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// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.
using System;
using System.Collections.Generic;
using System.Linq;
using System.Runtime.CompilerServices;
using System.Text;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Data.IO;
using Microsoft.ML.Internal.Utilities;
using Microsoft.ML.Runtime;
using Microsoft.ML.Tokenizers;
using Microsoft.ML.TorchSharp.Extensions;
using Microsoft.ML.TorchSharp.NasBert;
using Microsoft.ML.TorchSharp.NasBert.Models;
using TorchSharp;
using static Microsoft.ML.TorchSharp.NasBert.NasBertTrainer;
[assembly: LoadableClass(typeof(TextClassificationTransformer), null, typeof(SignatureLoadModel),
TextClassificationTransformer.UserName, TextClassificationTransformer.LoaderSignature)]
[assembly: LoadableClass(typeof(IRowMapper), typeof(TextClassificationTransformer), null, typeof(SignatureLoadRowMapper),
TextClassificationTransformer.UserName, TextClassificationTransformer.LoaderSignature)]
namespace Microsoft.ML.TorchSharp.NasBert
{
/// <summary>
/// The <see cref="IEstimator{TTransformer}"/> for training a Deep Neural Network (DNN) to classify text.
/// </summary>
/// <remarks>
/// <format type="text/markdown"><).
///
/// ### Input and output columns
/// The input label column data must be [key](xref:Microsoft.ML.Data.KeyDataViewType) type and the sentence columns must be of type <xref:Microsoft.ML.Data.TextDataViewType>.
///
/// This trainer outputs the following columns:
///
/// | Output column name | Column type | Description|
/// | -- | -- | -- |
/// | `PredictedLabel` | [key](xref:Microsoft.ML.Data.KeyDataViewType) type | The predicted label's index. If its value is `i`, the actual label would be the `i`-th category in the key-valued input label type. |
/// | `Score` | Vector of<xref:System.Single> | The scores of all classes. Higher value means higher probability to fall into the associated class. If the `i`-th element has the largest value, the predicted label index would be `i`. Note that `i` is a zero-based index. |
///
/// ### Trainer characteristics
/// | Characteristic | Value |
/// | -- | -- |
/// | Machine learning task | Multiclass classification |
/// | Is normalization required? | No |
/// | Is caching required? | No |
/// | Required NuGet in addition to Microsoft.ML | Microsoft.ML.TorchSharp and libtorch-cpu or libtorch-cuda-11.3 or any of the OS specific variants. |
/// | Exportable to ONNX | No |
///
/// ### Training algorithm details
/// Trains a Deep Neural Network (DNN) by leveraging an existing, pretrained NAS-BERT roBERTa model for the purpose of classifying text.
/// ]]>
/// </format>
/// </remarks>
///
public class TextClassificationTrainer : NasBertTrainer<UInt32, long>
{
public class TextClassificationOptions : NasBertTrainer.NasBertOptions
{
public TextClassificationOptions()
{
TaskType = BertTaskType.TextClassification;
BatchSize = 32;
MaxEpoch = 10;
}
}
internal TextClassificationTrainer(IHostEnvironment env, TextClassificationOptions options) : base(env, options)
{
}
internal TextClassificationTrainer(IHostEnvironment env,
string labelColumnName = DefaultColumnNames.Label,
string predictionColumnName = DefaultColumnNames.PredictedLabel,
string scoreColumnName = DefaultColumnNames.Score,
string sentence1ColumnName = "Sentence1",
string sentence2ColumnName = default,
int batchSize = 32,
int maxEpochs = 10,
IDataView validationSet = null,
BertArchitecture architecture = BertArchitecture.Roberta) :
this(env, new TextClassificationOptions
{
PredictionColumnName = predictionColumnName,
ScoreColumnName = scoreColumnName,
Sentence1ColumnName = sentence1ColumnName,
Sentence2ColumnName = sentence2ColumnName,
LabelColumnName = labelColumnName,
BatchSize = batchSize,
MaxEpoch = maxEpochs,
ValidationSet = validationSet,
TaskType = BertTaskType.TextClassification
})
{
}
private protected override TrainerBase CreateTrainer(TorchSharpBaseTrainer<uint, long> parent, IChannel ch, IDataView input)
{
return new Trainer(parent, ch, input);
}
private protected override TorchSharpBaseTransformer<uint, long> CreateTransformer(IHost host, Options options, torch.nn.Module model, DataViewSchema.DetachedColumn labelColumn)
{
return new TextClassificationTransformer(host, options as NasBertOptions, model as NasBertModel, labelColumn);
}
private protected class Trainer : NasBertTrainerBase
{
private const string ModelUrlString = "models/NasBert2000000.tsm";
public Trainer(TorchSharpBaseTrainer<uint, long> parent, IChannel ch, IDataView input) : base(parent, ch, input, ModelUrlString)
{
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private protected override long AddToTargets(uint target)
{
// keys are 1 based but the model is 0 based
return target - 1;
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private protected override torch.Tensor CreateTargetsTensor(ref List<long> targets, torch.Device device)
{
return torch.tensor(targets, device: Device);
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private protected override int GetNumCorrect(torch.Tensor predictions, torch.Tensor targets)
{
predictions = predictions ?? throw new ArgumentNullException(nameof(predictions));
return (int)predictions.eq(targets).sum().ToInt64();
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private protected override torch.Tensor GetPredictions(torch.Tensor logits)
{
logits = logits ?? throw new ArgumentNullException(nameof(logits));
var (_, indexes) = logits.max(-1, false);
return indexes;
}
private protected override int GetRowCountAndSetLabelCount(IDataView input)
{
var labelCol = input.GetColumn<uint>(Parent.Option.LabelColumnName);
var rowCount = 0;
var uniqueLabels = new HashSet<uint>();
foreach (var label in labelCol)
{
rowCount++;
uniqueLabels.Add(label);
}
Parent.Option.NumberOfClasses = uniqueLabels.Count;
return rowCount;
}
private protected override torch.Tensor GetTargets(torch.Tensor labels)
{
return labels.view(-1);
}
}
}
public sealed class TextClassificationTransformer : NasBertTransformer<UInt32, long>
{
internal const string LoadName = "TextClassTrainer";
internal const string UserName = "Text Classification Trainer";
internal const string ShortName = "TXTCLSS";
internal const string Summary = "NLP with NAS-BERT";
internal const string LoaderSignature = "TXTCLSS";
private static readonly FuncStaticMethodInfo1<object, Delegate> _decodeInitMethodInfo
= new FuncStaticMethodInfo1<object, Delegate>(DecodeInit<int>);
private static VersionInfo GetVersionInfo()
{
return new VersionInfo(
modelSignature: "TXT-CLSS",
//verWrittenCur: 0x00010001, // Initial
verWrittenCur: 0x00010002, // New refactor format
verReadableCur: 0x00010002,
verWeCanReadBack: 0x00010002,
loaderSignature: LoaderSignature,
loaderAssemblyName: typeof(TextClassificationTransformer).Assembly.FullName);
}
internal TextClassificationTransformer(IHostEnvironment env, NasBertOptions options, NasBertModel model, DataViewSchema.DetachedColumn labelColumn) : base(env, options, model, labelColumn)
{
}
private protected override IRowMapper GetRowMapper(TorchSharpBaseTransformer<uint, long> parent, DataViewSchema schema)
{
return new Mapper(parent, schema);
}
private protected override void SaveModel(ModelSaveContext ctx)
{
// *** Binary format ***
// BaseModel
// int: id of label column name
// int: id of the score column name
// int: id of output column name
// int: number of classes
// BinaryStream: TS Model
// int: id of sentence 1 column name
// int: id of sentence 2 column name
// LabelValues
SaveBaseModel(ctx, GetVersionInfo());
var labelColType = LabelColumn.Annotations.Schema[AnnotationUtils.Kinds.KeyValues].Type as VectorDataViewType;
Microsoft.ML.Internal.Utilities.Utils.MarshalActionInvoke(SaveLabelValues<int>, labelColType.ItemType.RawType, ctx);
}
private void SaveLabelValues<T>(ModelSaveContext ctx)
{
ValueGetter<VBuffer<T>> getter = LabelColumn.Annotations.GetGetter<VBuffer<T>>(LabelColumn.Annotations.Schema[AnnotationUtils.Kinds.KeyValues]);
var val = default(VBuffer<T>);
getter(ref val);
BinarySaver saver = new BinarySaver(Host, new BinarySaver.Arguments());
int bytesWritten;
var labelColType = LabelColumn.Annotations.Schema[AnnotationUtils.Kinds.KeyValues].Type as VectorDataViewType;
if (!saver.TryWriteTypeAndValue<VBuffer<T>>(ctx.Writer.BaseStream, labelColType, ref val, out bytesWritten))
throw Host.Except("We do not know how to serialize label names of type '{0}'", labelColType.ItemType);
}
//Factory method for SignatureLoadRowMapper.
private static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, DataViewSchema inputSchema)
=> Create(env, ctx).MakeRowMapper(inputSchema);
// Factory method for SignatureLoadModel.
private static TextClassificationTransformer Create(IHostEnvironment env, ModelLoadContext ctx)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());
// *** Binary format ***
// BaseModel
// int: id of label column name
// int: id of the score column name
// int: id of output column name
// int: number of classes
// BinaryStream: TS Model
// int: id of sentence 1 column name
// int: id of sentence 2 column name
// LabelValues
var options = new NasBertOptions()
{
LabelColumnName = ctx.LoadString(),
ScoreColumnName = ctx.LoadString(),
PredictionColumnName = ctx.LoadString(),
NumberOfClasses = ctx.Reader.ReadInt32(),
};
var ch = env.Start("Load Model");
var tokenizer = TokenizerExtensions.GetInstance(ch);
EnglishRobertaTokenizer tokenizerModel = tokenizer.RobertaModel();
var model = new ModelForPrediction(options, tokenizerModel.PadIndex, tokenizerModel.SymbolsCount, options.NumberOfClasses);
if (!ctx.TryLoadBinaryStream("TSModel", r => model.load(r)))
throw env.ExceptDecode();
options.Sentence1ColumnName = ctx.LoadString();
options.Sentence2ColumnName = ctx.LoadStringOrNull();
options.TaskType = BertTaskType.TextClassification;
BinarySaver saver = new BinarySaver(env, new BinarySaver.Arguments());
DataViewType type;
object value;
env.CheckDecode(saver.TryLoadTypeAndValue(ctx.Reader.BaseStream, out type, out value));
var vecType = type as VectorDataViewType;
env.CheckDecode(vecType != null);
env.CheckDecode(value != null);
var labelGetter = Microsoft.ML.Internal.Utilities.Utils.MarshalInvoke(_decodeInitMethodInfo, vecType.ItemType.RawType, value);
var meta = new DataViewSchema.Annotations.Builder();
meta.Add(AnnotationUtils.Kinds.KeyValues, type, labelGetter);
var labelCol = new DataViewSchema.DetachedColumn(options.LabelColumnName, type, meta.ToAnnotations());
return new TextClassificationTransformer(env, options, model, labelCol);
}
private static Delegate DecodeInit<T>(object value)
{
VBuffer<T> buffValue = (VBuffer<T>)value;
ValueGetter<VBuffer<T>> buffGetter = (ref VBuffer<T> dst) => buffValue.CopyTo(ref dst);
return buffGetter;
}
private sealed class Mapper : NasBertMapper
{
public Mapper(TorchSharpBaseTransformer<uint, long> parent, DataViewSchema inputSchema) : base(parent, inputSchema)
{
}
private protected override Delegate CreateGetter(DataViewRow input, int iinfo, TensorCacher outputCacher)
{
var ch = Host.Start("Make Getter");
if (iinfo == 0)
return MakePredictedLabelGetter(input, ch, outputCacher);
else
return MakeScoreGetter(input, ch, outputCacher);
}
private Delegate MakeScoreGetter(DataViewRow input, IChannel ch, TensorCacher outputCacher)
{
ValueGetter<ReadOnlyMemory<char>> getSentence1 = default;
ValueGetter<ReadOnlyMemory<char>> getSentence2 = default;
Tokenizer tokenizer = TokenizerExtensions.GetInstance(ch);
getSentence1 = input.GetGetter<ReadOnlyMemory<char>>(input.Schema[Parent.SentenceColumn.Name]);
if (Parent.SentenceColumn2.IsValid)
getSentence2 = input.GetGetter<ReadOnlyMemory<char>>(input.Schema[Parent.SentenceColumn2.Name]);
ReadOnlyMemory<char> sentence1 = default;
ReadOnlyMemory<char> sentence2 = default;
ValueGetter<VBuffer<float>> score = (ref VBuffer<float> dst) =>
{
using var disposeScope = torch.NewDisposeScope();
var editor = VBufferEditor.Create(ref dst, Parent.Options.NumberOfClasses);
UpdateCacheIfNeeded(input.Position, outputCacher, ref sentence1, ref sentence2, ref getSentence1, ref getSentence2, tokenizer);
var values = (outputCacher as BertTensorCacher).Result.cpu().ToArray<float>();
for (var i = 0; i < values.Length; i++)
{
editor.Values[i] = values[i];
}
dst = editor.Commit();
};
return score;
}
private Delegate MakePredictedLabelGetter(DataViewRow input, IChannel ch, TensorCacher outputCacher)
{
ValueGetter<ReadOnlyMemory<char>> getSentence1 = default;
ValueGetter<ReadOnlyMemory<char>> getSentence2 = default;
Tokenizer tokenizer = TokenizerExtensions.GetInstance(ch);
getSentence1 = input.GetGetter<ReadOnlyMemory<char>>(input.Schema[Parent.SentenceColumn.Name]);
if (Parent.SentenceColumn2.IsValid)
getSentence2 = input.GetGetter<ReadOnlyMemory<char>>(input.Schema[Parent.SentenceColumn2.Name]);
ReadOnlyMemory<char> sentence1 = default;
ReadOnlyMemory<char> sentence2 = default;
ValueGetter<UInt32> classification = (ref UInt32 dst) =>
{
using var disposeScope = torch.NewDisposeScope();
UpdateCacheIfNeeded(input.Position, outputCacher, ref sentence1, ref sentence2, ref getSentence1, ref getSentence2, tokenizer);
dst = (UInt32)(outputCacher as BertTensorCacher).Result.argmax(-1).cpu().item<long>() + 1;
};
return classification;
}
private protected override Func<int, bool> GetDependenciesCore(Func<int, bool> activeOutput)
{
return col => (activeOutput(0) || activeOutput(1)) && InputColIndices.Any(i => i == col);
}
}
}
}
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