Revert "Add type resolving for ONNX models"

1 files changed, 28 insertions, 191 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..c2fb2107604 100644
--- a/config-model/src/main/java/com/yahoo/searchdefinition/OnnxModel.java
+++ b/config-model/src/main/java/com/yahoo/searchdefinition/OnnxModel.java
@@ -2,22 +2,14 @@
 package com.yahoo.searchdefinition;
 
 import com.yahoo.config.FileReference;
-import com.yahoo.path.Path;
-import com.yahoo.searchlib.rankingexpression.ExpressionFunction;
-import com.yahoo.searchlib.rankingexpression.Reference;
-import com.yahoo.tensor.TensorType;
 import com.yahoo.vespa.model.AbstractService;
 import com.yahoo.vespa.model.utils.FileSender;
-import onnx.Onnx;
 
+import java.util.ArrayList;
 import java.util.Collection;
 import java.util.Collections;
-import java.util.HashMap;
-import java.util.HashSet;
-import java.util.Map;
+import java.util.List;
 import java.util.Objects;
-import java.util.Optional;
-import java.util.Set;
 
 /**
  * A global ONNX model distributed using file distribution, similar to ranking constants.
@@ -29,16 +21,16 @@ public class OnnxModel {
     public enum PathType {FILE, URI};
 
     private final String name;
-    private PathType pathType = PathType.FILE;
     private String path = null;
     private String fileReference = "";
-    private String defaultOutput = null;
-    private Map<String, String> inputMap = new HashMap<>();
-    private Map<String, String> outputMap = new HashMap<>();
+    private List<OnnxNameMapping> inputMap = new ArrayList<>();
+    private List<OnnxNameMapping> outputMap = new ArrayList<>();
 
-    private Map<String, Onnx.TypeProto> inputTypes = new HashMap<>();
-    private Map<String, Onnx.TypeProto> outputTypes = new HashMap<>();
-    private Map<String, TensorType>     vespaTypes = new HashMap<>();
+    public PathType getPathType() {
+        return pathType;
+    }
+
+    private PathType pathType = PathType.FILE;
 
     public OnnxModel(String name) {
         this.name = name;
@@ -57,52 +49,21 @@ public class OnnxModel {
     }
 
     public void setUri(String uri) {
-        throw new IllegalArgumentException("URI for ONNX models are not currently supported");
-    }
-
-    public PathType getPathType() {
-        return pathType;
-    }
-
-    public void setDefaultOutput(String onnxName) {
-        Objects.requireNonNull(onnxName, "Name cannot be null");
-        this.defaultOutput = onnxName;
+        Objects.requireNonNull(uri, "uri cannot be null");
+        this.path = uri;
+        this.pathType = PathType.URI;
     }
 
     public void addInputNameMapping(String onnxName, String vespaName) {
-        addInputNameMapping(onnxName, vespaName, true);
-    }
-
-    public void addInputNameMapping(String onnxName, String vespaName, boolean overwrite) {
         Objects.requireNonNull(onnxName, "Onnx name cannot be null");
         Objects.requireNonNull(vespaName, "Vespa name cannot be null");
-        if (overwrite || ! inputMap.containsKey(onnxName)) {
-            inputMap.put(onnxName, vespaName);
-        }
+        this.inputMap.add(new OnnxNameMapping(onnxName, vespaName));
     }
 
     public void addOutputNameMapping(String onnxName, String vespaName) {
-        addOutputNameMapping(onnxName, vespaName, true);
-    }
-
-    public void addOutputNameMapping(String onnxName, String vespaName, boolean overwrite) {
         Objects.requireNonNull(onnxName, "Onnx name cannot be null");
         Objects.requireNonNull(vespaName, "Vespa name cannot be null");
-        if (overwrite || ! outputMap.containsKey(onnxName)) {
-            outputMap.put(onnxName, vespaName);
-        }
-    }
-
-    public void addInputType(String onnxName, Onnx.TypeProto type) {
-        Objects.requireNonNull(onnxName, "Onnx name cannot be null");
-        Objects.requireNonNull(type, "Tensor type cannot be null");
-        inputTypes.put(onnxName, type);
-    }
-
-    public void addOutputType(String onnxName, Onnx.TypeProto type) {
-        Objects.requireNonNull(onnxName, "Onnx name cannot be null");
-        Objects.requireNonNull(type, "Tensor type cannot be null");
-        outputTypes.put(onnxName, type);
+        this.outputMap.add(new OnnxNameMapping(onnxName, vespaName));
     }
 
     /** Initiate sending of this constant to some services over file distribution */
@@ -115,16 +76,11 @@ public class OnnxModel {
 
     public String getName() { return name; }
     public String getFileName() { return path; }
-    public Path   getFilePath() { return Path.fromString(path); }
     public String getUri() { return path; }
     public String getFileReference() { return fileReference; }
 
-    public Map<String, String> getInputMap() { return Collections.unmodifiableMap(inputMap); }
-    public Map<String, String> getOutputMap() { return Collections.unmodifiableMap(outputMap); }
-
-    public String getDefaultOutput() {
-        return defaultOutput;
-    }
+    public List<OnnxNameMapping> getInputMap() { return Collections.unmodifiableList(inputMap); }
+    public List<OnnxNameMapping> getOutputMap() { return Collections.unmodifiableList(outputMap); }
 
     public void validate() {
         if (path == null || path.isEmpty())
@@ -134,142 +90,23 @@ public class OnnxModel {
     public String toString() {
         StringBuilder b = new StringBuilder();
         b.append("onnx-model '").append(name)
-                .append(pathType == PathType.FILE ? "' from file '" : " from uri ").append(path)
-                .append("' with ref '").append(fileReference)
-                .append("'");
+         .append(pathType == PathType.FILE ? "' from file '" : " from uri ").append(path)
+         .append("' with ref '").append(fileReference)
+         .append("'");
         return b.toString();
     }
 
-    /**
-     * Return the tensor type for an ONNX model for the given context.
-     * An ONNX model can have dynamic/symbolic dimension sizes. If so, the output
-     * type depends on the input types for the given context (rank profile).
-     */
-    public TensorType getTensorType(String onnxName, MapEvaluationTypeContext context) {
-        Onnx.TypeProto onnxOutputType = outputTypes.get(onnxName);
-        if (onnxOutputType == null) {
-            throw new IllegalArgumentException("Could not find type for output '" + onnxName + "' " + "in '" + name + "'");
-        }
-        if (containsSymbolicDimensionSizes(onnxOutputType)) {
-            return getTensorTypeWithSymbolicDimensions(onnxOutputType, context);
-        }
-        return vespaTypes.computeIfAbsent(onnxName, v -> typeFrom(onnxOutputType));
-    }
-
-    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 Map<String, Long> resolveSymbolicDimensionSizes(MapEvaluationTypeContext context) {
-        Map<String, Long> symbolicSizes = new HashMap<>();
-        for (String onnxInputName : inputTypes.keySet()) {
-
-            Onnx.TypeProto onnxType = inputTypes.get(onnxInputName);
-            if ( ! containsSymbolicDimensionSizes(onnxType)) {
-                continue;
-            }
-
-            Optional<TensorType> vespaType = resolveInputType(onnxInputName, context);
-            if (vespaType.isEmpty()) {
-                return Collections.emptyMap();
-            }
-
-            var onnxDimensions = onnxType.getTensorType().getShape().getDimList();
-            var vespaDimensions = vespaType.get().dimensions();
-            if (vespaDimensions.size() != onnxDimensions.size()) {
-                return Collections.emptyMap();
-            }
-
-            for (int i = 0; i < vespaDimensions.size(); ++i) {
-                if (vespaDimensions.get(i).size().isEmpty() || ! onnxDimensions.get(i).hasDimParam()) {
-                    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 + "'");
-                }
-                symbolicSizes.put(symbolicName, size);
-            }
-        }
-        return symbolicSizes;
-    }
-
-    private Optional<TensorType> resolveInputType(String onnxInputName, MapEvaluationTypeContext context) {
-        String source = inputMap.get(onnxInputName);
-        if (source != null) {
-            // Source is either a simple reference (query/attribute/constant)...
-            Optional<Reference> reference = Reference.simple(source);
-            if (reference.isPresent()) {
-                return Optional.of(context.getType(reference.get()));
-            }
-            // ... or a function
-            ExpressionFunction func = context.getFunction(source);
-            if (func != null) {
-                return Optional.of(func.getBody().type(context));
-            }
-        }
-        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);
-    }
-
-    private static TensorType typeFrom(Onnx.TypeProto type, Map<String, Long> symbolicSizes) {
-        String dimensionPrefix = "d"; // standard naming convention: d0, d1, ...
-        Onnx.TensorShapeProto shape = type.getTensorType().getShape();
-        TensorType.Builder builder = new TensorType.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();
-            if (onnxDimension.hasDimParam() && symbolicSizes != null && symbolicSizes.containsKey(onnxDimension.getDimParam())) {
-                onnxDimensionSize = symbolicSizes.get(onnxDimension.getDimParam());
-            }
-            if (onnxDimensionSize == 0 && symbolicSizes != null) {
-                // This is for the case where all symbolic dimensions have
-                // different names, but can be resolved to a single dimension size.
-                Set<Long> unknownSizes = new HashSet<>(symbolicSizes.values());
-                if (unknownSizes.size() == 1) {
-                    onnxDimensionSize = unknownSizes.iterator().next();
-                }
-            }
-            if (onnxDimensionSize <= 0) {
-                throw new IllegalArgumentException("Unable to determine fixed dimension size when converting from " +
-                        "ONNX type: " + type + " to Vespa tensor type.");
-            }
-            builder.indexed(dimensionName, onnxDimensionSize);
-        }
-        return builder.build();
-    }
+    public static class OnnxNameMapping {
+        private String onnxName;
+        private String vespaName;
 
-    private static TensorType.Value toValueType(Onnx.TensorProto.DataType dataType) {
-        switch (dataType) {
-            case FLOAT: return TensorType.Value.FLOAT;
-            case DOUBLE: return TensorType.Value.DOUBLE;
-            // Imperfect conversion, for now:
-            case BOOL: return TensorType.Value.FLOAT;
-            case INT8: return TensorType.Value.FLOAT;
-            case INT16: return TensorType.Value.FLOAT;
-            case INT32: return TensorType.Value.FLOAT;
-            case INT64: return TensorType.Value.FLOAT;
-            case UINT8: return TensorType.Value.FLOAT;
-            case UINT16: return TensorType.Value.FLOAT;
-            case UINT32: return TensorType.Value.FLOAT;
-            case UINT64: return TensorType.Value.FLOAT;
-            default: throw new IllegalArgumentException("A ONNX tensor with data type " + dataType +
-                    " cannot be converted to a Vespa tensor type");
+        private OnnxNameMapping(String onnxName, String vespaName) {
+            this.onnxName = onnxName;
+            this.vespaName = vespaName;
         }
+        public String getOnnxName() { return onnxName; }
+        public String getVespaName() { return vespaName; }
+        public void setVespaName(String vespaName) { this.vespaName = vespaName; }
     }
 
 }