1 files changed, 119 insertions, 0 deletions
diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/Split.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/Split.java
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+++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/operations/Split.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.evaluation.Value;
+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.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;
+
+public class Split extends IntermediateOperation {
+
+    private final AttributeMap attributes;
+    private final int output;
+
+    private final int axis;
+    private int start;
+    private int end;
+
+    public Split(String modelName, String nodeName, List<IntermediateOperation> inputs, AttributeMap attributes, int output) {
+        super(modelName, nodeName, inputs);
+        this.attributes = attributes;
+        this.output = output;
+        axis = (int) attributes.get("axis").orElse(DoubleValue.zero).asDouble();
+    }
+
+    @Override
+    protected OrderedTensorType lazyGetType() {
+        if (!allInputTypesPresent(1))
+            return null;
+        OrderedTensorType inputType = inputs.get(0).type().get();
+
+        // required as we use tensor create
+        inputs.get(0).exportAsRankingFunction = true;
+
+        int axisSize = inputType.dimensions().get(axis).size().get().intValue();
+        start = 0;
+        end = axisSize;
+
+        if (attributes.getList("split").isPresent()) {
+            List<Value> splitList = attributes.getList("split").get();
+            if (output > splitList.size()) {
+                throw new IllegalArgumentException("Split in " + name + ": output out of range of split list");
+            }
+            for (int i = 0; i < output; ++i) {
+                start += (int) splitList.get(i).asDouble();
+            }
+            if (output < splitList.size()) {
+                end = start + (int) splitList.get(output).asDouble();
+            }
+        } else {
+            start = axisSize / 2 * output;
+            end = start + axisSize / 2;
+        }
+
+        if (start >= axisSize || start < 0) {
+            throw new IllegalArgumentException("Split in " + name + ": split start index out of range (" + start + ")");
+        }
+        if (end > axisSize || end < 0) {
+            throw new IllegalArgumentException("Split in " + name + ": split end index out of range (" + end + ")");
+        }
+
+        OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(resultValueType());
+        for (int i = 0; i < inputType.rank(); ++i) {
+            TensorType.Dimension inputDimension = inputType.dimensions().get(i);
+            long dimSize = i == axis ? end - start : inputDimension.size().get();
+            typeBuilder.add(TensorType.Dimension.indexed(inputDimension.name(), dimSize));
+        }
+        return typeBuilder.build();
+    }
+
+    @Override
+    protected TensorFunction lazyGetFunction() {
+        if (!allInputFunctionsPresent(1)) 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 i = 0; i < inputType.rank(); ++i) {
+            String inputDimensionName = inputType.dimensions().get(i).name();
+            ExpressionNode reference = new ReferenceNode(inputDimensionName);
+            ExpressionNode offset = new ArithmeticNode(reference, ArithmeticOperator.PLUS, new ConstantNode(new DoubleValue(i == axis ? start : 0)));
+            dimensionValues.add(new com.yahoo.tensor.functions.Slice.DimensionValue<>(Optional.of(inputDimensionName), wrapScalar(new EmbracedNode(offset))));
+        }
+
+        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 Split withInputs(List<IntermediateOperation> inputs) {
+        return new Split(modelName(), name(), inputs, attributes, output);
+    }
+
+    @Override
+    public String operationName() { return "Split"; }
+
+}