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onnxruntime/csharp/test/Microsoft.ML.OnnxRuntime.Tests.Common/InferenceTest.cs
Dmitri Smirnov 0cdf36faeb
Expose SessionOtions.DisablePerSessionThreads (#19730) 
### Description

### Motivation and Context
ML.NET needs to run mltiple sessions on a single threadpool.
2024-03-04 13:46:51 -08:00

2136 lines
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// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
using Microsoft.ML.OnnxRuntime.Tensors;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Linq.Expressions;
using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;
using System.Text.RegularExpressions;
using System.Threading;
using System.Threading.Tasks;
using Xunit;
using Xunit.Abstractions;
// This runs in a separate package built from EndToEndTests
// and for this reason it can not refer to non-public members
// of Onnxruntime package
namespace Microsoft.ML.OnnxRuntime.Tests
{
// This is to make sure it does not run in parallel with OrtEnvTests
// or any other test class within the same collection
[Collection("Ort Inference Tests")]
public partial class InferenceTest
{
private readonly ITestOutputHelper output;
public InferenceTest(ITestOutputHelper o)
{
this.output = o;
}
[Fact(DisplayName = "TestSessionOptions")]
public void TestSessionOptions()
{
// get instance to setup logging
var ortEnvInstance = OrtEnv.Instance();
using (SessionOptions opt = new SessionOptions())
{
Assert.NotNull(opt);
// check default values of the properties
Assert.Equal(ExecutionMode.ORT_SEQUENTIAL, opt.ExecutionMode);
Assert.True(opt.EnableMemoryPattern);
Assert.False(opt.EnableProfiling);
Assert.Equal("onnxruntime_profile_", opt.ProfileOutputPathPrefix);
Assert.True(opt.EnableCpuMemArena);
Assert.Equal("", opt.LogId);
Assert.Equal(0, opt.LogVerbosityLevel);
Assert.Equal(OrtLoggingLevel.ORT_LOGGING_LEVEL_WARNING, opt.LogSeverityLevel);
Assert.Equal(0, opt.IntraOpNumThreads);
Assert.Equal(0, opt.InterOpNumThreads);
Assert.Equal(GraphOptimizationLevel.ORT_ENABLE_ALL, opt.GraphOptimizationLevel);
// No get, so no verify
opt.DisablePerSessionThreads();
// try setting options
opt.ExecutionMode = ExecutionMode.ORT_PARALLEL;
Assert.Equal(ExecutionMode.ORT_PARALLEL, opt.ExecutionMode);
opt.EnableMemoryPattern = false;
Assert.False(opt.EnableMemoryPattern);
opt.EnableProfiling = true;
Assert.True(opt.EnableProfiling);
Assert.Equal("onnxruntime_profile_", opt.ProfileOutputPathPrefix);
opt.ProfileOutputPathPrefix = "Ort_P_";
Assert.Equal("Ort_P_", opt.ProfileOutputPathPrefix);
opt.EnableCpuMemArena = false;
Assert.False(opt.EnableCpuMemArena);
opt.LogId = "MyLogId";
Assert.Equal("MyLogId", opt.LogId);
opt.LogVerbosityLevel = 1;
Assert.Equal(1, opt.LogVerbosityLevel);
opt.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_WARNING;
Assert.Equal(OrtLoggingLevel.ORT_LOGGING_LEVEL_WARNING, opt.LogSeverityLevel);
opt.IntraOpNumThreads = 4;
Assert.Equal(4, opt.IntraOpNumThreads);
opt.InterOpNumThreads = 4;
Assert.Equal(4, opt.InterOpNumThreads);
opt.GraphOptimizationLevel = GraphOptimizationLevel.ORT_ENABLE_EXTENDED;
Assert.Equal(GraphOptimizationLevel.ORT_ENABLE_EXTENDED, opt.GraphOptimizationLevel);
Assert.Throws<OnnxRuntimeException>(() => { opt.GraphOptimizationLevel = (GraphOptimizationLevel)10; });
opt.AddSessionConfigEntry("key", "value");
var ex = Assert.Throws<OnnxRuntimeException>(() => { opt.AddSessionConfigEntry("", "invalid key"); });
Assert.Contains("[ErrorCode:InvalidArgument] Config key is empty", ex.Message);
// SessionOptions.RegisterOrtExtensions can be manually tested by referencing the
// Microsoft.ML.OnnxRuntime.Extensions nuget package. After that is done, this should not throw.
ex = Assert.Throws<OnnxRuntimeException>(() => { opt.RegisterOrtExtensions(); });
Assert.Contains("Microsoft.ML.OnnxRuntime.Extensions NuGet package must be referenced", ex.Message);
#if USE_CUDA
opt.AppendExecutionProvider_CUDA(0);
#endif
#if USE_DML
// Explicitly set dll probe path so that the (potentially) stale system DirectML.dll
// doesn't get loaded by the test process when it is eventually delay loaded by onnruntime.dll
// The managed tests binary path already contains the right DirectML.dll, so use that
var directml_dll_path = AppDomain.CurrentDomain.BaseDirectory;
SetDllDirectory(directml_dll_path);
try
{
opt.AppendExecutionProvider_DML(0);
}
catch (OnnxRuntimeException ortException)
{
// if we run on a CI machine with the incorrect hardware we might get an error due to that.
// allow that as the call made it through to the DML EP so the C# layer is working correctly.
// any other exception type or error message is considered a failure.
Assert.Contains("The specified device interface or feature level is not supported on this system.",
ortException.Message);
}
// Restore the default dll search order
SetDllDirectory(null);
#endif
#if USE_DNNL
opt.AppendExecutionProvider_Dnnl(0);
#endif
#if USE_MIGRAPHX
opt.AppendExecutionProvider_MIGraphX(0);
#endif
#if USE_NNAPI
opt.AppendExecutionProvider_Nnapi(0);
#endif
#if USE_TVM
opt.AppendExecutionProvider_Tvm("Vulkan -device=amd_apu");
#endif
#if USE_OPENVINO
opt.AppendExecutionProvider_OpenVINO();
#endif
#if USE_ROCM
opt.AppendExecutionProvider_ROCm(0);
#endif
#if USE_TENSORRT
opt.AppendExecutionProvider_Tensorrt(0);
#endif
#if USE_XNNPACK
opt.AppendExecutionProvider("XNNPACK");
#else
ex = Assert.Throws<OnnxRuntimeException>(() => { opt.AppendExecutionProvider("XNNPACK"); });
Assert.Contains("XNNPACK execution provider is not supported in this build", ex.Message);
#endif
#if USE_SNPE
opt.AppendExecutionProvider("SNPE");
#else
ex = Assert.Throws<OnnxRuntimeException>(() => { opt.AppendExecutionProvider("SNPE"); });
Assert.Contains("SNPE execution provider is not supported in this build", ex.Message);
#endif
#if USE_QNN
opt.AppendExecutionProvider("QNN");
#else
ex = Assert.Throws<OnnxRuntimeException>(() => { opt.AppendExecutionProvider("QNN"); });
Assert.Contains("QNN execution provider is not supported in this build", ex.Message);
#endif
opt.AppendExecutionProvider_CPU(1);
}
}
#if! __MOBILE__
// Use to set dll probe path so that the right dll(s) is loaded by the test process
// Invoke only to specify Windows specific EPs' dll locations explicitly
[DllImport("kernel32.dll", CharSet = CharSet.Unicode, SetLastError = true)]
[return: MarshalAs(UnmanagedType.Bool)]
static extern bool SetDllDirectory(string lpPathName);
#else
static bool SetDllDirectory(string lpPathName)
{
throw new NotSupportedException();
}
#endif
[Fact(DisplayName = "TestRunOptions")]
public void TestRunOptions()
{
using (var opt = new RunOptions())
{
Assert.NotNull(opt);
//verify default options
Assert.False(opt.Terminate);
Assert.Equal(0, opt.LogVerbosityLevel);
Assert.Equal(OrtLoggingLevel.ORT_LOGGING_LEVEL_WARNING, opt.LogSeverityLevel);
Assert.Equal("", opt.LogId);
// try setting options
opt.Terminate = true;
Assert.True(opt.Terminate);
opt.LogVerbosityLevel = 1;
Assert.Equal(1, opt.LogVerbosityLevel);
opt.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_WARNING;
Assert.Equal(OrtLoggingLevel.ORT_LOGGING_LEVEL_WARNING, opt.LogSeverityLevel);
opt.LogId = "MyLogTag";
Assert.Equal("MyLogTag", opt.LogId);
opt.AddRunConfigEntry("key", "value");
var ex = Assert.Throws<OnnxRuntimeException>(() => { opt.AddRunConfigEntry("", "missing key"); });
Assert.Contains("[ErrorCode:InvalidArgument] Config key is empty", ex.Message);
}
}
[Fact(DisplayName = "TestThreadingOptions")]
public void TestThreadingOptions()
{
using (var opt = new OrtThreadingOptions())
{
Assert.NotNull(opt);
//verify default options
opt.GlobalSpinControl = false;
opt.GlobalInterOpNumThreads = 1;
opt.GlobalIntraOpNumThreads = 1;
opt.SetGlobalDenormalAsZero();
}
}
[Fact(DisplayName = "CanCreateAndDisposeSessionWithModel")]
public void CanCreateAndDisposeSessionWithModel()
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("squeezenet.onnx");
using (var session = new InferenceSession(model))
{
Assert.NotNull(session);
Assert.NotNull(session.InputMetadata);
Assert.Equal(1, session.InputMetadata.Count); // 1 input node
Assert.True(session.InputMetadata.ContainsKey("data_0")); // input node name
Assert.Equal(typeof(float), session.InputMetadata["data_0"].ElementType);
Assert.True(session.InputMetadata["data_0"].IsTensor);
var expectedInputDimensions = new int[] { 1, 3, 224, 224 };
Assert.Equal(expectedInputDimensions.Length, session.InputMetadata["data_0"].Dimensions.Length);
for (int i = 0; i < expectedInputDimensions.Length; i++)
{
Assert.Equal(expectedInputDimensions[i], session.InputMetadata["data_0"].Dimensions[i]);
}
Assert.NotNull(session.OutputMetadata);
Assert.Equal(1, session.OutputMetadata.Count); // 1 output node
Assert.True(session.OutputMetadata.ContainsKey("softmaxout_1")); // output node name
Assert.Equal(typeof(float), session.OutputMetadata["softmaxout_1"].ElementType);
Assert.True(session.OutputMetadata["softmaxout_1"].IsTensor);
var expectedOutputDimensions = new int[] { 1, 1000, 1, 1 };
Assert.Equal(expectedOutputDimensions.Length, session.OutputMetadata["softmaxout_1"].Dimensions.Length);
for (int i = 0; i < expectedOutputDimensions.Length; i++)
{
Assert.Equal(expectedOutputDimensions[i], session.OutputMetadata["softmaxout_1"].Dimensions[i]);
}
}
}
[Theory(DisplayName = "CanRunInferenceOnAModel")]
[InlineData(GraphOptimizationLevel.ORT_DISABLE_ALL, true)]
[InlineData(GraphOptimizationLevel.ORT_DISABLE_ALL, false)]
[InlineData(GraphOptimizationLevel.ORT_ENABLE_EXTENDED, true)]
[InlineData(GraphOptimizationLevel.ORT_ENABLE_EXTENDED, false)]
private void CanRunInferenceOnAModel(GraphOptimizationLevel graphOptimizationLevel, bool enableParallelExecution)
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("squeezenet.onnx");
using (var cleanUp = new DisposableListTest<IDisposable>())
{
// Set the graph optimization level for this session.
SessionOptions options = new SessionOptions();
cleanUp.Add(options);
options.GraphOptimizationLevel = graphOptimizationLevel;
if (enableParallelExecution) options.ExecutionMode = ExecutionMode.ORT_PARALLEL;
var session = new InferenceSession(model, options);
cleanUp.Add(session);
var inputMeta = session.InputMetadata;
var outputMeta = session.OutputMetadata;
var container = new List<NamedOnnxValue>();
float[] expectedOutput = TestDataLoader.LoadTensorFromEmbeddedResource("bench.expected_out");
int[] expectedDimensions = { 1, 1000, 1, 1 }; // hardcoded for now for the test data
ReadOnlySpan<int> expectedOutputDimensions = expectedDimensions;
string[] expectedOutputNames = new string[] { "softmaxout_1" };
float[] inputData = TestDataLoader.LoadTensorFromEmbeddedResource("bench.in"); // this is the data for only one input tensor for this model
foreach (var name in inputMeta.Keys)
{
Assert.Equal(typeof(float), inputMeta[name].ElementType);
Assert.True(inputMeta[name].IsTensor);
var tensor = new DenseTensor<float>(inputData, inputMeta[name].Dimensions);
container.Add(NamedOnnxValue.CreateFromTensor<float>(name, tensor));
}
// Run inference with named inputs and outputs created with in Run()
using (var results = session.Run(container)) // results is an IReadOnlyList<NamedOnnxValue> container
{
ValidateRunResults(results);
}
// Run inference with named inputs, outputs created with in Run() and RunOptions
using (var runOptions = new RunOptions())
{
runOptions.LogId = "CsharpTest";
runOptions.Terminate = false; // TODO: Test terminate = true, it currently crashes
runOptions.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_ERROR;
IReadOnlyCollection<string> outputNames = session.OutputMetadata.Keys.ToList();
using (var results = session.Run(container, outputNames, runOptions)) // results is an IReadOnlyList<NamedOnnxValue> container
{
ValidateRunResults(results);
}
}
// Run inference with pinned inputs and outputs created with in Run()
using (var pinnedInputs = new DisposableListTest<FixedBufferOnnxValue>())
{
var inputNames = container.Select(i => i.Name).ToArray();
pinnedInputs.AddRange(container.Select(i => FixedBufferOnnxValue.CreateFromTensor(i.AsTensor<float>())));
// output names not specified
using (var results = session.Run(inputNames, pinnedInputs)) // results is an IReadOnlyList<NamedOnnxValue> container
{
ValidateRunResults(results);
}
// output names specified explicitly
using (var results = session.Run(inputNames, pinnedInputs, expectedOutputNames)) // results is an IReadOnlyList<NamedOnnxValue> container
{
ValidateRunResults(results);
}
}
// Run inference with outputs pinned from buffers
using (var pinnedInputs = new DisposableListTest<FixedBufferOnnxValue>())
using (var pinnedOutputs = new DisposableListTest<FixedBufferOnnxValue>())
{
var memInfo = OrtMemoryInfo.DefaultInstance; // CPU
// Create inputs
Assert.Single(inputMeta.Keys);
var inputNames = inputMeta.Keys.ToArray();
var inputName = inputNames[0];
Assert.Equal(typeof(float), inputMeta[inputName].ElementType);
Assert.True(inputMeta[inputName].IsTensor);
var longShape = Array.ConvertAll<int, long>(inputMeta[inputName].Dimensions, Convert.ToInt64);
var byteSize = ShapeUtils.GetSizeForShape(longShape);
pinnedInputs.Add(FixedBufferOnnxValue.CreateFromMemory<float>(memInfo, inputData,
TensorElementType.Float, longShape, byteSize));
// Prepare output buffer
Assert.Single(outputMeta.Keys);
var outputNames = outputMeta.Keys.ToArray();
var outputName = outputNames[0];
Assert.Equal(typeof(float), outputMeta[outputName].ElementType);
Assert.True(outputMeta[outputName].IsTensor);
longShape = Array.ConvertAll<int, long>(outputMeta[outputName].Dimensions, Convert.ToInt64);
byteSize = ShapeUtils.GetSizeForShape(longShape);
float[] outputBuffer = new float[expectedOutput.Length];
pinnedOutputs.Add(FixedBufferOnnxValue.CreateFromMemory<float>(memInfo, outputBuffer,
TensorElementType.Float, longShape, byteSize));
session.Run(inputNames, pinnedInputs, outputNames, pinnedOutputs);
Assert.Equal(expectedOutput, outputBuffer, new FloatComparer());
}
// Run inference with named inputs and named outputs
{
// correct pre-allocated outputs
var expectedOutputValues = new List<NamedOnnxValue>()
{
NamedOnnxValue.CreateFromTensor("softmaxout_1", new DenseTensor<float>(expectedOutputDimensions))
};
session.Run(container, expectedOutputValues);
ValidateRunResultData(expectedOutputValues[0].AsTensor<float>(), expectedOutput, expectedDimensions);
}
// Run inference with pinned inputs and named outputs
using (var pinnedInputs = new DisposableListTest<FixedBufferOnnxValue>())
{
var inputNames = container.Select(i => i.Name).ToArray();
pinnedInputs.AddRange(container.Select(i => FixedBufferOnnxValue.CreateFromTensor(i.AsTensor<float>())));
// expected inputs and outputs
var expectedOutputValues = new List<NamedOnnxValue>()
{
NamedOnnxValue.CreateFromTensor("softmaxout_1", new DenseTensor<float>(expectedOutputDimensions))
};
session.Run(inputNames, pinnedInputs, expectedOutputValues);
ValidateRunResultData(expectedOutputValues[0].AsTensor<float>(), expectedOutput, expectedDimensions);
}
// Run inference with named inputs and pinned outputs
{
// correct pre-allocated outputs
using (var pinnedOutputs = new DisposableListTest<FixedBufferOnnxValue>())
{
var outputTensor = new DenseTensor<float>(expectedOutputDimensions);
pinnedOutputs.Add(FixedBufferOnnxValue.CreateFromTensor(outputTensor));
session.Run(container, expectedOutputNames, pinnedOutputs);
ValidateRunResultData(outputTensor, expectedOutput, expectedDimensions);
}
}
// Run inference with pinned inputs and pinned outputs
using (DisposableListTest<FixedBufferOnnxValue> pinnedInputs = new DisposableListTest<FixedBufferOnnxValue>(),
pinnedOutputs = new DisposableListTest<FixedBufferOnnxValue>())
{
var inputNames = container.Select(i => i.Name).ToArray();
pinnedInputs.AddRange(container.Select(i => FixedBufferOnnxValue.CreateFromTensor(i.AsTensor<float>())));
var outputTensor = new DenseTensor<float>(expectedOutputDimensions);
pinnedOutputs.Add(FixedBufferOnnxValue.CreateFromTensor(outputTensor));
session.Run(inputNames, pinnedInputs, expectedOutputNames, pinnedOutputs);
ValidateRunResultData(outputTensor, expectedOutput, expectedDimensions);
}
}
}
[Fact(DisplayName = "RunInferenceUsingPreAllocatedOutputsAndDictionary")]
public void RunInferenceUsingPreAllocatedOutputsAndDictionary()
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("squeezenet.onnx");
using (var cleanUp = new DisposableListTest<IDisposable>())
{
var runOptions = new RunOptions();
cleanUp.Add(runOptions);
var session = new InferenceSession(model);
cleanUp.Add(session);
var inputMeta = session.InputMetadata;
Assert.Single(inputMeta.Keys);
var inputNames = inputMeta.Keys.ToList().AsReadOnly();
Assert.Equal(TensorElementType.Float, inputMeta[inputNames[0]].ElementDataType);
Assert.True(inputMeta[inputNames[0]].IsTensor);
var inputShape = Array.ConvertAll<int, long>(inputMeta[inputNames[0]].Dimensions, Convert.ToInt64);
var outputMeta = session.OutputMetadata;
var expectedOutputNames = new List<string> { "softmaxout_1" }.AsReadOnly();
Assert.Contains(expectedOutputNames[0], outputMeta.Keys);
long[] expectedShape = { 1, 1000, 1, 1 }; // hardcoded for the test data
// this is the data for only one input tensor for this model
float[] inputData = TestDataLoader.LoadTensorFromEmbeddedResource("bench.in");
float[] expectedOutput = TestDataLoader.LoadTensorFromEmbeddedResource("bench.expected_out");
// Allocate input OrtValue on top of the inputData
// Input should stay pinned for the entire duration of the inference
var inputOrtValue = OrtValue.CreateTensorValueFromMemory<float>(inputData, inputShape);
cleanUp.Add(inputOrtValue);
// Create OrtValue and pre-allocate output buffer using the expected output shape
using (var outputOrtValue = OrtValue.CreateAllocatedTensorValue(OrtAllocator.DefaultInstance,
TensorElementType.Float, expectedShape))
{
// Run inference
var inputValues = new List<OrtValue> { inputOrtValue }.AsReadOnly();
var outputValues = new List<OrtValue> { outputOrtValue }.AsReadOnly();
session.Run(runOptions, inputNames, inputValues,
expectedOutputNames, outputValues);
ValidateRunResult(outputOrtValue, expectedOutput, expectedShape);
}
//Let's run this again with an interface that takes a Dictionary of name/OrtValue
var inputDict = new Dictionary<string, OrtValue>();
inputDict.Add(inputNames[0], inputOrtValue);
using (var results = session.Run(runOptions, inputDict, expectedOutputNames))
{
Assert.Single(results);
var outputOrtValue = results[0];
ValidateRunResult(outputOrtValue, expectedOutput, expectedShape);
}
}
}
[Fact(DisplayName = "InferenceSessionDisposed")]
public void InferenceSessionDisposed()
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("squeezenet.onnx");
// Set the graph optimization level for this session.
using (SessionOptions options = new SessionOptions())
{
options.ProfileOutputPathPrefix = "Ort_P_";
options.EnableProfiling = true;
using (var session = new InferenceSession(model, options))
{
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
float[] inputData = TestDataLoader.LoadTensorFromEmbeddedResource("bench.in"); // this is the data for only one input tensor for this model
foreach (var name in inputMeta.Keys)
{
Assert.Equal(typeof(float), inputMeta[name].ElementType);
Assert.True(inputMeta[name].IsTensor);
var tensor = new DenseTensor<float>(inputData, inputMeta[name].Dimensions);
container.Add(NamedOnnxValue.CreateFromTensor<float>(name, tensor));
}
// Run inference with named inputs and outputs created with in Run()
using (var results = session.Run(container)) // results is an IReadOnlyList<NamedOnnxValue> container
{
ValidateRunResults(results);
}
string profile_file = session.EndProfiling();
// Profile file should have the output path prefix in it
Assert.Contains("Ort_P_", profile_file);
}
}
}
[Fact(DisplayName = "InferenceSessionGetProfilingStartTimeNs")]
public void InferenceSessionGetProfilingStartTimeNs()
{
ulong getSingleSessionProfilingStartTime()
{
ulong startTime = 0;
using (SessionOptions options = new SessionOptions())
{
options.EnableProfiling = true;
var model = TestDataLoader.LoadModelFromEmbeddedResource("squeezenet.onnx");
using (var session = new InferenceSession(model, options))
{
startTime = session.ProfilingStartTimeNs;
}
}
return startTime;
}
// Get profiling's start time
ulong ProfilingStartTime = getSingleSessionProfilingStartTime();
// Check the profiling's start time has been updated
Assert.True(ProfilingStartTime != 0);
}
[Fact(DisplayName = "SessionOptionsFreeDimensionOverrides")]
public void SessionOptionsFreeDimensionOverrides()
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("abs_free_dimensions.onnx");
// By Name
using (SessionOptions options = new SessionOptions())
{
options.AddFreeDimensionOverrideByName("Dim1", 4);
options.AddFreeDimensionOverrideByName("Dim2", 6);
using (var session = new InferenceSession(model, options))
{
var inputMetadata = session.InputMetadata;
var dims = inputMetadata["x"].Dimensions;
Assert.Equal(3, dims.Length);
Assert.Equal(4, dims[0]);
Assert.Equal(6, dims[1]);
Assert.Equal(5, dims[2]);
}
}
// By Denotation
using (SessionOptions options = new SessionOptions())
{
options.AddFreeDimensionOverride("DATA_BATCH", 3);
options.AddFreeDimensionOverride("DATA_CHANNEL", 5);
using (var session = new InferenceSession(model, options))
{
var inputMetadata = session.InputMetadata;
var dims = inputMetadata["x"].Dimensions;
Assert.Equal(3, dims.Length);
Assert.Equal(3, dims[0]);
Assert.Equal(5, dims[1]);
Assert.Equal(5, dims[2]);
}
}
}
private void ValidateRunResults(IReadOnlyCollection<NamedOnnxValue> results)
{
// validate the results
foreach (var r in results)
{
Assert.Equal(1, results.Count);
Assert.Equal("softmaxout_1", r.Name);
float[] expectedOutput = TestDataLoader.LoadTensorFromEmbeddedResource("bench.expected_out");
int[] expectedDimensions = { 1, 1000, 1, 1 }; // hardcoded for now for the test data
ValidateRunResultData(r.AsTensor<float>(), expectedOutput, expectedDimensions);
}
}
private void ValidateRunResultData(Tensor<float> resultTensor, float[] expectedOutput, int[] expectedDimensions)
{
Assert.Equal(expectedDimensions.Length, resultTensor.Rank);
var resultDimensions = resultTensor.Dimensions;
for (int i = 0; i < expectedDimensions.Length; i++)
{
Assert.Equal(expectedDimensions[i], resultDimensions[i]);
}
var resultArray = resultTensor.ToArray();
Assert.Equal(expectedOutput.Length, resultArray.Length);
Assert.Equal(expectedOutput, resultArray, new FloatComparer());
}
private static void ValidateRunResult(OrtValue resultTensor, ReadOnlySpan<float> expectedOutput, long[] expectedShape)
{
Assert.True(resultTensor.IsTensor);
var typeShape = resultTensor.GetTensorTypeAndShape();
Assert.Equal(TensorElementType.Float, typeShape.ElementDataType);
Assert.Equal(typeShape.Shape, expectedShape);
var resultSpan = resultTensor.GetTensorDataAsSpan<float>().ToArray();
var expectedSpan = expectedOutput.ToArray();
Assert.Equal(expectedSpan, resultSpan, new FloatComparer());
}
[Fact(DisplayName = "ThrowWrongInputName")]
private void ThrowWrongInputName()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var tensor = tuple.Item3;
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
container.Add(NamedOnnxValue.CreateFromTensor<float>("wrong_name", tensor));
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
Assert.Contains("Input name: 'wrong_name' is not in the metadata", ex.Message);
session.Dispose();
}
[Fact(DisplayName = "ThrowWrongInputType")]
private void ThrowWrongInputType()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
int[] inputDataInt = inputData.Select(x => (int)x).ToArray();
var tensor = new DenseTensor<int>(inputDataInt, inputMeta["data_0"].Dimensions);
container.Add(NamedOnnxValue.CreateFromTensor<int>("data_0", tensor));
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
var msg = ex.ToString();
Assert.Contains("Tensor element data type discovered", msg);
session.Dispose();
}
[Fact(DisplayName = "ThrowExtraInputs")]
private void ThrowExtraInputs()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var tensor = tuple.Item3;
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
var nov1 = NamedOnnxValue.CreateFromTensor<float>("data_0", tensor);
var nov2 = NamedOnnxValue.CreateFromTensor<float>("extra", tensor);
container.Add(nov1);
container.Add(nov2);
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(container));
Assert.Contains("Input name: 'extra' is not in the metadata", ex.Message);
session.Dispose();
}
[Fact(DisplayName = "ThrowInconsistentPinnedInputs")]
private void ThrowInconsistentPinnedInputs()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var tensor = tuple.Item3;
using (var inputs = new DisposableListTest<FixedBufferOnnxValue>())
{
inputs.Add(FixedBufferOnnxValue.CreateFromTensor(tensor));
var ex = Assert.Throws<ArgumentException>(() => session.Run(new string[0], inputs));
Assert.StartsWith("Length of inputNames (0) must match that of inputValues (1).", ex.Message);
}
}
[Fact(DisplayName = "ThrowWrongOutputName")]
private void ThrowWrongOutputName()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var inputTensor = tuple.Item3;
var inputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("data_0", inputTensor) };
var outputTensor = new DenseTensor<float>((ReadOnlySpan<int>)new[] { 1, 2 });
// var outputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("bad_output_name", outputTensor) };
var bad_names = new string[] { "bad_output_name" };
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(inputs, bad_names));
Assert.Contains("Output name: 'bad_output_name' is not in the metadata", ex.Message);
session.Dispose();
}
[Fact(DisplayName = "ThrowWrongOutputType")]
private void ThrowWrongOutputType()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var inputTensor = tuple.Item3;
var inputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("data_0", inputTensor) };
var outputTensor = new DenseTensor<int>((ReadOnlySpan<int>)new[] { 1, 1000, 1, 1 });
var outputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor("softmaxout_1", outputTensor) };
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(inputs, outputs));
// TODO: check exception message
// InferenceSession::ValidateOutputs() does not check type so far. Currently this will finally trigger an error in Softmax.
session.Dispose();
}
[Fact(DisplayName = "ThrowWrongOutputDimension")]
private void ThrowWrongOutputDimension()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var inputTensor = tuple.Item3;
var inputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("data_0", inputTensor) };
var outputTensor = new DenseTensor<float>((ReadOnlySpan<int>)new[] { 1, 1001, 1, 1 });
var outputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor("softmaxout_1", outputTensor) };
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(inputs, outputs));
// TODO: check exception message
// InferenceSession::ValidateOutputs() does not check dims so far. Currently this will finally trigger an error in Softmax.
session.Dispose();
}
[Fact(DisplayName = "ThrowNoOutput")]
private void ThrowNoOutput()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var inputTensor = tuple.Item3;
var inputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("data_0", inputTensor) };
var outputTensor = new DenseTensor<float>((ReadOnlySpan<int>)new[] { 1, 1000, 1, 1 });
var outputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor("softmaxout_1", outputTensor) };
var ex = Assert.Throws<OnnxRuntimeException>(() => session.Run(inputs, new NamedOnnxValue[0]));
Assert.Contains("[ErrorCode:InvalidArgument] At least one output should be requested.", ex.Message);
session.Dispose();
}
[Fact(DisplayName = "ThrowInconsistentPinnedOutputs")]
private void ThrowInconsistentPinnedOutputs()
{
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var inputTensor = tuple.Item3;
var inputs = new List<NamedOnnxValue> { NamedOnnxValue.CreateFromTensor<float>("data_0", inputTensor) };
var outputTensor = new DenseTensor<float>((ReadOnlySpan<int>)new[] { 1, 1000, 1, 1 });
using (var outputs = new DisposableListTest<FixedBufferOnnxValue>())
{
var ex = Assert.Throws<ArgumentException>(() => session.Run(inputs, new string[] { "softmaxout_1" }, outputs));
Assert.StartsWith("Length of outputNames (1) must match that of outputValues (0).", ex.Message);
}
}
[Fact(DisplayName = "TestMultiThreads")]
private void TestMultiThreads()
{
var numThreads = 10;
var loop = 10;
var tuple = OpenSessionSqueezeNet();
var session = tuple.Item1;
var inputData = tuple.Item2;
var tensor = tuple.Item3;
var expectedOut = tuple.Item4;
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
container.Add(NamedOnnxValue.CreateFromTensor<float>("data_0", tensor));
var tasks = new Task[numThreads];
for (int i = 0; i < numThreads; i++)
{
tasks[i] = Task.Factory.StartNew((Action)(() =>
{
for (int j = 0; j < loop; j++)
{
var resnov = session.Run(container);
var res = resnov.ToArray()[0].AsTensor<float>().ToArray();
Assert.Equal(res, expectedOut, (IEqualityComparer<float>)new FloatComparer());
}
}));
};
Task.WaitAll(tasks);
session.Dispose();
}
[Fact(DisplayName = "TestOverridableInitializerMetadata")]
private void TestOverridableInitializerMetadata()
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("overridable_initializer.onnx");
using (var session = new InferenceSession(model))
{
Assert.Equal(2, session.InputMetadata.Count);
Assert.True(session.InputMetadata.ContainsKey("Label"));
Assert.True(session.InputMetadata.ContainsKey("F2"));
Assert.Equal(1, session.OverridableInitializerMetadata.Count);
Assert.True(session.OverridableInitializerMetadata.ContainsKey("F1"));
Assert.True(session.OverridableInitializerMetadata["F1"].IsTensor);
Assert.Equal(typeof(float), session.OverridableInitializerMetadata["F1"].ElementType);
Assert.Equal(2, session.OverridableInitializerMetadata["F1"].Dimensions.Length);
Assert.Equal(1, session.OverridableInitializerMetadata["F1"].Dimensions[0]);
Assert.Equal(1, session.OverridableInitializerMetadata["F1"].Dimensions[1]);
var container = new List<NamedOnnxValue>();
var Label_input = new DenseTensor<bool>(new bool[] { true }, new int[] { 1, 1 });
container.Add(NamedOnnxValue.CreateFromTensor("Label", Label_input));
var F2_input = new DenseTensor<string>(new string[] { "f2_string" }, new int[] { 1, 1 });
container.Add(NamedOnnxValue.CreateFromTensor("F2", F2_input));
var F1_initializer = new DenseTensor<float>(new float[] { 2.0f }, new int[] { 1, 1 });
container.Add(NamedOnnxValue.CreateFromTensor("F1", F1_initializer));
using (var result = session.Run(container))
{
var resultMap = new Dictionary<string, NamedOnnxValue>();
foreach (var output in result)
{
resultMap[output.Name] = output;
}
Assert.True(resultMap.ContainsKey("Label0"));
Assert.True(resultMap.ContainsKey("F20"));
Assert.True(resultMap.ContainsKey("F11"));
var overriddenInitializer = resultMap["F11"].AsTensor<float>();
Assert.NotNull(overriddenInitializer);
Assert.True(overriddenInitializer.SequenceEqual(F1_initializer));
}
}
}
[Fact(DisplayName = "TestSymbolicDimsMetadata")]
private void TestSymbolicDimsMetadata()
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("capi_symbolic_dims.onnx");
using (var session = new InferenceSession(model))
{
var inputs = session.InputMetadata;
var outputs = session.OutputMetadata;
Assert.Equal(2, inputs.Count);
Assert.Equal(1, session.OutputMetadata.Count);
Assert.True(inputs.ContainsKey("A"));
Assert.True(inputs.ContainsKey("B"));
Assert.True(outputs.ContainsKey("C"));
var inputA = inputs["A"];
var inputB = inputs["B"];
var outputC = outputs["C"];
// dimension values and any symbolic dimension info should have the same length
Assert.Equal(inputA.Dimensions.Length, inputA.SymbolicDimensions.Length);
Assert.Equal(inputB.Dimensions.Length, inputB.SymbolicDimensions.Length);
Assert.Equal(outputC.Dimensions.Length, outputC.SymbolicDimensions.Length);
Assert.Equal(inputA.Dimensions, new int[] { -1, 2 });
Assert.Equal(inputA.SymbolicDimensions, new string[] { "n", "" });
Assert.Equal(inputB.Dimensions, new int[] { -1 });
Assert.Equal(inputB.SymbolicDimensions, new string[] { "m" });
Assert.Equal(outputC.Dimensions, new int[] { -1 });
Assert.Equal(outputC.SymbolicDimensions, new string[] { "" }); // unnamed symbolic dim
}
}
[Fact(DisplayName = "TestModelInputFloat")]
private void TestModelInputFloat()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_FLOAT.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<float>(new float[] { 1.0f, 2.0f, -3.0f, float.MinValue, float.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var tensorOut = res.First().AsTensor<float>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
[Fact(DisplayName = "TestModelInputBOOL")]
private void TestModelInputBOOL()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_BOOL.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<bool>(new bool[] { true, false, true, false, true }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var tensorOut = res.First().AsTensor<bool>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
[Fact(DisplayName = "TestReusingRunOutputNonStringType")]
private void TestReusingRunOutputNonStringType()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_BOOL.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<bool>(new bool[] { true, false, true, false, true }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res1 = session.Run(container);
// change the name of the DisposableNamedOnnxValue
res1.First().Name = "input";
// Run inferencing 2 times using the output of the first Run()
for (int i = 0; i < 2; ++i)
{
using (var res2 = session.Run(res1))
{
var tensorOut = res2.First().AsTensor<bool>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
}
[Fact(DisplayName = "TestReusingRunOutputStringType")]
private void TestReusingRunOutputStringType()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_STRING.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<string>(new string[] { "a", "b", "c", "d", "e" }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
var res1 = session.Run(container);
// change the name of the DisposableNamedOnnxValue
res1.First().Name = "input";
// Run inferencing 2 times using the output of the first Run()
for (int i = 0; i < 2; ++i)
{
using (var res2 = session.Run(res1))
{
var tensorOut = res2.First().AsTensor<string>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
}
[Fact(DisplayName = "TestCreateFixedBufferOnnxValueFromStringTensor")]
public void TestCreateFixedBufferOnnxValueFromStringTensor()
{
var tensor = new DenseTensor<string>(new string[] { "a", "b" }, new int[] { 1, 2 });
using (var value = FixedBufferOnnxValue.CreateFromTensor(tensor)) { }
}
[Fact(DisplayName = "TestReusingStringFixedBufferOnnxValue")]
public void TestReusingStringFixedBufferOnnxValue()
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_STRING.pb");
using (var session = new InferenceSession(model))
{
var tensorA = new DenseTensor<string>(new string[] { "a", "b", "c", "d", "e" }, new int[] { 1, 5 });
var tensorB = new DenseTensor<string>(new string[] { "v", "w", "x", "y", "z" }, new int[] { 1, 5 });
var tensorC = new DenseTensor<string>(new string[] { "i", "j", "k", "l", "m" }, new int[] { 1, 5 });
var tensorD = new DenseTensor<string>(new string[] { "i", "j", "k", "l", "m" }, new int[] { 1, 5 });
using (FixedBufferOnnxValue a = FixedBufferOnnxValue.CreateFromTensor(tensorA),
b = FixedBufferOnnxValue.CreateFromTensor(tensorB),
c = FixedBufferOnnxValue.CreateFromTensor(tensorC),
d = FixedBufferOnnxValue.CreateFromTensor(tensorD))
{
// OK to use string type FixedBufferOnnxValue only in input
session.Run(new[] { "input" }, new[] { a });
// Cannot use string type FixedBufferOnnxValue in output
Assert.Throws<NotSupportedException>(() =>
{
// NamedOnnxValue inputs
session.Run(new[] { NamedOnnxValue.CreateFromTensor("input", tensorB) }, new[] { "output" }, new[] { b });
});
Assert.Throws<NotSupportedException>(() =>
{
// both FixedBufferOnnxValue for inputs and outputs
session.Run(new[] { "input" }, new[] { c }, new[] { "output" }, new[] { d });
});
}
}
}
[Fact(DisplayName = "TestReusingFixedBufferOnnxValue")]
private void TestReusingFixedBufferOnnxValue()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_BOOL.pb");
using (var session = new InferenceSession(model))
{
var bufferA = new bool[] { true, false, true, false, true };
var bufferB = new bool[bufferA.Length];
var bufferC = new bool[bufferA.Length];
var tensorA = new DenseTensor<bool>(bufferA, new int[] { 1, 5 });
var tensorB = new DenseTensor<bool>(bufferB, new int[] { 1, 5 });
var tensorC = new DenseTensor<bool>(bufferC, new int[] { 1, 5 });
using (FixedBufferOnnxValue a = FixedBufferOnnxValue.CreateFromTensor(tensorA),
b = FixedBufferOnnxValue.CreateFromTensor(tensorB),
c = FixedBufferOnnxValue.CreateFromTensor(tensorC))
{
session.Run(new[] { "input" }, new[] { a }, new[] { "output" }, new[] { b });
session.Run(new[] { "input" }, new[] { b }, new[] { "output" }, new[] { c });
}
Assert.True(tensorC.SequenceEqual(tensorA));
}
}
[Fact(DisplayName = "TestReusingFixedBufferOnnxValueMultiInferences")]
private void TestReusingFixedBufferOnnxValueMultiInferences()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_INT32.pb");
using (var session = new InferenceSession(model))
{
var bufferInput = new int[5];
var bufferOutput = new int[5];
var tensorInput = new DenseTensor<int>(bufferInput, new int[] { 1, 5 });
var tensorOutput = new DenseTensor<int>(bufferOutput, new int[] { 1, 5 });
using (FixedBufferOnnxValue valueInput = FixedBufferOnnxValue.CreateFromTensor(tensorInput),
valueOutput = FixedBufferOnnxValue.CreateFromTensor(tensorOutput))
{
var inputNames = new[] { "input" };
var outputNames = new[] { "output" };
var inputValues = new[] { valueInput };
var outputValues = new[] { valueOutput };
var rand = new Random();
// run the model for multiple times
for (var i = 0; i < 1000; i++)
{
// feed inputs ( 5 random integers )
var inputs = Enumerable.Range(0, 5).Select(x => rand.Next()).ToArray();
inputs.CopyTo(bufferInput, 0);
// run inference
session.Run(inputNames, inputValues, outputNames, outputValues);
// use outputs
Assert.Equal(inputs, bufferOutput);
}
}
}
}
[Fact(DisplayName = "TestModelInputINT32")]
private void TestModelInputINT32()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_INT32.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<int>(new int[] { 1, -2, -3, int.MinValue, int.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var tensorOut = res.First().AsTensor<int>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
[Fact(DisplayName = "TestModelInputDOUBLE")]
private void TestModelInputDOUBLE()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_DOUBLE.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<double>(new double[] { 1.0, 2.0, -3.0, 5, 5 }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var tensorOut = res.First().AsTensor<double>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
[Fact(DisplayName = "TestModelInputSTRING")]
private void TestModelInputSTRING()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_STRING.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<string>(new string[] {
"hello",
"École élémentaire",
"mit freundlichen grüßen",
"Понедельник",
"最好的问候,"+
"नमस्ते," +
"こんにちは," +
"안녕하세요"
}, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var tensorOut = res.First().AsTensor<string>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
[Fact(DisplayName = "TestModelInputSTRING_ShouldFailWithNullInput")]
private void TestModelInputSTRING_ShouldFailWithNullInput()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_STRING.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<string>(new string[5], // null
new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
Assert.Throws<ArgumentNullException>(() => { session.Run(container); });
}
}
[Fact(DisplayName = "TestModelInputINT8")]
private void TestModelInputINT8()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_INT8.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<sbyte>(new sbyte[] { 1, 2, -3, sbyte.MinValue, sbyte.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var tensorOut = res.First().AsTensor<sbyte>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
[Fact(DisplayName = "TestModelInputUINT8")]
private void TestModelInputUINT8()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_UINT8.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<byte>(new byte[] { 1, 2, 3, byte.MinValue, byte.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var tensorOut = res.First().AsTensor<byte>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
[Fact(DisplayName = "TestModelInputUINT16")]
private void TestModelInputUINT16()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_UINT16.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<UInt16>(new UInt16[] { 1, 2, 3, UInt16.MinValue, UInt16.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var tensorOut = res.First().AsTensor<UInt16>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
[Fact(DisplayName = "TestModelInputINT16")]
private void TestModelInputINT16()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_INT16.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<Int16>(new Int16[] { 1, 2, 3, Int16.MinValue, Int16.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var tensorOut = res.First().AsTensor<Int16>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
[Fact(DisplayName = "TestModelInputINT64")]
private void TestModelInputINT64()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_INT64.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<Int64>(new Int64[] { 1, 2, -3, Int64.MinValue, Int64.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var tensorOut = res.First().AsTensor<Int64>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
[Fact(DisplayName = "TestModelInputUINT32")]
private void TestModelInputUINT32()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_UINT32.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<UInt32>(new UInt32[] { 1, 2, 3, UInt32.MinValue, UInt32.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var tensorOut = res.First().AsTensor<UInt32>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
[Fact(DisplayName = "TestModelInputUINT64")]
private void TestModelInputUINT64()
{
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_UINT64.pb");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<UInt64>(new UInt64[] { 1, 2, 3, UInt64.MinValue, UInt64.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var tensorOut = res.First().AsTensor<UInt64>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
[Fact(DisplayName = "TestModelInputFLOAT16")]
private void TestModelInputFLOAT16()
{
// model takes 1x5 input of fixed type, echoes back
Float16[] modelInput = { new Float16(15360), new Float16(16384), new Float16(16896), new Float16(17408), new Float16(17664) };
int[] inputShape = { 1, 5 };
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_FLOAT16.onnx");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<Float16>(modelInput, inputShape);
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var valueOut = res.First();
Assert.Equal(OnnxValueType.ONNX_TYPE_TENSOR, valueOut.ValueType);
Assert.Equal(Tensors.TensorElementType.Float16, valueOut.ElementType);
var tensorOut = res.First().AsTensor<Float16>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
[Fact(DisplayName = "TestModelInputBFLOAT16")]
private void TestModelInputBFLOAT16()
{
BFloat16[] modelInput = { new BFloat16(16256), new BFloat16(16384),
new BFloat16(16448), new BFloat16(16512), new BFloat16(16544) };
int[] inputShape = { 1, 5 };
// model takes 1x5 input of fixed type, echoes back
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_BFLOAT16.onnx");
using (var session = new InferenceSession(model))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<BFloat16>(modelInput, inputShape);
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var valueOut = res.First();
Assert.Equal(OnnxValueType.ONNX_TYPE_TENSOR, valueOut.ValueType);
Assert.Equal(Tensors.TensorElementType.BFloat16, valueOut.ElementType);
var tensorOut = res.First().AsTensor<BFloat16>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
private class IgnoreWhenMlOpsDisabledFact : FactAttribute
{
public IgnoreWhenMlOpsDisabledFact()
{
var disableMlOpsEnvVar = Environment.GetEnvironmentVariable("DisableMlOps");
var isMlOpsDisabled = (disableMlOpsEnvVar != null) ? disableMlOpsEnvVar.Equals("ON") : false;
if (isMlOpsDisabled)
{
Skip = "Skipping this test since Ml Ops are disabled.";
}
}
}
[IgnoreWhenMlOpsDisabledFact(DisplayName = "TestModelSequenceOfMapIntFloat")]
private void TestModelSequenceOfMapIntFloat()
{
// test model trained using lightgbm classifier
// produces 2 named outputs
// "label" is a tensor,
// "probabilities" is a sequence<map<int64, float>>
// https://github.com/onnx/sklearn-onnx/blob/master/docs/examples/plot_pipeline_lightgbm.py
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_sequence_map_int_float.pb");
using (var session = new InferenceSession(model))
{
var outMeta = session.OutputMetadata;
var label_meta = outMeta["label"];
Assert.True(label_meta.IsTensor);
Assert.Equal(OnnxValueType.ONNX_TYPE_TENSOR, label_meta.OnnxValueType);
Assert.Equal(TensorElementType.Int64, label_meta.ElementDataType);
Assert.NotEmpty(label_meta.Dimensions);
// sequence<map<int64, float>>
var probabilities_meta = outMeta["probabilities"];
Assert.Equal(OnnxValueType.ONNX_TYPE_SEQUENCE, probabilities_meta.OnnxValueType);
var seqElementMetata = probabilities_meta.AsSequenceMetadata().ElementMeta;
Assert.Equal(OnnxValueType.ONNX_TYPE_MAP, seqElementMetata.OnnxValueType);
var mapMetadata = seqElementMetata.AsMapMetadata();
// Map<int64, float tensor>
Assert.Equal(Tensors.TensorElementType.Int64, mapMetadata.KeyDataType);
var valueTensorMeta = mapMetadata.ValueMetadata;
Assert.True(valueTensorMeta.IsTensor);
Assert.Equal(Tensors.TensorElementType.Float, valueTensorMeta.ElementDataType);
// tensor<float>
var inputMeta = session.InputMetadata["input"];
Assert.True(inputMeta.IsTensor);
Assert.Equal(Tensors.TensorElementType.Float, inputMeta.ElementDataType);
Assert.Equal(2, inputMeta.Dimensions.Length);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<float>(new float[] { 5.8f, 2.8f }, new int[] { 1, 2 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var outputs = session.Run(container))
{
// first output is a tensor containing label
var outNode0 = outputs.ElementAtOrDefault(0);
Assert.Equal("label", outNode0.Name);
Assert.Equal(OnnxValueType.ONNX_TYPE_TENSOR, outNode0.ValueType);
Assert.Equal(Tensors.TensorElementType.Int64, outNode0.ElementType);
// try-cast as a tensor
var outLabelTensor = outNode0.AsTensor<long>();
Assert.NotNull(outLabelTensor);
// Label 1 should have highest probability
Assert.Equal(1, outLabelTensor[0]);
// second output is a sequence<map<int64, float>>
// try-cast to an sequence of NOV
var outNode1 = outputs.ElementAtOrDefault(1);
Assert.Equal("probabilities", outNode1.Name);
Assert.Equal(OnnxValueType.ONNX_TYPE_SEQUENCE, outNode1.ValueType);
// try-cast to an sequence of NOV
var seq = outNode1.AsEnumerable<NamedOnnxValue>();
Assert.NotNull(seq);
// Try-cast into DisposableNov so we can control and check the process
// try-cast first element in sequence to map/dictionary type
if (System.Environment.Is64BitProcess)
{
var map = seq.First().AsDictionary<Int64, float>();
Assert.NotNull(map);
Assert.Equal(0.25938290, map[0], 6);
Assert.Equal(0.40904793, map[1], 6);
Assert.Equal(0.33156919, map[2], 6);
}
else // 32-bit
{
var map = seq.First().AsDictionary<long, float>();
Assert.NotNull(map);
Assert.Equal(0.25938290, map[0], 6);
Assert.Equal(0.40904793, map[1], 6);
Assert.Equal(0.33156919, map[2], 6);
}
}
}
}
[IgnoreWhenMlOpsDisabledFact(DisplayName = "TestModelSequenceOfMapStringFloat")]
private void TestModelSequenceOfMapStringFloat()
{
// test model trained using lightgbm classifier
// produces 2 named outputs
// "label" is a tensor,
// "probabilities" is a sequence<map<int64, float>>
// https://github.com/onnx/sklearn-onnx/blob/master/docs/examples/plot_pipeline_lightgbm.py
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_sequence_map_string_float.pb");
using (var session = new InferenceSession(model))
{
var outMeta = session.OutputMetadata;
var label_meta = outMeta["label"];
Assert.True(label_meta.IsTensor);
Assert.Equal(OnnxValueType.ONNX_TYPE_TENSOR, label_meta.OnnxValueType);
Assert.True(label_meta.IsString);
Assert.Equal(TensorElementType.String, label_meta.ElementDataType);
Assert.NotEmpty(label_meta.Dimensions);
// sequence<map<string, float>>
var probabilities_meta = outMeta["probabilities"];
Assert.Equal(OnnxValueType.ONNX_TYPE_SEQUENCE, probabilities_meta.OnnxValueType);
var seqElementMetata = probabilities_meta.AsSequenceMetadata().ElementMeta;
Assert.Equal(OnnxValueType.ONNX_TYPE_MAP, seqElementMetata.OnnxValueType);
var mapMetadata = seqElementMetata.AsMapMetadata();
Assert.Equal(Tensors.TensorElementType.String, mapMetadata.KeyDataType);
var valueTensorMeta = mapMetadata.ValueMetadata;
Assert.True(valueTensorMeta.IsTensor);
Assert.Equal(Tensors.TensorElementType.Float, valueTensorMeta.ElementDataType);
// tensor<float>
var inputMeta = session.InputMetadata["input"];
Assert.True(inputMeta.IsTensor);
Assert.False(inputMeta.IsString);
Assert.Equal(Tensors.TensorElementType.Float, inputMeta.ElementDataType);
Assert.Equal(2, inputMeta.Dimensions.Length);
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<float>(new float[] { 5.8f, 2.8f }, new int[] { 1, 2 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var outputs = session.Run(container))
{
// first output is a tensor containing label
var outNode0 = outputs.ElementAtOrDefault(0);
Assert.Equal("label", outNode0.Name);
Assert.Equal(OnnxValueType.ONNX_TYPE_TENSOR, outNode0.ValueType);
Assert.Equal(TensorElementType.String, outNode0.ElementType);
// try-cast as a tensor
var outLabelTensor = outNode0.AsTensor<string>();
Assert.NotNull(outLabelTensor);
// Label 1 should have highest probability
Assert.Equal("1", outLabelTensor[0]);
// second output is a sequence<map<string, float>>
// try-cast to an sequence of NOV
var outNode1 = outputs.ElementAtOrDefault(1);
Assert.Equal("probabilities", outNode1.Name);
Assert.Equal(OnnxValueType.ONNX_TYPE_SEQUENCE, outNode1.ValueType);
// try-cast to an sequence of NOV
var seq = outNode1.AsEnumerable<NamedOnnxValue>();
// try-cast first element in sequence to map/dictionary type
var map = seq.First().AsDictionary<string, float>();
Assert.NotNull(map);
//verify values are valid
Assert.Equal(0.25938290, map["0"], 6);
Assert.Equal(0.40904793, map["1"], 6);
Assert.Equal(0.33156919, map["2"], 6);
}
}
}
[Fact(DisplayName = "TestModelSequenceOfTensors")]
private void TestModelSequenceOfTensors()
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_sequence_tensors.onnx");
using (var session = new InferenceSession(model))
{
var outMeta = session.OutputMetadata;
var output_seq = outMeta["output_sequence"];
Assert.False(output_seq.IsTensor);
Assert.Equal(OnnxValueType.ONNX_TYPE_SEQUENCE, output_seq.OnnxValueType);
var elemMeta = output_seq.AsSequenceMetadata().ElementMeta;
Assert.True(elemMeta.IsTensor);
Assert.Equal(Tensors.TensorElementType.Int64, elemMeta.ElementDataType);
// Inputs
var tensor1Meta = session.InputMetadata["tensor1"];
Assert.True(tensor1Meta.IsTensor);
Assert.Equal(Tensors.TensorElementType.Int64, tensor1Meta.ElementDataType);
Assert.Equal(2, tensor1Meta.Dimensions.Length);
var container = new List<NamedOnnxValue>();
var firstInputTensor = new DenseTensor<Int64>(new Int64[] { 1, 2, 3, 4, 5, 6 }, new int[] { 2, 3 });
var secondInputTensor = new DenseTensor<Int64>(new Int64[] { 7, 8, 9, 10, 11, 12 }, new int[] { 2, 3 });
var firstNov = NamedOnnxValue.CreateFromTensor("tensor1", firstInputTensor);
var secondNov = NamedOnnxValue.CreateFromTensor("tensor2", secondInputTensor);
container.Add(firstNov);
container.Add(secondNov);
using (var outputs = session.Run(container))
{
// output is a sequence<tensors>
// try-cast to an sequence of NOV
var outNode = outputs.ElementAtOrDefault(0);
Assert.Equal("output_sequence", outNode.Name);
Assert.Equal(OnnxValueType.ONNX_TYPE_SEQUENCE, outNode.ValueType);
// try-cast to an sequence of NOV
var seq = outNode.AsEnumerable<NamedOnnxValue>();
// make sure that the sequence holds only 2 elements (tensors)
Assert.True(seq.Count() == 2);
// try-cast the elements in sequence to tensor type
var firstTensorInOuputSequence = seq.First().AsTensor<Int64>();
var secondTensorInOuputSequence = seq.Last().AsTensor<Int64>();
Assert.NotNull(firstTensorInOuputSequence);
Assert.NotNull(secondTensorInOuputSequence);
// make sure the tensors in the output sequence hold the correct values
Assert.True(firstTensorInOuputSequence.GetValue(0) == 1);
Assert.True(firstTensorInOuputSequence.GetValue(1) == 2);
Assert.True(firstTensorInOuputSequence.GetValue(2) == 3);
Assert.True(firstTensorInOuputSequence.GetValue(3) == 4);
Assert.True(firstTensorInOuputSequence.GetValue(4) == 5);
Assert.True(firstTensorInOuputSequence.GetValue(5) == 6);
Assert.True(secondTensorInOuputSequence.GetValue(0) == 7);
Assert.True(secondTensorInOuputSequence.GetValue(1) == 8);
Assert.True(secondTensorInOuputSequence.GetValue(2) == 9);
Assert.True(secondTensorInOuputSequence.GetValue(3) == 10);
Assert.True(secondTensorInOuputSequence.GetValue(4) == 11);
Assert.True(secondTensorInOuputSequence.GetValue(5) == 12);
}
}
}
[Fact(DisplayName = "TestModelMetadata")]
private void TestModelMetadata()
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("model_with_valid_ort_config_json.onnx");
using (var session = new InferenceSession(model))
{
var modelMetadata = session.ModelMetadata;
Assert.Equal(1, modelMetadata.Version);
Assert.Equal("Hari", modelMetadata.ProducerName);
Assert.Equal("matmul test", modelMetadata.GraphName);
Assert.Equal("", modelMetadata.Domain);
Assert.Equal("This is a test model with a valid ORT config Json", modelMetadata.Description);
Assert.Equal("graph description", modelMetadata.GraphDescription);
Assert.Equal(2, modelMetadata.CustomMetadataMap.Keys.Count);
Assert.Equal("dummy_value", modelMetadata.CustomMetadataMap["dummy_key"]);
Assert.Equal("{\"session_options\": {\"inter_op_num_threads\": 5, \"intra_op_num_threads\": 2, \"graph_optimization_level\": 99, \"enable_profiling\": 1}}",
modelMetadata.CustomMetadataMap["ort_config"]);
}
}
[Fact(DisplayName = "TestInferenceSessionWithByteArray")]
private void TestInferenceSessionWithByteArray()
{
// model takes 1x5 input of fixed type, echoes back
var modelData = TestDataLoader.LoadModelFromEmbeddedResource("test_types_FLOAT.pb");
using (var session = new InferenceSession(modelData))
{
var container = new List<NamedOnnxValue>();
var tensorIn = new DenseTensor<float>(new float[] { 1.0f, 2.0f, -3.0f, float.MinValue, float.MaxValue }, new int[] { 1, 5 });
var nov = NamedOnnxValue.CreateFromTensor("input", tensorIn);
container.Add(nov);
using (var res = session.Run(container))
{
var tensorOut = res.First().AsTensor<float>();
Assert.True(tensorOut.SequenceEqual(tensorIn));
}
}
}
void TestCPUAllocatorInternal(InferenceSession session)
{
int device_id = 0;
using (var info_cpu = new OrtMemoryInfo(OrtMemoryInfo.allocatorCPU, OrtAllocatorType.ArenaAllocator, device_id, OrtMemType.Default))
{
Assert.Equal("Cpu", info_cpu.Name);
Assert.Equal(device_id, info_cpu.Id);
Assert.Equal(OrtAllocatorType.ArenaAllocator, info_cpu.GetAllocatorType());
Assert.Equal(OrtMemType.Default, info_cpu.GetMemoryType());
using (var allocator = new OrtAllocator(session, info_cpu))
{
var alloc_info = allocator.Info;
// Allocator type returned may be different on x86 so we don't compare.
Assert.Equal(info_cpu.Name, alloc_info.Name);
Assert.Equal(info_cpu.GetMemoryType(), alloc_info.GetMemoryType());
Assert.Equal(info_cpu.Id, alloc_info.Id);
uint size = 1024;
OrtMemoryAllocation chunk = allocator.Allocate(size);
Assert.Equal(chunk.Size, size);
var chunk_info = chunk.Info;
// Allocator type returned may be different on x86 so we don't compare.
Assert.Equal(chunk_info.Name, alloc_info.Name);
Assert.Equal(chunk_info.GetMemoryType(), alloc_info.GetMemoryType());
Assert.Equal(chunk_info.Id, alloc_info.Id);
chunk.Dispose();
alloc_info.Dispose();
}
}
}
#if USE_CUDA
void TestCUDAAllocatorInternal(InferenceSession session)
{
int device_id = 0;
using (var info_cuda = new OrtMemoryInfo(OrtMemoryInfo.allocatorCUDA, OrtAllocatorType.ArenaAllocator, device_id, OrtMemType.Default))
{
Assert.Equal("Cuda", info_cuda.Name);
Assert.Equal(device_id, info_cuda.Id);
Assert.Equal(OrtAllocatorType.ArenaAllocator, info_cuda.GetAllocatorType());
Assert.Equal(OrtMemType.Default, info_cuda.GetMemoryType());
using (var allocator = new OrtAllocator(session, info_cuda))
{
var alloc_info = allocator.Info;
Assert.True(info_cuda.Equals(alloc_info));
uint size = 1024;
OrtMemoryAllocation chunk = allocator.Allocate(size);
Assert.Equal(chunk.Size, size);
Assert.True(chunk.Info.Equals(alloc_info));
chunk.Dispose();
alloc_info.Dispose();
}
}
}
#endif
#if USE_ROCM
void TestROCMAllocatorInternal(InferenceSession session)
{
int device_id = 0;
using (var info_rocm = new OrtMemoryInfo(OrtMemoryInfo.allocatorHIP, OrtAllocatorType.ArenaAllocator, device_id, OrtMemType.Default))
{
Assert.Equal("Hip", info_rocm.Name);
Assert.Equal(device_id, info_rocm.Id);
Assert.Equal(OrtAllocatorType.ArenaAllocator, info_rocm.GetAllocatorType());
Assert.Equal(OrtMemType.Default, info_rocm.GetMemoryType());
using (var allocator = new OrtAllocator(session, info_rocm))
{
var alloc_info = allocator.Info;
Assert.True(info_rocm.Equals(alloc_info));
uint size = 1024;
OrtMemoryAllocation chunk = allocator.Allocate(size);
Assert.Equal(chunk.Size, size);
Assert.True(chunk.Info.Equals(alloc_info));
chunk.Dispose();
alloc_info.Dispose();
}
}
}
#endif
[Fact(DisplayName = "TestAllocator")]
private void TestAllocator()
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("squeezenet.onnx");
using (SessionOptions options = new SessionOptions())
{
options.AppendExecutionProvider_CPU(1);
#if USE_CUDA
options.AppendExecutionProvider_CUDA(0);
#endif
#if USE_ROCM
options.AppendExecutionProvider_ROCm(0);
#endif
using (var session = new InferenceSession(model, options))
{
TestCPUAllocatorInternal(session);
#if USE_CUDA
TestCUDAAllocatorInternal(session);
#endif
#if USE_ROCM
TestROCMAllocatorInternal(session);
#endif
}
}
}
[Fact(DisplayName = "TestSharingOfInitializerAndItsPrepackedVersion")]
private void TestSharingOfInitializerAndItsPrepackedVersion()
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("matmul_1.onnx");
// create initializer to share
var ortCpuMemInfo = OrtMemoryInfo.DefaultInstance;
var dims = new long[] { 2, 1 };
var dataBuffer = new float[] { 2.0F, 1.0F };
var dataHandle = GCHandle.Alloc(dataBuffer, GCHandleType.Pinned);
try
{
unsafe
{
float* p = (float*)dataHandle.AddrOfPinnedObject();
for (int i = 0; i < dataBuffer.Length; ++i)
{
*p++ = dataBuffer[i];
}
}
var dataBufferNumBytes = (uint)dataBuffer.Length * sizeof(float);
using (var sharedInitializer = OrtValue.CreateTensorValueWithData(ortCpuMemInfo, Tensors.TensorElementType.Float,
dims, dataHandle.AddrOfPinnedObject(), dataBufferNumBytes))
{
using (var prepackedWeightsContainer = new PrePackedWeightsContainer())
{
using (var options = new SessionOptions())
{
// We want to share initializers between the two sessions
options.AddInitializer("W", sharedInitializer);
float[] expectedOutput = { 4.0F, 10.0F, 16.0F };
int[] expectedDimensions = { 3, 1 };
// We want the pre-packed weights of the shared initializer to be shared between sessions (memory savings)
// and hence we pass in the 'prepackedWeightsContainer' at session creation time
byte[] modelData = model;
// Test both InferenceSession ctors that take PrePackedWeightsContainer instances
using (var session = new InferenceSession(model, options, prepackedWeightsContainer))
using (var session2 = new InferenceSession(modelData, options, prepackedWeightsContainer))
{
var inputMeta = session.InputMetadata;
var container = new List<NamedOnnxValue>();
foreach (var name in inputMeta.Keys)
{
Assert.Equal(typeof(float), inputMeta[name].ElementType);
Assert.True(inputMeta[name].IsTensor);
var tensor = new DenseTensor<float>(new float[] { 1, 2, 3, 4, 5, 6 }, inputMeta[name].Dimensions);
container.Add(NamedOnnxValue.CreateFromTensor<float>(name, tensor));
}
ReadOnlySpan<int> expectedOutputDimensions = new int[] { 1, 1000, 1, 1 };
string[] expectedOutputNames = new string[] { "Y" };
// Run inference with named inputs and outputs created with in Run()
using (var results = session.Run(container)) // results is an IReadOnlyList<NamedOnnxValue> container
{
foreach (var r in results)
{
ValidateRunResultData(r.AsTensor<float>(), expectedOutput, expectedDimensions);
}
}
// Run inference with named inputs and outputs created with in Run()
using (var results2 = session2.Run(container)) // results is an IReadOnlyList<NamedOnnxValue> container
{
foreach (var r in results2)
{
ValidateRunResultData(r.AsTensor<float>(), expectedOutput, expectedDimensions);
}
}
}
}
}
}
}
finally
{
dataHandle.Free();
}
}
[Fact(DisplayName = "TestSharedAllocatorUsingCreateAndRegisterAllocator")]
private void TestSharedAllocatorUsingCreateAndRegisterAllocator()
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("mul_1.onnx");
using (var memInfo = new OrtMemoryInfo(OrtMemoryInfo.allocatorCPU,
OrtAllocatorType.ArenaAllocator, 0, OrtMemType.Default))
using (var arenaCfg = new OrtArenaCfg(0, -1, -1, -1))
{
var env = OrtEnv.Instance();
// Create and register the arena based allocator
env.CreateAndRegisterAllocator(memInfo, arenaCfg);
using (var sessionOptions = new SessionOptions())
{
// Key must match kOrtSessionOptionsConfigUseEnvAllocators in onnxruntime_session_options_config_keys.h
sessionOptions.AddSessionConfigEntry("session.use_env_allocators", "1");
// Create two sessions to share the allocator
// Create a thrid session that DOES NOT use the allocator in the environment
using (var session1 = new InferenceSession(model, sessionOptions))
using (var session2 = new InferenceSession(model, sessionOptions))
using (var session3 = new InferenceSession(model)) // Use the default SessionOptions instance
{
// Input data
var inputDims = new long[] { 3, 2 };
var input = new float[] { 1.0F, 2.0F, 3.0F, 4.0F, 5.0F, 6.0F };
// Output data
int[] outputDims = { 3, 2 };
float[] output = { 1.0F, 4.0F, 9.0F, 16.0F, 25.0F, 36.0F };
// Run inference on all three models
var inputMeta = session1.InputMetadata;
var container = new List<NamedOnnxValue>();
foreach (var name in inputMeta.Keys)
{
Assert.Equal(typeof(float), inputMeta[name].ElementType);
Assert.True(inputMeta[name].IsTensor);
var tensor = new DenseTensor<float>(input, inputMeta[name].Dimensions);
container.Add(NamedOnnxValue.CreateFromTensor<float>(name, tensor));
}
// Run inference with named inputs and outputs created with in Run()
using (var results = session1.Run(container)) // results is an IReadOnlyList<NamedOnnxValue> container
{
foreach (var r in results)
{
ValidateRunResultData(r.AsTensor<float>(), output, outputDims);
}
}
// Run inference with named inputs and outputs created with in Run()
using (var results = session2.Run(container)) // results is an IReadOnlyList<NamedOnnxValue> container
{
foreach (var r in results)
{
ValidateRunResultData(r.AsTensor<float>(), output, outputDims);
}
}
// Run inference with named inputs and outputs created with in Run()
using (var results = session3.Run(container)) // results is an IReadOnlyList<NamedOnnxValue> container
{
foreach (var r in results)
{
ValidateRunResultData(r.AsTensor<float>(), output, outputDims);
}
}
}
}
}
}
internal static Tuple<InferenceSession, float[], DenseTensor<float>, float[]> OpenSessionSqueezeNet(int? deviceId = null)
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("squeezenet.onnx");
#if USE_DML
// Explicitly set dll probe path so that the (potentially) stale system DirectML.dll
// doesn't get loaded by the test process when it is eventually delay loaded by onnruntime.dll
// The managed tests binary path already contains the right DirectML.dll, so use that
var directml_dll_path = AppDomain.CurrentDomain.BaseDirectory;
SetDllDirectory(directml_dll_path);
using (var option = new SessionOptions())
{
if (!deviceId.HasValue)
{
option.AppendExecutionProvider_CPU(1);
}
else
{
option.AppendExecutionProvider_DML(deviceId.Value);
}
// Restore the default dll search order
SetDllDirectory(null);
#elif USE_CUDA
using (var option = (deviceId.HasValue) ?
SessionOptions.MakeSessionOptionWithCudaProvider(deviceId.Value) :
new SessionOptions())
{
if(!deviceId.HasValue)
{
option.AppendExecutionProvider_CPU(1);
}
#elif USE_ROCM
using (var option = (deviceId.HasValue) ?
SessionOptions.MakeSessionOptionWithRocmProvider(deviceId.Value) :
new SessionOptions())
{
if(!deviceId.HasValue)
{
option.AppendExecutionProvider_CPU(1);
}
#else
using (var option = new SessionOptions())
{
option.AppendExecutionProvider_CPU(1);
#endif
var session = (deviceId.HasValue)
? new InferenceSession(model, option)
: new InferenceSession(model);
float[] inputData = TestDataLoader.LoadTensorFromEmbeddedResource("bench.in");
float[] expectedOutput = TestDataLoader.LoadTensorFromEmbeddedResource("bench.expected_out");
var inputMeta = session.InputMetadata;
var tensor = new DenseTensor<float>(inputData, inputMeta["data_0"].Dimensions);
return new Tuple<InferenceSession, float[], DenseTensor<float>, float[]>(session, inputData, tensor, expectedOutput);
}
}
private class GpuFact : FactAttribute
{
public GpuFact()
{
var testOnGpu = System.Environment.GetEnvironmentVariable("TESTONGPU");
if (testOnGpu == null || !testOnGpu.Equals("ON"))
{
Skip = "GPU testing not enabled";
}
}
}
private class SkipNonPackageTests : FactAttribute
{
public SkipNonPackageTests()
{
var skipNonPackageTests = System.Environment.GetEnvironmentVariable("SKIPNONPACKAGETESTS");
if (skipNonPackageTests != null && skipNonPackageTests.Equals("ON"))
{
Skip = "Test skipped while testing the package as it is not within the scope";
}
}
}
[Fact(DisplayName = "TestModelRunAsyncTask")]
private async void TestModelRunAsyncTask()
{
Float16[] inputData = { new Float16(15360), new Float16(16384), new Float16(16896), new Float16(17408), new Float16(17664) };
long[] shape = { 1, 5 };
var inputNames = new List<string> { "input" };
var inputValues = new List<OrtValue> { OrtValue.CreateTensorValueFromMemory(inputData, shape) };
var outputNames = new List<string> { "output" };
var outputValues = new List<OrtValue> { OrtValue.CreateAllocatedTensorValue(OrtAllocator.DefaultInstance,
TensorElementType.Float16, shape) };
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_FLOAT16.onnx");
using (SessionOptions opt = new SessionOptions())
{
opt.IntraOpNumThreads = 2;
using (var session = new InferenceSession(model, opt))
{
try
{
var task = session.RunAsync(null, inputNames, inputValues, outputNames, outputValues);
var outputs = await task;
var valueOut = outputs.ElementAt<OrtValue>(0);
var float16s = valueOut.GetTensorDataAsSpan<Float16>().ToArray();
Assert.Equal(new Float16(16896), float16s[2]);
}
catch
{
Assert.True(false);
}
}
}
}
[Fact(DisplayName = "TestModelRunAsyncTaskFail")]
private async void TestModelRunAsyncTaskFail()
{
Float16[] inputData = { new Float16(15360), new Float16(16384), new Float16(16896), new Float16(17408), new Float16(17664) };
long[] shape = { 1, 5 };
var inputNames = new List<string> { "input" };
var inputValues = new List<OrtValue> { OrtValue.CreateTensorValueFromMemory(inputData, shape) };
var outputNames = new List<string> { "output" };
var outputValues = new List<OrtValue> { OrtValue.CreateAllocatedTensorValue(OrtAllocator.DefaultInstance,
TensorElementType.Float16, shape) };
var model = TestDataLoader.LoadModelFromEmbeddedResource("test_types_FLOAT16.onnx");
using (SessionOptions opt = new SessionOptions())
{
opt.IntraOpNumThreads = 1; // this will make RunAsync fail
string err = "";
using (var session = new InferenceSession(model, opt))
{
try
{
var task = session.RunAsync(null, inputNames, inputValues, outputNames, outputValues);
var outputs = await task;
}
catch (Exception ex)
{
err = ex.Message;
}
finally
{
Assert.Contains("intra op thread pool must have at least one thread for RunAsync", err);
}
}
}
}
#if USE_AZURE
[Fact(DisplayName = "TestLoadAzureEP")]
private void TestLoadAzureEP()
{
var model = TestDataLoader.LoadModelFromEmbeddedResource("mul_1.onnx");
using (var memInfo = new OrtMemoryInfo(OrtMemoryInfo.allocatorCPU,
OrtAllocatorType.ArenaAllocator, 0, OrtMemType.Default))
using (var arenaCfg = new OrtArenaCfg(0, -1, -1, -1))
{
using (var sessionOptions = new SessionOptions())
{
sessionOptions.AppendExecutionProvider("AZURE");
try {
using (var session1 = new InferenceSession(model, sessionOptions))
{
}
}
catch (Exception) {
Assert.True(false);
}
}
}
}
#endif
}
}
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