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Merged
merged 3 commits into from
Nov 25, 2019
Merged

Added onnx export support for several multiclass classifiers #4462

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6423062
Added onnx export support for several multiclass classifiers
harishsk Nov 8, 2019
ed45b3e
Separated LightGBM tests for 64-bit and updated baseline for another …
harishsk Nov 14, 2019
cb57a28
Fixed bad merge
harishsk Nov 14, 2019
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24 changes: 23 additions & 1 deletion ...Microsoft.ML.StandardTrainers/Standard/LogisticRegression/MulticlassLogisticRegression.cs
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Expand Up @@ -978,13 +978,35 @@ private bool SaveAsOnnxCore(OnnxContext ctx, string[] outputs, string featureCol
{
Host.CheckValue(ctx, nameof(ctx));

string predictedLabelInt64 = null;
string predictedLabelUint32 = null;
// REVIEW: What is the right way to get the name of the predicted column?
for (int i = 0; i < outputs.Length; i++)
{
if (outputs[i] != DefaultColumnNames.PredictedLabel)
continue;
predictedLabelUint32 = DefaultColumnNames.PredictedLabel;
predictedLabelInt64 = ctx.AddIntermediateVariable(NumberDataViewType.Int64, "PredictedLabelInt64", true);
outputs[i] = predictedLabelInt64;
break;
}

Host.CheckValue(predictedLabelInt64, nameof(predictedLabelInt64));

string opType = "LinearClassifier";
var node = ctx.CreateNode(opType, new[] { featureColumn }, outputs, ctx.GetNodeName(opType));
node.AddAttribute("post_transform", GetOnnxPostTransform());
node.AddAttribute("multi_class", true);
node.AddAttribute("coefficients", Weights.SelectMany(w => w.DenseValues()));
node.AddAttribute("intercepts", Biases);
node.AddAttribute("classlabels_ints", Enumerable.Range(0, NumberOfClasses).Select(x => (long)x));
node.AddAttribute("classlabels_ints", Enumerable.Range(1, NumberOfClasses).Select(x => (long)x));

// Onnx outputs an Int64, but ML.NET outputs UInt32. So cast the Onnx output here
opType = "Cast";
var castNode = ctx.CreateNode(opType, predictedLabelInt64, predictedLabelUint32, ctx.GetNodeName(opType), "");
var t = InternalDataKindExtensions.ToInternalDataKind(DataKind.UInt32).ToType();
castNode.AddAttribute("to", t);

return true;
}

Expand Down
163 changes: 161 additions & 2 deletions src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/OneVersusAllTrainer.cs
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Expand Up @@ -16,6 +16,7 @@
using Microsoft.ML.Internal.Internallearn;
using Microsoft.ML.Internal.Utilities;
using Microsoft.ML.Model;
using Microsoft.ML.Model.OnnxConverter;
using Microsoft.ML.Model.Pfa;
using Microsoft.ML.Runtime;
using Microsoft.ML.Trainers;
Expand Down Expand Up @@ -244,7 +245,8 @@ public sealed class OneVersusAllModelParameters :
IValueMapper,
ICanSaveInSourceCode,
ICanSaveInTextFormat,
ISingleCanSavePfa
ISingleCanSavePfa,
ISingleCanSaveOnnx
{
internal const string LoaderSignature = "OVAExec";
internal const string RegistrationName = "OVAPredictor";
Expand Down Expand Up @@ -490,7 +492,11 @@ void ICanSaveInTextFormat.SaveAsText(TextWriter writer, RoleMappedSchema schema)
}
}

private abstract class ImplBase : ISingleCanSavePfa
bool ICanSaveOnnx.CanSaveOnnx(OnnxContext ctx) => _impl.CanSaveOnnx(ctx);

bool ISingleCanSaveOnnx.SaveAsOnnx(OnnxContext ctx, string[] outputNames, string featureColumn) => _impl.SaveAsOnnx(ctx, outputNames, featureColumn);

private abstract class ImplBase : ISingleCanSavePfa, ISingleCanSaveOnnx
{
public OutputFormula OutputFormula;
public abstract DataViewType InputType { get; }
Expand All @@ -499,6 +505,10 @@ private abstract class ImplBase : ISingleCanSavePfa
public abstract ValueMapper<VBuffer<float>, VBuffer<float>> GetMapper();
public abstract JToken SaveAsPfa(BoundPfaContext ctx, JToken input);

public bool CanSaveOnnx(OnnxContext ctx) => Predictors.All(pred => (pred as ICanSaveOnnx)?.CanSaveOnnx(ctx) == true);
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@ganik ganik Nov 19, 2019 •
edited by harishsk
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(pred as ICanSaveOnnx)? [](start = 79, length = 23)

What happens if it cant cast? #Resolved

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If it can't cast, it will return a null. And the question mark after the ')' ensures makes it a null conditional operator and will not execute further and the overall function will return false.

In reply to: 348211703 [](ancestors = 348211703)


public abstract bool SaveAsOnnx(OnnxContext ctx, string[] outputNames, string featureColumn);

protected bool IsValid(IValueMapper mapper, ref VectorDataViewType inputType)
{
Contracts.AssertValueOrNull(mapper);
Expand All @@ -521,6 +531,65 @@ protected bool IsValid(IValueMapper mapper, ref VectorDataViewType inputType)
}
return true;
}

public string[] SaveAsOnnxPreProcess(OnnxContext ctx, string featureColumn, bool clipToZero)
{
string[] outputs = new string[Predictors.Length];

string[] localOutputNames = { DefaultColumnNames.PredictedLabel, DefaultColumnNames.Score, DefaultColumnNames.Probability };

for (int i = 0; i < Predictors.Length; i++)
{
var predictorOutputNames = new string[localOutputNames.Length];

predictorOutputNames[0] = ctx.AddIntermediateVariable(NumberDataViewType.UInt32, $"{DefaultColumnNames.PredictedLabel}_{i}", true);
predictorOutputNames[1] = ctx.AddIntermediateVariable(NumberDataViewType.Single, $"{DefaultColumnNames.Score}_{i}", true);
predictorOutputNames[2] = ctx.AddIntermediateVariable(NumberDataViewType.Single, $"{DefaultColumnNames.Probability}_{i}", true);

string clipInput = predictorOutputNames[2];

var pred = Predictors[i] as ISingleCanSaveOnnx;
Contracts.AssertValue(pred);
pred.SaveAsOnnx(ctx, predictorOutputNames, featureColumn);

if (clipToZero)
{
var clipOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, $"ClipOutput_{i}", true);
outputs[i] = clipOutput;

string opType = "Clip";
var clipNode = ctx.CreateNode(opType, clipInput, outputs[i], ctx.GetNodeName(opType), "");
clipNode.AddAttribute("min", 0.0);
}
else
outputs[i] = predictorOutputNames[2];
}
return outputs;
}

public void SaveAsOnnxPostProcess(OnnxContext ctx, string inputName, string[] outputNames)
{
Contracts.Assert(outputNames.Length >= 2);

string opType;
opType = "ArgMax";
var argMaxOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "ArgMaxOutput", true);
var argMaxNode = ctx.CreateNode(opType, inputName, argMaxOutput, ctx.GetNodeName(opType), "");
argMaxNode.AddAttribute("keepdims", 0);

opType = "Add";
var one = ctx.AddInitializer(1);
var addOutput = ctx.AddIntermediateVariable(NumberDataViewType.Int64, "AddOutput", true);
var addNode = ctx.CreateNode(opType, new[] { argMaxOutput, one }, new[] { addOutput }, ctx.GetNodeName(opType), "");

opType = "Cast";
var castToUint32Node = ctx.CreateNode(opType, addOutput, outputNames[0], ctx.GetNodeName(opType), "");
var t2 = InternalDataKindExtensions.ToInternalDataKind(DataKind.UInt32).ToType();
castToUint32Node.AddAttribute("to", t2);

opType = "Max";
ctx.CreateNode(opType, inputName, outputNames[1], ctx.GetNodeName(opType), "");
}
}

private sealed class ImplRaw : ImplBase
Expand Down Expand Up @@ -586,6 +655,21 @@ public override JToken SaveAsPfa(BoundPfaContext ctx, JToken input)
JObject jobj = null;
return jobj.AddReturn("type", PfaUtils.Type.Array(PfaUtils.Type.Double)).AddReturn("new", rootObjects);
}

public override bool SaveAsOnnx(OnnxContext ctx, string[] outputNames, string featureColumn)
{
var probabilityOutputs = base.SaveAsOnnxPreProcess(ctx, featureColumn, true);

string opType = "Concat";
var concatOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "ConcatOutput", true);
var concatNode = ctx.CreateNode(opType, probabilityOutputs, new[] { concatOutput }, ctx.GetNodeName(opType), "");
concatNode.AddAttribute("axis", 0);

base.SaveAsOnnxPostProcess(ctx, concatOutput, outputNames);

return true;

}
}

private sealed class ImplDist : ImplBase
Expand Down Expand Up @@ -699,6 +783,51 @@ public override JToken SaveAsPfa(BoundPfaContext ctx, JToken input)
var factorVar = ctx.DeclareVar(null, PfaUtils.Call("/", 1.0, PfaUtils.Call("a.sum", resultVar)));
return PfaUtils.Call("la.scale", resultVar, factorVar);
}

public override bool SaveAsOnnx(OnnxContext ctx, string[] outputNames, string featureColumn)
{
Contracts.Assert(outputNames.Length >= 2);

string opType;
var probabilityOutputs = base.SaveAsOnnxPreProcess(ctx, featureColumn, true);

opType = "Sum";
var sumOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "SumOfScores", true);
var sumNode = ctx.CreateNode(opType, probabilityOutputs, new[] { sumOutput }, ctx.GetNodeName(opType), "");

opType = "Cast";
var castOutput = ctx.AddIntermediateVariable(BooleanDataViewType.Instance, "IsSumZero", true);
var castNode = ctx.CreateNode(opType, sumOutput, castOutput, ctx.GetNodeName(opType), "");
var t = InternalDataKindExtensions.ToInternalDataKind(DataKind.Boolean).ToType();
castNode.AddAttribute("to", t);

var castIsZeroSumToFloat = ctx.AddIntermediateVariable(BooleanDataViewType.Instance, "IsSumZeroAsFloat", true);
var castIsZeroSumToFloatNode = ctx.CreateNode(opType, castOutput, castIsZeroSumToFloat, ctx.GetNodeName(opType), "");
var t1 = InternalDataKindExtensions.ToInternalDataKind(DataKind.Single).ToType();
castIsZeroSumToFloatNode.AddAttribute("to", t1);

opType = "Sum";
var sumOutputNonZero = ctx.AddIntermediateVariable(NumberDataViewType.Single, "SumOfScoresNonZero", true);
var sumOutputNonZeroNode = ctx.CreateNode(opType, new[] { sumOutput, castIsZeroSumToFloat },
new[] { sumOutputNonZero }, ctx.GetNodeName(opType), "");

string[] divOutputs = new string[Predictors.Length];
for (int i = 0; i < Predictors.Length; i++)
{
opType = "Div";
divOutputs[i] = ctx.AddIntermediateVariable(NumberDataViewType.Single, $"DivOutput_{i}", true);
ctx.CreateNode(opType, new[] { probabilityOutputs[i], sumOutputNonZero }, new[] { divOutputs[i] }, ctx.GetNodeName(opType), "");
}

opType = "Concat";
var concatOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "ConcatOutput", true);
var concatNode = ctx.CreateNode(opType, divOutputs, new[] { concatOutput }, ctx.GetNodeName(opType), "");
concatNode.AddAttribute("axis", 0);

base.SaveAsOnnxPostProcess(ctx, concatOutput, outputNames);

return true;
}
}

private sealed class ImplSoftmax : ImplBase
Expand Down Expand Up @@ -768,6 +897,36 @@ public override JToken SaveAsPfa(BoundPfaContext ctx, JToken input)
{
throw new NotImplementedException("Softmax's PFA exporter is not implemented yet.");
}

public override bool SaveAsOnnx(OnnxContext ctx, string[] outputNames, string featureColumn)
{
Contracts.Assert(outputNames.Length >= 2);

var probabilityOutputs = base.SaveAsOnnxPreProcess(ctx, featureColumn, false);

string opType;
opType = "Concat";
var concatOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "ConcatOutput", true);
var concatNode = ctx.CreateNode(opType, probabilityOutputs, new[] { concatOutput }, ctx.GetNodeName(opType), "");
concatNode.AddAttribute("axis", 0);

opType = "Exp";
var expOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "ExpOutput", true);
var expNode = ctx.CreateNode(opType, concatOutput, expOutput, ctx.GetNodeName(opType), "");

opType = "ReduceSum";
var sumOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "SumOutput", true);
var sumNode = ctx.CreateNode(opType, expOutput, sumOutput, ctx.GetNodeName(opType), "");
sumNode.AddAttribute("keepdims", 0);

opType = "Div";
var divOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "DivOutput", true);
var divNode = ctx.CreateNode(opType, new[] { expOutput, sumOutput }, new[] { divOutput }, ctx.GetNodeName(opType), "");

base.SaveAsOnnxPostProcess(ctx, divOutput, outputNames);

return true;
}
}
}
}
23 changes: 20 additions & 3 deletions ...sClassification/BreastCancer/MultiClassificationLogisticRegressionSaveModelToOnnxTest.txt
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Expand Up @@ -118,7 +118,7 @@
"Features0"
],
"output": [
"PredictedLabel",
"PredictedLabelInt64",
"Score"
],
"name": "LinearClassifier",
Expand Down Expand Up @@ -239,7 +239,6 @@
{
"name": "classlabels_ints",
"ints": [
"0",
"1",
"2",
"3",
Expand All @@ -248,13 +247,31 @@
"6",
"7",
"8",
"9"
"9",
"10"
],
"type": "INTS"
}
],
"domain": "ai.onnx.ml"
},
{
"input": [
"PredictedLabelInt64"
],
"output": [
"PredictedLabel"
],
"name": "Cast0",
"opType": "Cast",
"attribute": [
{
"name": "to",
"i": "12",
"type": "INT"
}
]
},
{
"input": [
"Label0"
Expand Down
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