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diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/Tile.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/Tile.java
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+// Copyright 2020 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package ai.vespa.rankingexpression.importer.operations;
+
+import ai.vespa.rankingexpression.importer.OrderedTensorType;
+import com.yahoo.searchlib.rankingexpression.Reference;
+import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
+import com.yahoo.searchlib.rankingexpression.rule.ArithmeticNode;
+import com.yahoo.searchlib.rankingexpression.rule.ArithmeticOperator;
+import com.yahoo.searchlib.rankingexpression.rule.ConstantNode;
+import com.yahoo.searchlib.rankingexpression.rule.EmbracedNode;
+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.Tensor;
+import com.yahoo.tensor.TensorType;
+import com.yahoo.tensor.functions.Generate;
+import com.yahoo.tensor.functions.TensorFunction;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Optional;
+
+import static com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode.wrapScalar;
+
+/**
+ * Onnx tile operation.
+ */
+public class Tile extends IntermediateOperation {
+
+    public Tile(String modelName, String nodeName, List<IntermediateOperation> inputs) {
+        super(modelName, nodeName, inputs);
+    }
+
+    @Override
+    protected OrderedTensorType lazyGetType() {
+        if (!allInputTypesPresent(2)) return null;
+
+        // required as we use tensor create
+        inputs.get(0).exportAsRankingFunction = true;
+
+        IntermediateOperation repeats = inputs.get(1);
+        if (repeats.getConstantValue().isEmpty())
+            throw new IllegalArgumentException("Tile " + name + ": repeats input must be a constant.");
+
+        Tensor shape = repeats.getConstantValue().get().asTensor();
+        if (shape.type().rank() != 1)
+            throw new IllegalArgumentException("Tile " + name + ": repeats must be a 1-d tensor.");
+
+        OrderedTensorType inputType = inputs.get(0).type().get();
+        if (shape.type().dimensions().get(0).size().get() != inputType.rank())
+            throw new IllegalArgumentException("Tile " + name + ": repeats must be the same size as input rank.");
+
+        List<Integer> dimSizes = new ArrayList<>(inputType.rank());
+        shape.valueIterator().forEachRemaining(v -> dimSizes.add(v.intValue()));
+
+        OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(resultValueType());
+        for (int i = 0; i < dimSizes.size(); ++i) {
+            TensorType.Dimension inputDimension = inputType.dimensions().get(i);
+            typeBuilder.add(TensorType.Dimension.indexed(inputDimension.name(), inputDimension.size().get() * dimSizes.get(i)));
+        }
+        return typeBuilder.build();
+    }
+
+    @Override
+    protected TensorFunction lazyGetFunction() {
+        if (!allInputFunctionsPresent(2)) return null;
+
+        IntermediateOperation input = inputs.get(0);
+        OrderedTensorType inputType = input.type().get();
+        String inputFunctionName = input.rankingExpressionFunctionName();
+
+        List<com.yahoo.tensor.functions.Slice.DimensionValue<Reference>> dimensionValues = new ArrayList<>();
+
+        for (int axis = 0; axis < inputType.rank(); ++axis) {
+            String inputDimensionName = inputType.dimensions().get(axis).name();
+            long inputDimensionSize = inputType.dimensions().get(axis).size().get();
+
+            ExpressionNode size = new ConstantNode(new DoubleValue(inputDimensionSize));
+            ExpressionNode reference = new ReferenceNode(inputDimensionName);
+            ExpressionNode mod = new ArithmeticNode(reference, ArithmeticOperator.MODULO, size);
+            dimensionValues.add(new com.yahoo.tensor.functions.Slice.DimensionValue<>(Optional.of(inputDimensionName), wrapScalar(new EmbracedNode(mod))));
+        }
+
+        TensorFunction<Reference> inputIndices = new TensorFunctionNode.ExpressionTensorFunction(new ReferenceNode(inputFunctionName));
+        com.yahoo.tensor.functions.Slice<Reference> sliceIndices = new com.yahoo.tensor.functions.Slice<>(inputIndices, dimensionValues);
+        ExpressionNode sliceExpression = new TensorFunctionNode(sliceIndices);
+
+        TensorFunction generate = Generate.bound(type.type(), wrapScalar(sliceExpression));
+        return generate;
+    }
+
+    @Override
+    public Tile withInputs(List<IntermediateOperation> inputs) {
+        return new Tile(modelName(), name(), inputs);
+    }
+
+    @Override
+    public String operationName() { return "Tile"; }
+
+}