Revert "Add ONNX-RT evaluator to model-integration module"

21 files changed, 0 insertions, 680 deletions

diff --git a/model-integration/pom.xml b/model-integration/pom.xml
index dc3154c5c41..536d3578f8c 100644
--- a/model-integration/pom.xml
+++ b/model-integration/pom.xml
@@ -53,10 +53,6 @@
     </dependency>
 
     <dependency>
-      <groupId>com.microsoft.onnxruntime</groupId>
-      <artifactId>onnxruntime</artifactId>
-    </dependency>
-    <dependency>
       <groupId>com.google.protobuf</groupId>
       <artifactId>protobuf-java</artifactId>
     </dependency>
diff --git a/model-integration/src/main/java/ai/vespa/modelintegration/evaluator/OnnxEvaluator.java b/model-integration/src/main/java/ai/vespa/modelintegration/evaluator/OnnxEvaluator.java
deleted file mode 100644
index 59ad20b7714..00000000000
--- a/model-integration/src/main/java/ai/vespa/modelintegration/evaluator/OnnxEvaluator.java
+++ /dev/null
@@ -1,79 +0,0 @@
-// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-package ai.vespa.modelintegration.evaluator;
-
-import ai.onnxruntime.OnnxTensor;
-import ai.onnxruntime.OnnxValue;
-import ai.onnxruntime.OrtEnvironment;
-import ai.onnxruntime.OrtException;
-import ai.onnxruntime.OrtSession;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-
-import java.util.Collections;
-import java.util.HashMap;
-import java.util.Map;
-
-
-/**
- * Evaluates an ONNX Model by deferring to ONNX Runtime.
- *
- * @author lesters
- */
-public class OnnxEvaluator {
-
-    private final OrtEnvironment environment;
-    private final OrtSession session;
-
-    public OnnxEvaluator(String modelPath) {
-        try {
-            environment = OrtEnvironment.getEnvironment();
-            session = environment.createSession(modelPath, new OrtSession.SessionOptions());
-        } catch (OrtException e) {
-            throw new RuntimeException("ONNX Runtime exception", e);
-        }
-    }
-
-    public Tensor evaluate(Map<String, Tensor> inputs, String output) {
-        try {
-            Map<String, OnnxTensor> onnxInputs = TensorConverter.toOnnxTensors(inputs, environment, session);
-            try (OrtSession.Result result = session.run(onnxInputs, Collections.singleton(output))) {
-                return TensorConverter.toVespaTensor(result.get(0));
-            }
-        } catch (OrtException e) {
-            throw new RuntimeException("ONNX Runtime exception", e);
-        }
-    }
-
-    public Map<String, Tensor> evaluate(Map<String, Tensor> inputs) {
-        try {
-            Map<String, OnnxTensor> onnxInputs = TensorConverter.toOnnxTensors(inputs, environment, session);
-            Map<String, Tensor> outputs = new HashMap<>();
-            try (OrtSession.Result result = session.run(onnxInputs)) {
-                for (Map.Entry<String, OnnxValue> output : result) {
-                    outputs.put(output.getKey(), TensorConverter.toVespaTensor(output.getValue()));
-                }
-                return outputs;
-            }
-        } catch (OrtException e) {
-            throw new RuntimeException("ONNX Runtime exception", e);
-        }
-    }
-
-    public Map<String, TensorType> getInputInfo() {
-        try {
-            return TensorConverter.toVespaTypes(session.getInputInfo());
-        } catch (OrtException e) {
-            throw new RuntimeException("ONNX Runtime exception", e);
-        }
-    }
-
-    public Map<String, TensorType> getOutputInfo() {
-        try {
-            return TensorConverter.toVespaTypes(session.getOutputInfo());
-        } catch (OrtException e) {
-            throw new RuntimeException("ONNX Runtime exception", e);
-        }
-    }
-
-}
diff --git a/model-integration/src/main/java/ai/vespa/modelintegration/evaluator/TensorConverter.java b/model-integration/src/main/java/ai/vespa/modelintegration/evaluator/TensorConverter.java
deleted file mode 100644
index c1f973300d6..00000000000
--- a/model-integration/src/main/java/ai/vespa/modelintegration/evaluator/TensorConverter.java
+++ /dev/null
@@ -1,181 +0,0 @@
-// Copyright Verizon Media. 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 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.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);
-            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 -> e.getKey(), e -> toVespaType(e.getValue().getInfo())));
-    }
-
-    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;
-    }
-
-}
diff --git a/model-integration/src/main/java/ai/vespa/modelintegration/evaluator/package-info.java b/model-integration/src/main/java/ai/vespa/modelintegration/evaluator/package-info.java
deleted file mode 100644
index e44ea96c534..00000000000
--- a/model-integration/src/main/java/ai/vespa/modelintegration/evaluator/package-info.java
+++ /dev/null
@@ -1,5 +0,0 @@
-// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-@ExportPackage
-package ai.vespa.modelintegration.evaluator;
-
-import com.yahoo.osgi.annotation.ExportPackage;
\ No newline at end of file
diff --git a/model-integration/src/test/java/ai/vespa/modelintegration/evaluator/OnnxEvaluatorTest.java b/model-integration/src/test/java/ai/vespa/modelintegration/evaluator/OnnxEvaluatorTest.java
deleted file mode 100644
index 4b42e18d75e..00000000000
--- a/model-integration/src/test/java/ai/vespa/modelintegration/evaluator/OnnxEvaluatorTest.java
+++ /dev/null
@@ -1,93 +0,0 @@
-// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-package ai.vespa.modelintegration.evaluator;
-
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-import org.junit.Test;
-
-import java.util.HashMap;
-import java.util.Map;
-
-import static org.junit.Assert.assertEquals;
-
-/**
- * @author lesters
- */
-public class OnnxEvaluatorTest {
-
-    @Test
-    public void testSimpleMoodel() {
-        OnnxEvaluator evaluator = new OnnxEvaluator("src/test/models/onnx/simple/simple.onnx");
-
-        // Input types
-        Map<String, TensorType> inputTypes = evaluator.getInputInfo();
-        assertEquals(inputTypes.get("query_tensor"), TensorType.fromSpec("tensor<float>(d0[1],d1[4])"));
-        assertEquals(inputTypes.get("attribute_tensor"), TensorType.fromSpec("tensor<float>(d0[4],d1[1])"));
-        assertEquals(inputTypes.get("bias_tensor"), TensorType.fromSpec("tensor<float>(d0[1],d1[1])"));
-
-        // Output types
-        Map<String, TensorType> outputTypes = evaluator.getOutputInfo();
-        assertEquals(outputTypes.get("output"), TensorType.fromSpec("tensor<float>(d0[1],d1[1])"));
-
-        // Evaluation
-        Map<String, Tensor> inputs = new HashMap<>();
-        inputs.put("query_tensor", Tensor.from("tensor(d0[1],d1[4]):[0.1, 0.2, 0.3, 0.4]"));
-        inputs.put("attribute_tensor", Tensor.from("tensor(d0[4],d1[1]):[0.1, 0.2, 0.3, 0.4]"));
-        inputs.put("bias_tensor", Tensor.from("tensor(d0[1],d1[1]):[1.0]"));
-
-        assertEquals(evaluator.evaluate(inputs).get("output"), Tensor.from("tensor(d0[1],d1[1]):[1.3]"));
-        assertEquals(evaluator.evaluate(inputs, "output"), Tensor.from("tensor(d0[1],d1[1]):[1.3]"));
-    }
-
-    @Test
-    public void testBatchDimension() {
-        OnnxEvaluator evaluator = new OnnxEvaluator("src/test/models/onnx/pytorch/one_layer.onnx");
-
-        // Input types
-        Map<String, TensorType> inputTypes = evaluator.getInputInfo();
-        assertEquals(inputTypes.get("input"), TensorType.fromSpec("tensor<float>(d0[],d1[3])"));
-
-        // Output types
-        Map<String, TensorType> outputTypes = evaluator.getOutputInfo();
-        assertEquals(outputTypes.get("output"), TensorType.fromSpec("tensor<float>(d0[],d1[1])"));
-
-        // Evaluation
-        Map<String, Tensor> inputs = new HashMap<>();
-        inputs.put("input", Tensor.from("tensor<float>(d0[2],d1[3]):[[0.1, 0.2, 0.3],[0.4,0.5,0.6]]"));
-        assertEquals(evaluator.evaluate(inputs, "output"), Tensor.from("tensor<float>(d0[2],d1[1]):[0.6393113,0.67574286]"));
-    }
-
-    @Test
-    public void testMatMul() {
-        String expected = "tensor<float>(d0[2],d1[4]):[38,44,50,56,83,98,113,128]";
-        String input1 = "tensor<float>(d0[2],d1[3]):[1,2,3,4,5,6]";
-        String input2 = "tensor<float>(d0[3],d1[4]):[1,2,3,4,5,6,7,8,9,10,11,12]";
-        assertEvaluate("simple/matmul.onnx", expected, input1, input2);
-    }
-
-    @Test
-    public void testTypes() {
-        assertEvaluate("add_double.onnx", "tensor(d0[1]):[3]", "tensor(d0[1]):[1]", "tensor(d0[1]):[2]");
-        assertEvaluate("add_float.onnx", "tensor<float>(d0[1]):[3]", "tensor<float>(d0[1]):[1]", "tensor<float>(d0[1]):[2]");
-        assertEvaluate("add_int64.onnx", "tensor<double>(d0[1]):[3]", "tensor<double>(d0[1]):[1]", "tensor<double>(d0[1]):[2]");
-        assertEvaluate("cast_int8_float.onnx", "tensor<float>(d0[1]):[-128]", "tensor<int8>(d0[1]):[128]");
-        assertEvaluate("cast_float_int8.onnx", "tensor<int8>(d0[1]):[-1]", "tensor<float>(d0[1]):[255]");
-
-        // ONNX Runtime 1.7.0 does not support much of bfloat16 yet
-        // assertEvaluate("cast_bfloat16_float.onnx", "tensor<float>(d0[1]):[1]", "tensor<bfloat16>(d0[1]):[1]");
-    }
-
-    private void assertEvaluate(String model, String output, String... input) {
-        OnnxEvaluator evaluator = new OnnxEvaluator("src/test/models/onnx/" + model);
-        Map<String, Tensor> inputs = new HashMap<>();
-        for (int i = 0; i < input.length; ++i) {
-            inputs.put("input" + (i+1), Tensor.from(input[i]));
-        }
-        Tensor expected = Tensor.from(output);
-        Tensor result = evaluator.evaluate(inputs, "output");
-        assertEquals(expected, result);
-        assertEquals(expected.type().valueType(), result.type().valueType());
-    }
-
-}
diff --git a/model-integration/src/test/models/onnx/add_double.onnx b/model-integration/src/test/models/onnx/add_double.onnx
deleted file mode 100644
index 9264d1eb9f9..00000000000
--- a/model-integration/src/test/models/onnx/add_double.onnx
+++ /dev/null
@@ -1,16 +0,0 @@
-
add_double.py:f
-
-input1
-input2output"AddaddZ
-input1
-
-
-
Z
-input2
-
-
-
b
-output
-
-
-
B
\ No newline at end of file
diff --git a/model-integration/src/test/models/onnx/add_double.py b/model-integration/src/test/models/onnx/add_double.py
deleted file mode 100755
index fa9aa48f4b2..00000000000
--- a/model-integration/src/test/models/onnx/add_double.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-import onnx
-from onnx import helper, TensorProto
-
-INPUT_1 = helper.make_tensor_value_info('input1', TensorProto.DOUBLE, [1])
-INPUT_2 = helper.make_tensor_value_info('input2', TensorProto.DOUBLE, [1])
-OUTPUT = helper.make_tensor_value_info('output', TensorProto.DOUBLE, [1])
-
-nodes = [
-    helper.make_node(
-        'Add',
-        ['input1', 'input2'],
-        ['output'],
-    ),
-]
-graph_def = helper.make_graph(
-    nodes,
-    'add',
-    [
-        INPUT_1,
-        INPUT_2
-    ],
-    [OUTPUT],
-)
-model_def = helper.make_model(graph_def, producer_name='add_double.py', opset_imports=[onnx.OperatorSetIdProto(version=12)])
-onnx.save(model_def, 'add_double.onnx')
diff --git a/model-integration/src/test/models/onnx/add_float.onnx b/model-integration/src/test/models/onnx/add_float.onnx
deleted file mode 100644
index 0e3ad8f900c..00000000000
--- a/model-integration/src/test/models/onnx/add_float.onnx
+++ /dev/null
@@ -1,16 +0,0 @@
-add_float.py:f
-
-input1
-input2output"AddaddZ
-input1
-
-

-
Z
-input2
-
-

-
b
-output
-
-

-
B
\ No newline at end of file
diff --git a/model-integration/src/test/models/onnx/add_float.py b/model-integration/src/test/models/onnx/add_float.py
deleted file mode 100755
index e18b2c46d9d..00000000000
--- a/model-integration/src/test/models/onnx/add_float.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-import onnx
-from onnx import helper, TensorProto
-
-INPUT_1 = helper.make_tensor_value_info('input1', TensorProto.FLOAT, [1])
-INPUT_2 = helper.make_tensor_value_info('input2', TensorProto.FLOAT, [1])
-OUTPUT = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1])
-
-nodes = [
-    helper.make_node(
-        'Add',
-        ['input1', 'input2'],
-        ['output'],
-    ),
-]
-graph_def = helper.make_graph(
-    nodes,
-    'add',
-    [
-        INPUT_1,
-        INPUT_2
-    ],
-    [OUTPUT],
-)
-model_def = helper.make_model(graph_def, producer_name='add_float.py', opset_imports=[onnx.OperatorSetIdProto(version=12)])
-onnx.save(model_def, 'add_float.onnx')
diff --git a/model-integration/src/test/models/onnx/add_int64.onnx b/model-integration/src/test/models/onnx/add_int64.onnx
deleted file mode 100644
index 7b3a9ec6b95..00000000000
--- a/model-integration/src/test/models/onnx/add_int64.onnx
+++ /dev/null
@@ -1,16 +0,0 @@
-add_int64.py:f
-
-input1
-input2output"AddaddZ
-input1
-
-
-
Z
-input2
-
-
-
b
-output
-
-
-
B
\ No newline at end of file
diff --git a/model-integration/src/test/models/onnx/add_int64.py b/model-integration/src/test/models/onnx/add_int64.py
deleted file mode 100755
index 87908e292a2..00000000000
--- a/model-integration/src/test/models/onnx/add_int64.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-import onnx
-from onnx import helper, TensorProto
-
-INPUT_1 = helper.make_tensor_value_info('input1', TensorProto.INT64, [1])
-INPUT_2 = helper.make_tensor_value_info('input2', TensorProto.INT64, [1])
-OUTPUT = helper.make_tensor_value_info('output', TensorProto.INT64, [1])
-
-nodes = [
-    helper.make_node(
-        'Add',
-        ['input1', 'input2'],
-        ['output'],
-    ),
-]
-graph_def = helper.make_graph(
-    nodes,
-    'add',
-    [
-        INPUT_1,
-        INPUT_2
-    ],
-    [OUTPUT],
-)
-model_def = helper.make_model(graph_def, producer_name='add_int64.py', opset_imports=[onnx.OperatorSetIdProto(version=12)])
-onnx.save(model_def, 'add_int64.onnx')
diff --git a/model-integration/src/test/models/onnx/cast_bfloat16_float.onnx b/model-integration/src/test/models/onnx/cast_bfloat16_float.onnx
deleted file mode 100644
index cb19592abf4..00000000000
--- a/model-integration/src/test/models/onnx/cast_bfloat16_float.onnx
+++ /dev/null
@@ -1,12 +0,0 @@
-cast_bfloat16_float.py:U
-!
-input1output"Cast*	
-to
 
castZ
-input1
-
-
-
b
-output
-
-

-
B
\ No newline at end of file
diff --git a/model-integration/src/test/models/onnx/cast_bfloat16_float.py b/model-integration/src/test/models/onnx/cast_bfloat16_float.py
deleted file mode 100755
index 14b05347262..00000000000
--- a/model-integration/src/test/models/onnx/cast_bfloat16_float.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-import onnx
-from onnx import helper, TensorProto
-
-INPUT_1 = helper.make_tensor_value_info('input1', TensorProto.BFLOAT16, [1])
-OUTPUT = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1])
-
-nodes = [
-    helper.make_node(
-        'Cast',
-        ['input1'],
-        ['output'],
-        to=TensorProto.FLOAT
-    ),
-]
-graph_def = helper.make_graph(
-    nodes,
-    'cast',
-    [INPUT_1],
-    [OUTPUT],
-)
-model_def = helper.make_model(graph_def, producer_name='cast_bfloat16_float.py', opset_imports=[onnx.OperatorSetIdProto(version=12)])
-onnx.save(model_def, 'cast_bfloat16_float.onnx')
diff --git a/model-integration/src/test/models/onnx/cast_float_int8.onnx b/model-integration/src/test/models/onnx/cast_float_int8.onnx
deleted file mode 100644
index c30b023dd68..00000000000
--- a/model-integration/src/test/models/onnx/cast_float_int8.onnx
+++ /dev/null
@@ -1,12 +0,0 @@
-cast_float_int8.py:U
-!
-input1output"Cast*	
-to 
castZ
-input1
-
-

-
b
-output
-
-
-
B
\ No newline at end of file
diff --git a/model-integration/src/test/models/onnx/cast_float_int8.py b/model-integration/src/test/models/onnx/cast_float_int8.py
deleted file mode 100755
index bdc0850d033..00000000000
--- a/model-integration/src/test/models/onnx/cast_float_int8.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-import onnx
-from onnx import helper, TensorProto
-
-INPUT_1 = helper.make_tensor_value_info('input1', TensorProto.FLOAT, [1])
-OUTPUT = helper.make_tensor_value_info('output', TensorProto.INT8, [1])
-
-nodes = [
-    helper.make_node(
-        'Cast',
-        ['input1'],
-        ['output'],
-        to=TensorProto.INT8
-    ),
-]
-graph_def = helper.make_graph(
-    nodes,
-    'cast',
-    [INPUT_1],
-    [OUTPUT],
-)
-model_def = helper.make_model(graph_def, producer_name='cast_float_int8.py', opset_imports=[onnx.OperatorSetIdProto(version=12)])
-onnx.save(model_def, 'cast_float_int8.onnx')
diff --git a/model-integration/src/test/models/onnx/cast_int8_float.onnx b/model-integration/src/test/models/onnx/cast_int8_float.onnx
deleted file mode 100644
index 65aea4a36ae..00000000000
--- a/model-integration/src/test/models/onnx/cast_int8_float.onnx
+++ /dev/null
@@ -1,12 +0,0 @@
-cast_int8_float.py:U
-!
-input1output"Cast*	
-to
 
castZ
-input1
-
-
-
b
-output
-
-

-
B
\ No newline at end of file
diff --git a/model-integration/src/test/models/onnx/cast_int8_float.py b/model-integration/src/test/models/onnx/cast_int8_float.py
deleted file mode 100755
index 70bf2cf70ca..00000000000
--- a/model-integration/src/test/models/onnx/cast_int8_float.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-import onnx
-from onnx import helper, TensorProto
-
-INPUT_1 = helper.make_tensor_value_info('input1', TensorProto.INT8, [1])
-OUTPUT = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1])
-
-nodes = [
-    helper.make_node(
-        'Cast',
-        ['input1'],
-        ['output'],
-        to=TensorProto.FLOAT
-    ),
-]
-graph_def = helper.make_graph(
-    nodes,
-    'cast',
-    [INPUT_1],
-    [OUTPUT],
-)
-model_def = helper.make_model(graph_def, producer_name='cast_int8_float.py', opset_imports=[onnx.OperatorSetIdProto(version=12)])
-onnx.save(model_def, 'cast_int8_float.onnx')
diff --git a/model-integration/src/test/models/onnx/pytorch/one_layer.onnx b/model-integration/src/test/models/onnx/pytorch/one_layer.onnx
deleted file mode 100644
index dc9f664b943..00000000000
--- a/model-integration/src/test/models/onnx/pytorch/one_layer.onnx
+++ /dev/null
diff --git a/model-integration/src/test/models/onnx/pytorch/pytorch_one_layer.py b/model-integration/src/test/models/onnx/pytorch/pytorch_one_layer.py
deleted file mode 100755
index 1296d84e180..00000000000
--- a/model-integration/src/test/models/onnx/pytorch/pytorch_one_layer.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-import torch
-import torch.onnx
-
-
-class MyModel(torch.nn.Module):
-    def __init__(self):
-        super(MyModel, self).__init__()
-        self.linear = torch.nn.Linear(in_features=3, out_features=1)
-        self.logistic = torch.nn.Sigmoid()
-
-    def forward(self, vec):
-        return self.logistic(self.linear(vec))
-
-
-def main():
-    model = MyModel()
-
-    # Omit training - just export randomly initialized network
-
-    data = torch.FloatTensor([[0.1, 0.2, 0.3],[0.4, 0.5, 0.6]])
-    torch.onnx.export(model,
-                      data,
-                      "one_layer.onnx",
-                      input_names = ["input"],
-                      output_names = ["output"],
-                      dynamic_axes = {
-                          "input": {0: "batch"},
-                          "output": {0: "batch"},
-                      },
-                      opset_version=12)
-
-
-if __name__ == "__main__":
-    main()
-
-
diff --git a/model-integration/src/test/models/onnx/simple/matmul.onnx b/model-integration/src/test/models/onnx/simple/matmul.onnx
deleted file mode 100644
index 9bb88406116..00000000000
--- a/model-integration/src/test/models/onnx/simple/matmul.onnx
+++ /dev/null
@@ -1,16 +0,0 @@
-	matmul.py:x
- 
-input1
-input2output"MatMulmatmulZ
-input1
-

-
-Z
-input2
-

-
-b
-output
-

-
-B
\ No newline at end of file
diff --git a/model-integration/src/test/models/onnx/simple/matmul.py b/model-integration/src/test/models/onnx/simple/matmul.py
deleted file mode 100755
index beec55e9f5a..00000000000
--- a/model-integration/src/test/models/onnx/simple/matmul.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright 2020 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-import onnx
-from onnx import helper, TensorProto
-
-INPUT1 = helper.make_tensor_value_info('input1', TensorProto.FLOAT, [2, 3])
-INPUT2 = helper.make_tensor_value_info('input2', TensorProto.FLOAT, [3, 4])
-OUTPUT = helper.make_tensor_value_info('output', TensorProto.FLOAT, [2, 4])
-
-nodes = [
-    helper.make_node(
-        'MatMul',
-        ['input1', 'input2'],
-        ['output'],
-    ),
-]
-graph_def = helper.make_graph(
-    nodes,
-    'matmul',
-    [
-        INPUT1,
-        INPUT2,
-    ],
-    [OUTPUT],
-)
-model_def = helper.make_model(graph_def, producer_name='matmul.py', opset_imports=[onnx.OperatorSetIdProto(version=12)])
-onnx.save(model_def, 'matmul.onnx')