden2_bias_read), f(a,b)(a + b))";
+        final String macroExpression2 = "join(reduce(join(join(join(tf_macro_mnist_saved_dnn_hidden1_add, 0.009999999776482582, f(a,b)(a * b)), tf_macro_mnist_saved_dnn_hidden1_add, f(a,b)(max(a,b))), constant(mnist_saved_dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(mnist_saved_dnn_hidden2_bias_read), f(a,b)(a + b))";
 
         RankProfileSearchFixture search = fixtureWith("tensor(d0[1],d1[784])(0.0)",
                                     "tensorflow('mnist/saved')",
@@ -316,15 +316,15 @@ public class RankingExpressionWithTensorFlowTestCase {
                                                       "input",
                                                       new StoringApplicationPackage(applicationDir));
         search.assertFirstPhaseExpression(expression, "my_profile");
-        search.assertMacro(macroExpression1, "imported_ml_macro_mnist_saved_dnn_hidden1_add", "my_profile");
-        search.assertMacro(macroExpression2, "imported_ml_macro_mnist_saved_dnn_hidden2_add", "my_profile");
+        search.assertMacro(macroExpression1, "tf_macro_mnist_saved_dnn_hidden1_add", "my_profile");
+        search.assertMacro(macroExpression2, "tf_macro_mnist_saved_dnn_hidden2_add", "my_profile");
     }
 
     @Test
     public void testImportingFromStoredExpressionsWithSmallConstants() throws IOException {
-        final String expression = "join(join(reduce(join(join(join(imported_ml_macro_mnist_saved_dnn_hidden2_add, reduce(constant(mnist_saved_dnn_hidden2_Const), sum, d2), f(a,b)(a * b)), imported_ml_macro_mnist_saved_dnn_hidden2_add, f(a,b)(max(a,b))), constant(mnist_saved_dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(mnist_saved_dnn_outputs_bias_read), f(a,b)(a + b)), tensor(d0[1])(1.0), f(a,b)(a * b))";
+        final String expression = "join(join(reduce(join(join(join(tf_macro_mnist_saved_dnn_hidden2_add, reduce(constant(mnist_saved_dnn_hidden2_Const), sum, d2), f(a,b)(a * b)), tf_macro_mnist_saved_dnn_hidden2_add, f(a,b)(max(a,b))), constant(mnist_saved_dnn_outputs_weights_read), f(a,b)(a * b)), sum, d2), constant(mnist_saved_dnn_outputs_bias_read), f(a,b)(a + b)), tensor(d0[1])(1.0), f(a,b)(a * b))";
         final String macroExpression1 = "join(reduce(join(reduce(rename(input, (d0, d1), (d0, d4)), sum, d0), constant(mnist_saved_dnn_hidden1_weights_read), f(a,b)(a * b)), sum, d4), constant(mnist_saved_dnn_hidden1_bias_read), f(a,b)(a + b))";
-        final String macroExpression2 = "join(reduce(join(join(join(imported_ml_macro_mnist_saved_dnn_hidden1_add, 0.009999999776482582, f(a,b)(a * b)), imported_ml_macro_mnist_saved_dnn_hidden1_add, f(a,b)(max(a,b))), constant(mnist_saved_dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(mnist_saved_dnn_hidden2_bias_read), f(a,b)(a + b))";
+        final String macroExpression2 = "join(reduce(join(join(join(tf_macro_mnist_saved_dnn_hidden1_add, 0.009999999776482582, f(a,b)(a * b)), tf_macro_mnist_saved_dnn_hidden1_add, f(a,b)(max(a,b))), constant(mnist_saved_dnn_hidden2_weights_read), f(a,b)(a * b)), sum, d3), constant(mnist_saved_dnn_hidden2_bias_read), f(a,b)(a + b))";
 
         StoringApplicationPackage application = new StoringApplicationPackage(applicationDir);
         RankProfileSearchFixture search = fixtureWith("tensor(d0[1],d1[784])(0.0)",
@@ -335,8 +335,8 @@ public class RankingExpressionWithTensorFlowTestCase {
                 application);
         search.assertFirstPhaseExpression(expression, "my_profile");
         assertSmallConstant("mnist_saved_dnn_hidden1_mul_x", TensorType.fromSpec("tensor()"), search);
-        search.assertMacro(macroExpression1, "imported_ml_macro_mnist_saved_dnn_hidden1_add", "my_profile");
-        search.assertMacro(macroExpression2, "imported_ml_macro_mnist_saved_dnn_hidden2_add", "my_profile");
+        search.assertMacro(macroExpression1, "tf_macro_mnist_saved_dnn_hidden1_add", "my_profile");
+        search.assertMacro(macroExpression2, "tf_macro_mnist_saved_dnn_hidden2_add", "my_profile");
 
         // At this point the expression is stored - copy application to another location which do not have a models dir
         Path storedApplicationDirectory = applicationDir.getParentPath().append("copy");
@@ -353,8 +353,8 @@ public class RankingExpressionWithTensorFlowTestCase {
                     storedApplication);
             searchFromStored.assertFirstPhaseExpression(expression, "my_profile");
             assertSmallConstant("mnist_saved_dnn_hidden1_mul_x", TensorType.fromSpec("tensor()"), search);
-            searchFromStored.assertMacro(macroExpression1, "imported_ml_macro_mnist_saved_dnn_hidden1_add", "my_profile");
-            searchFromStored.assertMacro(macroExpression2, "imported_ml_macro_mnist_saved_dnn_hidden2_add", "my_profile");
+            searchFromStored.assertMacro(macroExpression1, "tf_macro_mnist_saved_dnn_hidden1_add", "my_profile");
+            searchFromStored.assertMacro(macroExpression2, "tf_macro_mnist_saved_dnn_hidden2_add", "my_profile");
         }
         finally {
             IOUtils.recursiveDeleteDir(storedApplicationDirectory.toFile());
@@ -465,7 +465,7 @@ public class RankingExpressionWithTensorFlowTestCase {
 
     }
 
-    static class StoringApplicationPackageFile extends ApplicationFile {
+    public static class StoringApplicationPackageFile extends ApplicationFile {
 
         /** The path to the application package root */
         private final Path root;
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ModelImporter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ModelImporter.java
deleted file mode 100644
index a658833b426..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ModelImporter.java
+++ /dev/null
@@ -1,242 +0,0 @@
-package com.yahoo.searchlib.rankingexpression.integration.ml;
-
-import com.yahoo.searchlib.rankingexpression.RankingExpression;
-import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
-import com.yahoo.searchlib.rankingexpression.evaluation.Value;
-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.Constant;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.IntermediateOperation;
-import com.yahoo.searchlib.rankingexpression.parser.ParseException;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.functions.Rename;
-import com.yahoo.tensor.functions.TensorFunction;
-import com.yahoo.yolean.Exceptions;
-
-import java.io.File;
-import java.util.List;
-import java.util.Map;
-import java.util.Optional;
-import java.util.logging.Logger;
-
-/**
- * Base class for importing ML models (ONNX/TensorFlow) as native Vespa
- * ranking expressions. The general mechanism for import is for the
- * specific ML platform import implementations to create an
- * IntermediateGraph. This class offers common code to convert the
- * IntermediateGraph to Vespa ranking expressions and macros.
- *
- * @author lesters
- */
-public abstract class ModelImporter {
-
-    private static final Logger log = Logger.getLogger(ModelImporter.class.getName());
-
-    /**
-     * The main import function.
-     */
-    public abstract ImportedModel importModel(String modelName, String modelPath);
-
-    public ImportedModel importModel(String modelName, File modelDir) {
-        return importModel(modelName, modelDir.toString());
-    }
-
-    /**
-     * Takes an IntermediateGraph and converts it to a ImportedModel containing
-     * the actual Vespa ranking expressions.
-     */
-    static ImportedModel convertIntermediateGraphToModel(IntermediateGraph graph) {
-        ImportedModel model = new ImportedModel(graph.name());
-
-        graph.optimize();
-
-        importSignatures(graph, model);
-        importExpressions(graph, model);
-        reportWarnings(graph, model);
-        logVariableTypes(graph);
-
-        return model;
-    }
-
-    private static void importSignatures(IntermediateGraph graph, ImportedModel model) {
-        for (String signatureName : graph.signatures()) {
-            ImportedModel.Signature signature = model.signature(signatureName);
-            for (Map.Entry<String, String> input : graph.inputs(signatureName).entrySet()) {
-                signature.input(input.getKey(), input.getValue());
-            }
-            for (Map.Entry<String, String> output : graph.outputs(signatureName).entrySet()) {
-                signature.output(output.getKey(), output.getValue());
-            }
-        }
-    }
-
-    private static boolean isSignatureInput(ImportedModel model, IntermediateOperation operation) {
-        for (ImportedModel.Signature signature : model.signatures().values()) {
-            for (String inputName : signature.inputs().values()) {
-                if (inputName.equals(operation.name())) {
-                    return true;
-                }
-            }
-        }
-        return false;
-    }
-
-    private static boolean isSignatureOutput(ImportedModel model, IntermediateOperation operation) {
-        for (ImportedModel.Signature signature : model.signatures().values()) {
-            for (String outputName : signature.outputs().values()) {
-                if (outputName.equals(operation.name())) {
-                    return true;
-                }
-            }
-        }
-        return false;
-    }
-
-    /**
-     * Convert intermediate representation to Vespa ranking expressions.
-     */
-    static void importExpressions(IntermediateGraph graph, ImportedModel model) {
-        for (ImportedModel.Signature signature : model.signatures().values()) {
-            for (String outputName : signature.outputs().values()) {
-                try {
-                    Optional<TensorFunction> function = importExpression(graph.get(outputName), model);
-                    if (!function.isPresent()) {
-                        signature.skippedOutput(outputName, "No valid output function could be found.");
-                    }
-                }
-                catch (IllegalArgumentException e) {
-                    signature.skippedOutput(outputName, Exceptions.toMessageString(e));
-                }
-            }
-        }
-    }
-
-    private static Optional<TensorFunction> importExpression(IntermediateOperation operation, ImportedModel model) {
-        if (!operation.type().isPresent()) {
-            return Optional.empty();
-        }
-        if (operation.isConstant()) {
-            return importConstant(operation, model);
-        }
-        importExpressionInputs(operation, model);
-        importRankingExpression(operation, model);
-        importArgumentExpression(operation, model);
-        importMacroExpression(operation, model);
-
-        return operation.function();
-    }
-
-    private static void importExpressionInputs(IntermediateOperation operation, ImportedModel model) {
-        operation.inputs().forEach(input -> importExpression(input, model));
-    }
-
-    private static Optional<TensorFunction> importConstant(IntermediateOperation operation, ImportedModel model) {
-        String name = operation.vespaName();
-        if (model.largeConstants().containsKey(name) || model.smallConstants().containsKey(name)) {
-            return operation.function();
-        }
-
-        Value value = operation.getConstantValue().orElseThrow(() ->
-                new IllegalArgumentException("Operation '" + operation.vespaName() + "' " +
-                        "is constant but does not have a value."));
-        if ( ! (value instanceof TensorValue)) {
-            return operation.function(); // scalar values are inserted directly into the expression
-        }
-
-        Tensor tensor = value.asTensor();
-        if (tensor.type().rank() == 0) {
-            model.smallConstant(name, tensor);
-        } else {
-            model.largeConstant(name, tensor);
-        }
-        return operation.function();
-    }
-
-    private static void importRankingExpression(IntermediateOperation operation, ImportedModel model) {
-        if (operation.function().isPresent()) {
-            String name = operation.name();
-            if (!model.expressions().containsKey(name)) {
-                TensorFunction function = operation.function().get();
-
-                if (isSignatureOutput(model, operation)) {
-                    OrderedTensorType operationType = operation.type().get();
-                    OrderedTensorType standardNamingType = OrderedTensorType.standardType(operationType);
-                    if ( ! operationType.equals(standardNamingType)) {
-                        List<String> renameFrom = operationType.dimensionNames();
-                        List<String> renameTo = standardNamingType.dimensionNames();
-                        function = new Rename(function, renameFrom, renameTo);
-                    }
-                }
-
-                try {
-                    // We add all intermediate nodes imported as separate expressions. Only
-                    // those referenced from the output will be used. We parse the
-                    // TensorFunction here to convert it to a RankingExpression tree.
-                    model.expression(name, new RankingExpression(name, function.toString()));
-                }
-                catch (ParseException e) {
-                    throw new RuntimeException("Imported function " + function +
-                            " cannot be parsed as a ranking expression", e);
-                }
-            }
-        }
-    }
-
-    private static void importArgumentExpression(IntermediateOperation operation, ImportedModel model) {
-        if (operation.isInput()) {
-            // All inputs must have dimensions with standard naming convention: d0, d1, ...
-            OrderedTensorType standardNamingConvention = OrderedTensorType.standardType(operation.type().get());
-            model.argument(operation.vespaName(), standardNamingConvention.type());
-            model.requiredMacro(operation.vespaName(), standardNamingConvention.type());
-        }
-    }
-
-    private static void importMacroExpression(IntermediateOperation operation, ImportedModel model) {
-        if (operation.macro().isPresent()) {
-            TensorFunction function = operation.macro().get();
-            try {
-                model.macro(operation.macroName(), new RankingExpression(operation.macroName(), function.toString()));
-            }
-            catch (ParseException e) {
-                throw new RuntimeException("Tensorflow function " + function +
-                                           " cannot be parsed as a ranking expression", e);
-            }
-        }
-    }
-
-    /**
-     * Add any import warnings to the signature in the ImportedModel.
-     */
-    private static void reportWarnings(IntermediateGraph graph, ImportedModel model) {
-        for (ImportedModel.Signature signature : model.signatures().values()) {
-            for (String outputName : signature.outputs().values()) {
-                reportWarnings(graph.get(outputName), model);
-            }
-        }
-    }
-
-    private static void reportWarnings(IntermediateOperation operation, ImportedModel model) {
-        for (String warning : operation.warnings()) {
-            model.defaultSignature().importWarning(warning);
-        }
-        for (IntermediateOperation input : operation.inputs()) {
-            reportWarnings(input, model);
-        }
-    }
-
-    /**
-     * Log all TensorFlow Variables (i.e file constants) imported as part of this with their ordered type.
-     * This allows users to learn the exact types (including dimension order after renaming) of the Variables
-     * such that these can be converted and fed to a parent document independently of the rest of the model
-     * for fast model weight updates.
-     */
-    private static void logVariableTypes(IntermediateGraph graph) {
-        for (IntermediateOperation operation : graph.operations()) {
-            if ( ! (operation instanceof Constant)) continue;
-            if ( ! operation.type().isPresent()) continue; // will not happen
-            log.info("Importing TensorFlow variable " + operation.name() + " as " + operation.vespaName() +
-                    " of type " + operation.type().get());
-        }
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/OnnxImporter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/OnnxImporter.java
deleted file mode 100644
index d3dd2a1d418..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/OnnxImporter.java
+++ /dev/null
@@ -1,30 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-package com.yahoo.searchlib.rankingexpression.integration.ml;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.IntermediateGraph;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.onnx.GraphImporter;
-import onnx.Onnx;
-
-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 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);
-        } catch (IOException e) {
-            throw new IllegalArgumentException("Could not import ONNX model from '" + modelPath + "'", e);
-        }
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/TensorFlowImporter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/TensorFlowImporter.java
deleted file mode 100644
index ff584559a83..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/TensorFlowImporter.java
+++ /dev/null
@@ -1,47 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.IntermediateGraph;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.tensorflow.GraphImporter;
-import org.tensorflow.SavedModelBundle;
-
-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 {
-
-    /**
-     * Imports a saved TensorFlow model from a directory.
-     * The model should be saved as a .pbtxt or .pb file.
-     * The name of the model is taken as the db/pbtxt file name (not including the file ending).
-     *
-     * @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
-     */
-    public ImportedModel importModel(String modelName, String modelDir) {
-        try (SavedModelBundle model = SavedModelBundle.load(modelDir, "serve")) {
-            return importModel(modelName, model);
-        }
-        catch (IllegalArgumentException e) {
-            throw new IllegalArgumentException("Could not import TensorFlow model from directory '" + modelDir + "'", e);
-        }
-    }
-
-    /** Imports a TensorFlow model */
-    ImportedModel importModel(String modelName, SavedModelBundle model) {
-        try {
-            IntermediateGraph graph = GraphImporter.importGraph(modelName, model);
-            return convertIntermediateGraphToModel(graph);
-        }
-        catch (IOException e) {
-            throw new IllegalArgumentException("Could not import TensorFlow model '" + model + "'", e);
-        }
-    }
-
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/IntermediateGraph.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/IntermediateGraph.java
deleted file mode 100644
index 39a8b211d09..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/IntermediateGraph.java
+++ /dev/null
@@ -1,107 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.IntermediateOperation;
-
-import java.util.Collection;
-import java.util.HashMap;
-import java.util.Map;
-import java.util.Set;
-
-/**
- * Holds an intermediate representation of an imported ONNX or TensorFlow
- * graph. After this intermediate representation is constructed, it is used to
- * simplify and optimize the computational graph and then converted into the
- * final ImportedModel that holds the Vespa ranking expressions for the model.
- *
- * @author lesters
- */
-public class IntermediateGraph {
-
-    private final String modelName;
-    private final Map<String, IntermediateOperation> index = new HashMap<>();
-    private final Map<String, GraphSignature> signatures = new HashMap<>();
-
-    private static class GraphSignature {
-        final Map<String, String> inputs = new HashMap<>();
-        final Map<String, String> outputs = new HashMap<>();
-    }
-
-    public IntermediateGraph(String modelName) {
-        this.modelName = modelName;
-    }
-
-    public String name() {
-        return modelName;
-    }
-
-    public IntermediateOperation put(String key, IntermediateOperation operation) {
-        return index.put(key, operation);
-    }
-
-    public IntermediateOperation get(String key) {
-        return index.get(key);
-    }
-
-    public Set<String> signatures() {
-        return signatures.keySet();
-    }
-
-    public Map<String, String> inputs(String signature) {
-        return signatures.computeIfAbsent(signature, (k) -> new GraphSignature()).inputs;
-    }
-
-    public Map<String, String> outputs(String signature) {
-        return signatures.computeIfAbsent(signature, (k) -> new GraphSignature()).outputs;
-    }
-
-    public String defaultSignature() {
-        return "default";
-    }
-
-    public boolean alreadyImported(String key) {
-        return index.containsKey(key);
-    }
-
-    public Collection<IntermediateOperation> operations() {
-        return index.values();
-    }
-
-    public void optimize() {
-        renameDimensions();
-    }
-
-    /**
-     * Find dimension names to avoid excessive renaming while evaluating the model.
-     */
-    private void renameDimensions() {
-        DimensionRenamer renamer = new DimensionRenamer();
-        for (String signature : signatures()) {
-            for (String output : outputs(signature).values()) {
-                addDimensionNameConstraints(index.get(output), renamer);
-            }
-        }
-        renamer.solve();
-        for (String signature : signatures()) {
-            for (String output : outputs(signature).values()) {
-                renameDimensions(index.get(output), renamer);
-            }
-        }
-    }
-
-    private static void addDimensionNameConstraints(IntermediateOperation operation, DimensionRenamer renamer) {
-        if (operation.type().isPresent()) {
-            operation.inputs().forEach(input -> addDimensionNameConstraints(input, renamer));
-            operation.addDimensionNameConstraints(renamer);
-        }
-    }
-
-    private static void renameDimensions(IntermediateOperation operation, DimensionRenamer renamer) {
-        if (operation.type().isPresent()) {
-            operation.inputs().forEach(input -> renameDimensions(input, renamer));
-            operation.renameDimensions(renamer);
-        }
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/onnx/GraphImporter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/onnx/GraphImporter.java
deleted file mode 100644
index 3fe92440cae..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/onnx/GraphImporter.java
+++ /dev/null
@@ -1,216 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-package com.yahoo.searchlib.rankingexpression.integration.ml.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
- */
-public class GraphImporter {
-
-    public 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;
-    }
-
-    public 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/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/onnx/TypeConverter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/onnx/TypeConverter.java
deleted file mode 100644
index 715c55d8323..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/onnx/TypeConverter.java
+++ /dev/null
@@ -1,52 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-package com.yahoo.searchlib.rankingexpression.integration.ml.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
- */
-public class TypeConverter {
-
-    public 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");
-                }
-            }
-        }
-    }
-
-    public static OrderedTensorType fromOnnxType(Onnx.TypeProto type) {
-        return fromOnnxType(type, "d");  // standard naming convention: d0, d1, ...
-    }
-
-    public 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/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/NoOp.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/NoOp.java
deleted file mode 100644
index 19ba146492c..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/NoOp.java
+++ /dev/null
@@ -1,26 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.Collections;
-import java.util.List;
-
-public class NoOp extends IntermediateOperation {
-
-    public NoOp(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, Collections.emptyList());  // don't propagate inputs
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        return null;
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        return null;
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/tensorflow/AttributeConverter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/tensorflow/AttributeConverter.java
deleted file mode 100644
index a815cbc3944..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/tensorflow/AttributeConverter.java
+++ /dev/null
@@ -1,85 +0,0 @@
-package com.yahoo.searchlib.rankingexpression.integration.ml.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
- */
-public class AttributeConverter implements IntermediateOperation.AttributeMap {
-
-    private final Map<String, AttrValue> attributeMap;
-
-    public AttributeConverter(NodeDef node) {
-        attributeMap = node.getAttrMap();
-    }
-
-    public 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/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/tensorflow/GraphImporter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/tensorflow/GraphImporter.java
deleted file mode 100644
index e1b292f9e61..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/tensorflow/GraphImporter.java
+++ /dev/null
@@ -1,234 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-package com.yahoo.searchlib.rankingexpression.integration.ml.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
- */
-public class GraphImporter {
-
-    public 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;
-    }
-
-    public 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 + "'");
-    }
-
-    public 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/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/tensorflow/TypeConverter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/tensorflow/TypeConverter.java
deleted file mode 100644
index 67ad1edc312..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/tensorflow/TypeConverter.java
+++ /dev/null
@@ -1,72 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-package com.yahoo.searchlib.rankingexpression.integration.ml.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
- */
-public class TypeConverter {
-
-    public 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?
-    }
-
-    public static OrderedTensorType fromTensorFlowType(NodeDef node) {
-        return fromTensorFlowType(node, "d");  // standard naming convention: d0, d1, ...
-    }
-
-    public 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/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/OnnxImporter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/OnnxImporter.java
new file mode 100644
index 00000000000..fa1f929cc80
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/OnnxImporter.java
@@ -0,0 +1,326 @@
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+
+package com.yahoo.searchlib.rankingexpression.integration.onnx;
+
+import com.yahoo.searchlib.rankingexpression.RankingExpression;
+import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
+import com.yahoo.searchlib.rankingexpression.evaluation.Value;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations.Constant;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations.Argument;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations.OnnxOperation;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.OperationMapper;
+import com.yahoo.searchlib.rankingexpression.parser.ParseException;
+import com.yahoo.tensor.Tensor;
+import com.yahoo.tensor.functions.Rename;
+import com.yahoo.tensor.functions.TensorFunction;
+import com.yahoo.yolean.Exceptions;
+import onnx.Onnx;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.IOException;
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Optional;
+import java.util.logging.Logger;
+import java.util.stream.Collectors;
+
+/**
+ * Converts a ONNX model into a ranking expression and set of constants.
+ *
+ * @author lesters
+ */
+public class OnnxImporter {
+
+    private static final Logger log = Logger.getLogger(OnnxImporter.class.getName());
+
+    public OnnxModel importModel(String modelName, File modelDir) {
+        return importModel(modelName, modelDir.toString());
+    }
+
+    public OnnxModel importModel(String modelName, String modelPath) {
+        try (FileInputStream inputStream = new FileInputStream(modelPath)) {
+            Onnx.ModelProto model = Onnx.ModelProto.parseFrom(inputStream);
+            return importModel(modelName, model);
+        } catch (IOException e) {
+            throw new IllegalArgumentException("Could not import ONNX model from '" + modelPath + "'", e);
+        }
+    }
+
+    public OnnxModel importModel(String modelName, Onnx.ModelProto model) {
+        return importGraph(modelName, model.getGraph());
+    }
+
+    private static OnnxModel importGraph(String modelName, Onnx.GraphProto graph) {
+        OnnxModel model = new OnnxModel(modelName);
+        OperationIndex index = new OperationIndex();
+
+        importNodes(graph, model, index);
+        verifyOutputTypes(graph, model, index);
+        findDimensionNames(model, index);
+        importExpressions(model, index);
+
+        reportWarnings(model, index);
+
+        return model;
+    }
+
+    private static void importNodes(Onnx.GraphProto graph, OnnxModel model, OperationIndex index) {
+        for (Onnx.ValueInfoProto valueInfo : graph.getOutputList()) {
+            importNode(valueInfo.getName(), graph, model, index);
+        }
+    }
+
+    private static OnnxOperation importNode(String name, Onnx.GraphProto graph, OnnxModel model, OperationIndex index) {
+        if (index.alreadyImported(name)) {
+            return index.get(name);
+        }
+        OnnxOperation operation;
+        if (isArgumentTensor(name, graph)) {
+            operation = new Argument(getArgumentTensor(name, graph));
+            model.input(OnnxOperation.namePartOf(name), operation.vespaName());
+        } else if (isConstantTensor(name, graph)) {
+            operation = new Constant(model.name(), getConstantTensor(name, graph));
+        } else {
+            Onnx.NodeProto node = getNodeFromGraph(name, graph);
+            List<OnnxOperation> inputs = importNodeInputs(node, graph, model, index);
+            operation = OperationMapper.get(node, inputs);
+            if (isOutputNode(name, graph)) {
+                model.output(OnnxOperation.namePartOf(name), operation.vespaName());
+            }
+        }
+        index.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 = OnnxOperation.namePartOf(valueInfo.getName());
+            if (nodeName.equals(name)) {
+                return valueInfo;
+            }
+        }
+        return null;
+    }
+
+    private static List<OnnxOperation> importNodeInputs(Onnx.NodeProto node,
+                                                        Onnx.GraphProto graph,
+                                                        OnnxModel model,
+                                                        OperationIndex index) {
+        return node.getInputList().stream()
+                .map(nodeName -> importNode(nodeName, graph, model, index))
+                .collect(Collectors.toList());
+    }
+
+    private static void verifyOutputTypes(Onnx.GraphProto graph, OnnxModel model, OperationIndex index) {
+        for (String outputName : model.outputs().values()) {
+            OnnxOperation operation = index.get(outputName);
+            Onnx.ValueInfoProto onnxNode = getOutputNode(outputName, graph);
+            operation.type().orElseThrow(
+                        () -> new IllegalArgumentException("Output of '" + outputName + "' has no type."))
+                    .verifyType(onnxNode.getType());
+        }
+    }
+
+
+    /** Find dimension names to avoid excessive renaming while evaluating the model. */
+    private static void findDimensionNames(OnnxModel model, OperationIndex index) {
+        DimensionRenamer renamer = new DimensionRenamer();
+        for (String output : model.outputs().values()) {
+            addDimensionNameConstraints(index.get(output), renamer);
+        }
+        renamer.solve();
+        for (String output : model.outputs().values()) {
+            renameDimensions(index.get(output), renamer);
+        }
+    }
+
+    private static void addDimensionNameConstraints(OnnxOperation operation, DimensionRenamer renamer) {
+        if (operation.type().isPresent()) {
+            operation.inputs().forEach(input -> addDimensionNameConstraints(input, renamer));
+            operation.addDimensionNameConstraints(renamer);
+        }
+    }
+
+    private static void renameDimensions(OnnxOperation operation, DimensionRenamer renamer) {
+        if (operation.type().isPresent()) {
+            operation.inputs().forEach(input -> renameDimensions(input, renamer));
+            operation.renameDimensions(renamer);
+        }
+    }
+
+    private static void importExpressions(OnnxModel model, OperationIndex index) {
+        for (String outputName : model.outputs().values()) {
+            try {
+                Optional<TensorFunction> function = importExpression(index.get(outputName), model);
+                if (!function.isPresent()) {
+                    model.skippedOutput(outputName, "No valid output function could be found.");
+                }
+            }
+            catch (IllegalArgumentException e) {
+                model.skippedOutput(outputName, Exceptions.toMessageString(e));
+            }
+        }
+    }
+
+    private static Optional<TensorFunction> importExpression(OnnxOperation operation, OnnxModel model) {
+        if (!operation.type().isPresent()) {
+            return Optional.empty();
+        }
+        if (operation.isConstant()) {
+            return importConstant(operation, model);
+        }
+        importInputExpressions(operation, model);
+        importRankingExpression(operation, model);
+        importArgumentExpression(operation, model);
+
+        return operation.function();
+    }
+
+    private static void importInputExpressions(OnnxOperation operation, OnnxModel model) {
+        operation.inputs().forEach(input -> importExpression(input, model));
+    }
+
+    private static Optional<TensorFunction> importConstant(OnnxOperation operation, OnnxModel model) {
+        String name = operation.vespaName();
+        if (model.largeConstants().containsKey(name) || model.smallConstants().containsKey(name)) {
+            return operation.function();
+        }
+
+        Value value = operation.getConstantValue().orElseThrow(() ->
+                new IllegalArgumentException("Operation '" + operation.vespaName() + "' " +
+                        "is constant but does not have a value."));
+        if ( ! (value instanceof TensorValue)) {
+            return operation.function(); // scalar values are inserted directly into the expression
+        }
+
+        Tensor tensor = value.asTensor();
+        if (tensor.type().rank() == 0) {
+            model.smallConstant(name, tensor);
+        } else {
+            model.largeConstant(name, tensor);
+        }
+        return operation.function();
+    }
+
+    private static void importRankingExpression(OnnxOperation operation, OnnxModel model) {
+        if (operation.function().isPresent()) {
+            String name = operation.vespaName();
+            if (!model.expressions().containsKey(name)) {
+                TensorFunction function = operation.function().get();
+
+                if (model.outputs().containsKey(name)) {
+                    OrderedTensorType operationType = operation.type().get();
+                    OrderedTensorType standardNamingType = OrderedTensorType.standardType(operationType);
+                    if ( ! operationType.equals(standardNamingType)) {
+                        List<String> renameFrom = operationType.dimensionNames();
+                        List<String> renameTo = standardNamingType.dimensionNames();
+                        function = new Rename(function, renameFrom, renameTo);
+                    }
+                }
+
+                try {
+                    // We add all intermediate nodes imported as separate expressions. Only
+                    // those referenced from the output will be used. We parse the
+                    // TensorFunction here to convert it to a RankingExpression tree.
+                    model.expression(name, new RankingExpression(name, function.toString()));
+                }
+                catch (ParseException e) {
+                    throw new RuntimeException("Tensorflow function " + function +
+                            " cannot be parsed as a ranking expression", e);
+                }
+            }
+        }
+    }
+
+    private static void importArgumentExpression(OnnxOperation operation, OnnxModel model) {
+        if (operation.isInput()) {
+            // All inputs must have dimensions with standard naming convention: d0, d1, ...
+            OrderedTensorType standardNamingConvention = OrderedTensorType.standardType(operation.type().get());
+            model.argument(operation.vespaName(), standardNamingConvention.type());
+            model.requiredMacro(operation.vespaName(), standardNamingConvention.type());
+        }
+    }
+
+    private static void reportWarnings(OnnxModel model, OperationIndex index) {
+        for (String output : model.outputs().values()) {
+            reportWarnings(model, index.get(output));
+        }
+    }
+
+    private static void reportWarnings(OnnxModel model, OnnxOperation operation) {
+        for (String warning : operation.warnings()) {
+            model.importWarning(warning);
+        }
+        for (OnnxOperation input : operation.inputs()) {
+            reportWarnings(model, input);
+        }
+    }
+
+    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");
+    }
+
+    private static class OperationIndex {
+        private final Map<String, OnnxOperation> index = new HashMap<>();
+        public OnnxOperation put(String key, OnnxOperation operation) { return index.put(key, operation); }
+        public OnnxOperation get(String key) { return index.get(key); }
+        public boolean alreadyImported(String key) { return index.containsKey(key); }
+        public Collection<OnnxOperation> operations() { return index.values(); }
+    }
+
+}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/OnnxModel.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/OnnxModel.java
new file mode 100644
index 00000000000..bd53afefc3f
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/OnnxModel.java
@@ -0,0 +1,112 @@
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+
+package com.yahoo.searchlib.rankingexpression.integration.onnx;
+
+import com.yahoo.searchlib.rankingexpression.RankingExpression;
+import com.yahoo.tensor.Tensor;
+import com.yahoo.tensor.TensorType;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.regex.Pattern;
+
+/**
+ * The result of importing an ONNX model into Vespa.
+ *
+ * @author bratseth
+ * @author lesters
+ */
+public class OnnxModel {
+
+    private static final Pattern nameRegexp = Pattern.compile("[A-Za-z0-9_]*");
+
+    private final String name;
+
+    public OnnxModel(String name) {
+        if ( ! nameRegexp.matcher(name).matches())
+            throw new IllegalArgumentException("A TensorFlow model name can only contain [A-Za-z0-9_], but is '" +
+                    name + "'");
+        this.name = name;
+    }
+
+    /** Returns the name of this model, which can only contain the characters in [A-Za-z0-9_] */
+    public String name() { return name; }
+
+    private final Map<String, String> inputs = new HashMap<>();
+    private final Map<String, String> outputs = new HashMap<>();
+    private final Map<String, String> skippedOutputs = new HashMap<>();
+    private final List<String> importWarnings = new ArrayList<>();
+
+    private final Map<String, TensorType> arguments = new HashMap<>();
+    private final Map<String, Tensor> smallConstants = new HashMap<>();
+    private final Map<String, Tensor> largeConstants = new HashMap<>();
+    private final Map<String, RankingExpression> expressions = new HashMap<>();
+    private final Map<String, RankingExpression> macros = new HashMap<>();
+    private final Map<String, TensorType> requiredMacros = new HashMap<>();
+
+    void input(String inputName, String argumentName) { inputs.put(inputName, argumentName); }
+    void output(String name, String expressionName) { outputs.put(name, expressionName); }
+    void skippedOutput(String name, String reason) { skippedOutputs.put(name, reason); }
+    void importWarning(String warning) { importWarnings.add(warning); }
+    void argument(String name, TensorType argumentType) { arguments.put(name, argumentType); }
+    void smallConstant(String name, Tensor constant) { smallConstants.put(name, constant); }
+    void largeConstant(String name, Tensor constant) { largeConstants.put(name, constant); }
+    void expression(String name, RankingExpression expression) { expressions.put(name, expression); }
+    void macro(String name, RankingExpression expression) { macros.put(name, expression); }
+    void requiredMacro(String name, TensorType type) { requiredMacros.put(name, type); }
+
+    /**
+     * Returns an immutable map of the inputs (evaluation context) of this. This is a map from input name
+     * to argument (Placeholder) name in the owner of this
+     */
+    public Map<String, String> inputs() { return Collections.unmodifiableMap(inputs); }
+
+    /** Returns arguments().get(inputs.get(name)), e.g the type of the argument this input references */
+    public TensorType inputArgument(String inputName) { return arguments().get(inputs.get(inputName)); }
+
+    /** Returns an immutable list of the expression names of this */
+    public Map<String, String> outputs() { return Collections.unmodifiableMap(outputs); }
+
+    /**
+     * Returns an immutable list of the outputs of this which could not be imported,
+     * with a string detailing the reason for each
+     */
+    public Map<String, String> skippedOutputs() { return Collections.unmodifiableMap(skippedOutputs); }
+
+    /**
+     * Returns an immutable list of possibly non-fatal warnings encountered during import.
+     */
+    public List<String> importWarnings() { return Collections.unmodifiableList(importWarnings); }
+
+    /** Returns expressions().get(outputs.get(outputName)), e.g the expression this output references */
+    public RankingExpression outputExpression(String outputName) { return expressions().get(outputs.get(outputName)); }
+
+    /** Returns an immutable map of the arguments (inputs) of this */
+    public Map<String, TensorType> arguments() { return Collections.unmodifiableMap(arguments); }
+
+    /**
+     * Returns an immutable map of the small constants of this.
+     */
+    public Map<String, Tensor> smallConstants() { return Collections.unmodifiableMap(smallConstants); }
+
+    /**
+     * Returns an immutable map of the large constants of this.
+     */
+    public Map<String, Tensor> largeConstants() { return Collections.unmodifiableMap(largeConstants); }
+
+    /**
+     * Returns an immutable map of the expressions of this - corresponding to ONNX nodes
+     * which are not inputs or constants.
+     */
+    public Map<String, RankingExpression> expressions() { return Collections.unmodifiableMap(expressions); }
+
+    /** Returns an immutable map of macros that are part of this model */
+    public Map<String, RankingExpression> macros() { return Collections.unmodifiableMap(macros); }
+
+    /** Returns an immutable map of the macros that must be provided by the environment running this model */
+    public Map<String, TensorType> requiredMacros() { return Collections.unmodifiableMap(requiredMacros); }
+
+}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/DimensionRenamer.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/DimensionRenamer.java
index 38f1d2329e2..2524417cee0 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/DimensionRenamer.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/DimensionRenamer.java
@@ -1,7 +1,7 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer;
+package com.yahoo.searchlib.rankingexpression.integration.onnx.importer;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations.IntermediateOperation;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations.OnnxOperation;
 
 import java.util.ArrayDeque;
 import java.util.ArrayList;
@@ -47,7 +47,7 @@ public class DimensionRenamer {
     /**
      * Add a constraint between dimension names.
      */
-    public void addConstraint(String from, String to, Constraint pred, IntermediateOperation operation) {
+    public void addConstraint(String from, String to, Constraint pred, OnnxOperation operation) {
         Arc arc = new Arc(from, to, operation);
         Arc opposite = arc.opposite();
         constraints.put(arc, pred);
@@ -175,9 +175,9 @@ public class DimensionRenamer {
 
         private final String from;
         private final String to;
-        private final IntermediateOperation operation;
+        private final OnnxOperation operation;
 
-        Arc(String from, String to, IntermediateOperation operation) {
+        Arc(String from, String to, OnnxOperation operation) {
             this.from = from;
             this.to = to;
             this.operation = operation;
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/OperationMapper.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/OperationMapper.java
new file mode 100644
index 00000000000..12090145d3a
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/OperationMapper.java
@@ -0,0 +1,26 @@
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+
+package com.yahoo.searchlib.rankingexpression.integration.onnx.importer;
+
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations.Join;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations.MatMul;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations.NoOp;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations.OnnxOperation;
+import com.yahoo.tensor.functions.ScalarFunctions;
+import onnx.Onnx;
+
+import java.util.List;
+
+public class OperationMapper {
+
+    public static OnnxOperation get(Onnx.NodeProto node, List<OnnxOperation> inputs) {
+        switch (node.getOpType().toLowerCase()) {
+            case "add":         return new Join(node, inputs, ScalarFunctions.add());
+            case "matmul":      return new MatMul(node, inputs);
+        }
+
+        OnnxOperation op = new NoOp(node, inputs);
+        op.warning("Operation '" + node.getOpType() + "' is currently not implemented");
+        return op;
+    }
+}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/OrderedTensorType.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/OrderedTensorType.java
index 209d73a9f38..812e9b8d678 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/OrderedTensorType.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/OrderedTensorType.java
@@ -1,9 +1,9 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
 
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer;
+package com.yahoo.searchlib.rankingexpression.integration.onnx.importer;
 
 import com.yahoo.tensor.TensorType;
-import com.yahoo.tensor.TensorTypeParser;
+import onnx.Onnx;
 
 import java.util.ArrayList;
 import java.util.Collections;
@@ -13,9 +13,9 @@ import java.util.stream.Collectors;
 
 /**
  * A Vespa tensor type is ordered by the lexicographical ordering of dimension
- * names. Imported tensors have an explicit ordering of their dimensions.
+ * names. ONNX tensors have an explicit ordering of their dimensions.
  * During import, we need to track the Vespa dimension that matches the
- * corresponding imported dimension as the ordering can change after
+ * corresponding ONNX dimension as the ordering can change after
  * dimension renaming. That is the purpose of this class.
  *
  * @author lesters
@@ -25,14 +25,14 @@ public class OrderedTensorType {
     private final TensorType type;
     private final List<TensorType.Dimension> dimensions;
 
-    private final long[] innerSizesOriginal;
+    private final long[] innerSizesOnnx;
     private final long[] innerSizesVespa;
     private final int[] dimensionMap;
 
     private OrderedTensorType(List<TensorType.Dimension> dimensions) {
         this.dimensions = Collections.unmodifiableList(dimensions);
         this.type = new TensorType.Builder(dimensions).build();
-        this.innerSizesOriginal = new long[dimensions.size()];
+        this.innerSizesOnnx = new long[dimensions.size()];
         this.innerSizesVespa = new long[dimensions.size()];
         this.dimensionMap = createDimensionMap();
     }
@@ -54,10 +54,10 @@ public class OrderedTensorType {
         if (numDimensions == 0) {
             return null;
         }
-        innerSizesOriginal[numDimensions - 1] = 1;
+        innerSizesOnnx[numDimensions - 1] = 1;
         innerSizesVespa[numDimensions - 1] = 1;
         for (int i = numDimensions - 1; --i >= 0; ) {
-            innerSizesOriginal[i] = dimensions().get(i+1).size().orElse(-1L) * innerSizesOriginal[i+1];
+            innerSizesOnnx[i] = dimensions().get(i+1).size().orElse(-1L) * innerSizesOnnx[i+1];
             innerSizesVespa[i] = type.dimensions().get(i+1).size().orElse(-1L) * innerSizesVespa[i+1];
         }
         int[] mapping = new int[numDimensions];
@@ -74,15 +74,11 @@ public class OrderedTensorType {
         return mapping;
     }
 
-    public int dimensionMap(int originalIndex) {
-        return dimensionMap[originalIndex];
-    }
-
     /**
-     * When dimension ordering between Vespa and imported differs, i.e.
+     * When dimension ordering between Vespa and Onnx differs, i.e.
      * after dimension renaming, use the dimension map to read in values
      * so that they are correctly laid out in memory for Vespa.
-     * Used when importing tensors.
+     * Used when importing tensors from Onnx.
      */
     public int toDirectIndex(int index) {
         if (dimensions.size() == 0) {
@@ -94,9 +90,9 @@ public class OrderedTensorType {
         int directIndex = 0;
         long rest = index;
         for (int i = 0; i < dimensions.size(); ++i) {
-            long address = rest / innerSizesOriginal[i];
+            long address = rest / innerSizesOnnx[i];
             directIndex += innerSizesVespa[dimensionMap[i]] * address;
-            rest %= innerSizesOriginal[i];
+            rest %= innerSizesOnnx[i];
         }
         return directIndex;
     }
@@ -120,6 +116,22 @@ public class OrderedTensorType {
         return true;
     }
 
+    public void verifyType(Onnx.TypeProto typeProto) {
+        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 < dimensions.size(); ++onnxIndex) {
+                int vespaIndex = dimensionMap[onnxIndex];
+                Onnx.TensorShapeProto.Dimension onnxDimension = shape.getDim(onnxIndex);
+                TensorType.Dimension vespaDimension = type().dimensions().get(vespaIndex);
+                if (onnxDimension.getDimValue() != vespaDimension.size().orElse(-1L)) {
+                    throw new IllegalArgumentException("TensorFlow dimensions of does not match Vespa dimensions");
+                }
+            }
+        }
+    }
     public OrderedTensorType rename(DimensionRenamer renamer) {
         List<TensorType.Dimension> renamedDimensions = new ArrayList<>(dimensions.size());
         for (TensorType.Dimension dimension : dimensions) {
@@ -139,13 +151,18 @@ public class OrderedTensorType {
         return new OrderedTensorType(renamedDimensions);
     }
 
-    public OrderedTensorType rename(String dimensionPrefix) {
-        OrderedTensorType.Builder builder = new OrderedTensorType.Builder();
-        for (int i = 0; i < dimensions.size(); ++ i) {
+    public static OrderedTensorType fromOnnxType(Onnx.TypeProto type) {
+        return fromOnnxType(type, "d");  // standard naming convention: d0, d1, ...
+    }
+
+    public static OrderedTensorType fromOnnxType(Onnx.TypeProto type, String dimensionPrefix) {
+        Onnx.TensorShapeProto shape = type.getTensorType().getShape();
+        Builder builder = new Builder(shape);
+        for (int i = 0; i < shape.getDimCount(); ++ i) {
             String dimensionName = dimensionPrefix + i;
-            Optional<Long> dimSize = dimensions.get(i).size();
-            if (dimSize.isPresent() && dimSize.get() >= 0) {
-                builder.add(TensorType.Dimension.indexed(dimensionName, dimSize.get()));
+            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));
             }
@@ -153,13 +170,13 @@ public class OrderedTensorType {
         return builder.build();
     }
 
-    public static OrderedTensorType standardType(OrderedTensorType type) {
-        OrderedTensorType.Builder builder = new OrderedTensorType.Builder();
-        for (int i = 0; i < type.dimensions().size(); ++ i) {
-            TensorType.Dimension dim = type.dimensions().get(i);
-            String dimensionName = "d" + i;
-            if (dim.size().isPresent() && dim.size().get() >= 0) {
-                builder.add(TensorType.Dimension.indexed(dimensionName, dim.size().get()));
+    public static OrderedTensorType fromOnnxType(List<Long> dims, String dimensionPrefix) {
+        Builder builder = new Builder();
+        for (int i = 0; i < dims.size(); ++ i) {
+            String dimensionName = dimensionPrefix + i;
+            Long dimSize = dims.get(i);
+            if (dimSize >= 0) {
+                builder.add(TensorType.Dimension.indexed(dimensionName, dimSize));
             } else {
                 builder.add(TensorType.Dimension.indexed(dimensionName));
             }
@@ -167,46 +184,13 @@ public class OrderedTensorType {
         return builder.build();
     }
 
-    public static Long tensorSize(TensorType type) {
-        Long size = 1L;
-        for (TensorType.Dimension dimension : type.dimensions()) {
-            size *= dimensionSize(dimension);
-        }
-        return size;
-    }
-
-    public static Long dimensionSize(TensorType.Dimension dim) {
-        return dim.size().orElseThrow(() -> new IllegalArgumentException("Dimension has no size"));
-    }
-
-    /**
-     * Returns a string representation of this: A standard tensor type string where dimensions
-     * are listed in the order of this rather than in the natural order of their names.
-     */
-    @Override
-    public String toString() {
-        return "tensor(" + dimensions.stream().map(TensorType.Dimension::toString).collect(Collectors.joining(",")) + ")";
-    }
-
-    /**
-     * Creates an instance from the string representation of this: A standard tensor type string
-     * where dimensions are listed in the order of this rather than the natural order of their names.
-     */
-    public static OrderedTensorType fromSpec(String typeSpec) {
-        return new OrderedTensorType(TensorTypeParser.dimensionsFromSpec(typeSpec));
-    }
-
-    public static OrderedTensorType fromDimensionList(List<Long> dims) {
-        return fromDimensionList(dims, "d");  // standard naming convention: d0, d1, ...
-    }
-
-    public static OrderedTensorType fromDimensionList(List<Long> dims, String dimensionPrefix) {
-        OrderedTensorType.Builder builder = new OrderedTensorType.Builder();
-        for (int i = 0; i < dims.size(); ++ i) {
-            String dimensionName = dimensionPrefix + i;
-            Long dimSize = dims.get(i);
-            if (dimSize >= 0) {
-                builder.add(TensorType.Dimension.indexed(dimensionName, dimSize));
+    public static OrderedTensorType standardType(OrderedTensorType type) {
+        Builder builder = new Builder();
+        for (int i = 0; i < type.dimensions().size(); ++ i) {
+            TensorType.Dimension dim = type.dimensions().get(i);
+            String dimensionName = "d" + i;
+            if (dim.size().isPresent() && dim.size().get() >= 0) {
+                builder.add(TensorType.Dimension.indexed(dimensionName, dim.size().get()));
             } else {
                 builder.add(TensorType.Dimension.indexed(dimensionName));
             }
@@ -216,13 +200,45 @@ public class OrderedTensorType {
 
     public static class Builder {
 
+        private final Onnx.TensorShapeProto shape;
         private final List<TensorType.Dimension> dimensions;
 
+        public Builder(Onnx.TensorShapeProto shape) {
+            this.shape = shape;
+            this.dimensions = new ArrayList<>(shape.getDimCount());
+        }
+
         public Builder() {
+            this.shape = null;
             this.dimensions = new ArrayList<>();
         }
 
         public Builder add(TensorType.Dimension vespaDimension) {
+            if (shape != null) {
+                int index = dimensions.size();
+                Onnx.TensorShapeProto.Dimension onnxDimension = shape.getDim(index);
+                long size = onnxDimension.getDimValue();
+                if (size >= 0) {
+                    if (vespaDimension.type() != TensorType.Dimension.Type.indexedBound) {
+                        throw new IllegalArgumentException("Non-agreement between Onnx and Vespa " +
+                                "dimension types");
+                    }
+                    if (!vespaDimension.size().isPresent()) {
+                        throw new IllegalArgumentException("Tensor dimension is indexed bound but does " +
+                                "not have a size");
+                    }
+                    if (vespaDimension.size().get() != size) {
+                        throw new IllegalArgumentException("Non-agreement between Onnx and Vespa " +
+                                "dimension sizes. TensorFlow: " + size + " Vespa: " +
+                                vespaDimension.size().get());
+                    }
+                } else {
+                    if (vespaDimension.type() != TensorType.Dimension.Type.indexedUnbound) {
+                        throw new IllegalArgumentException("Non-agreement between Onnx and Vespa " +
+                                "dimension types");
+                    }
+                }
+            }
             this.dimensions.add(vespaDimension);
             return this;
         }
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/onnx/TensorConverter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/TensorConverter.java
index 18856d4a25f..2912db03b5f 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/onnx/TensorConverter.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/TensorConverter.java
@@ -1,16 +1,17 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
 
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.onnx;
+package com.yahoo.searchlib.rankingexpression.integration.onnx.importer;
 
 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 com.yahoo.tensor.TensorType;
 import onnx.Onnx;
 
 import java.nio.ByteBuffer;
 import java.nio.ByteOrder;
 import java.nio.FloatBuffer;
+import java.util.List;
 
 /**
  * Converts Onnx tensors into Vespa tensors.
@@ -28,6 +29,7 @@ public class TensorConverter {
         return builder.build();
     }
 
+    /* todo: support more types */
     private static Values readValuesOf(Onnx.TensorProto tensorProto) {
         if (tensorProto.hasRawData()) {
             switch (tensorProto.getDataType()) {
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/Argument.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/Argument.java
new file mode 100644
index 00000000000..a8d8d63daf4
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/Argument.java
@@ -0,0 +1,64 @@
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations;
+
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.OrderedTensorType;
+import com.yahoo.tensor.TensorType;
+import com.yahoo.tensor.evaluation.VariableTensor;
+import com.yahoo.tensor.functions.Rename;
+import com.yahoo.tensor.functions.TensorFunction;
+import onnx.Onnx;
+
+import java.util.Collections;
+import java.util.List;
+
+public class Argument extends OnnxOperation {
+
+    private Onnx.ValueInfoProto valueInfo;
+    private OrderedTensorType standardNamingType;  // using standard naming convention: d0, d1, ...
+
+    public Argument(Onnx.ValueInfoProto valueInfoProto) {
+        super(null, Collections.emptyList());
+        valueInfo = valueInfoProto;
+        standardNamingType = OrderedTensorType.fromOnnxType(valueInfo.getType());
+    }
+
+    @Override
+    public String vespaName() {
+        return vespaName(valueInfo.getName());
+    }
+
+    @Override
+    protected OrderedTensorType lazyGetType() {
+        return OrderedTensorType.fromOnnxType(valueInfo.getType(), vespaName() + "_");
+    }
+
+    @Override
+    protected TensorFunction lazyGetFunction() {
+        TensorFunction output = new VariableTensor(vespaName(), standardNamingType.type());
+        if (!standardNamingType.equals(type)) {
+            List<String> renameFrom = standardNamingType.dimensionNames();
+            List<String> renameTo = type.dimensionNames();
+            output = new Rename(output, renameFrom, renameTo);
+        }
+        return output;
+    }
+
+    @Override
+    public void addDimensionNameConstraints(DimensionRenamer renamer) {
+        for (TensorType.Dimension dimension : type.type().dimensions()) {
+            renamer.addDimension(dimension.name());
+        }
+    }
+
+    @Override
+    public boolean isInput() {
+        return true;
+    }
+
+    @Override
+    public boolean isConstant() {
+        return false;
+    }
+
+}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Constant.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/Constant.java
index 5e4abeaa234..13043a61a8e 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Constant.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/Constant.java
@@ -1,34 +1,38 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations;
 
+import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
 import com.yahoo.searchlib.rankingexpression.evaluation.Value;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.TensorConverter;
 import com.yahoo.tensor.TensorType;
 import com.yahoo.tensor.functions.TensorFunction;
+import onnx.Onnx;
 
 import java.util.Collections;
 import java.util.Optional;
 
-public class Constant extends IntermediateOperation {
+public class Constant extends OnnxOperation {
 
-    private final String modelName;
+    final String modelName;
+    final Onnx.TensorProto tensorProto;
 
-    public Constant(String modelName, String nodeName, OrderedTensorType type) {
-        super(modelName, nodeName, Collections.emptyList());
+    public Constant(String modelName, Onnx.TensorProto tensorProto) {
+        super(null, Collections.emptyList());
         this.modelName = modelName;
-        this.type = type.rename(vespaName() + "_");
+        this.tensorProto = tensorProto;
     }
 
     /** Constant names are prefixed by "modelName_" to avoid name conflicts between models */
     @Override
     public String vespaName() {
-        return modelName + "_" + vespaName(name);
+        return modelName + "_" + vespaName(tensorProto.getName());
     }
 
     @Override
     protected OrderedTensorType lazyGetType() {
-        return type;
+        return OrderedTensorType.fromOnnxType(tensorProto.getDimsList(), vespaName() + "_");
     }
 
     @Override
@@ -36,14 +40,9 @@ public class Constant extends IntermediateOperation {
         return null;  // will be added by function() since this is constant.
     }
 
-    /**
-     * Constant values are sent in via the constantValueFunction, as the
-     * dimension names and thus the data layout depends on the dimension
-     * renaming which happens after the conversion to intermediate graph.
-     */
     @Override
     public Optional<Value> getConstantValue() {
-        return Optional.ofNullable(constantValueFunction).map(func -> func.apply(type));
+        return Optional.of(new TensorValue(TensorConverter.toVespaTensor(tensorProto, type)));
     }
 
     @Override
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Join.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/Join.java
index 8413ed74118..fe2004a528d 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Join.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/Join.java
@@ -1,22 +1,24 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.OrderedTensorType;
 import com.yahoo.tensor.TensorType;
 import com.yahoo.tensor.functions.Reduce;
 import com.yahoo.tensor.functions.TensorFunction;
+import onnx.Onnx;
 
 import java.util.ArrayList;
+import java.util.Collections;
 import java.util.List;
 import java.util.function.DoubleBinaryOperator;
 
-public class Join extends IntermediateOperation {
+public class Join extends OnnxOperation {
 
     private final DoubleBinaryOperator operator;
 
-    public Join(String modelName, String nodeName, List<IntermediateOperation> inputs, DoubleBinaryOperator operator) {
-        super(modelName, nodeName, inputs);
+    public Join(Onnx.NodeProto node, List<OnnxOperation> inputs, DoubleBinaryOperator operator) {
+        super(node, inputs);
         this.operator = operator;
     }
 
@@ -59,8 +61,8 @@ public class Join extends IntermediateOperation {
             return null;
         }
 
-        IntermediateOperation a = largestInput();
-        IntermediateOperation b = smallestInput();
+        OnnxOperation a = largestInput();
+        OnnxOperation b = smallestInput();
 
         List<String> aDimensionsToReduce = new ArrayList<>();
         List<String> bDimensionsToReduce = new ArrayList<>();
@@ -105,13 +107,13 @@ public class Join extends IntermediateOperation {
         }
     }
 
-    private IntermediateOperation largestInput() {
+    private OnnxOperation largestInput() {
         OrderedTensorType a = inputs.get(0).type().get();
         OrderedTensorType b = inputs.get(1).type().get();
         return a.rank() >= b.rank() ? inputs.get(0) : inputs.get(1);
     }
 
-    private IntermediateOperation smallestInput() {
+    private OnnxOperation smallestInput() {
         OrderedTensorType a = inputs.get(0).type().get();
         OrderedTensorType b = inputs.get(1).type().get();
         return a.rank() < b.rank() ? inputs.get(0) : inputs.get(1);
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/MatMul.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/MatMul.java
index 52e223f9518..1b388e2ae89 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/MatMul.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/MatMul.java
@@ -1,18 +1,21 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.OrderedTensorType;
 import com.yahoo.tensor.TensorType;
 import com.yahoo.tensor.functions.TensorFunction;
+import onnx.Onnx;
 
+import java.util.Collections;
 import java.util.List;
 import java.util.Optional;
+import java.util.function.DoubleBinaryOperator;
 
-public class MatMul extends IntermediateOperation {
+public class MatMul extends OnnxOperation {
 
-    public MatMul(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
+    public MatMul(Onnx.NodeProto node, List<OnnxOperation> inputs) {
+        super(node, inputs);
     }
 
     @Override
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/NoOp.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/NoOp.java
new file mode 100644
index 00000000000..b1136a0ce0a
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/NoOp.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 com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations;
+
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.OrderedTensorType;
+import com.yahoo.tensor.functions.TensorFunction;
+import onnx.Onnx;
+
+import java.util.Collections;
+import java.util.List;
+
+public class NoOp extends OnnxOperation {
+
+    public NoOp(Onnx.NodeProto node, List<OnnxOperation> inputs) {
+        super(node, Collections.emptyList());  // don't propagate inputs
+    }
+
+    @Override
+    protected OrderedTensorType lazyGetType() {
+        return null;
+    }
+
+    @Override
+    protected TensorFunction lazyGetFunction() {
+        return null;
+    }
+
+    @Override
+    public boolean isConstant() {
+        return true;
+    }
+
+}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/OnnxOperation.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/OnnxOperation.java
new file mode 100644
index 00000000000..30f7b4f4711
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/importer/operations/OnnxOperation.java
@@ -0,0 +1,139 @@
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+
+package com.yahoo.searchlib.rankingexpression.integration.onnx.importer.operations;
+
+import com.yahoo.searchlib.rankingexpression.Reference;
+import com.yahoo.searchlib.rankingexpression.evaluation.Value;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.onnx.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
+import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
+import com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode;
+import com.yahoo.tensor.functions.TensorFunction;
+import onnx.Onnx;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+import java.util.Optional;
+import java.util.function.Function;
+
+/**
+ * Wraps an ONNX node and produces the respective Vespa tensor operation.
+ * During import, a graph of these operations are constructed. Then, the
+ * types are used to deduce sensible dimension names using the
+ * DimensionRenamer. After the types have been renamed, the proper
+ * Vespa expressions can be extracted.
+ *
+ * @author lesters
+ */
+public abstract class OnnxOperation {
+
+    protected final Onnx.NodeProto node; // can be null for onnx inputs and constants
+    protected final List<OnnxOperation> inputs;
+    protected final List<OnnxOperation> outputs = new ArrayList<>();
+    protected final List<String> importWarnings = new ArrayList<>();
+
+    protected OrderedTensorType type;
+    protected TensorFunction function;
+    protected Value constantValue = null;
+
+    OnnxOperation(Onnx.NodeProto node, List<OnnxOperation> inputs) {
+        this.node = node;
+        this.inputs = Collections.unmodifiableList(inputs);
+        this.inputs.forEach(i -> i.outputs.add(this));
+    }
+
+    protected abstract OrderedTensorType lazyGetType();
+    protected abstract TensorFunction lazyGetFunction();
+
+    /** Returns the Vespa tensor type of this operation if it exists */
+    public Optional<OrderedTensorType> type() {
+        if (type == null) {
+            type = lazyGetType();
+        }
+        return Optional.ofNullable(type);
+    }
+
+    /** Returns the Vespa tensor function implementing all operations from this node with inputs */
+    public Optional<TensorFunction> function() {
+        if (function == null) {
+            if (isConstant()) {
+                ExpressionNode constant = new ReferenceNode(Reference.simple("constant", vespaName()));
+                function = new TensorFunctionNode.TensorFunctionExpressionNode(constant);
+            } else {
+                function = lazyGetFunction();
+            }
+        }
+        return Optional.ofNullable(function);
+    }
+
+    /** Return Onnx node */
+    public Onnx.NodeProto node() { return node; }
+
+    /** Return unmodifiable list of inputs */
+    public List<OnnxOperation> inputs() { return inputs; }
+
+    /** Return unmodifiable list of outputs. If a node has multiple outputs, consider adding a macro. */
+    public List<OnnxOperation> outputs() { return Collections.unmodifiableList(outputs); }
+
+    /** Add dimension name constraints for this operation */
+    public void addDimensionNameConstraints(DimensionRenamer renamer) { }
+
+    /** Performs dimension rename for this operation */
+    public void renameDimensions(DimensionRenamer renamer) { type = type.rename(renamer); }
+
+    /** Return true for operations that are inputs to the model itself (as opposed to inputs to the operation) */
+    public boolean isInput() { return false; }
+
+    /** Return true if this node is constant */
+    public boolean isConstant() { return inputs.stream().allMatch(OnnxOperation::isConstant); }
+
+    /** Gets the constant value if it exists */
+    public Optional<Value> getConstantValue() { return Optional.ofNullable(constantValue); }
+
+    /** Retrieve the valid Vespa name of this node */
+    public String vespaName() { return vespaName(node.getName()); }
+    public String vespaName(String name) { return name != null ? namePartOf(name).replace('/', '_') : null; }
+
+    /** Retrieve the list of warnings produced during its lifetime */
+    public List<String> warnings() { return Collections.unmodifiableList(importWarnings); }
+
+    /** Set an input warning */
+    public void warning(String warning) { importWarnings.add(warning); }
+
+    boolean verifyInputs(int expected, Function<OnnxOperation, Optional<?>> func) {
+        if (inputs.size() != expected) {
+            throw new IllegalArgumentException("Expected " + expected + " inputs " +
+                    "for '" + node.getName() + "', got " + inputs.size());
+        }
+        return inputs.stream().map(func).allMatch(Optional::isPresent);
+    }
+
+    boolean allInputTypesPresent(int expected) {
+        return verifyInputs(expected, OnnxOperation::type);
+    }
+
+    boolean allInputFunctionsPresent(int expected) {
+        return verifyInputs(expected, OnnxOperation::function);
+    }
+
+    /**
+     * A method signature input and output has the form name:index.
+     * This returns the name part without the index.
+     */
+    public static String namePartOf(String name) {
+        name = name.startsWith("^") ? name.substring(1) : name;
+        return name.split(":")[0];
+    }
+
+    /**
+     * This return the output index part. Indexes are used for nodes with
+     * multiple outputs.
+     */
+    public static int indexPartOf(String name) {
+        int i = name.indexOf(":");
+        return i < 0 ? 0 : Integer.parseInt(name.substring(i + 1));
+    }
+
+}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/package-info.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/package-info.java
index 1530754cc43..5cff8b03d40 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/package-info.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/onnx/package-info.java
@@ -3,6 +3,6 @@
  * ONNX integration
  */
 @ExportPackage
-package com.yahoo.searchlib.rankingexpression.integration.ml;
+package com.yahoo.searchlib.rankingexpression.integration.onnx;
 
 import com.yahoo.osgi.annotation.ExportPackage;
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/TensorFlowImporter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/TensorFlowImporter.java
new file mode 100644
index 00000000000..e3c72830095
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/TensorFlowImporter.java
@@ -0,0 +1,411 @@
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow;
+
+import com.yahoo.searchlib.rankingexpression.RankingExpression;
+import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
+import com.yahoo.searchlib.rankingexpression.evaluation.Value;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OperationMapper;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.TensorConverter;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Const;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.TensorFlowOperation;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Variable;
+import com.yahoo.searchlib.rankingexpression.parser.ParseException;
+import com.yahoo.tensor.Tensor;
+import com.yahoo.tensor.functions.Rename;
+import com.yahoo.tensor.functions.TensorFunction;
+import com.yahoo.yolean.Exceptions;
+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.File;
+import java.io.IOException;
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Optional;
+import java.util.logging.Logger;
+import java.util.stream.Collectors;
+
+/**
+ * Converts a saved TensorFlow model into a ranking expression and set of constants.
+ *
+ * @author bratseth
+ * @author lesters
+ */
+public class TensorFlowImporter {
+
+    private static final Logger log = Logger.getLogger(TensorFlowImporter.class.getName());
+
+    /**
+     * Imports a saved TensorFlow model from a directory.
+     * The model should be saved as a .pbtxt or .pb file.
+     * The name of the model is taken as the db/pbtxt file name (not including the file ending).
+     *
+     * @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
+     */
+    public TensorFlowModel importModel(String modelName, String modelDir) {
+        try (SavedModelBundle model = SavedModelBundle.load(modelDir, "serve")) {
+
+            return importModel(modelName, model);
+        }
+        catch (IllegalArgumentException e) {
+            throw new IllegalArgumentException("Could not import TensorFlow model from directory '" + modelDir + "'", e);
+        }
+    }
+
+    public TensorFlowModel importModel(String modelName, File modelDir) {
+        return importModel(modelName, modelDir.toString());
+    }
+
+    /** Imports a TensorFlow model */
+    public TensorFlowModel importModel(String modelName, SavedModelBundle model) {
+        try {
+            return importGraph(modelName, MetaGraphDef.parseFrom(model.metaGraphDef()), model);
+        }
+        catch (IOException e) {
+            throw new IllegalArgumentException("Could not import TensorFlow model '" + model + "'", e);
+        }
+    }
+
+    /**
+     * Imports the TensorFlow graph by first importing the tensor types, then
+     * finding a suitable set of dimensions names for each
+     * placeholder/constant/variable, then importing the expressions.
+     */
+    private static TensorFlowModel importGraph(String modelName, MetaGraphDef graph, SavedModelBundle bundle) {
+        TensorFlowModel model = new TensorFlowModel(modelName);
+        OperationIndex index = new OperationIndex();
+
+        importSignatures(graph, model);
+        importNodes(graph, model, index);
+        findDimensionNames(model, index);
+        importExpressions(model, index, bundle);
+
+        reportWarnings(model, index);
+        logVariableTypes(index);
+
+        return model;
+    }
+
+    private static void importSignatures(MetaGraphDef graph, TensorFlowModel model) {
+        for (Map.Entry<String, SignatureDef> signatureEntry : graph.getSignatureDefMap().entrySet()) {
+            String signatureName = signatureEntry.getKey();
+            TensorFlowModel.Signature signature = model.signature(signatureName);
+
+            Map<String, TensorInfo> inputInfoMap = signatureEntry.getValue().getInputsMap();
+            for (Map.Entry<String, TensorInfo> input : inputInfoMap.entrySet()) {
+                String inputName = input.getKey();
+                signature.input(inputName, namePartOf(input.getValue().getName()));
+            }
+
+            Map<String, TensorInfo> outputInfoMap = signatureEntry.getValue().getOutputsMap();
+            for (Map.Entry<String, TensorInfo> output : outputInfoMap.entrySet()) {
+                String outputName = output.getKey();
+                signature.output(outputName, namePartOf(output.getValue().getName()));
+            }
+        }
+    }
+
+    private static boolean isSignatureInput(TensorFlowModel model, TensorFlowOperation operation) {
+        for (TensorFlowModel.Signature signature : model.signatures().values()) {
+            for (String inputName : signature.inputs().values()) {
+                if (inputName.equals(operation.node().getName())) {
+                    return true;
+                }
+            }
+        }
+        return false;
+    }
+
+    private static boolean isSignatureOutput(TensorFlowModel model, TensorFlowOperation operation) {
+        for (TensorFlowModel.Signature signature : model.signatures().values()) {
+            for (String outputName : signature.outputs().values()) {
+                if (outputName.equals(operation.node().getName())) {
+                    return true;
+                }
+            }
+        }
+        return false;
+    }
+
+    private static void importNodes(MetaGraphDef graph, TensorFlowModel model, OperationIndex index) {
+        for (TensorFlowModel.Signature signature : model.signatures().values()) {
+            for (String outputName : signature.outputs().values()) {
+                importNode(model.name(), outputName, graph.getGraphDef(), index);
+            }
+        }
+    }
+
+    private static TensorFlowOperation importNode(String modelName, String nodeName, GraphDef graph, OperationIndex index) {
+        if (index.alreadyImported(nodeName)) {
+            return index.get(nodeName);
+        }
+        NodeDef node = getTensorFlowNodeFromGraph(namePartOf(nodeName), graph);
+        List<TensorFlowOperation> inputs = importNodeInputs(modelName, node, graph, index);
+        TensorFlowOperation operation = OperationMapper.get(modelName, node, inputs, portPartOf(nodeName));
+        index.put(nodeName, operation);
+
+        List<TensorFlowOperation> controlInputs = importControlInputs(modelName, node, graph, index);
+        if (controlInputs.size() > 0) {
+            operation.setControlInputs(controlInputs);
+        }
+
+        return operation;
+    }
+
+    private static List<TensorFlowOperation> importNodeInputs(String modelName, NodeDef node, GraphDef graph, OperationIndex index) {
+        return node.getInputList().stream()
+                .filter(name -> ! isControlDependency(name))
+                .map(nodeName -> importNode(modelName, nodeName, graph, index))
+                .collect(Collectors.toList());
+    }
+
+    private static List<TensorFlowOperation> importControlInputs(String modelName, NodeDef node, GraphDef graph, OperationIndex index) {
+        return node.getInputList().stream()
+                .filter(nodeName -> isControlDependency(nodeName))
+                .map(nodeName -> importNode(modelName, nodeName, graph, index))
+                .collect(Collectors.toList());
+    }
+
+    private static boolean isControlDependency(String name) {
+        return name.startsWith("^");
+    }
+
+    /** Find dimension names to avoid excessive renaming while evaluating the model. */
+    private static void findDimensionNames(TensorFlowModel model, OperationIndex index) {
+        DimensionRenamer renamer = new DimensionRenamer();
+        for (TensorFlowModel.Signature signature : model.signatures().values()) {
+            for (String output : signature.outputs().values()) {
+                addDimensionNameConstraints(index.get(output), renamer);
+            }
+        }
+        renamer.solve();
+        for (TensorFlowModel.Signature signature : model.signatures().values()) {
+            for (String output : signature.outputs().values()) {
+                renameDimensions(index.get(output), renamer);
+            }
+        }
+    }
+
+    private static void addDimensionNameConstraints(TensorFlowOperation operation, DimensionRenamer renamer) {
+        if (operation.type().isPresent()) {
+            operation.inputs().forEach(input -> addDimensionNameConstraints(input, renamer));
+            operation.addDimensionNameConstraints(renamer);
+        }
+    }
+
+    private static void renameDimensions(TensorFlowOperation operation, DimensionRenamer renamer) {
+        if (operation.type().isPresent()) {
+            operation.inputs().forEach(input -> renameDimensions(input, renamer));
+            operation.renameDimensions(renamer);
+        }
+    }
+
+    private static void importExpressions(TensorFlowModel model, OperationIndex index, SavedModelBundle bundle) {
+        for (TensorFlowModel.Signature signature : model.signatures().values()) {
+            for (String outputName : signature.outputs().values()) {
+                try {
+                    Optional<TensorFunction> function = importExpression(index.get(outputName), model, bundle);
+                    if (!function.isPresent()) {
+                        signature.skippedOutput(outputName, "No valid output function could be found.");
+                    }
+                }
+                catch (IllegalArgumentException e) {
+                    signature.skippedOutput(outputName, Exceptions.toMessageString(e));
+                }
+            }
+        }
+    }
+
+    private static Optional<TensorFunction> importExpression(TensorFlowOperation operation, TensorFlowModel model, SavedModelBundle bundle) {
+        if (!operation.type().isPresent()) {
+            return Optional.empty();
+        }
+        if (operation.isConstant()) {
+            return importConstant(model, operation, bundle);
+        }
+
+        importInputExpressions(operation, model, bundle);
+        importRankingExpression(model, operation);
+        importInputExpression(model, operation);
+        importMacroExpression(model, operation);
+
+        return operation.function();
+    }
+
+    private static void importInputExpressions(TensorFlowOperation operation, TensorFlowModel model,
+                                               SavedModelBundle bundle) {
+        operation.inputs().forEach(input -> importExpression(input, model, bundle));
+    }
+
+    private static void importMacroExpression(TensorFlowModel model, TensorFlowOperation operation) {
+        if (operation.macro().isPresent()) {
+            TensorFunction function = operation.macro().get();
+            try {
+                model.macro(operation.macroName(), new RankingExpression(operation.macroName(), function.toString()));
+            }
+            catch (ParseException e) {
+                throw new RuntimeException("Tensorflow function " + function +
+                                           " cannot be parsed as a ranking expression", e);
+            }
+        }
+    }
+
+    private static Optional<TensorFunction> importConstant(TensorFlowModel model, TensorFlowOperation operation,
+                                                           SavedModelBundle bundle) {
+        String name = operation.vespaName();
+        if (model.largeConstants().containsKey(name) || model.smallConstants().containsKey(name)) {
+            return operation.function();
+        }
+
+        Tensor tensor;
+        if (operation.getConstantValue().isPresent()) {
+            Value value = operation.getConstantValue().get();
+            if ( ! (value instanceof TensorValue)) {
+                return operation.function(); // scalar values are inserted directly into the expression
+            }
+            tensor = value.asTensor();
+        } else {
+            // Here we use the type from the operation, which will have correct dimension names after name resolving
+            tensor = TensorConverter.toVespaTensor(readVariable(operation.node().getName(), bundle),
+                                                   operation.type().get());
+            operation.setConstantValue(new TensorValue(tensor));
+        }
+
+        if (tensor.type().rank() == 0) {
+            model.smallConstant(name, tensor);
+        } else {
+            model.largeConstant(name, tensor);
+        }
+        return operation.function();
+    }
+
+    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 importRankingExpression(TensorFlowModel model, TensorFlowOperation operation) {
+        if (operation.function().isPresent()) {
+            String name = operation.node().getName();
+            if (!model.expressions().containsKey(operation.node().getName())) {
+                TensorFunction function = operation.function().get();
+
+                // Make sure output adheres to standard naming convention
+                if (isSignatureOutput(model, operation)) {
+                    OrderedTensorType operationType = operation.type().get();
+                    OrderedTensorType standardNamingType = OrderedTensorType.fromTensorFlowType(operation.node());
+                    if ( ! operationType.equals(standardNamingType)) {
+                        List<String> renameFrom = operationType.dimensionNames();
+                        List<String> renameTo = standardNamingType.dimensionNames();
+                        function = new Rename(function, renameFrom, renameTo);
+                    }
+                }
+
+                try {
+                    // We add all intermediate nodes imported as separate expressions. Only
+                    // those referenced  in a signature output will be used. We parse the
+                    // TensorFunction here to convert it to a RankingExpression tree.
+                    model.expression(name, new RankingExpression(name, function.toString()));
+                }
+                catch (ParseException e) {
+                    throw new RuntimeException("Tensorflow function " + function +
+                                               " cannot be parsed as a ranking expression", e);
+                }
+            }
+        }
+    }
+
+    private static void importInputExpression(TensorFlowModel model, TensorFlowOperation operation) {
+        if (operation.isInput() && isSignatureInput(model, operation)) {
+            // All inputs must have dimensions with standard naming convention: d0, d1, ...
+            OrderedTensorType standardNamingConvention = OrderedTensorType.fromTensorFlowType(operation.node());
+            model.argument(operation.node().getName(), standardNamingConvention.type());
+            model.requiredMacro(operation.vespaName(), standardNamingConvention.type());
+        }
+    }
+
+    private static void reportWarnings(TensorFlowModel model, OperationIndex index) {
+        for (TensorFlowModel.Signature signature : model.signatures().values()) {
+            for (String output : signature.outputs().values()) {
+                reportWarnings(index.get(output), signature);
+            }
+        }
+    }
+
+    /**
+     * Log all TensorFlow Variables (i.e file constants) imported as part of this with their ordered type.
+     * This allows users to learn the exact types (including dimension order after renaming) of the Variables
+     * such that these can be converted and fed to a parent document independently of the rest of the model
+     * for fast model weight updates.
+     */
+    private static void logVariableTypes(OperationIndex index) {
+        for (TensorFlowOperation operation : index.operations()) {
+            if ( ! (operation instanceof Variable)) continue;
+            if ( ! operation.type().isPresent()) continue; // will not happen
+
+            log.info("Importing TensorFlow variable " + operation.node().getName() + " as " + operation.vespaName() +
+                     " of type " + operation.type().get());
+        }
+    }
+
+    private static void reportWarnings(TensorFlowOperation operation, TensorFlowModel.Signature signature) {
+        for (String warning : operation.warnings()) {
+            signature.importWarning(warning);
+        }
+        for (TensorFlowOperation input : operation.inputs()) {
+            reportWarnings(input, signature);
+        }
+    }
+
+    private static NodeDef getTensorFlowNodeFromGraph(String name, GraphDef graph) {
+        for (NodeDef node : graph.getNodeList()) {
+            if (node.getName().equals(name)) {
+                return node;
+            }
+        }
+        throw new IllegalArgumentException("Could not find node '" + name + "'");
+    }
+
+    /**
+     * A method signature input and output has the form name:index.
+     * This returns the name part without the index.
+     */
+    private static String namePartOf(String name) {
+        name = name.startsWith("^") ? name.substring(1) : name;
+        return name.split(":")[0];
+    }
+
+    /**
+     * This return the output port part. Indexes are used for nodes with
+     * multiple outputs.
+     */
+    private static int portPartOf(String name) {
+        int i = name.indexOf(":");
+        return i < 0 ? 0 : Integer.parseInt(name.substring(i + 1));
+    }
+
+    private static class OperationIndex {
+
+        private final Map<String, TensorFlowOperation> index = new HashMap<>();
+        public TensorFlowOperation put(String key, TensorFlowOperation operation) { return index.put(key, operation); }
+        public TensorFlowOperation get(String key) { return index.get(key); }
+        public boolean alreadyImported(String key) { return index.containsKey(key); }
+        public Collection<TensorFlowOperation> operations() { return index.values(); }
+
+    }
+
+}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ImportedModel.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/TensorFlowModel.java
index 4b49f17f74e..721214f9e94 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ImportedModel.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/TensorFlowModel.java
@@ -1,4 +1,5 @@
-package com.yahoo.searchlib.rankingexpression.integration.ml;
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow;
 
 import com.yahoo.searchlib.rankingexpression.RankingExpression;
 import com.yahoo.tensor.Tensor;
@@ -12,61 +13,76 @@ import java.util.Map;
 import java.util.regex.Pattern;
 
 /**
- * The result of importing a model (TensorFlow or ONNX) into Vespa.
+ * The result of importing a TensorFlow model into Vespa.
+ * - A set of signatures which are named collections of inputs and outputs.
+ * - A set of named constant tensors represented by Variable nodes in TensorFlow.
+ * - A list of warning messages.
  *
  * @author bratseth
  */
-public class ImportedModel {
-
-    private static final String defaultSignatureName = "default";
+// This object can be built incrementally within this package, but is immutable when observed from outside the package
+public class TensorFlowModel {
 
     private static final Pattern nameRegexp = Pattern.compile("[A-Za-z0-9_]*");
-    private final String name;
 
-    private final Map<String, Signature> signatures = new HashMap<>();
-    private final Map<String, TensorType> arguments = new HashMap<>();
-    private final Map<String, Tensor> smallConstants = new HashMap<>();
-    private final Map<String, Tensor> largeConstants = new HashMap<>();
-    private final Map<String, RankingExpression> expressions = new HashMap<>();
-    private final Map<String, RankingExpression> macros = new HashMap<>();
-    private final Map<String, TensorType> requiredMacros = new HashMap<>();
+    private final String name;
 
     /**
-     * Creates a new imported model.
+     * Creates a TensorFlow model
      *
      * @param name the name of this mode, containing only characters in [A-Za-z0-9_]
      */
-    public ImportedModel(String name) {
+    public TensorFlowModel(String name) {
         if ( ! nameRegexp.matcher(name).matches())
-            throw new IllegalArgumentException("An imported model name can only contain [A-Za-z0-9_], but is '" +
-                    name + "'");
+            throw new IllegalArgumentException("A TensorFlow model name can only contain [A-Za-z0-9_], but is '" +
+                                               name + "'");
         this.name = name;
     }
 
     /** Returns the name of this model, which can only contain the characters in [A-Za-z0-9_] */
     public String name() { return name; }
 
+    private final Map<String, Signature> signatures = new HashMap<>();
+    private final Map<String, TensorType> arguments = new HashMap<>();
+    private final Map<String, Tensor> smallConstants = new HashMap<>();
+    private final Map<String, Tensor> largeConstants = new HashMap<>();
+    private final Map<String, RankingExpression> expressions = new HashMap<>();
+    private final Map<String, RankingExpression> macros = new HashMap<>();
+    private final Map<String, TensorType> requiredMacros = new HashMap<>();
+
+    void argument(String name, TensorType argumentType) { arguments.put(name, argumentType); }
+    void smallConstant(String name, Tensor constant) { smallConstants.put(name, constant); }
+    void largeConstant(String name, Tensor constant) { largeConstants.put(name, constant); }
+    void expression(String name, RankingExpression expression) { expressions.put(name, expression); }
+    void macro(String name, RankingExpression expression) { macros.put(name, expression); }
+    void requiredMacro(String name, TensorType type) { requiredMacros.put(name, type); }
+
+    /** Returns the given signature. If it does not already exist it is added to this. */
+    Signature signature(String name) {
+        return signatures.computeIfAbsent(name, Signature::new);
+    }
+
     /** Returns an immutable map of the arguments ("Placeholders") of this */
     public Map<String, TensorType> arguments() { return Collections.unmodifiableMap(arguments); }
 
     /**
      * Returns an immutable map of the small constants of this.
      * These should have sizes up to a few kb at most, and correspond to constant
-     * values given in the TensorFlow or ONNX source.
+     * values given in the TensorFlow source.
      */
     public Map<String, Tensor> smallConstants() { return Collections.unmodifiableMap(smallConstants); }
 
     /**
      * Returns an immutable map of the large constants of this.
-     * These can have sizes in gigabytes and must be distributed to nodes separately from configuration.
-     * For TensorFlow this corresponds to Variable files stored separately.
+     * These can have sizes in gigabytes and must be distributed to nodes separately from configuration,
+     * and correspond to Variable files stored separately in TensorFlow.
      */
     public Map<String, Tensor> largeConstants() { return Collections.unmodifiableMap(largeConstants); }
 
     /**
-     * Returns an immutable map of the expressions of this - corresponding to graph nodes
-     * which are not Inputs/Placeholders or Variables (which instead become respectively arguments and constants).
-     * Note that only nodes recursively referenced by a placeholder/input are added.
+     * Returns an immutable map of the expressions of this - corresponding to TensorFlow nodes
+     * which are not Placeholders or Variables (which instead become respectively arguments and constants).
+     * Note that only nodes recursively referenced by a placeholder are added.
      */
     public Map<String, RankingExpression> expressions() { return Collections.unmodifiableMap(expressions); }
 
@@ -79,26 +95,9 @@ public class ImportedModel {
     /** Returns an immutable map of the signatures of this */
     public Map<String, Signature> signatures() { return Collections.unmodifiableMap(signatures); }
 
-    /** Returns the given signature. If it does not already exist it is added to this. */
-    Signature signature(String name) {
-        return signatures.computeIfAbsent(name, Signature::new);
-    }
-
-    /** Convenience method for returning a default signature */
-    Signature defaultSignature() { return signature(defaultSignatureName); }
-
-    void argument(String name, TensorType argumentType) { arguments.put(name, argumentType); }
-    void smallConstant(String name, Tensor constant) { smallConstants.put(name, constant); }
-    void largeConstant(String name, Tensor constant) { largeConstants.put(name, constant); }
-    void expression(String name, RankingExpression expression) { expressions.put(name, expression); }
-    void macro(String name, RankingExpression expression) { macros.put(name, expression); }
-    void requiredMacro(String name, TensorType type) { requiredMacros.put(name, type); }
-
     /**
-     * A signature is a set of named inputs and outputs, where the inputs maps to argument
-     * ("placeholder") names+types, and outputs maps to expressions nodes.
-     * Note that TensorFlow supports multiple signatures in their format, but ONNX has no explicit
-     * concept of signatures. For now, we handle ONNX models as having a single signature.
+     * A signature is a set of named inputs and outputs, where the inputs maps to argument ("placeholder") names+types,
+     * and outputs maps to expressions nodes.
      */
     public class Signature {
 
@@ -108,14 +107,19 @@ public class ImportedModel {
         private final Map<String, String> skippedOutputs = new HashMap<>();
         private final List<String> importWarnings = new ArrayList<>();
 
-        public Signature(String name) {
+        Signature(String name) {
             this.name = name;
         }
 
+        void input(String inputName, String argumentName) { inputs.put(inputName, argumentName); }
+        void output(String name, String expressionName) { outputs.put(name, expressionName); }
+        void skippedOutput(String name, String reason) { skippedOutputs.put(name, reason); }
+        void importWarning(String warning) { importWarnings.add(warning); }
+
         public String name() { return name; }
 
         /** Returns the result this is part of */
-        public ImportedModel owner() { return ImportedModel.this; }
+        TensorFlowModel owner() { return TensorFlowModel.this; }
 
         /**
          * Returns an immutable map of the inputs (evaluation context) of this. This is a map from input name
@@ -123,7 +127,7 @@ public class ImportedModel {
          */
         public Map<String, String> inputs() { return Collections.unmodifiableMap(inputs); }
 
-        /** Returns the type of the argument this input references */
+        /** Returns owner().arguments().get(inputs.get(name)), e.g the type of the argument this input references */
         public TensorType inputArgument(String inputName) { return owner().arguments().get(inputs.get(inputName)); }
 
         /** Returns an immutable list of the expression names of this */
@@ -140,17 +144,12 @@ public class ImportedModel {
          */
         public List<String> importWarnings() { return Collections.unmodifiableList(importWarnings); }
 
-        /** Returns the expression this output references */
+        /** Returns owner().expressions().get(outputs.get(outputName)), e.g the expression this output references */
         public RankingExpression outputExpression(String outputName) { return owner().expressions().get(outputs.get(outputName)); }
 
         @Override
         public String toString() { return "signature '" + name + "'"; }
 
-        void input(String inputName, String argumentName) { inputs.put(inputName, argumentName); }
-        void output(String name, String expressionName) { outputs.put(name, expressionName); }
-        void skippedOutput(String name, String reason) { skippedOutputs.put(name, reason); }
-        void importWarning(String warning) { importWarnings.add(warning); }
-
     }
 
 }
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/VariableConverter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/VariableConverter.java
index e1294ec3e01..c5ac7ace0fc 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/VariableConverter.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/VariableConverter.java
@@ -1,8 +1,7 @@
-package com.yahoo.searchlib.rankingexpression.integration.ml;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.tensorflow.TensorConverter;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.tensorflow.GraphImporter;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.TensorConverter;
 import com.yahoo.tensor.serialization.JsonFormat;
 import com.yahoo.yolean.Exceptions;
 import org.tensorflow.SavedModelBundle;
@@ -25,7 +24,7 @@ public class VariableConverter {
      */
     public static byte[] importVariable(String modelDir, String tensorFlowVariableName, String orderedTypeSpec) {
         try (SavedModelBundle bundle = SavedModelBundle.load(modelDir, "serve")) {
-            return JsonFormat.encode(TensorConverter.toVespaTensor(GraphImporter.readVariable(tensorFlowVariableName,
+            return JsonFormat.encode(TensorConverter.toVespaTensor(TensorFlowImporter.readVariable(tensorFlowVariableName,
                                                                                                    bundle),
                                                                    OrderedTensorType.fromSpec(orderedTypeSpec)));
         }
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/DimensionRenamer.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/DimensionRenamer.java
new file mode 100644
index 00000000000..c1665d066a4
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/DimensionRenamer.java
@@ -0,0 +1,210 @@
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer;
+
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.TensorFlowOperation;
+
+import java.util.ArrayDeque;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Deque;
+import java.util.HashMap;
+import java.util.Iterator;
+import java.util.List;
+import java.util.Map;
+import java.util.Objects;
+import java.util.Optional;
+
+/**
+ * A constraint satisfier to find suitable dimension names to reduce the
+ * amount of necessary renaming during evaluation of an imported model.
+ *
+ * @author lesters
+ */
+public class DimensionRenamer {
+
+    private final String dimensionPrefix;
+    private final Map<String, List<Integer>> variables = new HashMap<>();
+    private final Map<Arc, Constraint> constraints = new HashMap<>();
+    private final Map<String, Integer> renames = new HashMap<>();
+
+    private int iterations = 0;
+
+    public DimensionRenamer() {
+        this("d");
+    }
+
+    public DimensionRenamer(String dimensionPrefix) {
+        this.dimensionPrefix = dimensionPrefix;
+    }
+
+    /**
+     * Add a dimension name variable.
+     */
+    public void addDimension(String name) {
+        variables.computeIfAbsent(name, d -> new ArrayList<>());
+    }
+
+    /**
+     * Add a constraint between dimension names.
+     */
+    public void addConstraint(String from, String to, Constraint pred, TensorFlowOperation operation) {
+        Arc arc = new Arc(from, to, operation);
+        Arc opposite = arc.opposite();
+        constraints.put(arc, pred);
+        constraints.put(opposite, (x,y) -> pred.test(y, x));  // make constraint graph symmetric
+    }
+
+    /**
+     * Retrieve resulting name of dimension after solving for constraints.
+     */
+    public Optional<String> dimensionNameOf(String name) {
+        if (!renames.containsKey(name)) {
+            return Optional.empty();
+        }
+        return Optional.of(String.format("%s%d", dimensionPrefix, renames.get(name)));
+    }
+
+    /**
+     * Perform iterative arc consistency until we have found a solution. After
+     * an initial iteration, the variables (dimensions) will have multiple
+     * valid values. Find a single valid assignment by iteratively locking one
+     * dimension after another, and running the arc consistency algorithm
+     * multiple times.
+     *
+     * This requires having constraints that result in an absolute ordering:
+     * equals, lesserThan and greaterThan do that, but adding notEquals does
+     * not typically result in a guaranteed ordering. If that is needed, the
+     * algorithm below needs to be adapted with a backtracking (tree) search
+     * to find solutions.
+     */
+    public void solve(int maxIterations) {
+        initialize();
+
+        // Todo: evaluate possible improved efficiency by using a heuristic such as min-conflicts
+
+        for (String dimension : variables.keySet()) {
+            List<Integer> values = variables.get(dimension);
+            if (values.size() > 1) {
+                if (!ac3()) {
+                    throw new IllegalArgumentException("Dimension renamer unable to find a solution.");
+                }
+                values.sort(Integer::compare);
+                variables.put(dimension, Collections.singletonList(values.get(0)));
+            }
+            renames.put(dimension, variables.get(dimension).get(0));
+            if (iterations > maxIterations) {
+                throw new IllegalArgumentException("Dimension renamer unable to find a solution within " +
+                        maxIterations + " iterations");
+            }
+        }
+
+        // Todo: handle failure more gracefully:
+        // If a solution can't be found, look at the operation node in the arc
+        // with the most remaining constraints, and inject a rename operation.
+        // Then run this algorithm again.
+    }
+
+    public void solve() {
+        solve(100000);
+    }
+
+    private void initialize() {
+        for (Map.Entry<String, List<Integer>> variable : variables.entrySet()) {
+            List<Integer> values = variable.getValue();
+            for (int i = 0; i < variables.size(); ++i) {
+                values.add(i);  // invariant: values are in increasing order
+            }
+        }
+    }
+
+    private boolean ac3() {
+        Deque<Arc> workList = new ArrayDeque<>(constraints.keySet());
+        while (!workList.isEmpty()) {
+            Arc arc = workList.pop();
+            iterations += 1;
+            if (revise(arc)) {
+                if (variables.get(arc.from).size() == 0) {
+                    return false;  // no solution found
+                }
+                for (Arc constraint : constraints.keySet()) {
+                    if (arc.from.equals(constraint.to) && !arc.to.equals(constraint.from)) {
+                        workList.add(constraint);
+                    }
+                }
+            }
+        }
+        return true;
+    }
+
+    private boolean revise(Arc arc) {
+        boolean revised = false;
+        for(Iterator<Integer> fromIterator = variables.get(arc.from).iterator(); fromIterator.hasNext(); ) {
+            Integer from = fromIterator.next();
+            boolean satisfied = false;
+            for (Iterator<Integer> toIterator = variables.get(arc.to).iterator(); toIterator.hasNext(); ) {
+                Integer to = toIterator.next();
+                if (constraints.get(arc).test(from, to)) {
+                    satisfied = true;
+                }
+            }
+            if (!satisfied) {
+                fromIterator.remove();
+                revised = true;
+            }
+        }
+        return revised;
+    }
+
+    public interface Constraint {
+        boolean test(Integer x, Integer y);
+    }
+
+    public static boolean equals(Integer x, Integer y) {
+        return Objects.equals(x, y);
+    }
+
+    public static boolean lesserThan(Integer x, Integer y) {
+        return x < y;
+    }
+
+    public static boolean greaterThan(Integer x, Integer y) {
+        return x > y;
+    }
+
+    private static class Arc {
+
+        private final String from;
+        private final String to;
+        private final TensorFlowOperation operation;
+
+        Arc(String from, String to, TensorFlowOperation operation) {
+            this.from = from;
+            this.to = to;
+            this.operation = operation;
+        }
+
+        Arc opposite() {
+            return new Arc(to, from, operation);
+        }
+
+        @Override
+        public int hashCode() {
+            return Objects.hash(from, to);
+        }
+
+        @Override
+        public boolean equals(Object obj) {
+            if (obj == null || !(obj instanceof Arc)) {
+                return false;
+            }
+            Arc other = (Arc) obj;
+            return Objects.equals(from, other.from) && Objects.equals(to, other.to);
+        }
+
+        @Override
+        public String toString() {
+            return String.format("%s -> %s", from, to);
+        }
+    }
+
+}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/OperationMapper.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/OperationMapper.java
new file mode 100644
index 00000000000..b665413a6b2
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/OperationMapper.java
@@ -0,0 +1,97 @@
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer;
+
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.ConcatV2;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Const;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.ExpandDims;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Identity;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Join;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Map;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Matmul;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Mean;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Merge;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.NoOp;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Placeholder;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.PlaceholderWithDefault;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Reshape;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Select;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Shape;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Squeeze;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Switch;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.TensorFlowOperation;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations.Variable;
+import com.yahoo.tensor.functions.ScalarFunctions;
+import org.tensorflow.framework.NodeDef;
+
+import java.util.List;
+
+/**
+ * Maps from TensorFlow operations to Vespa operations.
+ *
+ * @author bratseth
+ * @author lesters
+ */
+public class OperationMapper {
+
+    public static TensorFlowOperation get(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        switch (node.getOp().toLowerCase()) {
+            // array ops
+            case "concatv2":    return new ConcatV2(modelName, node, inputs, port);
+            case "const":       return new Const(modelName, node, inputs, port);
+            case "expanddims":  return new ExpandDims(modelName, node, inputs, port);
+            case "identity":    return new Identity(modelName, node, inputs, port);
+            case "placeholder": return new Placeholder(modelName, node, inputs, port);
+            case "placeholderwithdefault": return new PlaceholderWithDefault(modelName, node, inputs, port);
+            case "reshape":     return new Reshape(modelName, node, inputs, port);
+            case "shape":       return new Shape(modelName, node, inputs, port);
+            case "squeeze":     return new Squeeze(modelName, node, inputs, port);
+
+            // control flow
+            case "merge":       return new Merge(modelName, node, inputs, port);
+            case "switch":      return new Switch(modelName, node, inputs, port);
+
+            // math ops
+            case "add":         return new Join(modelName, node, inputs, port, ScalarFunctions.add());
+            case "add_n":       return new Join(modelName, node, inputs, port, ScalarFunctions.add());
+            case "acos":        return new Map(modelName, node, inputs, port, ScalarFunctions.acos());
+            case "div":         return new Join(modelName, node, inputs, port, ScalarFunctions.divide());
+            case "realdiv":     return new Join(modelName, node, inputs, port, ScalarFunctions.divide());
+            case "floor":       return new Map(modelName, node, inputs, port, ScalarFunctions.floor());
+            case "matmul":      return new Matmul(modelName, node, inputs, port);
+            case "maximum":     return new Join(modelName, node, inputs, port, ScalarFunctions.max());
+            case "mean":        return new Mean(modelName, node, inputs, port);
+            case "reducemean":  return new Mean(modelName, node, inputs, port);
+            case "mul":         return new Join(modelName, node, inputs, port, ScalarFunctions.multiply());
+            case "multiply":    return new Join(modelName, node, inputs, port, ScalarFunctions.multiply());
+            case "rsqrt":       return new Map(modelName, node, inputs, port, ScalarFunctions.rsqrt());
+            case "select":      return new Select(modelName, node, inputs, port);
+            case "where3":      return new Select(modelName, node, inputs, port);
+            case "sigmoid":     return new Map(modelName, node, inputs, port, ScalarFunctions.sigmoid());
+            case "squareddifference": return new Join(modelName, node, inputs, port, ScalarFunctions.squareddifference());
+            case "sub":         return new Join(modelName, node, inputs, port, ScalarFunctions.subtract());
+            case "subtract":    return new Join(modelName, node, inputs, port, ScalarFunctions.subtract());
+
+            // nn ops
+            case "biasadd":     return new Join(modelName, node, inputs, port, ScalarFunctions.add());
+            case "elu":         return new Map(modelName, node, inputs, port, ScalarFunctions.elu());
+            case "relu":        return new Map(modelName, node, inputs, port, ScalarFunctions.relu());
+            case "selu":        return new Map(modelName, node, inputs, port, ScalarFunctions.selu());
+
+            // state ops
+            case "variable":    return new Variable(modelName, node, inputs, port);
+            case "variablev2":  return new Variable(modelName, node, inputs, port);
+
+            // evaluation no-ops
+            case "stopgradient":return new Identity(modelName, node, inputs, port);
+            case "noop":        return new NoOp(modelName, node, inputs, port);
+        }
+
+        TensorFlowOperation op = new NoOp(modelName, node, inputs, port);
+        op.warning("Operation '" + node.getOp() + "' is currently not implemented");
+        return op;
+    }
+
+}
+
+
+
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/OrderedTensorType.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/OrderedTensorType.java
new file mode 100644
index 00000000000..03a65333192
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/OrderedTensorType.java
@@ -0,0 +1,255 @@
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer;
+
+import com.yahoo.tensor.TensorType;
+import com.yahoo.tensor.TensorTypeParser;
+import org.tensorflow.framework.AttrValue;
+import org.tensorflow.framework.NodeDef;
+import org.tensorflow.framework.TensorShapeProto;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+import java.util.Optional;
+import java.util.stream.Collectors;
+
+/**
+ * A Vespa tensor type is ordered by the lexicographical ordering of dimension
+ * names. TensorFlow tensors have an explicit ordering of their dimensions.
+ * During import, we need to track the Vespa dimension that matches the
+ * corresponding TensorFlow dimension as the ordering can change after
+ * dimension renaming. That is the purpose of this class.
+ *
+ * @author lesters
+ */
+public class OrderedTensorType {
+
+    private final TensorType type;
+    private final List<TensorType.Dimension> dimensions;
+
+    private final long[] innerSizesTensorFlow;
+    private final long[] innerSizesVespa;
+    private final int[] dimensionMap;
+
+    private OrderedTensorType(List<TensorType.Dimension> dimensions) {
+        this.dimensions = Collections.unmodifiableList(dimensions);
+        this.type = new TensorType.Builder(dimensions).build();
+        this.innerSizesTensorFlow = new long[dimensions.size()];
+        this.innerSizesVespa = new long[dimensions.size()];
+        this.dimensionMap = createDimensionMap();
+    }
+
+    public TensorType type() {
+        return this.type;
+    }
+
+    public int rank() { return dimensions.size(); }
+
+    public List<TensorType.Dimension> dimensions() {
+        return dimensions;
+    }
+
+    public List<String> dimensionNames() {
+        return dimensions.stream().map(TensorType.Dimension::name).collect(Collectors.toList());
+    }
+
+    private int[] createDimensionMap() {
+        int numDimensions = dimensions.size();
+        if (numDimensions == 0) {
+            return null;
+        }
+        innerSizesTensorFlow[numDimensions - 1] = 1;
+        innerSizesVespa[numDimensions - 1] = 1;
+        for (int i = numDimensions - 1; --i >= 0; ) {
+            innerSizesTensorFlow[i] = dimensions().get(i+1).size().orElse(-1L) * innerSizesTensorFlow[i+1];
+            innerSizesVespa[i] = type.dimensions().get(i+1).size().orElse(-1L) * innerSizesVespa[i+1];
+        }
+        int[] mapping = new int[numDimensions];
+        for (int i = 0; i < numDimensions; ++i) {
+            TensorType.Dimension dim1 = dimensions().get(i);
+            for (int j = 0; j < numDimensions; ++j) {
+                TensorType.Dimension dim2 = type.dimensions().get(j);
+                if (dim1.equals(dim2)) {
+                    mapping[i] = j;
+                    break;
+                }
+            }
+        }
+        return mapping;
+    }
+
+    /**
+     * When dimension ordering between Vespa and TensorFlow differs, i.e.
+     * after dimension renaming, use the dimension map to read in values
+     * so that they are correctly laid out in memory for Vespa.
+     * Used when importing tensors from TensorFlow.
+     */
+    public int toDirectIndex(int index) {
+        if (dimensions.size() == 0) {
+            return 0;
+        }
+        if (dimensionMap == null)  {
+            throw new IllegalArgumentException("Dimension map is not available");
+        }
+        int directIndex = 0;
+        long rest = index;
+        for (int i = 0; i < dimensions.size(); ++i) {
+            long address = rest / innerSizesTensorFlow[i];
+            directIndex += innerSizesVespa[dimensionMap[i]] * address;
+            rest %= innerSizesTensorFlow[i];
+        }
+        return directIndex;
+    }
+
+    @Override
+    public boolean equals(Object obj) {
+        if (obj == null || !(obj instanceof OrderedTensorType)) {
+            return false;
+        }
+        OrderedTensorType other = (OrderedTensorType) obj;
+        if (dimensions.size() != dimensions.size()) {
+            return false;
+        }
+        List<TensorType.Dimension> thisDimensions = this.dimensions();
+        List<TensorType.Dimension> otherDimensions = other.dimensions();
+        for (int i = 0; i < thisDimensions.size(); ++i) {
+            if (!thisDimensions.get(i).equals(otherDimensions.get(i))) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+    public void verifyType(NodeDef node) {
+        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 < dimensions.size(); ++tensorFlowIndex) {
+                int vespaIndex = dimensionMap[tensorFlowIndex];
+                TensorShapeProto.Dim tensorFlowDimension = shape.getDim(tensorFlowIndex);
+                TensorType.Dimension vespaDimension = 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?
+    }
+
+    public OrderedTensorType rename(DimensionRenamer renamer) {
+        List<TensorType.Dimension> renamedDimensions = new ArrayList<>(dimensions.size());
+        for (TensorType.Dimension dimension : dimensions) {
+            String oldName = dimension.name();
+            Optional<String> newName = renamer.dimensionNameOf(oldName);
+            if (!newName.isPresent())
+                return this; // presumably, already renamed
+            TensorType.Dimension.Type dimensionType = dimension.type();
+            if (dimensionType == TensorType.Dimension.Type.indexedBound) {
+                renamedDimensions.add(TensorType.Dimension.indexed(newName.get(), dimension.size().get()));
+            } else if (dimensionType == TensorType.Dimension.Type.indexedUnbound) {
+                renamedDimensions.add(TensorType.Dimension.indexed(newName.get()));
+            } else if (dimensionType == TensorType.Dimension.Type.mapped) {
+                renamedDimensions.add(TensorType.Dimension.mapped(newName.get()));
+            }
+        }
+        return new OrderedTensorType(renamedDimensions);
+    }
+
+    /**
+     * Returns a string representation of this: A standard tensor type string where dimensions
+     * are listed in the order of this rather than in the natural order of their names.
+     */
+    @Override
+    public String toString() {
+        return "tensor(" + dimensions.stream().map(TensorType.Dimension::toString).collect(Collectors.joining(",")) + ")";
+    }
+
+    /**
+     * Creates an instance from the string representation of this: A standard tensor type string
+     * where dimensions are listed in the order of this rather than the natural order of their names.
+     */
+    public static OrderedTensorType fromSpec(String typeSpec) {
+        return new OrderedTensorType(TensorTypeParser.dimensionsFromSpec(typeSpec));
+    }
+
+    public static OrderedTensorType fromTensorFlowType(NodeDef node) {
+        return fromTensorFlowType(node, "d");  // standard naming convention: d0, d1, ...
+    }
+
+    public static OrderedTensorType fromTensorFlowType(NodeDef node, String dimensionPrefix) {
+        Builder builder = new Builder(node);
+        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();
+    }
+
+    public static class Builder {
+
+        private final TensorShapeProto shape;
+        private final List<TensorType.Dimension> dimensions;
+
+        public Builder(NodeDef node) {
+            this.shape = tensorFlowShape(node);
+            this.dimensions = new ArrayList<>(shape.getDimCount());
+        }
+
+        public Builder add(TensorType.Dimension vespaDimension) {
+            int index = dimensions.size();
+            TensorShapeProto.Dim tensorFlowDimension = shape.getDim(index);
+            long size = tensorFlowDimension.getSize();
+            if (size >= 0) {
+                if (vespaDimension.type() != TensorType.Dimension.Type.indexedBound) {
+                    throw new IllegalArgumentException("Non-agreement between TensorFlow and Vespa " +
+                                                       "dimension types");
+                }
+                if (!vespaDimension.size().isPresent()) {
+                    throw new IllegalArgumentException("Tensor dimension is indexed bound but does " +
+                                                       "not have a size");
+                }
+                if (vespaDimension.size().get() != size) {
+                    throw new IllegalArgumentException("Non-agreement between TensorFlow and Vespa " +
+                                                       "dimension sizes. TensorFlow: " + size + " Vespa: " +
+                                                       vespaDimension.size().get());
+                }
+            } else {
+                if (vespaDimension.type() != TensorType.Dimension.Type.indexedUnbound) {
+                    throw new IllegalArgumentException("Non-agreement between TensorFlow and Vespa " +
+                                                       "dimension types");
+                }
+            }
+            this.dimensions.add(vespaDimension);
+            return this;
+        }
+
+        public OrderedTensorType build() {
+            return new OrderedTensorType(dimensions);
+        }
+
+    }
+
+}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/tensorflow/TensorConverter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/TensorConverter.java
index d2d0acfc964..3f55e622fdf 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/tensorflow/TensorConverter.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/TensorConverter.java
@@ -1,7 +1,6 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.tensorflow;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
 import com.yahoo.tensor.IndexedTensor;
 import com.yahoo.tensor.Tensor;
 import com.yahoo.tensor.TensorType;
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/ConcatV2.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/ConcatV2.java
index 1b8c62fe0e9..4f5d61d75f9 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/ConcatV2.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/ConcatV2.java
@@ -1,37 +1,38 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
 import com.yahoo.tensor.Tensor;
 import com.yahoo.tensor.TensorType;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.List;
 import java.util.Optional;
 
-public class ConcatV2 extends IntermediateOperation {
+public class ConcatV2 extends TensorFlowOperation {
 
     private String concatDimensionName;
 
-    public ConcatV2(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
+    public ConcatV2(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
     }
 
     @Override
     protected OrderedTensorType lazyGetType() {
-        if (!inputs.stream().map(IntermediateOperation::type).allMatch(Optional::isPresent)) {
+        if (!inputs.stream().map(TensorFlowOperation::type).allMatch(Optional::isPresent)) {
             return null;
         }
 
-        IntermediateOperation concatDimOp = inputs.get(inputs.size() - 1);  // ConcatV2: concat dimension is the last input
+        TensorFlowOperation concatDimOp = inputs.get(inputs.size() - 1);  // ConcatV2: concat dimension is the last input
         if (!concatDimOp.getConstantValue().isPresent()) {
-            throw new IllegalArgumentException("ConcatV2 in " + name + ": " +
+            throw new IllegalArgumentException("ConcatV2 in " + node.getName() + ": " +
                                                "concat dimension must be a constant.");
         }
         Tensor concatDimTensor = concatDimOp.getConstantValue().get().asTensor();
         if (concatDimTensor.type().rank() != 0) {
-            throw new IllegalArgumentException("ConcatV2 in " + name + ": " +
+            throw new IllegalArgumentException("ConcatV2 in " + node.getName() + ": " +
                                                "concat dimension must be a scalar.");
         }
 
@@ -43,7 +44,7 @@ public class ConcatV2 extends IntermediateOperation {
         for (int i = 1; i < inputs.size() - 1; ++i) {
             OrderedTensorType bType = inputs.get(i).type().get();
             if (bType.rank() != aType.rank()) {
-                throw new IllegalArgumentException("ConcatV2 in " + name + ": " +
+                throw new IllegalArgumentException("ConcatV2 in " + node.getName() + ": " +
                                                    "inputs must have save rank.");
             }
             for (int j = 0; j < aType.rank(); ++j) {
@@ -52,13 +53,13 @@ public class ConcatV2 extends IntermediateOperation {
                 if (j == concatDim) {
                     concatDimSize += dimSizeB;
                 } else if (dimSizeA != dimSizeB) {
-                    throw new IllegalArgumentException("ConcatV2 in " + name + ": " +
+                    throw new IllegalArgumentException("ConcatV2 in " + node.getName() + ": " +
                                                        "input dimension " + j + " differs in input tensors.");
                 }
             }
         }
 
-        OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder();
+        OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(node);
         int dimensionIndex = 0;
         for (TensorType.Dimension dimension : aType.dimensions()) {
             if (dimensionIndex == concatDim) {
@@ -74,7 +75,7 @@ public class ConcatV2 extends IntermediateOperation {
 
     @Override
     protected TensorFunction lazyGetFunction() {
-        if (!inputs.stream().map(IntermediateOperation::function).allMatch(Optional::isPresent)) {
+        if (!inputs.stream().map(TensorFlowOperation::function).allMatch(Optional::isPresent)) {
             return null;
         }
         TensorFunction result = inputs.get(0).function().get();
@@ -87,7 +88,7 @@ public class ConcatV2 extends IntermediateOperation {
 
     @Override
     public void addDimensionNameConstraints(DimensionRenamer renamer) {
-        if (!inputs.stream().map(IntermediateOperation::type).allMatch(Optional::isPresent)) {
+        if (!inputs.stream().map(TensorFlowOperation::type).allMatch(Optional::isPresent)) {
             return;
         }
         OrderedTensorType a = inputs.get(0).type().get();
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Const.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Const.java
index 3c0f8569c47..718e2a4b3c2 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Const.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Const.java
@@ -1,38 +1,36 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
 import com.yahoo.searchlib.rankingexpression.Reference;
+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.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.TensorConverter;
 import com.yahoo.searchlib.rankingexpression.rule.ConstantNode;
 import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
 import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
 import com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode;
 import com.yahoo.tensor.TensorType;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.AttrValue;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.List;
 import java.util.Optional;
 
-public class Const extends IntermediateOperation {
+public class Const extends TensorFlowOperation {
 
-    private final AttributeMap attributeMap;
-
-    public Const(String modelName,
-                 String nodeName,
-                 List<IntermediateOperation> inputs,
-                 AttributeMap attributeMap,
-                 OrderedTensorType type) {
-        super(modelName, nodeName, inputs);
-        this.attributeMap = attributeMap;
-        this.type = type.rename(vespaName() + "_");
+    public Const(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
         setConstantValue(value());
     }
 
     @Override
     protected OrderedTensorType lazyGetType() {
-        return type;
+        return OrderedTensorType.fromTensorFlowType(node, vespaName() + "_");
     }
 
     @Override
@@ -57,7 +55,7 @@ public class Const extends IntermediateOperation {
     /** Constant names are prefixed by "modelName_" to avoid name conflicts between models */
     @Override
     public String vespaName() {
-        return modelName + "_" + super.vespaName();
+        return modelName() + "_" + super.vespaName();
     }
 
     @Override
@@ -79,11 +77,24 @@ public class Const extends IntermediateOperation {
     }
 
     private Value value() {
-        Optional<Value> value = attributeMap.get("value", type);
-        if ( ! value.isPresent()) {
-            throw new IllegalArgumentException("Node '" + name + "' of type " +
-                                               "const has missing or non-recognized 'value' attribute");
+        if ( ! node.getAttrMap().containsKey("value")) {
+            throw new IllegalArgumentException("Node '" + node.getName() + "' of type " +
+                                               "const has missing 'value' attribute");
+        }
+        AttrValue attrValue = node.getAttrMap().get("value");
+        if (attrValue.getValueCase() == AttrValue.ValueCase.TENSOR) {
+            return new TensorValue(TensorConverter.toVespaTensor(attrValue.getTensor(), type().get().type()));
+        }
+        if (attrValue.getValueCase() == AttrValue.ValueCase.B) {
+            return new BooleanValue(attrValue.getB());
+        }
+        if (attrValue.getValueCase() == AttrValue.ValueCase.I) {
+            return new DoubleValue(attrValue.getI());
+        }
+        if (attrValue.getValueCase() == AttrValue.ValueCase.F) {
+            return new DoubleValue(attrValue.getF());
         }
-        return value.get();
+        throw new IllegalArgumentException("Requesting value of constant in " +
+                                           node.getName() + " but type is not recognized.");
     }
 }
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/ExpandDims.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/ExpandDims.java
index 742ed8b89ab..2d0f4c7042b 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/ExpandDims.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/ExpandDims.java
@@ -1,9 +1,9 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
 import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
 import com.yahoo.searchlib.rankingexpression.rule.ConstantNode;
 import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
 import com.yahoo.searchlib.rankingexpression.rule.GeneratorLambdaFunctionNode;
@@ -12,17 +12,18 @@ import com.yahoo.tensor.TensorType;
 import com.yahoo.tensor.functions.Generate;
 import com.yahoo.tensor.functions.ScalarFunctions;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.ArrayList;
 import java.util.List;
 import java.util.Optional;
 
-public class ExpandDims extends IntermediateOperation {
+public class ExpandDims extends TensorFlowOperation {
 
     private List<String> expandDimensions;
 
-    public ExpandDims(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
+    public ExpandDims(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
     }
 
     @Override
@@ -31,14 +32,14 @@ public class ExpandDims extends IntermediateOperation {
             return null;
         }
 
-        IntermediateOperation axisOperation = inputs().get(1);
+        TensorFlowOperation axisOperation = inputs().get(1);
         if (!axisOperation.getConstantValue().isPresent()) {
-            throw new IllegalArgumentException("ExpandDims in " + name + ": " +
+            throw new IllegalArgumentException("ExpandDims in " + node.getName() + ": " +
                     "axis must be a constant.");
         }
         Tensor axis = axisOperation.getConstantValue().get().asTensor();
         if (axis.type().rank() != 0) {
-            throw new IllegalArgumentException("ExpandDims in " + name + ": " +
+            throw new IllegalArgumentException("ExpandDims in " + node.getName() + ": " +
                     "axis argument must be a scalar.");
         }
 
@@ -48,7 +49,7 @@ public class ExpandDims extends IntermediateOperation {
             dimensionToInsert = inputType.dimensions().size() - dimensionToInsert;
         }
 
-        OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder();
+        OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(node);
         expandDimensions = new ArrayList<>();
         int dimensionIndex = 0;
         for (TensorType.Dimension dimension : inputType.dimensions()) {
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Identity.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Identity.java
index d29bd4b7a9e..1408e7e04f0 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Identity.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Identity.java
@@ -1,21 +1,22 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.List;
 
-public class Identity extends IntermediateOperation {
+public class Identity extends TensorFlowOperation {
 
-    public Identity(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
+    public Identity(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
     }
 
     /** Constant names are prefixed by "modelName_" to avoid name conflicts between models */
     @Override
     public String vespaName() {
-        return modelName + "_" + super.vespaName();
+        return modelName() + "_" + super.vespaName();
     }
 
     @Override
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Join.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Join.java
new file mode 100644
index 00000000000..6cbfe0dfb05
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Join.java
@@ -0,0 +1,145 @@
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
+
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
+import com.yahoo.tensor.TensorType;
+import com.yahoo.tensor.functions.Reduce;
+import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Optional;
+import java.util.function.DoubleBinaryOperator;
+
+public class Join extends TensorFlowOperation {
+
+    private final DoubleBinaryOperator operator;
+
+    public Join(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port, DoubleBinaryOperator operator) {
+        super(modelName, node, inputs, port);
+        this.operator = operator;
+    }
+
+    @Override
+    protected OrderedTensorType lazyGetType() {
+        if (!allInputTypesPresent(2)) {
+            return null;
+        }
+        OrderedTensorType a = largestInput().type().get();
+        OrderedTensorType b = smallestInput().type().get();
+
+        // Well now we have potentially entered the wonderful world of "broadcasting"
+        // https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html
+        // In broadcasting, the size of each dimension is compared element-wise,
+        // starting with the trailing dimensions and working forward. A special
+        // case occurs when the size of one dimension is 1, while the other is not.
+        // Then the dimension with size 1 is "stretched" to be of compatible size.
+        //
+        // An example:
+        //
+        // Tensor A: d0[5], d1[1], d2[3], d3[1]
+        // Tensor B:        d1[4], d2[1], d3[2]
+        //
+        // In TensorFlow and using the above rules of broadcasting, the resulting
+        // type is:
+        //           d0[5], d1[4], d2[3], d2[2]
+        //
+        // However, in Vespa's tensor logic, the join of the two above tensors would
+        // result in a tensor of type:
+        //           d0[5], d1[1], d2[1], d3[1]
+        //
+        // By reducing the dimensions of size 1 in each tensor before joining,
+        // we get equal results as in TensorFlow.
+
+        OrderedTensorType.Builder builder = new OrderedTensorType.Builder(node);
+        int sizeDifference = a.rank() - b.rank();
+        for (int i = 0; i < a.rank(); ++i) {
+            TensorType.Dimension aDim = a.dimensions().get(i);
+            long size = aDim.size().orElse(-1L);
+
+            if (i - sizeDifference >= 0) {
+                TensorType.Dimension bDim = b.dimensions().get(i - sizeDifference);
+                size = Math.max(size, bDim.size().orElse(-1L));
+            }
+
+            if (aDim.type() == TensorType.Dimension.Type.indexedBound) {
+                builder.add(TensorType.Dimension.indexed(aDim.name(), size));
+            } else if (aDim.type() == TensorType.Dimension.Type.indexedUnbound) {
+                builder.add(TensorType.Dimension.indexed(aDim.name()));
+            } else if (aDim.type() == TensorType.Dimension.Type.mapped) {
+                builder.add(TensorType.Dimension.mapped(aDim.name()));
+            }
+        }
+        return builder.build();
+    }
+
+    @Override
+    protected TensorFunction lazyGetFunction() {
+        if (!allInputTypesPresent(2)) {
+            return null;
+        }
+        if (!allInputFunctionsPresent(2)) {
+            return null;
+        }
+
+        TensorFlowOperation a = largestInput();
+        TensorFlowOperation b = smallestInput();
+
+        List<String> aDimensionsToReduce = new ArrayList<>();
+        List<String> bDimensionsToReduce = new ArrayList<>();
+        int sizeDifference = a.type().get().rank() - b.type().get().rank();
+        for (int i = 0; i < b.type().get().rank(); ++i) {
+            TensorType.Dimension bDim = b.type().get().dimensions().get(i);
+            TensorType.Dimension aDim = a.type().get().dimensions().get(i + sizeDifference);
+            long bSize = bDim.size().orElse(-1L);
+            long aSize = aDim.size().orElse(-1L);
+            if (bSize == 1L && aSize != 1L) {
+                bDimensionsToReduce.add(bDim.name());
+            }
+            if (aSize == 1L && bSize != 1L) {
+                aDimensionsToReduce.add(bDim.name());
+            }
+        }
+
+        TensorFunction aReducedFunction = a.function().get();
+        if (aDimensionsToReduce.size() > 0) {
+            aReducedFunction = new Reduce(a.function().get(), Reduce.Aggregator.sum, aDimensionsToReduce);
+        }
+        TensorFunction bReducedFunction = b.function().get();
+        if (bDimensionsToReduce.size() > 0) {
+            bReducedFunction = new Reduce(b.function().get(), Reduce.Aggregator.sum, bDimensionsToReduce);
+        }
+
+        return new com.yahoo.tensor.functions.Join(aReducedFunction, bReducedFunction, operator);
+    }
+
+    @Override
+    public void addDimensionNameConstraints(DimensionRenamer renamer) {
+        if (!allInputTypesPresent(2)) {
+            return;
+        }
+        OrderedTensorType a = largestInput().type().get();
+        OrderedTensorType b = smallestInput().type().get();
+        int sizeDifference = a.rank() - b.rank();
+        for (int i = 0; i < b.rank(); ++i) {
+            String bDim = b.dimensions().get(i).name();
+            String aDim = a.dimensions().get(i + sizeDifference).name();
+            renamer.addConstraint(aDim, bDim, DimensionRenamer::equals, this);
+        }
+    }
+
+    private TensorFlowOperation largestInput() {
+        OrderedTensorType a = inputs.get(0).type().get();
+        OrderedTensorType b = inputs.get(1).type().get();
+        return a.rank() >= b.rank() ? inputs.get(0) : inputs.get(1);
+    }
+
+    private TensorFlowOperation smallestInput() {
+        OrderedTensorType a = inputs.get(0).type().get();
+        OrderedTensorType b = inputs.get(1).type().get();
+        return a.rank() < b.rank() ? inputs.get(0) : inputs.get(1);
+    }
+
+}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Map.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Map.java
index f54ae83052f..c015f5ecba8 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Map.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Map.java
@@ -1,19 +1,20 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.List;
 import java.util.Optional;
 import java.util.function.DoubleUnaryOperator;
 
-public class Map extends IntermediateOperation {
+public class Map extends TensorFlowOperation {
 
     private final DoubleUnaryOperator operator;
 
-    public Map(String modelName, String nodeName, List<IntermediateOperation> inputs, DoubleUnaryOperator operator) {
-        super(modelName, nodeName, inputs);
+    public Map(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port, DoubleUnaryOperator operator) {
+        super(modelName, node, inputs, port);
         this.operator = operator;
     }
 
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Matmul.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Matmul.java
new file mode 100644
index 00000000000..b2b9530a161
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Matmul.java
@@ -0,0 +1,74 @@
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
+
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
+import com.yahoo.tensor.TensorType;
+import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
+
+import java.util.List;
+import java.util.Optional;
+
+public class Matmul extends TensorFlowOperation {
+
+    public Matmul(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
+    }
+
+    @Override
+    protected OrderedTensorType lazyGetType() {
+        if (!allInputTypesPresent(2)) {
+            return null;
+        }
+        OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(node);
+        typeBuilder.add(inputs.get(0).type().get().dimensions().get(0));
+        typeBuilder.add(inputs.get(1).type().get().dimensions().get(1));
+        return typeBuilder.build();
+    }
+
+    @Override
+    protected TensorFunction lazyGetFunction() {
+        if (!allInputTypesPresent(2)) {
+            return null;
+        }
+        OrderedTensorType aType = inputs.get(0).type().get();
+        OrderedTensorType bType = inputs.get(1).type().get();
+        if (aType.type().rank() < 2 || bType.type().rank() < 2)
+            throw new IllegalArgumentException("Tensors in matmul must have rank of at least 2");
+        if (aType.type().rank() != bType.type().rank())
+            throw new IllegalArgumentException("Tensors in matmul must have the same rank");
+
+        Optional<TensorFunction> aFunction = inputs.get(0).function();
+        Optional<TensorFunction> bFunction = inputs.get(1).function();
+        if (!aFunction.isPresent() || !bFunction.isPresent()) {
+            return null;
+        }
+        return new com.yahoo.tensor.functions.Matmul(aFunction.get(), bFunction.get(), aType.dimensions().get(1).name());
+    }
+
+    @Override
+    public void addDimensionNameConstraints(DimensionRenamer renamer) {
+        if (!allInputTypesPresent(2)) {
+            return;
+        }
+        List<TensorType.Dimension> aDimensions = inputs.get(0).type().get().dimensions();
+        List<TensorType.Dimension> bDimensions = inputs.get(1).type().get().dimensions();
+
+        String aDim0 = aDimensions.get(0).name();
+        String aDim1 = aDimensions.get(1).name();
+        String bDim0 = bDimensions.get(0).name();
+        String bDim1 = bDimensions.get(1).name();
+
+        // The second dimension of a should have the same name as the first dimension of b
+        renamer.addConstraint(aDim1, bDim0, DimensionRenamer::equals, this);
+
+        // The first dimension of a should have a different name than the second dimension of b
+        renamer.addConstraint(aDim0, bDim1, DimensionRenamer::lesserThan, this);
+
+        // For efficiency, the dimensions to join over should be innermost - soft constraint
+        renamer.addConstraint(aDim0, aDim1, DimensionRenamer::lesserThan, this);
+        renamer.addConstraint(bDim0, bDim1, DimensionRenamer::greaterThan, this);
+    }
+
+}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Mean.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Mean.java
index 95a77c07590..3eba872c6a0 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Mean.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Mean.java
@@ -1,10 +1,9 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
 import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
-import com.yahoo.searchlib.rankingexpression.evaluation.Value;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
 import com.yahoo.searchlib.rankingexpression.rule.ConstantNode;
 import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
 import com.yahoo.searchlib.rankingexpression.rule.GeneratorLambdaFunctionNode;
@@ -14,20 +13,20 @@ import com.yahoo.tensor.functions.Generate;
 import com.yahoo.tensor.functions.Reduce;
 import com.yahoo.tensor.functions.ScalarFunctions;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.AttrValue;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.ArrayList;
 import java.util.Iterator;
 import java.util.List;
 import java.util.Optional;
 
-public class Mean extends IntermediateOperation {
+public class Mean extends TensorFlowOperation {
 
-    private final AttributeMap attributeMap;
     private List<String> reduceDimensions;
 
-    public Mean(String modelName, String nodeName, List<IntermediateOperation> inputs, AttributeMap attributeMap) {
-        super(modelName, nodeName, inputs);
-        this.attributeMap = attributeMap;
+    public Mean(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
     }
 
     @Override
@@ -35,9 +34,9 @@ public class Mean extends IntermediateOperation {
         if (!allInputTypesPresent(2)) {
             return null;
         }
-        IntermediateOperation reductionIndices = inputs.get(1);
+        TensorFlowOperation reductionIndices = inputs.get(1);
         if (!reductionIndices.getConstantValue().isPresent()) {
-            throw new IllegalArgumentException("Mean in " + name + ": " +
+            throw new IllegalArgumentException("Mean in " + node.getName() + ": " +
                                                "reduction indices must be a constant.");
         }
         Tensor indices = reductionIndices.getConstantValue().get().asTensor();
@@ -55,7 +54,7 @@ public class Mean extends IntermediateOperation {
         return reducedType(inputType, shouldKeepDimensions());
     }
 
-    // optimization: if keepDims and one reduce dimension that has size 1: same as identity.
+    // todo: optimization: if keepDims and one reduce dimension that has size 1: same as identity.
 
     @Override
     protected TensorFunction lazyGetFunction() {
@@ -94,12 +93,12 @@ public class Mean extends IntermediateOperation {
     }
 
     private boolean shouldKeepDimensions() {
-        Optional<Value> keepDims = attributeMap.get("keep_dims");
-        return keepDims.isPresent() && keepDims.get().asBoolean();
+        AttrValue keepDimsAttr = node.getAttrMap().get("keep_dims");
+        return keepDimsAttr != null && keepDimsAttr.getB();
     }
 
     private OrderedTensorType reducedType(OrderedTensorType inputType, boolean keepDimensions) {
-        OrderedTensorType.Builder builder = new OrderedTensorType.Builder();
+        OrderedTensorType.Builder builder = new OrderedTensorType.Builder(node);
         for (TensorType.Dimension dimension: inputType.type().dimensions()) {
             if (!reduceDimensions.contains(dimension.name())) {
                 builder.add(dimension);
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Merge.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Merge.java
index 9d9eca47b1c..4c95e67e184 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Merge.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Merge.java
@@ -1,20 +1,21 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.List;
 
-public class Merge extends IntermediateOperation {
+public class Merge extends TensorFlowOperation {
 
-    public Merge(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
+    public Merge(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
     }
 
     @Override
     protected OrderedTensorType lazyGetType() {
-        for (IntermediateOperation operation : inputs) {
+        for (TensorFlowOperation operation : inputs) {
             if (operation.type().isPresent()) {
                 return operation.type().get();
             }
@@ -24,7 +25,7 @@ public class Merge extends IntermediateOperation {
 
     @Override
     protected TensorFunction lazyGetFunction() {
-        for (IntermediateOperation operation : inputs) {
+        for (TensorFlowOperation operation : inputs) {
             if (operation.function().isPresent()) {
                 return operation.function().get();
             }
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/NoOp.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/NoOp.java
new file mode 100644
index 00000000000..d558ec89e87
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/NoOp.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 com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
+
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
+import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
+
+import java.util.Collections;
+import java.util.List;
+
+public class NoOp extends TensorFlowOperation {
+
+    public NoOp(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, Collections.emptyList(), port);  // don't propagate inputs
+    }
+
+    @Override
+    protected OrderedTensorType lazyGetType() {
+        return null;
+    }
+
+    @Override
+    protected TensorFunction lazyGetFunction() {
+        return null;
+    }
+
+    @Override
+    public boolean isConstant() {
+        return true;
+    }
+
+}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Argument.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Placeholder.java
index 7fc2aae87d1..1619c11427a 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Argument.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Placeholder.java
@@ -1,29 +1,28 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
 import com.yahoo.tensor.TensorType;
 import com.yahoo.tensor.evaluation.VariableTensor;
 import com.yahoo.tensor.functions.Rename;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
 
-import java.util.Collections;
 import java.util.List;
 
-public class Argument extends IntermediateOperation {
+public class Placeholder extends TensorFlowOperation {
 
     private OrderedTensorType standardNamingType;  // using standard naming convention: d0, d1, ...
 
-    public Argument(String modelName, String nodeName, OrderedTensorType type) {
-        super(modelName, nodeName, Collections.emptyList());
-        this.type = type.rename(vespaName() + "_");
-        standardNamingType = OrderedTensorType.standardType(type);
+    public Placeholder(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
+        standardNamingType = OrderedTensorType.fromTensorFlowType(node);
     }
 
     @Override
     protected OrderedTensorType lazyGetType() {
-        return type;
+        return OrderedTensorType.fromTensorFlowType(node, vespaName() + "_");
     }
 
     @Override
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/PlaceholderWithDefault.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/PlaceholderWithDefault.java
index 9299ae9be12..65ce7f00e34 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/PlaceholderWithDefault.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/PlaceholderWithDefault.java
@@ -1,16 +1,17 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.List;
 import java.util.Optional;
 
-public class PlaceholderWithDefault extends IntermediateOperation {
+public class PlaceholderWithDefault extends TensorFlowOperation {
 
-    public PlaceholderWithDefault(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
+    public PlaceholderWithDefault(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
     }
 
     @Override
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Reshape.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Reshape.java
index e91c2305f7d..e7d90e5fc1f 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Reshape.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Reshape.java
@@ -1,9 +1,10 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
 import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.TensorConverter;
 import com.yahoo.searchlib.rankingexpression.rule.ArithmeticNode;
 import com.yahoo.searchlib.rankingexpression.rule.ArithmeticOperator;
 import com.yahoo.searchlib.rankingexpression.rule.ComparisonNode;
@@ -18,18 +19,19 @@ import com.yahoo.tensor.functions.Generate;
 import com.yahoo.tensor.functions.Reduce;
 import com.yahoo.tensor.functions.ScalarFunctions;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.ArrayList;
 import java.util.Iterator;
 import java.util.List;
 import java.util.stream.Collectors;
 
-import static com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType.tensorSize;
+import static com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.TensorConverter.tensorSize;
 
-public class Reshape extends IntermediateOperation {
+public class Reshape extends TensorFlowOperation {
 
-    public Reshape(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
+    public Reshape(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
     }
 
     @Override
@@ -37,15 +39,15 @@ public class Reshape extends IntermediateOperation {
         if (!allInputTypesPresent(2)) {
             return null;
         }
-        IntermediateOperation newShape = inputs.get(1);
+        TensorFlowOperation newShape = inputs.get(1);
         if (!newShape.getConstantValue().isPresent()) {
-            throw new IllegalArgumentException("Reshape in " + name + ": " +
+            throw new IllegalArgumentException("Reshape in " + node.getName() + ": " +
                     "shape input must be a constant.");
         }
         Tensor shape = newShape.getConstantValue().get().asTensor();
 
         OrderedTensorType inputType = inputs.get(0).type().get();
-        OrderedTensorType.Builder outputTypeBuilder = new OrderedTensorType.Builder();
+        OrderedTensorType.Builder outputTypeBuilder = new OrderedTensorType.Builder(node);
         int dimensionIndex = 0;
         for (Iterator<Tensor.Cell> cellIterator = shape.cellIterator(); cellIterator.hasNext();) {
             Tensor.Cell cell = cellIterator.next();
@@ -122,7 +124,7 @@ public class Reshape extends IntermediateOperation {
                 operators.add(0, ArithmeticOperator.MULTIPLY);
                 children.add(0, new ConstantNode(new DoubleValue(size)));
             }
-            size *= OrderedTensorType.dimensionSize(dimension);
+            size *= TensorConverter.dimensionSize(dimension);
             if (i > 0) {
                 operators.add(0, ArithmeticOperator.PLUS);
             }
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Select.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Select.java
index 927a4a368f9..5fdcb5a695f 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Select.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Select.java
@@ -1,23 +1,24 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
 import com.yahoo.tensor.Tensor;
 import com.yahoo.tensor.TensorType;
 import com.yahoo.tensor.functions.ScalarFunctions;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.List;
 import java.util.function.DoubleBinaryOperator;
 
-import static com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType.dimensionSize;
-import static com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType.tensorSize;
+import static com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.TensorConverter.dimensionSize;
+import static com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.TensorConverter.tensorSize;
 
-public class Select extends IntermediateOperation {
+public class Select extends TensorFlowOperation {
 
-    public Select(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
+    public Select(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
     }
 
     @Override
@@ -38,7 +39,7 @@ public class Select extends IntermediateOperation {
         if (!allInputFunctionsPresent(3)) {
             return null;
         }
-        IntermediateOperation conditionOperation = inputs().get(0);
+        TensorFlowOperation conditionOperation = inputs().get(0);
         TensorFunction a = inputs().get(1).function().get();
         TensorFunction b = inputs().get(2).function().get();
 
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Shape.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Shape.java
index da566909adc..af49d2c108b 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Shape.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Shape.java
@@ -1,19 +1,20 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
 import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
 import com.yahoo.tensor.IndexedTensor;
 import com.yahoo.tensor.Tensor;
 import com.yahoo.tensor.TensorType;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.List;
 
-public class Shape extends IntermediateOperation {
+public class Shape extends TensorFlowOperation {
 
-    public Shape(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
+    public Shape(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
         createConstantValue();
     }
 
@@ -23,7 +24,7 @@ public class Shape extends IntermediateOperation {
             return null;
         }
         OrderedTensorType inputType = inputs.get(0).type().get();
-        return new OrderedTensorType.Builder()
+        return new OrderedTensorType.Builder(node)
                 .add(TensorType.Dimension.indexed(vespaName(), inputType.dimensions().size()))
                 .build();
     }
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Squeeze.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Squeeze.java
index c750c47e27e..17ce9e8b7cb 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Squeeze.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Squeeze.java
@@ -1,26 +1,26 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
-import com.yahoo.searchlib.rankingexpression.evaluation.Value;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.TensorConverter;
 import com.yahoo.tensor.TensorType;
 import com.yahoo.tensor.functions.Reduce;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.AttrValue;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.ArrayList;
 import java.util.List;
 import java.util.Optional;
 import java.util.stream.Collectors;
 
-public class Squeeze extends IntermediateOperation {
+public class Squeeze extends TensorFlowOperation {
 
-    private final AttributeMap attributeMap;
     private List<String> squeezeDimensions;
 
-    public Squeeze(String modelName, String nodeName, List<IntermediateOperation> inputs, AttributeMap attributeMap) {
-        super(modelName, nodeName, inputs);
-        this.attributeMap = attributeMap;
+    public Squeeze(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
     }
 
     @Override
@@ -31,21 +31,20 @@ public class Squeeze extends IntermediateOperation {
         OrderedTensorType inputType = inputs.get(0).type().get();
         squeezeDimensions = new ArrayList<>();
 
-        Optional<List<Value>> squeezeDimsAttr = attributeMap.getList("squeeze_dims");
-        if ( ! squeezeDimsAttr.isPresent()) {
+        AttrValue squeezeDimsAttr = node.getAttrMap().get("squeeze_dims");
+        if (squeezeDimsAttr == null) {
             squeezeDimensions = inputType.type().dimensions().stream().
-                    filter(dim -> OrderedTensorType.dimensionSize(dim) == 1).
+                    filter(dim -> TensorConverter.dimensionSize(dim) == 1).
                     map(TensorType.Dimension::name).
                     collect(Collectors.toList());
         } else {
-            squeezeDimensions = squeezeDimsAttr.get().stream().map(Value::asDouble).map(Double::intValue).
+            squeezeDimensions = squeezeDimsAttr.getList().getIList().stream().
                     map(i -> i < 0 ? inputType.type().dimensions().size() - i : i).
-                    map(i -> inputType.type().dimensions().get(i)).
-                    filter(dim -> OrderedTensorType.dimensionSize(dim) == 1).
+                    map(i -> inputType.type().dimensions().get(i.intValue())).
+                    filter(dim -> TensorConverter.dimensionSize(dim) == 1).
                     map(TensorType.Dimension::name).
                     collect(Collectors.toList());
         }
-
         return squeezeDimensions.isEmpty() ? inputType : reducedType(inputType);
     }
 
@@ -73,7 +72,7 @@ public class Squeeze extends IntermediateOperation {
     }
 
     private OrderedTensorType reducedType(OrderedTensorType inputType) {
-        OrderedTensorType.Builder builder = new OrderedTensorType.Builder();
+        OrderedTensorType.Builder builder = new OrderedTensorType.Builder(node);
         for (TensorType.Dimension dimension: inputType.type().dimensions()) {
             if ( ! squeezeDimensions.contains(dimension.name())) {
                 builder.add(dimension);
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Switch.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Switch.java
index 0171d1ea171..de4d8862fd6 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/Switch.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Switch.java
@@ -1,19 +1,17 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.List;
 import java.util.Optional;
 
-public class Switch extends IntermediateOperation {
+public class Switch extends TensorFlowOperation {
 
-    private final int port;
-
-    public Switch(String modelName, String nodeName, List<IntermediateOperation> inputs, int port) {
-        super(modelName, nodeName, inputs);
-        this.port = port;
+    public Switch(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
     }
 
     @Override
@@ -23,7 +21,7 @@ public class Switch extends IntermediateOperation {
         }
         Optional<OrderedTensorType> predicate = inputs.get(1).type();
         if (predicate.get().type().rank() != 0) {
-            throw new IllegalArgumentException("Switch in " + name + ": " +
+            throw new IllegalArgumentException("Switch in " + node.getName() + ": " +
                     "predicate must be a scalar");
         }
         return inputs.get(0).type().orElse(null);
@@ -31,13 +29,13 @@ public class Switch extends IntermediateOperation {
 
     @Override
     protected TensorFunction lazyGetFunction() {
-        IntermediateOperation predicateOperation = inputs().get(1);
+        TensorFlowOperation predicateOperation = inputs().get(1);
         if (!predicateOperation.getConstantValue().isPresent()) {
-            throw new IllegalArgumentException("Switch in " + name + ": " +
+            throw new IllegalArgumentException("Switch in " + node.getName() + ": " +
                     "predicate must be a constant");
         }
         if (port < 0 || port > 1) {
-            throw new IllegalArgumentException("Switch in " + name + ": " +
+            throw new IllegalArgumentException("Switch in " + node.getName() + ": " +
                     "choice should be boolean");
         }
 
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/IntermediateOperation.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/TensorFlowOperation.java
index 43de29cedd5..3687bba8b85 100644
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/importer/operations/IntermediateOperation.java
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/TensorFlowOperation.java
@@ -1,16 +1,17 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
 
-package com.yahoo.searchlib.rankingexpression.integration.ml.importer.operations;
-
+import com.yahoo.searchlib.rankingexpression.RankingExpression;
 import com.yahoo.searchlib.rankingexpression.Reference;
 import com.yahoo.searchlib.rankingexpression.evaluation.Value;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
 import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
 import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
 import com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode;
 import com.yahoo.tensor.evaluation.VariableTensor;
 import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
 
 import java.util.ArrayList;
 import java.util.Collections;
@@ -19,40 +20,43 @@ import java.util.Optional;
 import java.util.function.Function;
 
 /**
- * Wraps an imported operation node and produces the respective Vespa tensor
- * operation. During import, a graph of these operations are constructed. Then,
- * the types are used to deduce sensible dimension names using the
- * DimensionRenamer. After the types have been renamed, the proper Vespa
- * expressions can be extracted.
+ * Wraps a TensorFlow node and produces the respective Vespa tensor operation.
+ * During import, a graph of these operations are constructed. Then, the
+ * types are used to deduce sensible dimension names using the
+ * DimensionRenamer. After the types have been renamed, the proper
+ * Vespa expressions can be extracted.
  *
  * @author lesters
  */
-public abstract class IntermediateOperation {
+public abstract class TensorFlowOperation {
+
+    protected final static String MACRO_PREFIX = "tf_macro_";
 
-    private final static String MACRO_PREFIX = "imported_ml_macro_";
+    private final String modelName;
 
-    protected final String name;
-    protected final String modelName;
-    protected final List<IntermediateOperation> inputs;
-    protected final List<IntermediateOperation> outputs = new ArrayList<>();
+    protected final NodeDef node;
+    protected final int port;
+    protected final List<TensorFlowOperation> inputs;
+    protected final List<TensorFlowOperation> outputs = new ArrayList<>();
+    protected final List<String> importWarnings = new ArrayList<>();
 
     protected OrderedTensorType type;
     protected TensorFunction function;
     protected TensorFunction macro = null;
 
-    private final List<String> importWarnings = new ArrayList<>();
     private Value constantValue = null;
-    private List<IntermediateOperation> controlInputs = Collections.emptyList();
+    private List<TensorFlowOperation> controlInputs = Collections.emptyList();
 
-    protected Function<OrderedTensorType, Value> constantValueFunction = null;
-
-    IntermediateOperation(String modelName, String name, List<IntermediateOperation> inputs) {
-        this.name = name;
+    TensorFlowOperation(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
         this.modelName = modelName;
+        this.node = node;
+        this.port = port;
         this.inputs = Collections.unmodifiableList(inputs);
         this.inputs.forEach(i -> i.outputs.add(this));
     }
 
+    protected String modelName() { return modelName; }
+
     protected abstract OrderedTensorType lazyGetType();
     protected abstract TensorFunction lazyGetFunction();
 
@@ -61,6 +65,9 @@ public abstract class IntermediateOperation {
         if (type == null) {
             type = lazyGetType();
         }
+        if (type != null) {
+            type.verifyType(node);
+        }
         return Optional.ofNullable(type);
     }
 
@@ -80,14 +87,14 @@ public abstract class IntermediateOperation {
         return Optional.ofNullable(function);
     }
 
-    /** Returns original name of this operation node */
-    public String name() { return name; }
+    /** Return TensorFlow node */
+    public NodeDef node() { return node; }
 
     /** Return unmodifiable list of inputs */
-    public List<IntermediateOperation> inputs() { return inputs; }
+    public List<TensorFlowOperation> inputs() { return inputs; }
 
     /** Return unmodifiable list of outputs. If a node has multiple outputs, consider adding a macro. */
-    public List<IntermediateOperation> outputs() { return Collections.unmodifiableList(outputs); }
+    public List<TensorFlowOperation> outputs() { return Collections.unmodifiableList(outputs); }
 
     /** Returns a Vespa ranking expression that should be added as a macro */
     public Optional<TensorFunction> macro() { return Optional.ofNullable(macro); }
@@ -102,34 +109,22 @@ public abstract class IntermediateOperation {
     public boolean isInput() { return false; }
 
     /** Return true if this node is constant */
-    public boolean isConstant() { return inputs.stream().allMatch(IntermediateOperation::isConstant); }
+    public boolean isConstant() { return inputs.stream().allMatch(TensorFlowOperation::isConstant); }
 
     /** Sets the constant value */
     public void setConstantValue(Value value) { constantValue = value; }
 
     /** Gets the constant value if it exists */
-    public Optional<Value> getConstantValue() {
-        if (constantValue != null) {
-            return Optional.of(constantValue);
-        }
-        if (constantValueFunction != null) {
-            return Optional.of(constantValueFunction.apply(type));
-        }
-        return Optional.empty();
-    }
-
-    /** Set the constant value function */
-    public void setConstantValueFunction(Function<OrderedTensorType, Value> func) { this.constantValueFunction = func; }
+    public Optional<Value> getConstantValue() { return Optional.ofNullable(constantValue); }
 
     /** Sets the external control inputs */
-    public void setControlInputs(List<IntermediateOperation> inputs) { this.controlInputs = inputs; }
+    public void setControlInputs(List<TensorFlowOperation> inputs) { this.controlInputs = inputs; }
 
     /** Retrieve the control inputs for this operation */
-    public List<IntermediateOperation> getControlInputs() { return Collections.unmodifiableList(this.controlInputs); }
+    public List<TensorFlowOperation> getControlInputs() { return Collections.unmodifiableList(this.controlInputs); }
 
     /** Retrieve the valid Vespa name of this node */
-    public String vespaName() { return vespaName(name); }
-    public String vespaName(String name) { return name != null ? namePartOf(name).replace('/', '_') : null; }
+    public String vespaName() { return node.getName() != null ? node.getName().replace('/', '_') : null; }
 
     /** Retrieve the valid Vespa name of this node if it is a macro */
     public String macroName() { return vespaName() != null ? MACRO_PREFIX + modelName + "_" + vespaName() : null; }
@@ -140,48 +135,23 @@ public abstract class IntermediateOperation {
     /** Set an input warning */
     public void warning(String warning) { importWarnings.add(warning); }
 
-    boolean verifyInputs(int expected, Function<IntermediateOperation, Optional<?>> func) {
+    boolean verifyInputs(int expected, Function<TensorFlowOperation, Optional<?>> func) {
+        if (!controlInputs.stream().map(func).allMatch(Optional::isPresent)) {
+            return false;
+        }
         if (inputs.size() != expected) {
             throw new IllegalArgumentException("Expected " + expected + " inputs " +
-                    "for '" + name + "', got " + inputs.size());
+                                               "for '" + node.getName() + "', got " + inputs.size());
         }
         return inputs.stream().map(func).allMatch(Optional::isPresent);
     }
 
     boolean allInputTypesPresent(int expected) {
-        return verifyInputs(expected, IntermediateOperation::type);
+        return verifyInputs(expected, TensorFlowOperation::type);
     }
 
     boolean allInputFunctionsPresent(int expected) {
-        return verifyInputs(expected, IntermediateOperation::function);
-    }
-
-    /**
-     * A method signature input and output has the form name:index.
-     * This returns the name part without the index.
-     */
-    public static String namePartOf(String name) {
-        name = name.startsWith("^") ? name.substring(1) : name;
-        return name.split(":")[0];
-    }
-
-    /**
-     * This return the output index part. Indexes are used for nodes with
-     * multiple outputs.
-     */
-    public static int indexPartOf(String name) {
-        int i = name.indexOf(":");
-        return i < 0 ? 0 : Integer.parseInt(name.substring(i + 1));
-    }
-
-    /**
-     * An interface mapping operation attributes to Vespa Values.
-     * Adapter for differences in ONNX/TensorFlow.
-     */
-    public interface AttributeMap {
-        Optional<Value> get(String key);
-        Optional<Value> get(String key, OrderedTensorType type);
-        Optional<List<Value>> getList(String key);
+        return verifyInputs(expected, TensorFlowOperation::function);
     }
 
 }
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Variable.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Variable.java
new file mode 100644
index 00000000000..b18a8a9b212
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/importer/operations/Variable.java
@@ -0,0 +1,46 @@
+// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.operations;
+
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
+import com.yahoo.tensor.TensorType;
+import com.yahoo.tensor.functions.TensorFunction;
+import org.tensorflow.framework.NodeDef;
+
+import java.util.List;
+
+public class Variable extends TensorFlowOperation {
+
+    public Variable(String modelName, NodeDef node, List<TensorFlowOperation> inputs, int port) {
+        super(modelName, node, inputs, port);
+    }
+
+    /** Constant names are prefixed by "modelName_" to avoid name conflicts between models */
+    @Override
+    public String vespaName() {
+        return modelName() + "_" + super.vespaName();
+    }
+
+    @Override
+    protected OrderedTensorType lazyGetType() {
+        return OrderedTensorType.fromTensorFlowType(node, super.vespaName() + "_");
+    }
+
+    @Override
+    protected TensorFunction lazyGetFunction() {
+        return null;  // will be added by function() since this is constant.
+    }
+
+    @Override
+    public void addDimensionNameConstraints(DimensionRenamer renamer) {
+        for (TensorType.Dimension dimension : type.type().dimensions()) {
+            renamer.addDimension(dimension.name());
+        }
+    }
+
+    @Override
+    public boolean isConstant() {
+        return true;
+    }
+
+}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/package-info.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/package-info.java
new file mode 100644
index 00000000000..9e53990a9d6
--- /dev/null
+++ b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/package-info.java
@@ -0,0 +1,8 @@
+// Copyright 2017 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+/**
+ * Tensorflow integration
+ */
+@ExportPackage
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow;
+
+import com.yahoo.osgi.annotation.ExportPackage;
diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/OnnxMnistSoftmaxImportTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/onnx/OnnxMnistSoftmaxImportTestCase.java
index a7926cd2e02..4b68cd40a08 100644
--- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/OnnxMnistSoftmaxImportTestCase.java
+++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/onnx/OnnxMnistSoftmaxImportTestCase.java
@@ -1,9 +1,11 @@
-package com.yahoo.searchlib.rankingexpression.integration.ml;
+package com.yahoo.searchlib.rankingexpression.integration.onnx;
 
 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.tensorflow.TensorFlowImporter;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.TensorFlowModel;
 import com.yahoo.tensor.Tensor;
 import com.yahoo.tensor.TensorType;
 import org.junit.Test;
@@ -22,7 +24,7 @@ public class OnnxMnistSoftmaxImportTestCase {
 
     @Test
     public void testMnistSoftmaxImport() throws IOException {
-        ImportedModel model = new OnnxImporter().importModel("test", "src/test/files/integration/onnx/mnist_softmax/mnist_softmax.onnx");
+        OnnxModel model = new OnnxImporter().importModel("test", "src/test/files/integration/onnx/mnist_softmax/mnist_softmax.onnx");
 
         // Check constants
         assertEquals(2, model.largeConstants().size());
@@ -46,7 +48,7 @@ public class OnnxMnistSoftmaxImportTestCase {
                 model.requiredMacros().get("Placeholder"));
 
         // Check outputs
-        RankingExpression output = model.defaultSignature().outputExpression("add");
+        RankingExpression output = model.outputExpression("add");
         assertNotNull(output);
         assertEquals("add", output.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))",
@@ -66,12 +68,13 @@ public class OnnxMnistSoftmaxImportTestCase {
     }
 
     private Tensor evaluateTensorFlowModel(String path, Tensor argument, String input, String output) {
-        ImportedModel model = new TensorFlowImporter().importModel("test", path);
+        SavedModelBundle tensorFlowModel = SavedModelBundle.load(path, "serve");
+        TensorFlowModel model = new TensorFlowImporter().importModel("test", tensorFlowModel);
         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);
+        OnnxModel model = new OnnxImporter().importModel("test", path);
         return evaluateExpression(model.expressions().get(output), contextFrom(model), argument, input);
     }
 
@@ -80,7 +83,14 @@ public class OnnxMnistSoftmaxImportTestCase {
         return expression.evaluate(context).asTensor();
     }
 
-    private Context contextFrom(ImportedModel result) {
+    private Context contextFrom(TensorFlowModel 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 Context contextFrom(OnnxModel 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)));
diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/BatchNormImportTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/BatchNormImportTestCase.java
index bf9684082f4..0f5eec93feb 100644
--- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/BatchNormImportTestCase.java
+++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/BatchNormImportTestCase.java
@@ -1,5 +1,5 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow;
 
 import com.yahoo.searchlib.rankingexpression.RankingExpression;
 import org.junit.Test;
@@ -15,7 +15,7 @@ public class BatchNormImportTestCase {
     @Test
     public void testBatchNormImport() {
         TestableTensorFlowModel model = new TestableTensorFlowModel("test", "src/test/files/integration/tensorflow/batch_norm/saved");
-        ImportedModel.Signature signature = model.get().signature("serving_default");
+        TensorFlowModel.Signature signature = model.get().signature("serving_default");
 
         assertEquals("Has skipped outputs",
                      0, model.get().signature("serving_default").skippedOutputs().size());
diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/DimensionRenamerTest.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/DimensionRenamerTest.java
index c8c7ec798bb..74b0d11f1d6 100644
--- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/DimensionRenamerTest.java
+++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/DimensionRenamerTest.java
@@ -1,6 +1,6 @@
-package com.yahoo.searchlib.rankingexpression.integration.ml;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.DimensionRenamer;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.DimensionRenamer;
 import org.junit.Test;
 
 import static org.junit.Assert.assertTrue;
diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/DropoutImportTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/DropoutImportTestCase.java
index a63c7346335..50a467ec581 100644
--- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/DropoutImportTestCase.java
+++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/DropoutImportTestCase.java
@@ -1,5 +1,5 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow;
 
 import com.yahoo.searchlib.rankingexpression.RankingExpression;
 import com.yahoo.tensor.TensorType;
@@ -24,7 +24,7 @@ public class DropoutImportTestCase {
         assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(),
                      model.get().requiredMacros().get("X"));
 
-        ImportedModel.Signature signature = model.get().signature("serving_default");
+        TensorFlowModel.Signature signature = model.get().signature("serving_default");
 
         assertEquals("Has skipped outputs",
                      0, model.get().signature("serving_default").skippedOutputs().size());
@@ -32,7 +32,7 @@ public class DropoutImportTestCase {
         RankingExpression output = signature.outputExpression("y");
         assertNotNull(output);
         assertEquals("outputs/Maximum", output.getName());
-        assertEquals("join(join(imported_ml_macro_test_outputs_BiasAdd, reduce(constant(test_outputs_Const), sum, d1), f(a,b)(a * b)), imported_ml_macro_test_outputs_BiasAdd, f(a,b)(max(a,b)))",
+        assertEquals("join(join(tf_macro_test_outputs_BiasAdd, reduce(constant(test_outputs_Const), sum, d1), f(a,b)(a * b)), tf_macro_test_outputs_BiasAdd, f(a,b)(max(a,b)))",
                 output.getRoot().toString());
         model.assertEqualResult("X", output.getName());
     }
diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/MnistSoftmaxImportTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/MnistSoftmaxImportTestCase.java
index bd7644be23b..9f919c452d6 100644
--- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/MnistSoftmaxImportTestCase.java
+++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/MnistSoftmaxImportTestCase.java
@@ -1,5 +1,5 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow;
 
 import com.yahoo.searchlib.rankingexpression.RankingExpression;
 import com.yahoo.tensor.Tensor;
@@ -45,7 +45,7 @@ public class MnistSoftmaxImportTestCase {
 
         // Check signatures
         assertEquals(1, model.get().signatures().size());
-        ImportedModel.Signature signature = model.get().signatures().get("serving_default");
+        TensorFlowModel.Signature signature = model.get().signatures().get("serving_default");
         assertNotNull(signature);
 
         // ... signature inputs
diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/OrderedTensorTypeTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/OrderedTensorTypeTestCase.java
index b2443082ab1..beec2ab1ead 100644
--- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/OrderedTensorTypeTestCase.java
+++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/OrderedTensorTypeTestCase.java
@@ -1,6 +1,6 @@
-package com.yahoo.searchlib.rankingexpression.integration.ml;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow;
 
-import com.yahoo.searchlib.rankingexpression.integration.ml.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.OrderedTensorType;
 import org.junit.Test;
 
 import static org.junit.Assert.assertEquals;
diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/TestableTensorFlowModel.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/TestableTensorFlowModel.java
index 723c5f27914..7ca16939477 100644
--- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/TestableTensorFlowModel.java
+++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/TestableTensorFlowModel.java
@@ -1,11 +1,11 @@
 // Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package com.yahoo.searchlib.rankingexpression.integration.ml;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow;
 
 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.tensorflow.TensorConverter;
+import com.yahoo.searchlib.rankingexpression.integration.tensorflow.importer.TensorConverter;
 import com.yahoo.searchlib.rankingexpression.rule.CompositeNode;
 import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
 import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
@@ -28,7 +28,7 @@ import static org.junit.Assert.assertEquals;
 public class TestableTensorFlowModel {
 
     private SavedModelBundle tensorFlowModel;
-    private ImportedModel model;
+    private TensorFlowModel model;
 
     // Sizes of the input vector
     private final int d0Size = 1;
@@ -39,7 +39,7 @@ public class TestableTensorFlowModel {
         model = new TensorFlowImporter().importModel(modelName, tensorFlowModel);
     }
 
-    public ImportedModel get() { return model; }
+    public TensorFlowModel get() { return model; }
 
     public void assertEqualResult(String inputName, String operationName) {
         Tensor tfResult = tensorFlowExecute(tensorFlowModel, inputName, operationName);
@@ -66,7 +66,7 @@ public class TestableTensorFlowModel {
         return TensorConverter.toVespaTensor(results.get(0));
     }
 
-    private Context contextFrom(ImportedModel result) {
+    private Context contextFrom(TensorFlowModel 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)));
@@ -81,7 +81,7 @@ public class TestableTensorFlowModel {
         return b.build();
     }
 
-    private void evaluateMacro(Context context, ImportedModel model, String macroName) {
+    private void evaluateMacro(Context context, TensorFlowModel model, String macroName) {
         if (!context.names().contains(macroName)) {
             RankingExpression e = model.macros().get(macroName);
             evaluateMacroDependencies(context, model, e.getRoot());
@@ -89,7 +89,7 @@ public class TestableTensorFlowModel {
         }
     }
 
-    private void evaluateMacroDependencies(Context context, ImportedModel model, ExpressionNode node) {
+    private void evaluateMacroDependencies(Context context, TensorFlowModel model, ExpressionNode node) {
         if (node instanceof ReferenceNode) {
             String name = node.toString();
             if (model.macros().containsKey(name)) {
diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/VariableConverterTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/VariableConverterTestCase.java
index f94098e6255..051c2c60c95 100644
--- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/VariableConverterTestCase.java
+++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/VariableConverterTestCase.java
@@ -1,4 +1,4 @@
-package com.yahoo.searchlib.rankingexpression.integration.ml;
+package com.yahoo.searchlib.rankingexpression.integration.tensorflow;
 
 import org.junit.Test;
 
diff --git a/vespajlib/src/main/java/com/yahoo/tensor/functions/ScalarFunctions.java b/vespajlib/src/main/java/com/yahoo/tensor/functions/ScalarFunctions.java
index 3a66eef258d..944755c9db2 100644
--- a/vespajlib/src/main/java/com/yahoo/tensor/functions/ScalarFunctions.java
+++ b/vespajlib/src/main/java/com/yahoo/tensor/functions/ScalarFunctions.java
@@ -22,37 +22,22 @@ public class ScalarFunctions {
     public static DoubleBinaryOperator add() { return new Add(); }
     public static DoubleBinaryOperator divide() { return new Divide(); }
     public static DoubleBinaryOperator equal() { return new Equal(); }
-    public static DoubleBinaryOperator greater() { return new Greater(); }
-    public static DoubleBinaryOperator less() { return new Less(); }
     public static DoubleBinaryOperator max() { return new Max(); }
     public static DoubleBinaryOperator min() { return new Min(); }
-    public static DoubleBinaryOperator mean() { return new Mean(); }
     public static DoubleBinaryOperator multiply() { return new Multiply(); }
-    public static DoubleBinaryOperator pow() { return new Pow(); }
     public static DoubleBinaryOperator squareddifference() { return new SquaredDifference(); }
     public static DoubleBinaryOperator subtract() { return new Subtract(); }
 
-    public static DoubleUnaryOperator abs() { return new Abs(); }
     public static DoubleUnaryOperator acos() { return new Acos(); }
-    public static DoubleUnaryOperator asin() { return new Asin(); }
-    public static DoubleUnaryOperator atan() { return new Atan(); }
-    public static DoubleUnaryOperator ceil() { return new Ceil(); }
-    public static DoubleUnaryOperator cos() { return new Cos(); }
     public static DoubleUnaryOperator elu() { return new Elu(); }
     public static DoubleUnaryOperator exp() { return new Exp(); }
     public static DoubleUnaryOperator floor() { return new Floor(); }
-    public static DoubleUnaryOperator log() { return new Log(); }
-    public static DoubleUnaryOperator neg() { return new Neg(); }
-    public static DoubleUnaryOperator reciprocal() { return new Reciprocal(); }
     public static DoubleUnaryOperator relu() { return new Relu(); }
     public static DoubleUnaryOperator rsqrt() { return new Rsqrt(); }
     public static DoubleUnaryOperator selu() { return new Selu(); }
-    public static DoubleUnaryOperator sin() { return new Sin(); }
     public static DoubleUnaryOperator sigmoid() { return new Sigmoid(); }
     public static DoubleUnaryOperator sqrt() { return new Sqrt(); }
     public static DoubleUnaryOperator square() { return new Square(); }
-    public static DoubleUnaryOperator tan() { return new Tan(); }
-    public static DoubleUnaryOperator tanh() { return new Tanh(); }
 
     public static Function<List<Long>, Double> random() { return new Random(); }
     public static Function<List<Long>, Double> equal(List<String> argumentNames) { return new EqualElements(argumentNames); }
@@ -74,20 +59,6 @@ public class ScalarFunctions {
         public String toString() { return "f(a,b)(a==b)"; }
     }
 
-    public static class Greater implements DoubleBinaryOperator {
-        @Override
-        public double applyAsDouble(double left, double right) { return left > right ? 1 : 0; }
-        @Override
-        public String toString() { return "f(a,b)(a > b)"; }
-    }
-
-    public static class Less implements DoubleBinaryOperator {
-        @Override
-        public double applyAsDouble(double left, double right) { return left < right ? 1 : 0; }
-        @Override
-        public String toString() { return "f(a,b)(a < b)"; }
-    }
-
     public static class Max implements DoubleBinaryOperator {
         @Override
         public double applyAsDouble(double left, double right) { return Math.max(left, right); }
@@ -102,13 +73,6 @@ public class ScalarFunctions {
         public String toString() { return "f(a,b)(min(a, b))"; }
     }
 
-    public static class Mean implements DoubleBinaryOperator {
-        @Override
-        public double applyAsDouble(double left, double right) { return (left + right) / 2; }
-        @Override
-        public String toString() { return "f(a,b)((a + b) / 2)"; }
-    }
-
     public static class Multiply implements DoubleBinaryOperator {
         @Override
         public double applyAsDouble(double left, double right) { return left * right; }
@@ -116,13 +80,6 @@ public class ScalarFunctions {
         public String toString() { return "f(a,b)(a * b)"; }
     }
 
-    public static class Pow implements DoubleBinaryOperator {
-        @Override
-        public double applyAsDouble(double left, double right) { return Math.pow(left, right); }
-        @Override
-        public String toString() { return "f(a,b)(pow(a, b))"; }
-    }
-
     public static class Divide implements DoubleBinaryOperator {
         @Override
         public double applyAsDouble(double left, double right) { return left / right; }
@@ -147,13 +104,6 @@ public class ScalarFunctions {
 
     // Unary operators ------------------------------------------------------------------------------
 
-    public static class Abs implements DoubleUnaryOperator {
-        @Override
-        public double applyAsDouble(double operand) { return Math.abs(operand); }
-        @Override
-        public String toString() { return "f(a)(fabs(a))"; }
-    }
-
     public static class Acos implements DoubleUnaryOperator {
         @Override
         public double applyAsDouble(double operand) { return Math.acos(operand); }
@@ -161,34 +111,6 @@ public class ScalarFunctions {
         public String toString() { return "f(a)(acos(a))"; }
     }
 
-    public static class Asin implements DoubleUnaryOperator {
-        @Override
-        public double applyAsDouble(double operand) { return Math.asin(operand); }
-        @Override
-        public String toString() { return "f(a)(asin(a))"; }
-    }
-
-    public static class Atan implements DoubleUnaryOperator {
-        @Override
-        public double applyAsDouble(double operand) { return Math.atan(operand); }
-        @Override
-        public String toString() { return "f(a)(atan(a))"; }
-    }
-
-    public static class Ceil implements DoubleUnaryOperator {
-        @Override
-        public double applyAsDouble(double operand) { return Math.ceil(operand); }
-        @Override
-        public String toString() { return "f(a)(ceil(a))"; }
-    }
-
-    public static class Cos implements DoubleUnaryOperator {
-        @Override
-        public double applyAsDouble(double operand) { return Math.cos(operand); }
-        @Override
-        public String toString() { return "f(a)(cos(a))"; }
-    }
-
     public static class Elu implements DoubleUnaryOperator {
         @Override
         public double applyAsDouble(double operand) { return operand < 0 ? Math.exp(operand) -1 : operand; }
@@ -210,26 +132,6 @@ public class ScalarFunctions {
         public String toString() { return "f(a)(floor(a))"; }
     }
 
-    public static class Log implements DoubleUnaryOperator {
-        @Override
-        public double applyAsDouble(double operand) { return Math.log(operand); }
-        @Override
-        public String toString() { return "f(a)(log(a))"; }
-    }
-
-    public static class Neg implements DoubleUnaryOperator {
-        @Override
-        public double applyAsDouble(double operand) { return -operand; }
-        @Override
-        public String toString() { return "f(a)(-a)"; }
-    }
-
-    public static class Reciprocal implements DoubleUnaryOperator {
-        @Override
-        public double applyAsDouble(double operand) { return 1.0 / operand; }
-        @Override
-        public String toString() { return "f(a)(1 / a)"; }
-    }
 
     public static class Relu implements DoubleUnaryOperator {
         @Override
@@ -248,13 +150,6 @@ public class ScalarFunctions {
         public String toString() { return String.format("f(a)(%f * if(a >= 0, a, %f*(exp(a)-1)))", scale, alpha); }
     }
 
-    public static class Sin implements DoubleUnaryOperator {
-        @Override
-        public double applyAsDouble(double operand) { return Math.sin(operand); }
-        @Override
-        public String toString() { return "f(a)(sin(a))"; }
-    }
-
     public static class Rsqrt implements DoubleUnaryOperator {
         @Override
         public double applyAsDouble(double operand) { return 1.0 / Math.sqrt(operand); }
@@ -277,29 +172,15 @@ public class ScalarFunctions {
     }
 
     public static class Square implements DoubleUnaryOperator {
+
         @Override
         public double applyAsDouble(double operand) { return operand * operand; }
-        @Override
-        public String toString() { return "f(a)(a * a)"; }
-    }
 
-    public static class Tan implements DoubleUnaryOperator {
-        @Override
-        public double applyAsDouble(double operand) { return Math.tan(operand); }
         @Override
-        public String toString() { return "f(a)(tan(a))"; }
-    }
+        public String toString() { return "f(a)(a * a)"; }
 
-    public static class Tanh implements DoubleUnaryOperator {
-        @Override
-        public double applyAsDouble(double operand) { return Math.tanh(operand); }
-        @Override
-        public String toString() { return "f(a)(tanh(a))"; }
     }
 
-
-
-
     // Variable-length operators -----------------------------------------------------------------------------
 
     public static class EqualElements implements Function<List<Long>, Double> {