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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.Bert
import com.johnsnowlabs.ml.onnx.{OnnxWrapper, ReadOnnxModel, WriteOnnxModel}
import com.johnsnowlabs.ml.openvino.{OpenvinoWrapper, ReadOpenvinoModel, WriteOpenvinoModel}
import com.johnsnowlabs.ml.tensorflow._
import com.johnsnowlabs.ml.util.LoadExternalModel.{
loadTextAsset,
modelSanityCheck,
notSupportedEngineError
}
import com.johnsnowlabs.ml.util.{ModelArch, ONNX, Openvino, 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 com.johnsnowlabs.util.FileHelper
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}
import org.slf4j.{Logger, LoggerFactory}
import java.nio.file.Files
import java.util.UUID
/** Token-level embeddings using BERT. BERT (Bidirectional Encoder Representations from
* Transformers) provides dense vector representations for natural language by using a deep,
* pre-trained neural network with the Transformer architecture.
*
* Pretrained models can be loaded with `pretrained` of the companion object:
* {{{
* val embeddings = BertEmbeddings.pretrained()
* .setInputCols("token", "document")
* .setOutputCol("bert_embeddings")
* }}}
* The default model is `"small_bert_L2_768"`, 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/training/english/dl-ner/ner_bert.ipynb Examples]]
* and the
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/BertEmbeddingsTestSpec.scala BertEmbeddingsTestSpec]].
* To see which models are compatible and how to import them see
* [[https://github.com/JohnSnowLabs/spark-nlp/discussions/5669]].
*
* '''Sources''' :
*
* [[https://arxiv.org/abs/1810.04805 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]]
*
* [[https://github.com/google-research/bert]]
*
* ''' Paper abstract '''
*
* ''We introduce a new language representation model called BERT, which stands for Bidirectional
* Encoder Representations from Transformers. Unlike recent language representation models, BERT
* is designed to pre-train deep bidirectional representations from unlabeled text by jointly
* conditioning on both left and right context in all layers. As a result, the pre-trained BERT
* model can be fine-tuned with just one additional output layer to create state-of-the-art
* models for a wide range of tasks, such as question answering and language inference, without
* substantial task-specific architecture modifications. BERT is conceptually simple and
* empirically powerful. It obtains new state-of-the-art results on eleven natural language
* processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement),
* MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1
* to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute
* improvement).''
*
* ==Example==
* {{{
* import spark.implicits._
* import com.johnsnowlabs.nlp.base.DocumentAssembler
* import com.johnsnowlabs.nlp.annotators.Tokenizer
* import com.johnsnowlabs.nlp.embeddings.BertEmbeddings
* import com.johnsnowlabs.nlp.EmbeddingsFinisher
* import org.apache.spark.ml.Pipeline
*
* val documentAssembler = new DocumentAssembler()
* .setInputCol("text")
* .setOutputCol("document")
*
* val tokenizer = new Tokenizer()
* .setInputCols("document")
* .setOutputCol("token")
*
* val embeddings = BertEmbeddings.pretrained("small_bert_L2_128", "en")
* .setInputCols("token", "document")
* .setOutputCol("bert_embeddings")
*
* val embeddingsFinisher = new EmbeddingsFinisher()
* .setInputCols("bert_embeddings")
* .setOutputCols("finished_embeddings")
* .setOutputAsVector(true)
*
* 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|
* +--------------------------------------------------------------------------------+
* |[-2.3497989177703857,0.480538547039032,-0.3238905668258667,-1.612930893898010...|
* |[-2.1357314586639404,0.32984697818756104,-0.6032363176345825,-1.6791689395904...|
* |[-1.8244884014129639,-0.27088963985443115,-1.059438943862915,-0.9817547798156...|
* |[-1.1648050546646118,-0.4725411534309387,-0.5938255786895752,-1.5780693292617...|
* |[-0.9125322699546814,0.4563939869403839,-0.3975459933280945,-1.81611204147338...|
* +--------------------------------------------------------------------------------+
* }}}
*
* @see
* [[BertSentenceEmbeddings]] for sentence-level embeddings
* @see
* [[com.johnsnowlabs.nlp.annotators.classifier.dl.BertForTokenClassification BertForTokenClassification]]
* For BertEmbeddings with a token classification layer on top
* @see
* [[https://sparknlp.org/docs/en/annotators Annotators Main Page]] for a list of transformer
* based embeddings
* @param uid
* required uid for storing annotator to disk
* @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 BertEmbeddings(override val uid: String)
extends AnnotatorModel[BertEmbeddings]
with HasBatchedAnnotate[BertEmbeddings]
with WriteTensorflowModel
with WriteOnnxModel
with WriteOpenvinoModel
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("BERT_EMBEDDINGS"))
/** Input Annotator Types: DOCUMENT, TOKEN
*
* @group anno
*/
override val inputAnnotatorTypes: Array[String] =
Array(AnnotatorType.DOCUMENT, AnnotatorType.TOKEN)
/** Output Annotator Types: WORD_EMBEDDINGS
*
* @group anno
*/
override val outputAnnotatorType: AnnotatorType = AnnotatorType.WORD_EMBEDDINGS
/** @group setParam */
def sentenceStartTokenId: Int = {
$$(vocabulary)("[CLS]")
}
/** @group setParam */
def sentenceEndTokenId: Int = {
$$(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]): BertEmbeddings.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,
"BERT 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 = $(maxSentenceLength)
/** It contains TF model signatures for the laded saved model
*
* @group param
*/
val signatures =
new MapFeature[String, String](model = this, name = "signatures").setProtected()
/** @group setParam */
def setSignatures(value: Map[String, String]): this.type = {
set(signatures, value)
this
}
/** @group getParam */
def getSignatures: Option[Map[String, String]] = get(this.signatures)
private var _model: Option[Broadcast[Bert]] = None
/** @group setParam */
def setModelIfNotSet(
spark: SparkSession,
tensorflowWrapper: Option[TensorflowWrapper],
onnxWrapper: Option[OnnxWrapper],
openvinoWrapper: Option[OpenvinoWrapper]): BertEmbeddings = {
if (_model.isEmpty) {
_model = Some(
spark.sparkContext.broadcast(
new Bert(
tensorflowWrapper,
onnxWrapper,
openvinoWrapper,
sentenceStartTokenId,
sentenceEndTokenId,
configProtoBytes = getConfigProtoBytes,
signatures = getSignatures,
modelArch = ModelArch.wordEmbeddings)))
}
this
}
def getModelIfNotSet: Bert = _model.get.value
/** Set Embeddings dimensions for the BERT model Only possible to set this when the first time
* is saved dimension is not changeable, it comes from BERT config file
*
* @group setParam
*/
override def setDimension(value: Int): this.type = {
set(this.dimension, value)
}
/** Whether to lowercase tokens or not
*
* @group setParam
*/
override def setCaseSensitive(value: Boolean): this.type = {
set(this.caseSensitive, value)
}
setDefault(dimension -> 768, batchSize -> 8, maxSentenceLength -> 128, caseSensitive -> false)
def tokenizeWithAlignment(tokens: Seq[TokenizedSentence]): Seq[WordpieceTokenizedSentence] = {
val basicTokenizer = new BasicTokenizer($(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 = "_bert"
getEngine match {
case TensorFlow.name =>
writeTensorflowModelV2(
path,
spark,
getModelIfNotSet.tensorflowWrapper.get,
suffix,
BertEmbeddings.tfFile,
configProtoBytes = getConfigProtoBytes)
case ONNX.name =>
writeOnnxModel(
path,
spark,
getModelIfNotSet.onnxWrapper.get,
suffix,
BertEmbeddings.onnxFile)
case Openvino.name =>
writeOpenvinoModel(
path,
spark,
getModelIfNotSet.openvinoWrapper.get,
suffix,
BertEmbeddings.openvinoFile)
case _ =>
throw new Exception(notSupportedEngineError)
}
}
}
trait ReadablePretrainedBertModel
extends ParamsAndFeaturesReadable[BertEmbeddings]
with HasPretrained[BertEmbeddings] {
override val defaultModelName: Some[String] = Some("small_bert_L2_768")
/** Java compliant-overrides */
override def pretrained(): BertEmbeddings = super.pretrained()
override def pretrained(name: String): BertEmbeddings = super.pretrained(name)
override def pretrained(name: String, lang: String): BertEmbeddings =
super.pretrained(name, lang)
override def pretrained(name: String, lang: String, remoteLoc: String): BertEmbeddings =
super.pretrained(name, lang, remoteLoc)
}
trait ReadBertDLModel extends ReadTensorflowModel with ReadOnnxModel with ReadOpenvinoModel {
this: ParamsAndFeaturesReadable[BertEmbeddings] =>
override val tfFile: String = "bert_tensorflow"
override val onnxFile: String = "bert_onnx"
override val openvinoFile: String = "bert_openvino"
def readModel(instance: BertEmbeddings, path: String, spark: SparkSession): Unit = {
instance.getEngine match {
case TensorFlow.name =>
val tfWrapper = readTensorflowModel(path, spark, "_bert_tf", initAllTables = false)
instance.setModelIfNotSet(spark, Some(tfWrapper), None, None)
case ONNX.name =>
val onnxWrapper =
readOnnxModel(path, spark, "_bert_onnx", zipped = true, useBundle = false, None)
instance.setModelIfNotSet(spark, None, Some(onnxWrapper), None)
case Openvino.name =>
val openvinoWrapper = readOpenvinoModel(path, spark, "_bert_ov")
instance.setModelIfNotSet(spark, None, None, Some(openvinoWrapper))
case _ =>
throw new Exception(notSupportedEngineError)
}
}
addReader(readModel)
def loadSavedModel(
modelPath: String,
spark: SparkSession,
useOpenvino: Boolean = false): BertEmbeddings = {
val (localModelPath, detectedEngine) = modelSanityCheck(modelPath)
val vocabs = loadTextAsset(localModelPath, "vocab.txt").zipWithIndex.toMap
/*Universal parameters for all engines*/
val annotatorModel = new BertEmbeddings()
.setVocabulary(vocabs)
val modelEngine =
if (useOpenvino)
Openvino.name
else
detectedEngine
annotatorModel.set(annotatorModel.engine, modelEngine)
modelEngine match {
case Openvino.name =>
val ovWrapper: OpenvinoWrapper =
OpenvinoWrapper.read(
spark,
localModelPath,
zipped = false,
useBundle = true,
detectedEngine = detectedEngine)
annotatorModel
.setModelIfNotSet(spark, None, None, Some(ovWrapper))
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, None)
case ONNX.name =>
val onnxWrapper =
OnnxWrapper.read(spark, localModelPath, zipped = false, useBundle = true)
annotatorModel
.setModelIfNotSet(spark, None, Some(onnxWrapper), None)
case _ =>
throw new Exception(notSupportedEngineError)
}
annotatorModel
}
}
/** This is the companion object of [[BertEmbeddings]]. Please refer to that class for the
* documentation.
*/
object BertEmbeddings extends ReadablePretrainedBertModel with ReadBertDLModel {
private[BertEmbeddings] val logger: Logger = LoggerFactory.getLogger("BertEmbeddings")
}
