1 files changed, 91 insertions, 5 deletions
diff --git a/vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java b/vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java
index cb7539d8565..87157495485 100644
--- a/vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java
+++ b/vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java
@@ -11,12 +11,21 @@ import com.yahoo.slime.Slime;
 import com.yahoo.slime.Type;
 import com.yahoo.tensor.DimensionSizes;
 import com.yahoo.tensor.IndexedTensor;
+import com.yahoo.tensor.MappedTensor;
 import com.yahoo.tensor.MixedTensor;
 import com.yahoo.tensor.Tensor;
 import com.yahoo.tensor.TensorAddress;
 import com.yahoo.tensor.TensorType;
+import com.yahoo.tensor.evaluation.Name;
+import com.yahoo.tensor.functions.ConstantTensor;
+import com.yahoo.tensor.functions.Slice;
 
+import java.util.ArrayList;
+import java.util.HashSet;
 import java.util.Iterator;
+import java.util.List;
+import java.util.Set;
+import java.util.stream.Collectors;
 
 /**
  * Writes tensors on the JSON format used in Vespa tensor document fields:
@@ -46,12 +55,33 @@ public class JsonFormat {
     }
 
     /** Serializes the given tensor type and value into a short-form JSON format */
-    public static byte[] encodeShortForm(IndexedTensor tensor) {
+    public static byte[] encodeShortForm(Tensor tensor) {
         Slime slime = new Slime();
         Cursor root = slime.setObject();
         root.setString("type", tensor.type().toString());
-        Cursor value = root.setArray("value");
-        encodeList(tensor, value, new long[tensor.dimensionSizes().dimensions()], 0);
+
+        // Encode as nested lists if indexed tensor
+        if (tensor instanceof IndexedTensor) {
+            IndexedTensor denseTensor = (IndexedTensor) tensor;
+            encodeValues(denseTensor, root.setArray("values"), new long[denseTensor.dimensionSizes().dimensions()], 0);
+        }
+
+        // Short form for a single mapped dimension
+        else if (tensor instanceof MappedTensor && tensor.type().dimensions().size() == 1) {
+            encodeSingleDimensionCells((MappedTensor) tensor, root);
+        }
+
+        // Short form for a mixed tensor
+        else if (tensor instanceof MixedTensor &&
+                tensor.type().dimensions().stream().filter(TensorType.Dimension::isMapped).count() >= 1) {
+            encodeBlocks((MixedTensor) tensor, root);
+        }
+
+        // No other short forms exist: default to standard cell address output
+        else {
+            encodeCells(tensor, root);
+        }
+
         return com.yahoo.slime.JsonFormat.toJsonBytes(slime);
     }
 
@@ -65,22 +95,78 @@ public class JsonFormat {
         }
     }
 
+    private static void encodeSingleDimensionCells(MappedTensor tensor, Cursor cursor) {
+        Cursor cells = cursor.setObject("cells");
+        if (tensor.type().dimensions().size() > 1)
+            throw new IllegalStateException("JSON encode of mapped tensor can only contain a single dimension");
+        tensor.cells().forEach((k,v) -> cells.setDouble(k.label(0), v));
+    }
+
     private static void encodeAddress(TensorType type, TensorAddress address, Cursor addressObject) {
         for (int i = 0; i < address.size(); i++)
             addressObject.setString(type.dimensions().get(i).name(), address.label(i));
     }
 
-    private static void encodeList(IndexedTensor tensor, Cursor cursor, long[] indexes, int dimension) {
+    private static void encodeValues(IndexedTensor tensor, Cursor cursor, long[] indexes, int dimension) {
         DimensionSizes sizes = tensor.dimensionSizes();
         for (indexes[dimension] = 0; indexes[dimension] < sizes.size(dimension); ++indexes[dimension]) {
             if (dimension < (sizes.dimensions() - 1)) {
-                encodeList(tensor, cursor.addArray(), indexes, dimension + 1);
+                encodeValues(tensor, cursor.addArray(), indexes, dimension + 1);
             } else {
                 cursor.addDouble(tensor.get(indexes));
             }
         }
     }
 
+    private static void encodeBlocks(MixedTensor tensor, Cursor cursor) {
+        var mappedDimensions = tensor.type().dimensions().stream().filter(d -> d.isMapped())
+                .map(d -> TensorType.Dimension.mapped(d.name())).collect(Collectors.toList());
+        if (mappedDimensions.size() < 1) {
+            throw new IllegalArgumentException("Should be ensured by caller");
+        }
+        cursor = (mappedDimensions.size() == 1) ? cursor.setObject("blocks") : cursor.setArray("blocks");
+
+        // Create tensor type for mapped dimensions subtype
+        TensorType mappedSubType = new TensorType.Builder(mappedDimensions).build();
+
+        // Find all unique indices for the mapped dimensions
+        Set<TensorAddress> denseSubSpaceAddresses = new HashSet<>();
+        tensor.cellIterator().forEachRemaining((cell) -> {
+            denseSubSpaceAddresses.add(subAddress(cell.getKey(), mappedSubType, tensor.type()));
+        });
+
+        // Slice out dense subspace of each and encode dense subspace as a list
+        for (TensorAddress denseSubSpaceAddress : denseSubSpaceAddresses) {
+            IndexedTensor denseSubspace = (IndexedTensor) sliceSubAddress(tensor, denseSubSpaceAddress, mappedSubType);
+
+            if (mappedDimensions.size() == 1) {
+                encodeValues(denseSubspace, cursor.setArray(denseSubSpaceAddress.label(0)), new long[denseSubspace.dimensionSizes().dimensions()], 0);
+            } else {
+                Cursor block = cursor.addObject();
+                encodeAddress(mappedSubType, denseSubSpaceAddress, block.setObject("address"));
+                encodeValues(denseSubspace, block.setArray("values"), new long[denseSubspace.dimensionSizes().dimensions()], 0);
+            }
+
+        }
+    }
+
+    private static TensorAddress subAddress(TensorAddress address, TensorType subType, TensorType origType) {
+        TensorAddress.Builder builder = new TensorAddress.Builder(subType);
+        for (TensorType.Dimension dim : subType.dimensions()) {
+            builder.add(dim.name(), address.label(origType.indexOfDimension(dim.name()).
+                    orElseThrow(() -> new IllegalStateException("Could not find mapped dimension index"))));
+        }
+        return builder.build();
+    }
+
+    private static Tensor sliceSubAddress(Tensor tensor, TensorAddress subAddress, TensorType subType) {
+        List<Slice.DimensionValue<Name>> sliceDims = new ArrayList<>(subAddress.size());
+        for (int i = 0; i < subAddress.size(); ++i) {
+            sliceDims.add(new Slice.DimensionValue<>(subType.dimensions().get(i).name(), subAddress.label(i)));
+        }
+        return new Slice<>(new ConstantTensor<>(tensor), sliceDims).evaluate();
+    }
+
     /** Deserializes the given tensor from JSON format */
     // NOTE: This must be kept in sync with com.yahoo.document.json.readers.TensorReader in the document module
     public static Tensor decode(TensorType type, byte[] jsonTensorValue) {