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/*
* Copyright 2017-2022 John Snow Labs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.johnsnowlabs.nlp.embeddings
import com.johnsnowlabs.ml.ai.DistilBert
import com.johnsnowlabs.ml.onnx.{OnnxWrapper, ReadOnnxModel, WriteOnnxModel}
import com.johnsnowlabs.ml.tensorflow._
import com.johnsnowlabs.ml.util.LoadExternalModel.{
loadTextAsset,
modelSanityCheck,
notSupportedEngineError
}
import com.johnsnowlabs.ml.util.{ModelArch, ONNX, TensorFlow}
import com.johnsnowlabs.nlp._
import com.johnsnowlabs.nlp.annotators.common._
import com.johnsnowlabs.nlp.annotators.tokenizer.wordpiece.{BasicTokenizer, WordpieceEncoder}
import com.johnsnowlabs.nlp.serialization.MapFeature
import com.johnsnowlabs.storage.HasStorageRef
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.ml.param.{IntArrayParam, IntParam}
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.{DataFrame, SparkSession}
/** DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT
* base. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving
* over 95% of BERT's performances as measured on the GLUE language understanding benchmark.
*
* Pretrained models can be loaded with `pretrained` of the companion object:
* {{{
* val embeddings = DistilBertEmbeddings.pretrained()
* .setInputCols("document", "token")
* .setOutputCol("embeddings")
* }}}
* The default model is `"distilbert_base_cased"`, if no name is provided. For available
* pretrained models please see the [[https://sparknlp.org/models?task=Embeddings Models Hub]].
*
* For extended examples of usage, see the
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/transformers/HuggingFace%20in%20Spark%20NLP%20-%20DistilBERT.ipynb Examples]]
* and the
* [[https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/DistilBertEmbeddingsTestSpec.scala DistilBertEmbeddingsTestSpec]].
* To see which models are compatible and how to import them see
* [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]].
*
* The DistilBERT model was proposed in the paper
* [[https://arxiv.org/abs/1910.01108 DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter]].
*
* '''Paper Abstract:'''
*
* ''As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural
* Language Processing (NLP), operating these large models in on-the-edge and/or under
* constrained computational training or inference budgets remains challenging. In this work, we
* propose a method to pre-train a smaller general-purpose language representation model, called
* DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like
* its larger counterparts. While most prior work investigated the use of distillation for
* building task-specific models, we leverage knowledge distillation during the pretraining phase
* and show that it is possible to reduce the size of a BERT model by 40%, while retaining 97% of
* its language understanding capabilities and being 60% faster. To leverage the inductive biases
* learned by larger models during pretraining, we introduce a triple loss combining language
* modeling, distillation and cosine-distance losses. Our smaller, faster and lighter model is
* cheaper to pre-train and we demonstrate its capabilities for on-device computations in a
* proof-of-concept experiment and a comparative on-device study.''
*
* Tips:
* - DistilBERT doesn't have `:obj:token_type_ids`, you don't need to indicate which token
* belongs to which segment. Just separate your segments with the separation token
* `:obj:tokenizer.sep_token` (or `:obj:[SEP]`).
* - DistilBERT doesn't have options to select the input positions (`:obj:position_ids` input).
* This could be added if necessary though, just let us know if you need this option.
*
* ==Example==
* {{{
* import spark.implicits._
* import com.johnsnowlabs.nlp.base.DocumentAssembler
* import com.johnsnowlabs.nlp.annotators.Tokenizer
* import com.johnsnowlabs.nlp.embeddings.DistilBertEmbeddings
* import com.johnsnowlabs.nlp.EmbeddingsFinisher
* import org.apache.spark.ml.Pipeline
*
* val documentAssembler = new DocumentAssembler()
* .setInputCol("text")
* .setOutputCol("document")
*
* val tokenizer = new Tokenizer()
* .setInputCols(Array("document"))
* .setOutputCol("token")
*
* val embeddings = DistilBertEmbeddings.pretrained()
* .setInputCols("document", "token")
* .setOutputCol("embeddings")
* .setCaseSensitive(true)
*
* val embeddingsFinisher = new EmbeddingsFinisher()
* .setInputCols("embeddings")
* .setOutputCols("finished_embeddings")
* .setOutputAsVector(true)
* .setCleanAnnotations(false)
*
* val pipeline = new Pipeline()
* .setStages(Array(
* documentAssembler,
* tokenizer,
* embeddings,
* embeddingsFinisher
* ))
*
* val data = Seq("This is a sentence.").toDF("text")
* val result = pipeline.fit(data).transform(data)
*
* result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
* +--------------------------------------------------------------------------------+
* | result|
* +--------------------------------------------------------------------------------+
* |[0.1127224713563919,-0.1982710212469101,0.5360898375511169,-0.272536993026733...|
* |[0.35534414649009705,0.13215228915214539,0.40981462597846985,0.14036104083061...|
* |[0.328085333108902,-0.06269335001707077,-0.017595693469047546,-0.024373905733...|
* |[0.15617232024669647,0.2967822253704071,0.22324979305267334,-0.04568954557180...|
* |[0.45411425828933716,0.01173491682857275,0.190129816532135,0.1178255230188369...|
* +--------------------------------------------------------------------------------+
* }}}
*
* @see
* [[com.johnsnowlabs.nlp.annotators.classifier.dl.DistilBertForTokenClassification DistilBertForTokenClassification]]
* for DistilBertEmbeddings with a token classification layer on top
* @see
* [[com.johnsnowlabs.nlp.annotators.classifier.dl.DistilBertForSequenceClassification DistilBertForSequenceClassification]]
* for DistilBertEmbeddings with a sequence classification layer on top
* @see
* [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of transformer
* based embeddings
* @groupname anno Annotator types
* @groupdesc anno
* Required input and expected output annotator types
* @groupname Ungrouped Members
* @groupname param Parameters
* @groupname setParam Parameter setters
* @groupname getParam Parameter getters
* @groupname Ungrouped Members
* @groupprio param 1
* @groupprio anno 2
* @groupprio Ungrouped 3
* @groupprio setParam 4
* @groupprio getParam 5
* @groupdesc param
* A list of (hyper-)parameter keys this annotator can take. Users can set and get the
* parameter values through setters and getters, respectively.
*/
class DistilBertEmbeddings(override val uid: String)
extends AnnotatorModel[DistilBertEmbeddings]
with HasBatchedAnnotate[DistilBertEmbeddings]
with WriteTensorflowModel
with WriteOnnxModel
with HasEmbeddingsProperties
with HasStorageRef
with HasCaseSensitiveProperties
with HasEngine {
/** Annotator reference id. Used to identify elements in metadata or to refer to this annotator
* type
*/
def this() = this(Identifiable.randomUID("DISTILBERT_EMBEDDINGS"))
/** Input Annotator Types: DOCUMENT. TOKEN
*
* @group param
*/
override val inputAnnotatorTypes: Array[String] =
Array(AnnotatorType.DOCUMENT, AnnotatorType.TOKEN)
/** Output Annotator Types: WORD_EMBEDDINGS
*
* @group param
*/
override val outputAnnotatorType: AnnotatorType = AnnotatorType.WORD_EMBEDDINGS
def sentenceStartTokenId: Int = {
$$(vocabulary)("[CLS]")
}
def sentenceEndTokenId: Int = {
$$(vocabulary)("[SEP]")
}
/** Vocabulary used to encode the words to ids with WordPieceEncoder
*
* @group param
*/
val vocabulary: MapFeature[String, Int] = new MapFeature(this, "vocabulary").setProtected()
/** @group setParam */
def setVocabulary(value: Map[String, Int]): this.type = set(vocabulary, value)
/** ConfigProto from tensorflow, serialized into byte array. Get with
* config_proto.SerializeToString()
*
* @group param
*/
val configProtoBytes = new IntArrayParam(
this,
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()")
/** @group setParam */
def setConfigProtoBytes(bytes: Array[Int]): DistilBertEmbeddings.this.type =
set(this.configProtoBytes, bytes)
/** @group getParam */
def getConfigProtoBytes: Option[Array[Byte]] = get(this.configProtoBytes).map(_.map(_.toByte))
/** Max sentence length to process (Default: `128`)
*
* @group param
*/
val maxSentenceLength =
new IntParam(this, "maxSentenceLength", "Max sentence length to process")
/** @group setParam */
def setMaxSentenceLength(value: Int): this.type = {
require(
value <= 512,
"DistilBERT models do not support sequences longer than 512 because of trainable positional embeddings.")
require(value >= 1, "The maxSentenceLength must be at least 1")
set(maxSentenceLength, value)
this
}
/** @group getParam */
def getMaxSentenceLength: Int = $(maxSentenceLength)
/** It contains TF model signatures for the laded saved model
*
* @group param
*/
val signatures =
new MapFeature[String, String](model = this, name = "signatures").setProtected()
/** @group setParam */
def setSignatures(value: Map[String, String]): this.type = {
set(signatures, value)
this
}
/** @group getParam */
def getSignatures: Option[Map[String, String]] = get(this.signatures)
private var _model: Option[Broadcast[DistilBert]] = None
/** @group setParam */
def setModelIfNotSet(
spark: SparkSession,
tensorflowWrapper: Option[TensorflowWrapper],
onnxWrapper: Option[OnnxWrapper]): DistilBertEmbeddings = {
if (_model.isEmpty) {
_model = Some(
spark.sparkContext.broadcast(
new DistilBert(
tensorflowWrapper,
onnxWrapper,
sentenceStartTokenId,
sentenceEndTokenId,
configProtoBytes = getConfigProtoBytes,
signatures = getSignatures,
modelArch = ModelArch.wordEmbeddings)))
}
this
}
/** @group getParam */
def getModelIfNotSet: DistilBert = _model.get.value
/** Set Embeddings dimensions for the DistilBERT model. Only possible to set this when the first
* time is saved dimension is not changeable, it comes from DistilBERT config file.
*
* @group setParam
*/
override def setDimension(value: Int): this.type = {
set(this.dimension, value)
}
/** Whether to lowercase tokens or not
*
* @group setParam
*/
override def setCaseSensitive(value: Boolean): this.type = {
set(this.caseSensitive, value)
}
setDefault(dimension -> 768, batchSize -> 8, maxSentenceLength -> 128, caseSensitive -> false)
def tokenizeWithAlignment(tokens: Seq[TokenizedSentence]): Seq[WordpieceTokenizedSentence] = {
val basicTokenizer = new BasicTokenizer($(caseSensitive))
val encoder = new WordpieceEncoder($$(vocabulary))
tokens.map { tokenIndex =>
// filter empty and only whitespace tokens
val bertTokens =
tokenIndex.indexedTokens.filter(x => x.token.nonEmpty && !x.token.equals(" ")).map {
token =>
val content = if ($(caseSensitive)) token.token else token.token.toLowerCase()
val sentenceBegin = token.begin
val sentenceEnd = token.end
val sentenceIndex = tokenIndex.sentenceIndex
val result = basicTokenizer.tokenize(
Sentence(content, sentenceBegin, sentenceEnd, sentenceIndex))
if (result.nonEmpty) result.head else IndexedToken("")
}
val wordpieceTokens =
bertTokens.flatMap(token => encoder.encode(token)).take($(maxSentenceLength))
WordpieceTokenizedSentence(wordpieceTokens)
}
}
/** takes a document and annotations and produces new annotations of this annotator's annotation
* type
*
* @param batchedAnnotations
* Annotations that correspond to inputAnnotationCols generated by previous annotators if any
* @return
* any number of annotations processed for every input annotation. Not necessary one to one
* relationship
*/
override def batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]] = {
// Unpack annotations and zip each sentence to the index or the row it belongs to
val sentencesWithRow = batchedAnnotations.zipWithIndex
.flatMap { case (annotations, i) =>
TokenizedWithSentence.unpack(annotations).toArray.map(x => (x, i))
}
// Tokenize sentences
val tokenizedSentences = tokenizeWithAlignment(sentencesWithRow.map(_._1))
// Process all sentences
val sentenceWordEmbeddings = getModelIfNotSet.predict(
tokenizedSentences,
sentencesWithRow.map(_._1),
$(batchSize),
$(maxSentenceLength),
$(caseSensitive))
// Group resulting annotations by rows. If there are not sentences in a given row, return empty sequence
batchedAnnotations.indices.map(rowIndex => {
val rowEmbeddings = sentenceWordEmbeddings
// zip each annotation with its corresponding row index
.zip(sentencesWithRow)
// select the sentences belonging to the current row
.filter(_._2._2 == rowIndex)
// leave the annotation only
.map(_._1)
if (rowEmbeddings.nonEmpty)
WordpieceEmbeddingsSentence.pack(rowEmbeddings)
else
Seq.empty[Annotation]
})
}
override protected def afterAnnotate(dataset: DataFrame): DataFrame = {
dataset.withColumn(
getOutputCol,
wrapEmbeddingsMetadata(dataset.col(getOutputCol), $(dimension), Some($(storageRef))))
}
override def onWrite(path: String, spark: SparkSession): Unit = {
super.onWrite(path, spark)
val suffix = "_distilbert"
getEngine match {
case TensorFlow.name =>
writeTensorflowModelV2(
path,
spark,
getModelIfNotSet.tensorflowWrapper.get,
suffix,
DistilBertEmbeddings.tfFile,
configProtoBytes = getConfigProtoBytes)
case ONNX.name =>
writeOnnxModel(
path,
spark,
getModelIfNotSet.onnxWrapper.get,
suffix,
DistilBertEmbeddings.onnxFile)
case _ =>
throw new Exception(notSupportedEngineError)
}
}
}
trait ReadablePretrainedDistilBertModel
extends ParamsAndFeaturesReadable[DistilBertEmbeddings]
with HasPretrained[DistilBertEmbeddings] {
override val defaultModelName: Some[String] = Some("distilbert_base_cased")
/** Java compliant-overrides */
override def pretrained(): DistilBertEmbeddings = super.pretrained()
override def pretrained(name: String): DistilBertEmbeddings = super.pretrained(name)
override def pretrained(name: String, lang: String): DistilBertEmbeddings =
super.pretrained(name, lang)
override def pretrained(name: String, lang: String, remoteLoc: String): DistilBertEmbeddings =
super.pretrained(name, lang, remoteLoc)
}
trait ReadDistilBertDLModel extends ReadTensorflowModel with ReadOnnxModel {
this: ParamsAndFeaturesReadable[DistilBertEmbeddings] =>
override val tfFile: String = "distilbert_tensorflow"
override val onnxFile: String = "bert_onnx"
def readModel(instance: DistilBertEmbeddings, path: String, spark: SparkSession): Unit = {
instance.getEngine match {
case TensorFlow.name =>
val tfWrapper = readTensorflowModel(path, spark, "_distilbert_tf", initAllTables = false)
instance.setModelIfNotSet(spark, Some(tfWrapper), None)
case ONNX.name => {
val onnxWrapper =
readOnnxModel(path, spark, "_distilbert_onnx", zipped = true, useBundle = false, None)
instance.setModelIfNotSet(spark, None, Some(onnxWrapper))
}
case _ =>
throw new Exception(notSupportedEngineError)
}
}
addReader(readModel)
def loadSavedModel(modelPath: String, spark: SparkSession): DistilBertEmbeddings = {
val (localModelPath, detectedEngine) = modelSanityCheck(modelPath)
val vocabs = loadTextAsset(localModelPath, "vocab.txt").zipWithIndex.toMap
/*Universal parameters for all engines*/
val annotatorModel = new DistilBertEmbeddings()
.setVocabulary(vocabs)
annotatorModel.set(annotatorModel.engine, detectedEngine)
detectedEngine match {
case TensorFlow.name =>
val (tfWrapper, signatures) =
TensorflowWrapper.read(localModelPath, zipped = false, useBundle = true)
val _signatures = signatures match {
case Some(s) => s
case None => throw new Exception("Cannot load signature definitions from model!")
}
/** the order of setSignatures is important if we use getSignatures inside
* setModelIfNotSet
*/
annotatorModel
.setSignatures(_signatures)
.setModelIfNotSet(spark, Some(tfWrapper), None)
case ONNX.name =>
val onnxWrapper =
OnnxWrapper.read(spark, localModelPath, zipped = false, useBundle = true)
annotatorModel
.setModelIfNotSet(spark, None, Some(onnxWrapper))
case _ =>
throw new Exception(notSupportedEngineError)
}
annotatorModel
}
}
/** This is the companion object of [[DistilBertEmbeddings]]. Please refer to that class for the
* documentation.
*/
object DistilBertEmbeddings extends ReadablePretrainedDistilBertModel with ReadDistilBertDLModel
