diff options
Diffstat (limited to 'config-model/src/main/java/com/yahoo/searchdefinition/OnnxModel.java')
-rw-r--r-- | config-model/src/main/java/com/yahoo/searchdefinition/OnnxModel.java | 63 |
1 files changed, 36 insertions, 27 deletions
diff --git a/config-model/src/main/java/com/yahoo/searchdefinition/OnnxModel.java b/config-model/src/main/java/com/yahoo/searchdefinition/OnnxModel.java index 58213186f78..5e8b8579ee6 100644 --- a/config-model/src/main/java/com/yahoo/searchdefinition/OnnxModel.java +++ b/config-model/src/main/java/com/yahoo/searchdefinition/OnnxModel.java @@ -150,54 +150,64 @@ public class OnnxModel { if (onnxOutputType == null) { throw new IllegalArgumentException("Could not find type for output '" + onnxName + "' " + "in '" + name + "'"); } - if (containsSymbolicDimensionSizes(onnxOutputType)) { - return getTensorTypeWithSymbolicDimensions(onnxOutputType, context); + if (allDimensionSizesAreKnown(onnxOutputType)) { + return vespaTypes.computeIfAbsent(onnxName, v -> typeFrom(onnxOutputType)); } - return vespaTypes.computeIfAbsent(onnxName, v -> typeFrom(onnxOutputType)); + return getTensorTypeWithUnknownDimensions(onnxOutputType, context); } - private TensorType getTensorTypeWithSymbolicDimensions(Onnx.TypeProto onnxOutputType, MapEvaluationTypeContext context) { - Map<String, Long> symbolicSizes = resolveSymbolicDimensionSizes(context); - if (symbolicSizes.isEmpty()) { - return TensorType.empty; // Context is probably a rank profile not using this ONNX model - } - return typeFrom(onnxOutputType, symbolicSizes); + private static boolean allDimensionSizesAreKnown(Onnx.TypeProto type) { + return type.getTensorType().getShape().getDimList().stream().noneMatch(d -> + (d.hasDimParam() && ! d.hasDimValue()) || d.getDimValue() == -1); } - private Map<String, Long> resolveSymbolicDimensionSizes(MapEvaluationTypeContext context) { + private TensorType getTensorTypeWithUnknownDimensions(Onnx.TypeProto onnxOutputType, MapEvaluationTypeContext context) { + long unboundSize = 0; Map<String, Long> symbolicSizes = new HashMap<>(); - for (String onnxInputName : inputTypes.keySet()) { + for (String onnxInputName : inputTypes.keySet()) { Onnx.TypeProto onnxType = inputTypes.get(onnxInputName); - if ( ! containsSymbolicDimensionSizes(onnxType)) { + if (allDimensionSizesAreKnown(onnxType)) { continue; } Optional<TensorType> vespaType = resolveInputType(onnxInputName, context); if (vespaType.isEmpty()) { - return Collections.emptyMap(); + return TensorType.empty; } var onnxDimensions = onnxType.getTensorType().getShape().getDimList(); var vespaDimensions = vespaType.get().dimensions(); if (vespaDimensions.size() != onnxDimensions.size()) { - return Collections.emptyMap(); + return TensorType.empty; } for (int i = 0; i < vespaDimensions.size(); ++i) { - if (vespaDimensions.get(i).size().isEmpty() || ! onnxDimensions.get(i).hasDimParam()) { + if (vespaDimensions.get(i).size().isEmpty()) { continue; } - String symbolicName = onnxDimensions.get(i).getDimParam(); Long size = vespaDimensions.get(i).size().get(); - if (symbolicSizes.containsKey(symbolicName) && ! symbolicSizes.get(symbolicName).equals(size)) { - throw new IllegalArgumentException("Found conflicting sizes for symbolic dimension " + - "'" + symbolicName + "' for input '" + onnxInputName + "' in ONNX model '" + name + "'"); + + // Handle dimensions with size -1 - typically batch dimensions + if (onnxDimensions.get(i).getDimValue() == -1) { + if (unboundSize != 0 && unboundSize != size) { + throw new IllegalArgumentException("Found conflicting sizes for unbound dimension " + + "for type '" + onnxOutputType + "' in ONNX model '" + name + "'"); + } + unboundSize = size; + + // Handle dimensions with symbolic names + } else if (onnxDimensions.get(i).hasDimParam()) { + String symbolicName = onnxDimensions.get(i).getDimParam(); + if (symbolicSizes.containsKey(symbolicName) && ! symbolicSizes.get(symbolicName).equals(size)) { + throw new IllegalArgumentException("Found conflicting sizes for symbolic dimension '" + + symbolicName + "' for input '" + onnxInputName + "' in ONNX model '" + name + "'"); + } + symbolicSizes.put(symbolicName, size); } - symbolicSizes.put(symbolicName, size); } } - return symbolicSizes; + return typeFrom(onnxOutputType, symbolicSizes, unboundSize); } private Optional<TensorType> resolveInputType(String onnxInputName, MapEvaluationTypeContext context) { @@ -217,15 +227,11 @@ public class OnnxModel { return Optional.empty(); // if this context does not contain this input } - private static boolean containsSymbolicDimensionSizes(Onnx.TypeProto type) { - return type.getTensorType().getShape().getDimList().stream().anyMatch(d -> d.hasDimParam() && ! d.hasDimValue()); - } - private static TensorType typeFrom(Onnx.TypeProto type) { - return typeFrom(type, null); + return typeFrom(type, null, 0); } - private static TensorType typeFrom(Onnx.TypeProto type, Map<String, Long> symbolicSizes) { + private static TensorType typeFrom(Onnx.TypeProto type, Map<String, Long> symbolicSizes, long unboundSize) { String dimensionPrefix = "d"; // standard naming convention: d0, d1, ... Onnx.TensorShapeProto shape = type.getTensorType().getShape(); TensorType.Builder builder = new TensorType.Builder(toValueType(type.getTensorType().getElemType())); @@ -244,6 +250,9 @@ public class OnnxModel { onnxDimensionSize = unknownSizes.iterator().next(); } } + if (onnxDimensionSize < 0) { + onnxDimensionSize = unboundSize; + } if (onnxDimensionSize <= 0) { throw new IllegalArgumentException("Unable to determine fixed dimension size when converting from " + "ONNX type: " + type + " to Vespa tensor type."); |