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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.modelintegration.evaluator;
import ai.onnxruntime.NodeInfo;
import ai.onnxruntime.OnnxJavaType;
import ai.onnxruntime.OnnxTensor;
import ai.onnxruntime.OnnxValue;
import ai.onnxruntime.OrtEnvironment;
import ai.onnxruntime.OrtException;
import ai.onnxruntime.OrtSession;
import ai.onnxruntime.TensorInfo;
import ai.onnxruntime.ValueInfo;
import ai.vespa.rankingexpression.importer.onnx.OnnxImporter;
import com.yahoo.tensor.DimensionSizes;
import com.yahoo.tensor.IndexedTensor;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.nio.DoubleBuffer;
import java.nio.FloatBuffer;
import java.nio.IntBuffer;
import java.nio.LongBuffer;
import java.nio.ShortBuffer;
import java.util.HashMap;
import java.util.Map;
import java.util.Set;
import java.util.stream.Collectors;
/**
* @author lesters
*/
class TensorConverter {
static Map<String, OnnxTensor> toOnnxTensors(Map<String, Tensor> tensorMap, OrtEnvironment env, OrtSession session)
throws OrtException
{
Map<String, OnnxTensor> result = new HashMap<>();
for (String name : tensorMap.keySet()) {
Tensor vespaTensor = tensorMap.get(name);
name = toOnnxName(name, session.getInputInfo().keySet());
TensorInfo onnxTensorInfo = toTensorInfo(session.getInputInfo().get(name).getInfo());
OnnxTensor onnxTensor = toOnnxTensor(vespaTensor, onnxTensorInfo, env);
result.put(name, onnxTensor);
}
return result;
}
static OnnxTensor toOnnxTensor(Tensor vespaTensor, TensorInfo onnxTensorInfo, OrtEnvironment environment)
throws OrtException
{
if ( ! (vespaTensor instanceof IndexedTensor)) {
throw new IllegalArgumentException("OnnxEvaluator currently only supports tensors with indexed dimensions");
}
IndexedTensor tensor = (IndexedTensor) vespaTensor;
ByteBuffer buffer = ByteBuffer.allocateDirect((int)tensor.size() * onnxTensorInfo.type.size).order(ByteOrder.nativeOrder());
if (onnxTensorInfo.type == OnnxJavaType.FLOAT) {
for (int i = 0; i < tensor.size(); i++)
buffer.putFloat(tensor.getFloat(i));
return OnnxTensor.createTensor(environment, buffer.rewind().asFloatBuffer(), tensor.shape());
}
if (onnxTensorInfo.type == OnnxJavaType.DOUBLE) {
for (int i = 0; i < tensor.size(); i++)
buffer.putDouble(tensor.get(i));
return OnnxTensor.createTensor(environment, buffer.rewind().asDoubleBuffer(), tensor.shape());
}
if (onnxTensorInfo.type == OnnxJavaType.INT8) {
for (int i = 0; i < tensor.size(); i++)
buffer.put((byte) tensor.get(i));
return OnnxTensor.createTensor(environment, buffer.rewind(), tensor.shape());
}
if (onnxTensorInfo.type == OnnxJavaType.INT16) {
for (int i = 0; i < tensor.size(); i++)
buffer.putShort((short) tensor.get(i));
return OnnxTensor.createTensor(environment, buffer.rewind().asShortBuffer(), tensor.shape());
}
if (onnxTensorInfo.type == OnnxJavaType.INT32) {
for (int i = 0; i < tensor.size(); i++)
buffer.putInt((int) tensor.get(i));
return OnnxTensor.createTensor(environment, buffer.rewind().asIntBuffer(), tensor.shape());
}
if (onnxTensorInfo.type == OnnxJavaType.INT64) {
for (int i = 0; i < tensor.size(); i++)
buffer.putLong((long) tensor.get(i));
return OnnxTensor.createTensor(environment, buffer.rewind().asLongBuffer(), tensor.shape());
}
throw new IllegalArgumentException("OnnxEvaluator does not currently support value type " + onnxTensorInfo.type);
}
static Tensor toVespaTensor(OnnxValue onnxValue) {
if ( ! (onnxValue instanceof OnnxTensor)) {
throw new IllegalArgumentException("ONNX value is not a tensor: maps and sequences are not yet supported");
}
OnnxTensor onnxTensor = (OnnxTensor) onnxValue;
TensorInfo tensorInfo = onnxTensor.getInfo();
TensorType type = toVespaType(onnxTensor.getInfo());
DimensionSizes sizes = sizesFromType(type);
IndexedTensor.BoundBuilder builder = (IndexedTensor.BoundBuilder) Tensor.Builder.of(type, sizes);
if (tensorInfo.type == OnnxJavaType.FLOAT) {
FloatBuffer buffer = onnxTensor.getFloatBuffer();
for (long i = 0; i < sizes.totalSize(); i++)
builder.cellByDirectIndex(i, buffer.get());
}
else if (tensorInfo.type == OnnxJavaType.DOUBLE) {
DoubleBuffer buffer = onnxTensor.getDoubleBuffer();
for (long i = 0; i < sizes.totalSize(); i++)
builder.cellByDirectIndex(i, buffer.get());
}
else if (tensorInfo.type == OnnxJavaType.INT8) {
ByteBuffer buffer = onnxTensor.getByteBuffer();
for (long i = 0; i < sizes.totalSize(); i++)
builder.cellByDirectIndex(i, buffer.get());
}
else if (tensorInfo.type == OnnxJavaType.INT16) {
ShortBuffer buffer = onnxTensor.getShortBuffer();
for (long i = 0; i < sizes.totalSize(); i++)
builder.cellByDirectIndex(i, buffer.get());
}
else if (tensorInfo.type == OnnxJavaType.INT32) {
IntBuffer buffer = onnxTensor.getIntBuffer();
for (long i = 0; i < sizes.totalSize(); i++)
builder.cellByDirectIndex(i, buffer.get());
}
else if (tensorInfo.type == OnnxJavaType.INT64) {
LongBuffer buffer = onnxTensor.getLongBuffer();
for (long i = 0; i < sizes.totalSize(); i++)
builder.cellByDirectIndex(i, buffer.get());
}
else {
throw new IllegalArgumentException("OnnxEvaluator does not currently support value type " + onnxTensor.getInfo().type);
}
return builder.build();
}
static private DimensionSizes sizesFromType(TensorType type) {
DimensionSizes.Builder builder = new DimensionSizes.Builder(type.dimensions().size());
for (int i = 0; i < type.dimensions().size(); i++)
builder.set(i, type.dimensions().get(i).size().get());
return builder.build();
}
static Map<String, TensorType> toVespaTypes(Map<String, NodeInfo> infoMap) {
return infoMap.entrySet().stream().collect(Collectors.toMap(e -> asValidName(e.getKey()),
e -> toVespaType(e.getValue().getInfo())));
}
static String asValidName(String name) {
return OnnxImporter.asValidIdentifier(name);
}
static String toOnnxName(String name, Set<String> onnxNames) {
if (onnxNames.contains(name))
return name;
for (String onnxName : onnxNames) {
if (asValidName(onnxName).equals(name))
return onnxName;
}
throw new IllegalArgumentException("ONNX model has no input with name " + name);
}
static TensorType toVespaType(ValueInfo valueInfo) {
TensorInfo tensorInfo = toTensorInfo(valueInfo);
TensorType.Builder builder = new TensorType.Builder(toVespaValueType(tensorInfo.onnxType));
long[] shape = tensorInfo.getShape();
for (int i = 0; i < shape.length; ++i) {
long dimSize = shape[i];
String dimName = "d" + i; // standard naming convention
if (dimSize > 0)
builder.indexed(dimName, dimSize);
else
builder.indexed(dimName); // unbound dimension for dim size -1
}
return builder.build();
}
static private TensorType.Value toVespaValueType(TensorInfo.OnnxTensorType onnxType) {
switch (onnxType) {
case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT8: return TensorType.Value.INT8;
case ONNX_TENSOR_ELEMENT_DATA_TYPE_BFLOAT16: return TensorType.Value.BFLOAT16;
case ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT: return TensorType.Value.FLOAT;
case ONNX_TENSOR_ELEMENT_DATA_TYPE_DOUBLE: return TensorType.Value.DOUBLE;
}
return TensorType.Value.DOUBLE;
}
static private TensorInfo toTensorInfo(ValueInfo valueInfo) {
if ( ! (valueInfo instanceof TensorInfo)) {
throw new IllegalArgumentException("ONNX value is not a tensor: maps and sequences are not yet supported");
}
return (TensorInfo) valueInfo;
}
}
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