Move all importing to model-integration

29 files changed, 6 insertions, 2713 deletions

diff --git a/searchlib/pom.xml b/searchlib/pom.xml
index e0ce822e593..87058f8dfa1 100644
--- a/searchlib/pom.xml
+++ b/searchlib/pom.xml
@@ -35,6 +35,12 @@
       <version>${project.version}</version>
     </dependency>
     <dependency>
+      <groupId>com.yahoo.vespa</groupId>
+      <artifactId>config-model-api</artifactId>
+      <version>${project.version}</version>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
       <groupId>com.fasterxml.jackson.core</groupId>
       <artifactId>jackson-core</artifactId>
       <scope>provided</scope>
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/DimensionRenamer.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/DimensionRenamer.java
deleted file mode 100644
index 86c4c287f05..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/DimensionRenamer.java
+++ /dev/null
@@ -1,210 +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.operations.IntermediateOperation;
-
-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, IntermediateOperation 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 IntermediateOperation operation;
-
-        Arc(String from, String to, IntermediateOperation 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/ml/ImportedModel.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ImportedModel.java
deleted file mode 100644
index e66d0ab6f35..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ImportedModel.java
+++ /dev/null
@@ -1,226 +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.google.common.collect.ImmutableMap;
-import com.yahoo.collections.Pair;
-import com.yahoo.searchlib.rankingexpression.ExpressionFunction;
-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.LinkedHashMap;
-import java.util.List;
-import java.util.Map;
-import java.util.Optional;
-import java.util.regex.Pattern;
-
-/**
- * The result of importing an ML model into Vespa.
- *
- * @author bratseth
- */
-public class ImportedModel {
-
-    private static final String defaultSignatureName = "default";
-
-    private static final Pattern nameRegexp = Pattern.compile("[A-Za-z0-9_]*");
-    private final String name;
-    private final String source;
-
-    private final Map<String, Signature> signatures = new HashMap<>();
-    private final Map<String, TensorType> inputs = 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> functions = new HashMap<>();
-
-    /**
-     * Creates a new imported model.
-     *
-     * @param name the name of this mode, containing only characters in [A-Za-z0-9_]
-     * @param source the source path (directory or file) of this model
-     */
-    public ImportedModel(String name, String source) {
-        if ( ! nameRegexp.matcher(name).matches())
-            throw new IllegalArgumentException("An imported model name can only contain [A-Za-z0-9_], but is '" + name + "'");
-        this.name = name;
-        this.source = source;
-    }
-
-    /** Returns the name of this model, which can only contain the characters in [A-Za-z0-9_] */
-    public String name() { return name; }
-
-    /** Returns the source path (directory or file) of this model */
-    public String source() { return source; }
-
-    /** Returns an immutable map of the inputs of this */
-    public Map<String, TensorType> inputs() { return Collections.unmodifiableMap(inputs); }
-
-    /**
-     * 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 source model.
-     */
-    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.
-     */
-    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 inputs and constants).
-     * Note that only nodes recursively referenced by a placeholder/input are added.
-     */
-    public Map<String, RankingExpression> expressions() { return Collections.unmodifiableMap(expressions); }
-
-    /**
-     * Returns an immutable map of the functions that are part of this model.
-     * Note that the functions themselves are *not* copies and *not* immutable - they must be copied before modification.
-     */
-    public Map<String, RankingExpression> functions() { return Collections.unmodifiableMap(functions); }
-
-    /** 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. */
-    public Signature signature(String name) {
-        return signatures.computeIfAbsent(name, Signature::new);
-    }
-
-    /** Convenience method for returning a default signature */
-    public Signature defaultSignature() { return signature(defaultSignatureName); }
-
-    public void input(String name, TensorType argumentType) { inputs.put(name, argumentType); }
-    public void smallConstant(String name, Tensor constant) { smallConstants.put(name, constant); }
-    public void largeConstant(String name, Tensor constant) { largeConstants.put(name, constant); }
-    public void expression(String name, RankingExpression expression) { expressions.put(name, expression); }
-    public void function(String name, RankingExpression expression) { functions.put(name, expression); }
-
-    /**
-     * Returns all the output expressions of this indexed by name. The names consist of one or two parts
-     * separated by dot, where the first part is the signature name
-     * if signatures are used, or the expression name if signatures are not used and there are multiple
-     * expressions, and the second is the output name if signature names are used.
-     */
-    public List<Pair<String, ExpressionFunction>> outputExpressions() {
-        List<Pair<String, ExpressionFunction>> expressions = new ArrayList<>();
-        for (Map.Entry<String, Signature> signatureEntry : signatures().entrySet()) {
-            for (Map.Entry<String, String> outputEntry : signatureEntry.getValue().outputs().entrySet())
-                expressions.add(new Pair<>(signatureEntry.getKey() + "" + outputEntry.getKey(),
-                                           signatureEntry.getValue().outputExpression(outputEntry.getKey())
-                                                         .withName(signatureEntry.getKey() + "" + outputEntry.getKey())));
-            if (signatureEntry.getValue().outputs().isEmpty()) // fallback: Signature without outputs
-                expressions.add(new Pair<>(signatureEntry.getKey(),
-                                           new ExpressionFunction(signatureEntry.getKey(),
-                                                                  new ArrayList<>(signatureEntry.getValue().inputs().values()),
-                                                                  expressions().get(signatureEntry.getKey()),
-                                                                  signatureEntry.getValue().inputMap(),
-                                                                  Optional.empty())));
-        }
-        if (signatures().isEmpty()) { // fallback for models without signatures
-            if (expressions().size() == 1) {
-                Map.Entry<String, RankingExpression> singleEntry = this.expressions.entrySet().iterator().next();
-                expressions.add(new Pair<>(singleEntry.getKey(),
-                                           new ExpressionFunction(singleEntry.getKey(),
-                                                                  new ArrayList<>(inputs.keySet()),
-                                                                  singleEntry.getValue(),
-                                                                  inputs,
-                                                                  Optional.empty())));
-            }
-            else {
-                for (Map.Entry<String, RankingExpression> expressionEntry : expressions().entrySet()) {
-                    expressions.add(new Pair<>(expressionEntry.getKey(),
-                                               new ExpressionFunction(expressionEntry.getKey(),
-                                                                      new ArrayList<>(inputs.keySet()),
-                                                                      expressionEntry.getValue(),
-                                                                      inputs,
-                                                                      Optional.empty())));
-                }
-            }
-        }
-        return expressions;
-    }
-
-    /**
-     * A signature is a set of named inputs and outputs, where the inputs maps to input
-     * ("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.
-     */
-    public class Signature {
-
-        private final String name;
-        private final Map<String, String> inputs = new LinkedHashMap<>();
-        private final Map<String, String> outputs = new LinkedHashMap<>();
-        private final Map<String, String> skippedOutputs = new HashMap<>();
-        private final List<String> importWarnings = new ArrayList<>();
-
-        public Signature(String name) {
-            this.name = name;
-        }
-
-        public String name() { return name; }
-
-        /** Returns the result this is part of */
-        public ImportedModel owner() { return ImportedModel.this; }
-
-        /**
-         * Returns an immutable map of the inputs (evaluation context) of this. This is a map from input name
-         * in this signature to input name in the owning model
-         */
-        public Map<String, String> inputs() { return Collections.unmodifiableMap(inputs); }
-
-        /** Returns the name and type of all inputs in this signature as an immutable map */
-        public Map<String, TensorType> inputMap() {
-            ImmutableMap.Builder<String, TensorType> inputs = new ImmutableMap.Builder<>();
-            // Note: We're naming inputs by their actual name (used in the expression, given by what the input maps *to*
-            // in the model, as these are the names which must actually be bound, if we are to avoid creating an
-            // "input mapping" to accomodate this complexity in
-            for (Map.Entry<String, String> inputEntry : inputs().entrySet())
-                inputs.put(inputEntry.getValue(), owner().inputs().get(inputEntry.getValue()));
-            return inputs.build();
-        }
-
-        /** Returns the type of the input this input references */
-        public TensorType inputArgument(String inputName) { return owner().inputs().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 the expression this output references */
-        public ExpressionFunction outputExpression(String outputName) {
-            return new ExpressionFunction(outputName,
-                                          new ArrayList<>(inputs.values()),
-                                          owner().expressions().get(outputs.get(outputName)),
-                                          inputMap(),
-                                          Optional.empty());
-        }
-
-        @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/ImportedModels.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ImportedModels.java
deleted file mode 100644
index 896cd2e8d21..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ImportedModels.java
+++ /dev/null
@@ -1,109 +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.google.common.collect.ImmutableMap;
-import com.yahoo.path.Path;
-
-import java.io.File;
-import java.util.Arrays;
-import java.util.Collection;
-import java.util.HashMap;
-import java.util.Map;
-import java.util.Optional;
-
-/**
- * All models imported from the models/ directory in the application package.
- * If this is empty it may be due to either not having any models in the application package,
- * or this being created for a ZooKeeper application package, which does not have imported models.
- *
- * @author bratseth
- */
-public class ImportedModels {
-
-    /** All imported models, indexed by their names */
-    private final ImmutableMap<String, ImportedModel> importedModels;
-
-    /** Create a null imported models */
-    public ImportedModels() {
-        importedModels = ImmutableMap.of();
-    }
-
-    public ImportedModels(File modelsDirectory, Collection<ModelImporter> importers) {
-        Map<String, ImportedModel> models = new HashMap<>();
-
-        // Find all subdirectories recursively which contains a model we can read
-        importRecursively(modelsDirectory, models, importers);
-        importedModels = ImmutableMap.copyOf(models);
-    }
-
-    private static void importRecursively(File dir,
-                                          Map<String, ImportedModel> models,
-                                          Collection<ModelImporter> importers) {
-        if ( ! dir.isDirectory()) return;
-
-        Arrays.stream(dir.listFiles()).sorted().forEach(child -> {
-            Optional<ModelImporter> importer = findImporterOf(child, importers);
-            if (importer.isPresent()) {
-                String name = toName(child);
-                ImportedModel existing = models.get(name);
-                if (existing != null)
-                    throw new IllegalArgumentException("The models in " + child + " and " + existing.source() +
-                                                       " both resolve to the model name '" + name + "'");
-                models.put(name, importer.get().importModel(name, child));
-            }
-            else {
-                importRecursively(child, models, importers);
-            }
-        });
-    }
-
-    private static Optional<ModelImporter> findImporterOf(File path, Collection<ModelImporter> importers) {
-        return importers.stream().filter(item -> item.canImport(path.toString())).findFirst();
-    }
-
-    /**
-     * Returns the model at the given location in the application package.
-     *
-     * @param modelPath the path to this model (file or directory, depending on model type)
-     *                  under the application package, both from the root or relative to the
-     *                  models directory works
-     * @return the model at this path or null if none
-     */
-    public ImportedModel get(File modelPath) {
-        return importedModels.get(toName(modelPath));
-    }
-
-    public ImportedModel get(String modelName) {
-        return importedModels.get(modelName);
-    }
-
-    /** Returns an immutable collection of all the imported models */
-    public Collection<ImportedModel> all() {
-        return importedModels.values();
-    }
-
-    private static String toName(File modelFile) {
-        Path modelPath = Path.fromString(modelFile.toString());
-        if (modelFile.isFile())
-            modelPath = stripFileEnding(modelPath);
-        String localPath = concatenateAfterModelsDirectory(modelPath);
-        return localPath.replace('.', '_');
-    }
-
-    private static Path stripFileEnding(Path path) {
-        int dotIndex = path.last().lastIndexOf("");
-        if (dotIndex <= 0) return path;
-        return path.withLast(path.last().substring(0, dotIndex));
-    }
-
-    private static String concatenateAfterModelsDirectory(Path path) {
-        boolean afterModels = false;
-        StringBuilder result = new StringBuilder();
-        for (String element : path.elements()) {
-            if (afterModels) result.append(element).append("_");
-            if (element.equals("models")) afterModels = true;
-        }
-        return result.substring(0, result.length()-1);
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/IntermediateGraph.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/IntermediateGraph.java
deleted file mode 100644
index 81c176707e5..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/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;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.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 model 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/ModelImporter.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ModelImporter.java
deleted file mode 100644
index 47bf6a2a240..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/ModelImporter.java
+++ /dev/null
@@ -1,243 +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.RankingExpression;
-import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
-import com.yahoo.searchlib.rankingexpression.evaluation.Value;
-import com.yahoo.searchlib.rankingexpression.integration.ml.operations.Constant;
-import com.yahoo.searchlib.rankingexpression.integration.ml.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 functions.
- *
- * @author lesters
- */
-public abstract class ModelImporter {
-
-    private static final Logger log = Logger.getLogger(ModelImporter.class.getName());
-
-    /** Returns whether the file or directory at the given path is of the type which can be imported by this */
-    public abstract boolean canImport(String modelPath);
-
-    /** Imports the given model */
-    public abstract ImportedModel importModel(String modelName, String modelPath);
-
-    public final ImportedModel importModel(String modelName, File modelPath) {
-        return importModel(modelName, modelPath.toString());
-    }
-
-    /**
-     * Takes an IntermediateGraph and converts it to a ImportedModel containing
-     * the actual Vespa ranking expressions.
-     */
-    public static ImportedModel convertIntermediateGraphToModel(IntermediateGraph graph, String modelSource) {
-        ImportedModel model = new ImportedModel(graph.name(), modelSource);
-
-        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);
-        importFunctionExpression(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.input(operation.vespaName(), standardNamingConvention.type());
-        }
-    }
-
-    private static void importFunctionExpression(IntermediateOperation operation, ImportedModel model) {
-        if (operation.rankingExpressionFunction().isPresent()) {
-            TensorFunction function = operation.rankingExpressionFunction().get();
-            try {
-                model.function(operation.rankingExpressionFunctionName(),
-                               new RankingExpression(operation.rankingExpressionFunctionName(),
-                                                     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/OrderedTensorType.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/OrderedTensorType.java
deleted file mode 100644
index dfd073f82b4..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/OrderedTensorType.java
+++ /dev/null
@@ -1,235 +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.tensor.TensorType;
-import com.yahoo.tensor.TensorTypeParser;
-
-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. Imported 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
- * 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[] innerSizesOriginal;
-    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.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;
-        }
-        innerSizesOriginal[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];
-            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;
-    }
-
-    public int dimensionMap(int originalIndex) {
-        return dimensionMap[originalIndex];
-    }
-
-    /**
-     * When dimension ordering between Vespa and imported 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.
-     */
-    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 / innerSizesOriginal[i];
-            directIndex += innerSizesVespa[dimensionMap[i]] * address;
-            rest %= innerSizesOriginal[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 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);
-    }
-
-    public OrderedTensorType rename(String dimensionPrefix) {
-        OrderedTensorType.Builder builder = new OrderedTensorType.Builder();
-        for (int i = 0; i < dimensions.size(); ++ 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()));
-            } else {
-                builder.add(TensorType.Dimension.indexed(dimensionName));
-            }
-        }
-        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()));
-            } else {
-                builder.add(TensorType.Dimension.indexed(dimensionName));
-            }
-        }
-        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));
-            } else {
-                builder.add(TensorType.Dimension.indexed(dimensionName));
-            }
-        }
-        return builder.build();
-    }
-
-    public static class Builder {
-
-        private final List<TensorType.Dimension> dimensions;
-
-        public Builder() {
-            this.dimensions = new ArrayList<>();
-        }
-
-        public Builder add(TensorType.Dimension vespaDimension) {
-            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/operations/Argument.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Argument.java
deleted file mode 100644
index 57e4ec13e45..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Argument.java
+++ /dev/null
@@ -1,57 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.searchlib.rankingexpression.integration.ml.DimensionRenamer;
-import com.yahoo.tensor.TensorType;
-import com.yahoo.tensor.evaluation.VariableTensor;
-import com.yahoo.tensor.functions.Rename;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.Collections;
-import java.util.List;
-
-public class Argument extends IntermediateOperation {
-
-    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);
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        return type;
-    }
-
-    @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/operations/ConcatV2.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/ConcatV2.java
deleted file mode 100644
index 413b856e43d..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/ConcatV2.java
+++ /dev/null
@@ -1,108 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.searchlib.rankingexpression.integration.ml.DimensionRenamer;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.List;
-import java.util.Optional;
-
-public class ConcatV2 extends IntermediateOperation {
-
-    private String concatDimensionName;
-
-    public ConcatV2(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        if (!inputs.stream().map(IntermediateOperation::type).allMatch(Optional::isPresent)) {
-            return null;
-        }
-
-        IntermediateOperation concatDimOp = inputs.get(inputs.size() - 1);  // ConcatV2: concat dimension is the last input
-        if (!concatDimOp.getConstantValue().isPresent()) {
-            throw new IllegalArgumentException("ConcatV2 in " + name + ": " +
-                                               "concat dimension must be a constant.");
-        }
-        Tensor concatDimTensor = concatDimOp.getConstantValue().get().asTensor();
-        if (concatDimTensor.type().rank() != 0) {
-            throw new IllegalArgumentException("ConcatV2 in " + name + ": " +
-                                               "concat dimension must be a scalar.");
-        }
-
-        OrderedTensorType aType = inputs.get(0).type().get();
-
-        int concatDim = (int)concatDimTensor.asDouble();
-        long concatDimSize = aType.dimensions().get(concatDim).size().orElse(-1L);
-
-        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 + ": " +
-                                                   "inputs must have save rank.");
-            }
-            for (int j = 0; j < aType.rank(); ++j) {
-                long dimSizeA = aType.dimensions().get(j).size().orElse(-1L);
-                long dimSizeB = bType.dimensions().get(j).size().orElse(-1L);
-                if (j == concatDim) {
-                    concatDimSize += dimSizeB;
-                } else if (dimSizeA != dimSizeB) {
-                    throw new IllegalArgumentException("ConcatV2 in " + name + ": " +
-                                                       "input dimension " + j + " differs in input tensors.");
-                }
-            }
-        }
-
-        OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder();
-        int dimensionIndex = 0;
-        for (TensorType.Dimension dimension : aType.dimensions()) {
-            if (dimensionIndex == concatDim) {
-                concatDimensionName = dimension.name();
-                typeBuilder.add(TensorType.Dimension.indexed(concatDimensionName, concatDimSize));
-            } else {
-                typeBuilder.add(dimension);
-            }
-            dimensionIndex++;
-        }
-        return typeBuilder.build();
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        if (!inputs.stream().map(IntermediateOperation::function).allMatch(Optional::isPresent)) {
-            return null;
-        }
-        TensorFunction result = inputs.get(0).function().get();
-        for (int i = 1; i < inputs.size() - 1; ++i) {
-            TensorFunction b = inputs.get(i).function().get();
-            result = new com.yahoo.tensor.functions.Concat(result, b, concatDimensionName);
-        }
-        return result;
-    }
-
-    @Override
-    public void addDimensionNameConstraints(DimensionRenamer renamer) {
-        if (!inputs.stream().map(IntermediateOperation::type).allMatch(Optional::isPresent)) {
-            return;
-        }
-        OrderedTensorType a = inputs.get(0).type().get();
-        for (int i = 1; i < inputs.size() - 1; ++i) {
-            OrderedTensorType b = inputs.get(i).type().get();
-            String bDim = b.dimensions().get(i).name();
-            String aDim = a.dimensions().get(i).name();
-            renamer.addConstraint(aDim, bDim, DimensionRenamer::equals, this);
-        }
-    }
-
-    @Override
-    public void renameDimensions(DimensionRenamer renamer) {
-        super.renameDimensions(renamer);
-        concatDimensionName = renamer.dimensionNameOf(concatDimensionName).orElse(concatDimensionName);
-   }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Const.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Const.java
deleted file mode 100644
index 49c0bb712c5..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Const.java
+++ /dev/null
@@ -1,89 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.Reference;
-import com.yahoo.searchlib.rankingexpression.evaluation.Value;
-import com.yahoo.searchlib.rankingexpression.integration.ml.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-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 java.util.List;
-import java.util.Optional;
-
-public class Const extends IntermediateOperation {
-
-    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() + "_");
-        setConstantValue(value());
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        return type;
-    }
-
-    @Override
-    public Optional<TensorFunction> function() {
-        if (function == null) {
-            function = lazyGetFunction();
-        }
-        return Optional.ofNullable(function);
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        ExpressionNode expressionNode;
-        if (type.type().rank() == 0 && getConstantValue().isPresent()) {
-            expressionNode = new ConstantNode(getConstantValue().get().asDoubleValue());
-        } else {
-            expressionNode = new ReferenceNode(Reference.simple("constant", vespaName()));
-        }
-        return new TensorFunctionNode.TensorFunctionExpressionNode(expressionNode);
-    }
-
-    /** Constant names are prefixed by "modelName_" to avoid name conflicts between models */
-    @Override
-    public String vespaName() {
-        return modelName + "_" + super.vespaName();
-    }
-
-    @Override
-    public void addDimensionNameConstraints(DimensionRenamer renamer) {
-        for (TensorType.Dimension dimension : type.type().dimensions()) {
-            renamer.addDimension(dimension.name());
-        }
-    }
-
-    @Override
-    public void renameDimensions(DimensionRenamer renamer) {
-        super.renameDimensions(renamer);
-        setConstantValue(value());
-    }
-
-    @Override
-    public boolean isConstant() {
-        return true;
-    }
-
-    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");
-        }
-        return value.get();
-    }
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Constant.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Constant.java
deleted file mode 100644
index 670274376a3..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Constant.java
+++ /dev/null
@@ -1,61 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.evaluation.Value;
-import com.yahoo.searchlib.rankingexpression.integration.ml.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.tensor.TensorType;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.Collections;
-import java.util.Optional;
-
-public class Constant extends IntermediateOperation {
-
-    private final String modelName;
-
-    public Constant(String modelName, String nodeName, OrderedTensorType type) {
-        super(modelName, nodeName, Collections.emptyList());
-        this.modelName = modelName;
-        this.type = type.rename(vespaName() + "_");
-    }
-
-    /** Constant names are prefixed by "modelName_" to avoid name conflicts between models */
-    @Override
-    public String vespaName() {
-        return modelName + "_" + vespaName(name);
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        return type;
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        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));
-    }
-
-    @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/ml/operations/ExpandDims.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/ExpandDims.java
deleted file mode 100644
index e037a9a2497..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/ExpandDims.java
+++ /dev/null
@@ -1,106 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
-import com.yahoo.searchlib.rankingexpression.integration.ml.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.searchlib.rankingexpression.rule.ConstantNode;
-import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
-import com.yahoo.searchlib.rankingexpression.rule.GeneratorLambdaFunctionNode;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-import com.yahoo.tensor.functions.Generate;
-import com.yahoo.tensor.functions.ScalarFunctions;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Optional;
-
-public class ExpandDims extends IntermediateOperation {
-
-    private List<String> expandDimensions;
-
-    public ExpandDims(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        if (!allInputTypesPresent(2)) {
-            return null;
-        }
-
-        IntermediateOperation axisOperation = inputs().get(1);
-        if (!axisOperation.getConstantValue().isPresent()) {
-            throw new IllegalArgumentException("ExpandDims in " + name + ": " +
-                    "axis must be a constant.");
-        }
-        Tensor axis = axisOperation.getConstantValue().get().asTensor();
-        if (axis.type().rank() != 0) {
-            throw new IllegalArgumentException("ExpandDims in " + name + ": " +
-                    "axis argument must be a scalar.");
-        }
-
-        OrderedTensorType inputType = inputs.get(0).type().get();
-        int dimensionToInsert = (int)axis.asDouble();
-        if (dimensionToInsert < 0) {
-            dimensionToInsert = inputType.dimensions().size() - dimensionToInsert;
-        }
-
-        OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder();
-        expandDimensions = new ArrayList<>();
-        int dimensionIndex = 0;
-        for (TensorType.Dimension dimension : inputType.dimensions()) {
-            if (dimensionIndex == dimensionToInsert) {
-                String name = String.format("%s_%d", vespaName(), dimensionIndex);
-                expandDimensions.add(name);
-                typeBuilder.add(TensorType.Dimension.indexed(name, 1L));
-            }
-            typeBuilder.add(dimension);
-            dimensionIndex++;
-        }
-
-        return typeBuilder.build();
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        if (!allInputFunctionsPresent(2)) {
-            return null;
-        }
-
-        // multiply with a generated tensor created from the reduced dimensions
-        TensorType.Builder typeBuilder = new TensorType.Builder();
-        for (String name : expandDimensions) {
-            typeBuilder.indexed(name, 1);
-        }
-        TensorType generatedType = typeBuilder.build();
-        ExpressionNode generatedExpression = new ConstantNode(new DoubleValue(1));
-        Generate generatedFunction = new Generate(generatedType,
-                new GeneratorLambdaFunctionNode(generatedType, generatedExpression).asLongListToDoubleOperator());
-        return new com.yahoo.tensor.functions.Join(inputs().get(0).function().get(), generatedFunction, ScalarFunctions.multiply());
-    }
-
-    @Override
-    public void addDimensionNameConstraints(DimensionRenamer renamer) {
-        for (TensorType.Dimension dimension : type.type().dimensions()) {
-            renamer.addDimension(dimension.name());
-        }
-    }
-
-    @Override
-    public void renameDimensions(DimensionRenamer renamer) {
-        super.renameDimensions(renamer);
-        List<String> renamedDimensions = new ArrayList<>(expandDimensions.size());
-        for (String name : expandDimensions) {
-            Optional<String> newName = renamer.dimensionNameOf(name);
-            if (!newName.isPresent()) {
-                return;  // presumably, already renamed
-            }
-            renamedDimensions.add(newName.get());
-        }
-        expandDimensions = renamedDimensions;
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Identity.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Identity.java
deleted file mode 100644
index c52b3357848..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Identity.java
+++ /dev/null
@@ -1,35 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.List;
-
-public class Identity extends IntermediateOperation {
-
-    public Identity(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
-    }
-
-    /** Constant names are prefixed by "modelName_" to avoid name conflicts between models */
-    @Override
-    public String vespaName() {
-        return modelName + "_" + super.vespaName();
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        if (!allInputTypesPresent(1))
-            return null;
-        return inputs.get(0).type().orElse(null);
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        if (!allInputFunctionsPresent(1))
-            return null;
-        return inputs.get(0).function().orElse(null);
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/IntermediateOperation.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/IntermediateOperation.java
deleted file mode 100644
index 1b62ef67d71..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/IntermediateOperation.java
+++ /dev/null
@@ -1,191 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.Reference;
-import com.yahoo.searchlib.rankingexpression.evaluation.Value;
-import com.yahoo.searchlib.rankingexpression.integration.ml.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.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 java.util.ArrayList;
-import java.util.Collections;
-import java.util.List;
-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.
- *
- * @author lesters
- */
-public abstract class IntermediateOperation {
-
-    public final static String FUNCTION_PREFIX = "imported_ml_function_";
-
-    protected final String name;
-    protected final String modelName;
-    protected final List<IntermediateOperation> inputs;
-    protected final List<IntermediateOperation> outputs = new ArrayList<>();
-
-    protected OrderedTensorType type;
-    protected TensorFunction function;
-    protected TensorFunction rankingExpressionFunction = null;
-
-    private final List<String> importWarnings = new ArrayList<>();
-    private Value constantValue = null;
-    private List<IntermediateOperation> controlInputs = Collections.emptyList();
-
-    protected Function<OrderedTensorType, Value> constantValueFunction = null;
-
-    IntermediateOperation(String modelName, String name, List<IntermediateOperation> inputs) {
-        this.name = name;
-        this.modelName = modelName;
-        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 if (outputs.size() > 1) {
-                rankingExpressionFunction = lazyGetFunction();
-                function = new VariableTensor(rankingExpressionFunctionName(), type.type());
-            } else {
-                function = lazyGetFunction();
-            }
-        }
-        return Optional.ofNullable(function);
-    }
-
-    /** Returns original name of this operation node */
-    public String name() { return name; }
-
-    /** Return unmodifiable list of inputs */
-    public List<IntermediateOperation> inputs() { return inputs; }
-
-    /** Return unmodifiable list of outputs. If a node has multiple outputs, consider adding a function. */
-    public List<IntermediateOperation> outputs() { return Collections.unmodifiableList(outputs); }
-
-    /** Returns a function that should be added as a ranking expression function */
-    public Optional<TensorFunction> rankingExpressionFunction() {
-        return Optional.ofNullable(rankingExpressionFunction);
-    }
-
-    /** 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(IntermediateOperation::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; }
-
-    /** Sets the external control inputs */
-    public void setControlInputs(List<IntermediateOperation> inputs) { this.controlInputs = inputs; }
-
-    /** Retrieve the control inputs for this operation */
-    public List<IntermediateOperation> 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; }
-
-    /** Retrieve the valid Vespa name of this node if it is a ranking expression function */
-    public String rankingExpressionFunctionName() {
-        return vespaName() != null ? FUNCTION_PREFIX + modelName + "_" + vespaName() : 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<IntermediateOperation, Optional<?>> func) {
-        if (inputs.size() != expected) {
-            throw new IllegalArgumentException("Expected " + expected + " inputs " +
-                    "for '" + name + "', got " + inputs.size());
-        }
-        return inputs.stream().map(func).allMatch(Optional::isPresent);
-    }
-
-    boolean allInputTypesPresent(int expected) {
-        return verifyInputs(expected, IntermediateOperation::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 different model types.
-     */
-    public interface AttributeMap {
-        Optional<Value> get(String key);
-        Optional<Value> get(String key, OrderedTensorType type);
-        Optional<List<Value>> getList(String key);
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Join.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Join.java
deleted file mode 100644
index c98bcb43331..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Join.java
+++ /dev/null
@@ -1,120 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.searchlib.rankingexpression.integration.ml.DimensionRenamer;
-import com.yahoo.tensor.TensorType;
-import com.yahoo.tensor.functions.Reduce;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.ArrayList;
-import java.util.List;
-import java.util.function.DoubleBinaryOperator;
-
-public class Join extends IntermediateOperation {
-
-    private final DoubleBinaryOperator operator;
-
-    public Join(String modelName, String nodeName, List<IntermediateOperation> inputs, DoubleBinaryOperator operator) {
-        super(modelName, nodeName, inputs);
-        this.operator = operator;
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        if (!allInputTypesPresent(2)) {
-            return null;
-        }
-        OrderedTensorType a = largestInput().type().get();
-        OrderedTensorType b = smallestInput().type().get();
-
-        OrderedTensorType.Builder builder = new OrderedTensorType.Builder();
-        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;
-        }
-
-        IntermediateOperation a = largestInput();
-        IntermediateOperation 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 IntermediateOperation 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() {
-        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/operations/Map.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Map.java
deleted file mode 100644
index 9bf6836ea9b..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Map.java
+++ /dev/null
@@ -1,37 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.List;
-import java.util.Optional;
-import java.util.function.DoubleUnaryOperator;
-
-public class Map extends IntermediateOperation {
-
-    private final DoubleUnaryOperator operator;
-
-    public Map(String modelName, String nodeName, List<IntermediateOperation> inputs, DoubleUnaryOperator operator) {
-        super(modelName, nodeName, inputs);
-        this.operator = operator;
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        if (!allInputTypesPresent(1)) {
-            return null;
-        }
-        return inputs.get(0).type().get();
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        if (!allInputFunctionsPresent(1)) {
-            return null;
-        }
-        Optional<TensorFunction> input = inputs.get(0).function();
-        return new com.yahoo.tensor.functions.Map(input.get(), operator);
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/MatMul.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/MatMul.java
deleted file mode 100644
index 287c23080de..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/MatMul.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.operations;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.tensor.TensorType;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.List;
-import java.util.Optional;
-
-public class MatMul extends IntermediateOperation {
-
-    public MatMul(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        if (!allInputTypesPresent(2)) {
-            return null;
-        }
-        OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder();
-        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/operations/Mean.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Mean.java
deleted file mode 100644
index f313831ea56..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Mean.java
+++ /dev/null
@@ -1,113 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
-import com.yahoo.searchlib.rankingexpression.evaluation.Value;
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.searchlib.rankingexpression.integration.ml.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.rule.ConstantNode;
-import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
-import com.yahoo.searchlib.rankingexpression.rule.GeneratorLambdaFunctionNode;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-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 java.util.ArrayList;
-import java.util.Iterator;
-import java.util.List;
-import java.util.Optional;
-
-public class Mean extends IntermediateOperation {
-
-    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;
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        if (!allInputTypesPresent(2)) {
-            return null;
-        }
-        IntermediateOperation reductionIndices = inputs.get(1);
-        if (!reductionIndices.getConstantValue().isPresent()) {
-            throw new IllegalArgumentException("Mean in " + name + ": " +
-                                               "reduction indices must be a constant.");
-        }
-        Tensor indices = reductionIndices.getConstantValue().get().asTensor();
-        reduceDimensions = new ArrayList<>();
-
-        OrderedTensorType inputType = inputs.get(0).type().get();
-        for (Iterator<Tensor.Cell> cellIterator = indices.cellIterator(); cellIterator.hasNext();) {
-            Tensor.Cell cell = cellIterator.next();
-            int dimensionIndex = cell.getValue().intValue();
-            if (dimensionIndex < 0) {
-                dimensionIndex = inputType.dimensions().size() - dimensionIndex;
-            }
-            reduceDimensions.add(inputType.dimensions().get(dimensionIndex).name());
-        }
-        return reducedType(inputType, shouldKeepDimensions());
-    }
-
-    // optimization: if keepDims and one reduce dimension that has size 1: same as identity.
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        if (!allInputTypesPresent(2)) {
-            return null;
-        }
-        TensorFunction inputFunction = inputs.get(0).function().get();
-        TensorFunction output = new Reduce(inputFunction, Reduce.Aggregator.avg, reduceDimensions);
-        if (shouldKeepDimensions()) {
-            // multiply with a generated tensor created from the reduced dimensions
-            TensorType.Builder typeBuilder = new TensorType.Builder();
-            for (String name : reduceDimensions) {
-                typeBuilder.indexed(name, 1);
-            }
-            TensorType generatedType = typeBuilder.build();
-            ExpressionNode generatedExpression = new ConstantNode(new DoubleValue(1));
-            Generate generatedFunction = new Generate(generatedType,
-                    new GeneratorLambdaFunctionNode(generatedType, generatedExpression).asLongListToDoubleOperator());
-            output = new com.yahoo.tensor.functions.Join(output, generatedFunction, ScalarFunctions.multiply());
-        }
-        return output;
-    }
-
-    @Override
-    public void renameDimensions(DimensionRenamer renamer) {
-        super.renameDimensions(renamer);
-        List<String> renamedDimensions = new ArrayList<>(reduceDimensions.size());
-        for (String name : reduceDimensions) {
-            Optional<String> newName = renamer.dimensionNameOf(name);
-            if (!newName.isPresent()) {
-                return;  // presumably, already renamed
-            }
-            renamedDimensions.add(newName.get());
-        }
-        reduceDimensions = renamedDimensions;
-    }
-
-    private boolean shouldKeepDimensions() {
-        Optional<Value> keepDims = attributeMap.get("keep_dims");
-        return keepDims.isPresent() && keepDims.get().asBoolean();
-    }
-
-    private OrderedTensorType reducedType(OrderedTensorType inputType, boolean keepDimensions) {
-        OrderedTensorType.Builder builder = new OrderedTensorType.Builder();
-        for (TensorType.Dimension dimension: inputType.type().dimensions()) {
-            if (!reduceDimensions.contains(dimension.name())) {
-                builder.add(dimension);
-            } else if (keepDimensions) {
-                builder.add(TensorType.Dimension.indexed(dimension.name(), 1L));
-            }
-        }
-        return builder.build();
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Merge.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Merge.java
deleted file mode 100644
index 9ce0ea6151b..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Merge.java
+++ /dev/null
@@ -1,35 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.List;
-
-public class Merge extends IntermediateOperation {
-
-    public Merge(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        for (IntermediateOperation operation : inputs) {
-            if (operation.type().isPresent()) {
-                return operation.type().get();
-            }
-        }
-        return null;
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        for (IntermediateOperation operation : inputs) {
-            if (operation.function().isPresent()) {
-                return operation.function().get();
-            }
-        }
-        return null;
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/NoOp.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/NoOp.java
deleted file mode 100644
index f78c945d09f..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/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.operations;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.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/operations/PlaceholderWithDefault.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/PlaceholderWithDefault.java
deleted file mode 100644
index 5da3300cd7a..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/PlaceholderWithDefault.java
+++ /dev/null
@@ -1,48 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.List;
-import java.util.Optional;
-
-public class PlaceholderWithDefault extends IntermediateOperation {
-
-    public PlaceholderWithDefault(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        if (!allInputTypesPresent(1)) {
-            return null;
-        }
-        return inputs().get(0).type().orElse(null);
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        if (!allInputFunctionsPresent(1)) {
-            return null;
-        }
-        // This should be a call to the function we add below, but for now
-        // we treat this as as identity function and just pass the constant.
-        return inputs.get(0).function().orElse(null);
-    }
-
-    @Override
-    public Optional<TensorFunction> rankingExpressionFunction() {
-        // For now, it is much more efficient to assume we always will return
-        // the default value, as we can prune away large parts of the expression
-        // tree by having it calculated as a constant. If a case arises where
-        // it is important to support this, implement this.
-        return Optional.empty();
-    }
-
-    @Override
-    public boolean isConstant() {
-        return true;  // not true if we add to function
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Reshape.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Reshape.java
deleted file mode 100644
index 3165890ff03..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Reshape.java
+++ /dev/null
@@ -1,131 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.searchlib.rankingexpression.integration.ml.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.rule.ArithmeticNode;
-import com.yahoo.searchlib.rankingexpression.rule.ArithmeticOperator;
-import com.yahoo.searchlib.rankingexpression.rule.ComparisonNode;
-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.TruthOperator;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-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 java.util.ArrayList;
-import java.util.Iterator;
-import java.util.List;
-import java.util.stream.Collectors;
-
-public class Reshape extends IntermediateOperation {
-
-    public Reshape(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        if (!allInputTypesPresent(2)) {
-            return null;
-        }
-        IntermediateOperation newShape = inputs.get(1);
-        if (!newShape.getConstantValue().isPresent()) {
-            throw new IllegalArgumentException("Reshape in " + name + ": " +
-                    "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();
-        int dimensionIndex = 0;
-        for (Iterator<Tensor.Cell> cellIterator = shape.cellIterator(); cellIterator.hasNext();) {
-            Tensor.Cell cell = cellIterator.next();
-            int size = cell.getValue().intValue();
-            if (size < 0) {
-                size = -1 * (int)shape.reduce(Reduce.Aggregator.prod).asDouble() /
-                       OrderedTensorType.tensorSize(inputType.type()).intValue();
-            }
-            outputTypeBuilder.add(TensorType.Dimension.indexed(
-                    String.format("%s_%d", vespaName(), dimensionIndex), size));
-            dimensionIndex++;
-        }
-        return outputTypeBuilder.build();
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        if (!allInputTypesPresent(2)) {
-            return null;
-        }
-        if (!allInputFunctionsPresent(2)) {
-            return null;
-        }
-        OrderedTensorType inputType = inputs.get(0).type().get();
-        TensorFunction inputFunction = inputs.get(0).function().get();
-        return reshape(inputFunction, inputType.type(), type.type());
-    }
-
-    @Override
-    public void addDimensionNameConstraints(DimensionRenamer renamer) {
-        for (TensorType.Dimension dimension : type.type().dimensions()) {
-            renamer.addDimension(dimension.name());
-        }
-    }
-
-    public static TensorFunction reshape(TensorFunction inputFunction, TensorType inputType, TensorType outputType) {
-        if (!OrderedTensorType.tensorSize(inputType).equals(OrderedTensorType.tensorSize(outputType))) {
-            throw new IllegalArgumentException("New and old shape of tensor must have the same size when reshaping");
-        }
-
-        // Conceptually, reshaping consists on unrolling a tensor to an array using the dimension order,
-        // then use the dimension order of the new shape to roll back into a tensor.
-        // Here we create a transformation tensor that is multiplied with the from tensor to map into
-        // the new shape. We have to introduce temporary dimension names and rename back if dimension names
-        // in the new and old tensor type overlap.
-
-        ExpressionNode unrollFrom = unrollTensorExpression(inputType);
-        ExpressionNode unrollTo = unrollTensorExpression(outputType);
-        ExpressionNode transformExpression = new ComparisonNode(unrollFrom, TruthOperator.EQUAL, unrollTo);
-
-        TensorType transformationType = new TensorType.Builder(inputType, outputType).build();
-        Generate transformTensor = new Generate(transformationType,
-                new GeneratorLambdaFunctionNode(transformationType, transformExpression).asLongListToDoubleOperator());
-
-        TensorFunction outputFunction = new Reduce(
-                new com.yahoo.tensor.functions.Join(inputFunction, transformTensor, ScalarFunctions.multiply()),
-                Reduce.Aggregator.sum,
-                inputType.dimensions().stream().map(TensorType.Dimension::name).collect(Collectors.toList()));
-
-        return outputFunction;
-    }
-
-    private static ExpressionNode unrollTensorExpression(TensorType type) {
-        if (type.rank() == 0) {
-            return new ConstantNode(DoubleValue.zero);
-        }
-        List<ExpressionNode> children = new ArrayList<>();
-        List<ArithmeticOperator> operators = new ArrayList<>();
-        int size = 1;
-        for (int i = type.dimensions().size() - 1; i >= 0; --i) {
-            TensorType.Dimension dimension = type.dimensions().get(i);
-            children.add(0, new ReferenceNode(dimension.name()));
-            if (size > 1) {
-                operators.add(0, ArithmeticOperator.MULTIPLY);
-                children.add(0, new ConstantNode(new DoubleValue(size)));
-            }
-            size *= OrderedTensorType.dimensionSize(dimension);
-            if (i > 0) {
-                operators.add(0, ArithmeticOperator.PLUS);
-            }
-        }
-        return new ArithmeticNode(children, operators);
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Select.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Select.java
deleted file mode 100644
index 176e6523e5f..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Select.java
+++ /dev/null
@@ -1,88 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-import com.yahoo.tensor.functions.ScalarFunctions;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.List;
-import java.util.function.DoubleBinaryOperator;
-
-import static com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType.dimensionSize;
-import static com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType.tensorSize;
-
-public class Select extends IntermediateOperation {
-
-    public Select(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        if (!allInputTypesPresent(3)) {
-            return null;
-        }
-        OrderedTensorType a = inputs.get(1).type().get();
-        OrderedTensorType b = inputs.get(2).type().get();
-        if ((a.type().rank() != b.type().rank()) || !(tensorSize(a.type()).equals(tensorSize(b.type())))) {
-            throw new IllegalArgumentException("'Select': input tensors must have the same shape");
-        }
-        return a;
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        if (!allInputFunctionsPresent(3)) {
-            return null;
-        }
-        IntermediateOperation conditionOperation = inputs().get(0);
-        TensorFunction a = inputs().get(1).function().get();
-        TensorFunction b = inputs().get(2).function().get();
-
-        // Shortcut: if we know during import which tensor to select, do that directly here.
-        if (conditionOperation.getConstantValue().isPresent()) {
-            Tensor condition = conditionOperation.getConstantValue().get().asTensor();
-            if (condition.type().rank() == 0) {
-                return ((int) condition.asDouble() == 0) ? b : a;
-            }
-            if (condition.type().rank() == 1 && dimensionSize(condition.type().dimensions().get(0)) == 1) {
-                return condition.cellIterator().next().getValue().intValue() == 0 ? b : a;
-            }
-        }
-
-        // The task is to select cells from 'x' or 'y' based on 'condition'.
-        // If 'condition' is 0 (false), select from 'y', if 1 (true) select
-        // from 'x'. We do this by individually joining 'x' and 'y' with
-        // 'condition', and then joining the resulting two tensors.
-
-        TensorFunction conditionFunction = conditionOperation.function().get();
-        TensorFunction aCond = new com.yahoo.tensor.functions.Join(a, conditionFunction, ScalarFunctions.multiply());
-        TensorFunction bCond = new com.yahoo.tensor.functions.Join(b, conditionFunction, new DoubleBinaryOperator() {
-             @Override public double applyAsDouble(double a, double b) { return a * (1.0 - b); }
-             @Override public String toString() { return "f(a,b)(a * (1-b))"; }
-         });
-        return new com.yahoo.tensor.functions.Join(aCond, bCond, ScalarFunctions.add());
-    }
-
-    @Override
-    public void addDimensionNameConstraints(DimensionRenamer renamer) {
-        if (!allInputTypesPresent(3)) {
-            return;
-        }
-        List<TensorType.Dimension> aDimensions = inputs.get(1).type().get().dimensions();
-        List<TensorType.Dimension> bDimensions = inputs.get(2).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();
-
-        // These tensors should have the same dimension names
-        renamer.addConstraint(aDim0, bDim0, DimensionRenamer::equals, this);
-        renamer.addConstraint(aDim1, bDim1, DimensionRenamer::equals, this);
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Shape.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Shape.java
deleted file mode 100644
index 56f05936541..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Shape.java
+++ /dev/null
@@ -1,54 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.tensor.IndexedTensor;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.List;
-
-public class Shape extends IntermediateOperation {
-
-    public Shape(String modelName, String nodeName, List<IntermediateOperation> inputs) {
-        super(modelName, nodeName, inputs);
-        createConstantValue();
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        if (!allInputTypesPresent(1)) {
-            return null;
-        }
-        OrderedTensorType inputType = inputs.get(0).type().get();
-        return new OrderedTensorType.Builder()
-                .add(TensorType.Dimension.indexed(vespaName(), inputType.dimensions().size()))
-                .build();
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        return null; // will be added by function() since this is constant.
-    }
-
-    @Override
-    public boolean isConstant() {
-        return true;
-    }
-
-    private void createConstantValue() {
-        if (!allInputTypesPresent(1)) {
-            return;
-        }
-        OrderedTensorType inputType = inputs.get(0).type().get();
-        IndexedTensor.BoundBuilder builder = (IndexedTensor.BoundBuilder) Tensor.Builder.of(type().get().type());
-        List<TensorType.Dimension> inputDimensions = inputType.dimensions();
-        for (int i = 0; i < inputDimensions.size(); i++) {
-            builder.cellByDirectIndex(i, inputDimensions.get(i).size().orElse(-1L));
-        }
-        this.setConstantValue(new TensorValue(builder.build()));
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Squeeze.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Squeeze.java
deleted file mode 100644
index f0946064213..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Squeeze.java
+++ /dev/null
@@ -1,85 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.evaluation.Value;
-import com.yahoo.searchlib.rankingexpression.integration.ml.DimensionRenamer;
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.tensor.TensorType;
-import com.yahoo.tensor.functions.Reduce;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Optional;
-import java.util.stream.Collectors;
-
-public class Squeeze extends IntermediateOperation {
-
-    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;
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        if (!allInputTypesPresent(1)) {
-            return null;
-        }
-        OrderedTensorType inputType = inputs.get(0).type().get();
-        squeezeDimensions = new ArrayList<>();
-
-        Optional<List<Value>> squeezeDimsAttr = attributeMap.getList("squeeze_dims");
-        if ( ! squeezeDimsAttr.isPresent()) {
-            squeezeDimensions = inputType.type().dimensions().stream().
-                    filter(dim -> OrderedTensorType.dimensionSize(dim) == 1).
-                    map(TensorType.Dimension::name).
-                    collect(Collectors.toList());
-        } else {
-            squeezeDimensions = squeezeDimsAttr.get().stream().map(Value::asDouble).map(Double::intValue).
-                    map(i -> i < 0 ? inputType.type().dimensions().size() - i : i).
-                    map(i -> inputType.type().dimensions().get(i)).
-                    filter(dim -> OrderedTensorType.dimensionSize(dim) == 1).
-                    map(TensorType.Dimension::name).
-                    collect(Collectors.toList());
-        }
-
-        return squeezeDimensions.isEmpty() ? inputType : reducedType(inputType);
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        if (!allInputFunctionsPresent(1)) {
-            return null;
-        }
-        TensorFunction inputFunction = inputs.get(0).function().get();
-        return new Reduce(inputFunction, Reduce.Aggregator.sum, squeezeDimensions);
-    }
-
-    @Override
-    public void renameDimensions(DimensionRenamer renamer) {
-        super.renameDimensions(renamer);
-        List<String> renamedDimensions = new ArrayList<>(squeezeDimensions.size());
-        for (String name : squeezeDimensions) {
-            Optional<String> newName = renamer.dimensionNameOf(name);
-            if (!newName.isPresent()) {
-                return;  // presumably, already renamed
-            }
-            renamedDimensions.add(newName.get());
-        }
-        squeezeDimensions = renamedDimensions;
-    }
-
-    private OrderedTensorType reducedType(OrderedTensorType inputType) {
-        OrderedTensorType.Builder builder = new OrderedTensorType.Builder();
-        for (TensorType.Dimension dimension: inputType.type().dimensions()) {
-            if ( ! squeezeDimensions.contains(dimension.name())) {
-                builder.add(dimension);
-            }
-        }
-        return builder.build();
-    }
-
-}
diff --git a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Switch.java b/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Switch.java
deleted file mode 100644
index 24212ef175c..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/operations/Switch.java
+++ /dev/null
@@ -1,50 +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.operations;
-
-import com.yahoo.searchlib.rankingexpression.integration.ml.OrderedTensorType;
-import com.yahoo.tensor.functions.TensorFunction;
-
-import java.util.List;
-import java.util.Optional;
-
-public class Switch extends IntermediateOperation {
-
-    private final int port;
-
-    public Switch(String modelName, String nodeName, List<IntermediateOperation> inputs, int port) {
-        super(modelName, nodeName, inputs);
-        this.port = port;
-    }
-
-    @Override
-    protected OrderedTensorType lazyGetType() {
-        if (!allInputTypesPresent(2)) {
-            return null;
-        }
-        Optional<OrderedTensorType> predicate = inputs.get(1).type();
-        if (predicate.get().type().rank() != 0) {
-            throw new IllegalArgumentException("Switch in " + name + ": " +
-                    "predicate must be a scalar");
-        }
-        return inputs.get(0).type().orElse(null);
-    }
-
-    @Override
-    protected TensorFunction lazyGetFunction() {
-        IntermediateOperation predicateOperation = inputs().get(1);
-        if (!predicateOperation.getConstantValue().isPresent()) {
-            throw new IllegalArgumentException("Switch in " + name + ": " +
-                    "predicate must be a constant");
-        }
-        if (port < 0 || port > 1) {
-            throw new IllegalArgumentException("Switch in " + name + ": " +
-                    "choice should be boolean");
-        }
-
-        double predicate = predicateOperation.getConstantValue().get().asDouble();
-        return predicate == port ? inputs().get(0).function().get() : null;
-    }
-
-}
-
-
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/ml/package-info.java
deleted file mode 100644
index 0eb782b5a6e..00000000000
--- a/searchlib/src/main/java/com/yahoo/searchlib/rankingexpression/integration/ml/package-info.java
+++ /dev/null
@@ -1,8 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-/**
- * Model integration
- */
-@ExportPackage
-package com.yahoo.searchlib.rankingexpression.integration.ml;
-
-import com.yahoo.osgi.annotation.ExportPackage;
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/ml/DimensionRenamerTest.java
deleted file mode 100644
index 04e9933e40b..00000000000
--- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/DimensionRenamerTest.java
+++ /dev/null
@@ -1,48 +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 org.junit.Test;
-
-import static org.junit.Assert.assertTrue;
-
-public class DimensionRenamerTest {
-
-    @Test
-    public void testMnistRenaming() {
-        DimensionRenamer renamer = new DimensionRenamer();
-
-        renamer.addDimension("first_dimension_of_x");
-        renamer.addDimension("second_dimension_of_x");
-        renamer.addDimension("first_dimension_of_w");
-        renamer.addDimension("second_dimension_of_w");
-        renamer.addDimension("first_dimension_of_b");
-
-        // which dimension to join on matmul
-        renamer.addConstraint("second_dimension_of_x", "first_dimension_of_w", DimensionRenamer::equals, null);
-
-        // other dimensions in matmul can't be equal
-        renamer.addConstraint("first_dimension_of_x", "second_dimension_of_w", DimensionRenamer::lesserThan, null);
-
-        // for efficiency, put dimension to join on innermost
-        renamer.addConstraint("first_dimension_of_x", "second_dimension_of_x", DimensionRenamer::lesserThan, null);
-        renamer.addConstraint("first_dimension_of_w", "second_dimension_of_w", DimensionRenamer::greaterThan, null);
-
-        // bias
-        renamer.addConstraint("second_dimension_of_w", "first_dimension_of_b", DimensionRenamer::equals, null);
-
-        renamer.solve();
-
-        String firstDimensionOfXName = renamer.dimensionNameOf("first_dimension_of_x").get();
-        String secondDimensionOfXName = renamer.dimensionNameOf("second_dimension_of_x").get();
-        String firstDimensionOfWName = renamer.dimensionNameOf("first_dimension_of_w").get();
-        String secondDimensionOfWName = renamer.dimensionNameOf("second_dimension_of_w").get();
-        String firstDimensionOfBName = renamer.dimensionNameOf("first_dimension_of_b").get();
-
-        assertTrue(firstDimensionOfXName.compareTo(secondDimensionOfXName) < 0);
-        assertTrue(firstDimensionOfWName.compareTo(secondDimensionOfWName) > 0);
-        assertTrue(secondDimensionOfXName.compareTo(firstDimensionOfWName) == 0);
-        assertTrue(firstDimensionOfXName.compareTo(secondDimensionOfWName) < 0);
-        assertTrue(secondDimensionOfWName.compareTo(firstDimensionOfBName) == 0);
-    }
-
-}
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/ml/OrderedTensorTypeTestCase.java
deleted file mode 100644
index f22e7cb087a..00000000000
--- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/ml/OrderedTensorTypeTestCase.java
+++ /dev/null
@@ -1,21 +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 org.junit.Test;
-
-import static org.junit.Assert.assertEquals;
-
-/**
- * @author bratseth
- */
-public class OrderedTensorTypeTestCase {
-
-    @Test
-    public void testToFromSpec() {
-        String spec = "tensor(b[],c{},a[3])";
-        OrderedTensorType type = OrderedTensorType.fromSpec(spec);
-        assertEquals(spec, type.toString());
-        assertEquals("tensor(a[3],b[],c{})", type.type().toString());
-    }
-
-}