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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.Xlnet
import com.johnsnowlabs.ml.tensorflow._
import com.johnsnowlabs.ml.tensorflow.sentencepiece.{
ReadSentencePieceModel,
SentencePieceWrapper,
WriteSentencePieceModel
}
import com.johnsnowlabs.ml.util.LoadExternalModel.{
loadSentencePieceAsset,
modelSanityCheck,
notSupportedEngineError
}
import com.johnsnowlabs.ml.util.{ModelEngine, TensorFlow}
import com.johnsnowlabs.nlp._
import com.johnsnowlabs.nlp.annotators.common._
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}
/** XlnetEmbeddings (XLNet): Generalized Autoregressive Pretraining for Language Understanding
*
* XLNet is a new unsupervised language representation learning method based on a novel
* generalized permutation language modeling objective. Additionally, XLNet employs
* Transformer-XL as the backbone model, exhibiting excellent performance for language tasks
* involving long context. Overall, XLNet achieves state-of-the-art (SOTA) results on various
* downstream language tasks including question answering, natural language inference, sentiment
* analysis, and document ranking.
*
* These word embeddings represent the outputs generated by the XLNet models.
*
* Note that this is a very computationally expensive module compared to word embedding modules
* that only perform embedding lookups. The use of an accelerator is recommended.
*
* `"xlnet_large_cased"` =
* [[https://storage.googleapis.com/xlnet/released_models/cased_L-24_H-1024_A-16.zip XLNet-Large]]
* \| 24-layer, 1024-hidden, 16-heads
*
* `"xlnet_base_cased"` =
* [[https://storage.googleapis.com/xlnet/released_models/cased_L-12_H-768_A-12.zip XLNet-Base]]
* \| 12-layer, 768-hidden, 12-heads. This model is trained on full data (different from the one
* in the paper).
*
* Pretrained models can be loaded with `pretrained` of the companion object:
* {{{
* val embeddings = XlnetEmbeddings.pretrained()
* .setInputCols("sentence", "token")
* .setOutputCol("embeddings")
* }}}
* The default model is `"xlnet_base_cased"`, if no name is provided.
*
* For extended examples of usage, see the
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/english/dl-ner/ner_xlnet.ipynb Examples]]
* and the
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/XlnetEmbeddingsTestSpec.scala XlnetEmbeddingsTestSpec]].
* 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/1906.08237 XLNet: Generalized Autoregressive Pretraining for Language Understanding]]
*
* [[https://github.com/zihangdai/xlnet]]
*
* '''Paper abstract: '''
*
* ''With the capability of modeling bidirectional contexts, denoising autoencoding based
* pretraining like BERT achieves better performance than pretraining approaches based on
* autoregressive language modeling. However, relying on corrupting the input with masks, BERT
* neglects dependency between the masked positions and suffers from a pretrain-finetune
* discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive
* pretraining method that (1) enables learning bidirectional contexts by maximizing the expected
* likelihood over all permutations of the factorization order and (2) overcomes the limitations
* of BERT thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from
* Transformer-XL, the state-of-the-art autoregressive model, into pretraining. Empirically,
* under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large
* margin, including question answering, natural language inference, sentiment analysis, and
* document ranking.''
*
* ==Example==
* {{{
* import spark.implicits._
* import com.johnsnowlabs.nlp.base.DocumentAssembler
* import com.johnsnowlabs.nlp.annotators.Tokenizer
* import com.johnsnowlabs.nlp.embeddings.XlnetEmbeddings
* 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 = XlnetEmbeddings.pretrained()
* .setInputCols("token", "document")
* .setOutputCol("embeddings")
*
* 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|
* +--------------------------------------------------------------------------------+
* |[-0.6287205219268799,-0.4865287244319916,-0.186111718416214,0.234187275171279...|
* |[-1.1967450380325317,0.2746637463569641,0.9481253027915955,0.3431355059146881...|
* |[-1.0777631998062134,-2.092679977416992,-1.5331977605819702,-1.11190271377563...|
* |[-0.8349916934967041,-0.45627787709236145,-0.7890847325325012,-1.028069257736...|
* |[-0.134845569729805,-0.11672890186309814,0.4945235550403595,-0.66587203741073...|
* +--------------------------------------------------------------------------------+
* }}}
*
* @see
* [[com.johnsnowlabs.nlp.annotators.classifier.dl.XlnetForTokenClassification XlnetForTokenClassification]]
* For Xlnet embeddings 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 internal uid for saving annotator
* @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 XlnetEmbeddings(override val uid: String)
extends AnnotatorModel[XlnetEmbeddings]
with HasBatchedAnnotate[XlnetEmbeddings]
with WriteTensorflowModel
with WriteSentencePieceModel
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("XLNET_EMBEDDINGS"))
/** Input Annotator Type : TOKEN, DOCUMENT
*
* @group anno
*/
override val inputAnnotatorTypes: Array[String] =
Array(AnnotatorType.DOCUMENT, AnnotatorType.TOKEN)
/** Output Annotator Type : WORD_EMBEDDINGS
*
* @group anno
*/
override val outputAnnotatorType: AnnotatorType = AnnotatorType.WORD_EMBEDDINGS
/** 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 getSaram */
def setConfigProtoBytes(bytes: Array[Int]): XlnetEmbeddings.this.type =
set(this.configProtoBytes, bytes)
/** @group setGaram */
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,
"XLNet model does 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)
/** Set dimension of Embeddings Since output shape depends on the model selected, see
* [[https://github.com/zihangdai/xlnet]]for further reference
*
* @group setParam
*/
override def setDimension(value: Int): this.type = {
set(this.dimension, value)
}
/** 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)
/** The Tensorflow XLNet Model */
private var _model: Option[Broadcast[Xlnet]] = None
/** Sets XLNet tensorflow Model */
def setModelIfNotSet(
spark: SparkSession,
tensorflow: TensorflowWrapper,
spp: SentencePieceWrapper): this.type = {
if (_model.isEmpty) {
_model = Some(
spark.sparkContext.broadcast(
new Xlnet(
tensorflow,
spp,
configProtoBytes = getConfigProtoBytes,
signatures = getSignatures)))
}
this
}
/** Gets XLNet tensorflow Model */
def getModelIfNotSet: Xlnet = _model.get.value
setDefault(batchSize -> 8, dimension -> 768, maxSentenceLength -> 128, caseSensitive -> true)
/** 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))
}
/*Return empty if the real tokens are empty*/
val sentenceWordEmbeddings = getModelIfNotSet.predict(
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 def onWrite(path: String, spark: SparkSession): Unit = {
super.onWrite(path, spark)
writeTensorflowModelV2(
path,
spark,
getModelIfNotSet.tensorflowWrapper,
"_xlnet",
XlnetEmbeddings.tfFile,
configProtoBytes = getConfigProtoBytes)
writeSentencePieceModel(path, spark, getModelIfNotSet.spp, "_xlnet", XlnetEmbeddings.sppFile)
}
override protected def afterAnnotate(dataset: DataFrame): DataFrame = {
dataset.withColumn(
getOutputCol,
wrapEmbeddingsMetadata(dataset.col(getOutputCol), $(dimension), Some($(storageRef))))
}
}
trait ReadablePretrainedXlnetModel
extends ParamsAndFeaturesReadable[XlnetEmbeddings]
with HasPretrained[XlnetEmbeddings] {
override val defaultModelName: Some[String] = Some("xlnet_base_cased")
/** Java compliant-overrides */
override def pretrained(): XlnetEmbeddings = super.pretrained()
override def pretrained(name: String): XlnetEmbeddings = super.pretrained(name)
override def pretrained(name: String, lang: String): XlnetEmbeddings =
super.pretrained(name, lang)
override def pretrained(name: String, lang: String, remoteLoc: String): XlnetEmbeddings =
super.pretrained(name, lang, remoteLoc)
}
trait ReadXlnetDLModel extends ReadTensorflowModel with ReadSentencePieceModel {
this: ParamsAndFeaturesReadable[XlnetEmbeddings] =>
override val tfFile: String = "xlnet_tensorflow"
override val sppFile: String = "xlnet_spp"
def readModel(instance: XlnetEmbeddings, path: String, spark: SparkSession): Unit = {
val tf = readTensorflowModel(path, spark, "_xlnet_tf", initAllTables = false)
val spp = readSentencePieceModel(path, spark, "_xlnet_spp", sppFile)
instance.setModelIfNotSet(spark, tf, spp)
}
addReader(readModel)
def loadSavedModel(modelPath: String, spark: SparkSession): XlnetEmbeddings = {
val (localModelPath, detectedEngine) = modelSanityCheck(modelPath)
val spModel = loadSentencePieceAsset(localModelPath, "spiece.model")
/*Universal parameters for all engines*/
val annotatorModel = new XlnetEmbeddings()
annotatorModel.set(annotatorModel.engine, detectedEngine)
detectedEngine match {
case TensorFlow.name =>
val (wrapper, 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, wrapper, spModel)
case _ =>
throw new Exception(notSupportedEngineError)
}
annotatorModel
}
}
/** This is the companion object of [[XlnetEmbeddings]]. Please refer to that class for the
* documentation.
*/
object XlnetEmbeddings extends ReadablePretrainedXlnetModel with ReadXlnetDLModel
