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// Copyright Yahoo. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package ai.vespa.rankingexpression.importer.onnx;
import ai.vespa.rankingexpression.importer.OrderedTensorType;
import com.yahoo.tensor.TensorType;
import onnx.Onnx;
/**
* Converts and verifies ONNX tensor types into Vespa tensor types.
*
* @author lesters
*/
class TypeConverter {
static void verifyType(Onnx.TypeProto typeProto, OrderedTensorType type) {
Onnx.TensorShapeProto shape = typeProto.getTensorType().getShape();
if (shape != null) {
if (shape.getDimCount() != type.rank()) {
throw new IllegalArgumentException("Onnx shape of does not match Vespa shape");
}
for (int onnxIndex = 0; onnxIndex < type.dimensions().size(); ++onnxIndex) {
int vespaIndex = type.dimensionMap(onnxIndex);
Onnx.TensorShapeProto.Dimension onnxDimension = shape.getDim(onnxIndex);
long onnxDimensionSize = onnxDimension.getDimValue() == 0 ? 1 : onnxDimension.getDimValue();
if (onnxDimensionSize == -1) {
continue; // disregard batch dimensions
}
TensorType.Dimension vespaDimension = type.type().dimensions().get(vespaIndex);
if (onnxDimensionSize != vespaDimension.size().orElse(-1L)) {
throw new IllegalArgumentException("Onnx dimensions of does not match Vespa dimensions");
}
}
}
}
static OrderedTensorType typeFrom(Onnx.TypeProto type) {
String dimensionPrefix = "d"; // standard naming convention: d0, d1, ...
Onnx.TensorShapeProto shape = type.getTensorType().getShape();
OrderedTensorType.Builder builder = new OrderedTensorType.Builder(toValueType(type.getTensorType().getElemType()));
for (int i = 0; i < shape.getDimCount(); ++ i) {
String dimensionName = dimensionPrefix + i;
Onnx.TensorShapeProto.Dimension onnxDimension = shape.getDim(i);
long onnxDimensionSize = onnxDimension.getDimValue() == 0 ? 1 : onnxDimension.getDimValue();
if (onnxDimensionSize >= 0) {
builder.add(TensorType.Dimension.indexed(dimensionName, onnxDimensionSize));
} else {
builder.add(TensorType.Dimension.indexed(dimensionName));
}
}
return builder.build();
}
static OrderedTensorType typeFrom(Onnx.TensorProto tensor) {
return OrderedTensorType.fromDimensionList(toValueType(tensor.getDataType()),
tensor.getDimsList());
}
private static TensorType.Value toValueType(Onnx.TensorProto.DataType onnxType) {
// NOTE:
// should match best_cell_type in onnx_wrapper.cpp
switch (onnxType) {
case BOOL: // Imperfect conversion fallthrough
case INT8:
return TensorType.Value.INT8;
case BFLOAT16:
return TensorType.Value.BFLOAT16;
case UINT8: // Imperfect conversion fallthrough
case INT16: // Imperfect conversion fallthrough
case UINT16: // Imperfect conversion fallthrough
case FLOAT:
return TensorType.Value.FLOAT;
case INT32: // Imperfect conversion fallthrough
case INT64: // Imperfect conversion fallthrough
case UINT32: // Imperfect conversion fallthrough
case UINT64: // Imperfect conversion fallthrough
case DOUBLE:
return TensorType.Value.DOUBLE;
default: throw new IllegalArgumentException("A ONNX tensor with data type " + onnxType +
" cannot be converted to a Vespa tensor type");
}
}
}
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