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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.Instructor
import com.johnsnowlabs.ml.onnx.{OnnxWrapper, ReadOnnxModel, WriteOnnxModel}
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.{ONNX, TensorFlow}
import com.johnsnowlabs.nlp._
import com.johnsnowlabs.nlp.serialization.MapFeature
import com.johnsnowlabs.storage.HasStorageRef
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.ml.param._
import org.apache.spark.ml.util.Identifiable
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.slf4j.{Logger, LoggerFactory}
/** Sentence embeddings using INSTRUCTOR.
*
* Instructor👨🏫, an instruction-finetuned text embedding model that can generate text
* embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation,
* etc.) and domains (e.g., science, finance, etc.) by simply providing the task instruction,
* without any finetuning. Instructor👨 achieves sota on 70 diverse embedding tasks!
*
* Pretrained models can be loaded with `pretrained` of the companion object:
* {{{
* val embeddings = InstructorEmbeddings.pretrained()
* .setInputCols("document")
* .setOutputCol("instructor_embeddings")
* }}}
* The default model is `"instructor_base"`, if no name is provided.
*
* For available pretrained models please see the
* [[https://sparknlp.org/models?q=Instructor Models Hub]].
*
* For extended examples of usage, see
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/InstructorEmbeddingsTestSpec.scala InstructorEmbeddingsTestSpec]].
*
* '''Sources''' :
*
* [[https://arxiv.org/abs/2212.09741 One Embedder, Any Task: Instruction-Finetuned Text Embeddings]]
*
* [[https://github.com/HKUNLP/instructor-embedding/ INSTRUCTOR Github Repository]]
*
* ''' Paper abstract '''
*
* ''We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions:
* every text input is embedded together with instructions explaining the use case (e.g., task
* and domain descriptions). Unlike encoders from prior work that are more specialized,
* INSTRUCTOR is a single embedder that can generate text embeddings tailored to different
* downstream tasks and domains, without any further training. We first annotate instructions for
* 330 diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive loss. We
* evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training),
* ranging from classification and information retrieval to semantic textual similarity and text
* generation evaluation. INSTRUCTOR, while having an order of magnitude fewer parameters than
* the previous best model, achieves state-of-the-art performance, with an average improvement of
* 3.4% compared to the previous best results on the 70 diverse datasets. Our analysis suggests
* that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning
* mitigates the challenge of training a single model on diverse datasets. Our model, code, and
* data are available at this https URL. [[https://instructor-embedding.github.io/]] ''
*
* ==Example==
* {{{
* import spark.implicits._
* import com.johnsnowlabs.nlp.base.DocumentAssembler
* import com.johnsnowlabs.nlp.annotators.Tokenizer
* import com.johnsnowlabs.nlp.embeddings.InstructorEmbeddings
* import com.johnsnowlabs.nlp.EmbeddingsFinisher
* import org.apache.spark.ml.Pipeline
*
* val documentAssembler = new DocumentAssembler()
* .setInputCol("text")
* .setOutputCol("document")
*
* val embeddings = InstructorEmbeddings.pretrained("instructor_base", "en")
* .setInputCols("document")
* .setInstruction("Represent the Medicine sentence for clustering: ")
* .setOutputCol("instructor_embeddings")
*
* val embeddingsFinisher = new EmbeddingsFinisher()
* .setInputCols("instructor_embeddings")
* .setOutputCols("finished_embeddings")
* .setOutputAsVector(true)
*
* val pipeline = new Pipeline().setStages(Array(
* documentAssembler,
* embeddings,
* embeddingsFinisher
* ))
*
* val data = Seq("Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity").toDF("text")
* val result = pipeline.fit(data).transform(data)
*
* result.selectExpr("explode(finished_embeddings) as result").show(1, 80)
* +--------------------------------------------------------------------------------+
* | result|
* +--------------------------------------------------------------------------------+
* |[-2.3497989177703857,0.480538547039032,-0.3238905668258667,-1.612930893898010...|
* +--------------------------------------------------------------------------------+
* }}}
*
* @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 InstructorEmbeddings(override val uid: String)
extends AnnotatorModel[InstructorEmbeddings]
with HasBatchedAnnotate[InstructorEmbeddings]
with WriteTensorflowModel
with WriteOnnxModel
with HasEmbeddingsProperties
with HasStorageRef
with WriteSentencePieceModel
with HasCaseSensitiveProperties
with HasEngine {
/** Annotator reference id. Used to identify elements in metadata or to refer to this annotator
* type
*/
override val inputAnnotatorTypes: Array[String] =
Array(AnnotatorType.DOCUMENT)
override val outputAnnotatorType: AnnotatorType = AnnotatorType.SENTENCE_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()")
/** Max sentence length to process (Default: `128`)
*
* @group param
*/
val maxSentenceLength =
new IntParam(this, "maxSentenceLength", "Max sentence length to process")
/** Set transformer instruction, e.g. 'summarize' format: `"instruction:"`.
*
* @group param
*/
val instruction =
new Param[String](this, "instruction", "Set transformer instruction, e.g. 'summarize'")
/** It contains TF model signatures for the laded saved model
*
* @group param
*/
val signatures =
new MapFeature[String, String](model = this, name = "signatures").setProtected()
private var _model: Option[Broadcast[Instructor]] = None
def this() = this(Identifiable.randomUID("INSTRUCTOR_EMBEDDINGS"))
/** @group setParam */
def setConfigProtoBytes(bytes: Array[Int]): InstructorEmbeddings.this.type =
set(this.configProtoBytes, bytes)
/** @group setParam */
def setMaxSentenceLength(value: Int): this.type = {
require(
value <= 512,
"Instructor 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)
def setInstruction(value: String): InstructorEmbeddings.this.type = {
set(instruction, value)
this
}
/** @group setParam */
def setSignatures(value: Map[String, String]): this.type = {
if (get(signatures).isEmpty)
set(signatures, value)
this
}
/** @group setParam */
def setModelIfNotSet(
spark: SparkSession,
tensorflowWrapper: Option[TensorflowWrapper],
onnxWrapper: Option[OnnxWrapper],
spp: SentencePieceWrapper): InstructorEmbeddings = {
if (_model.isEmpty) {
_model = Some(
spark.sparkContext.broadcast(
new Instructor(
tensorflowWrapper,
onnxWrapper,
spp = spp,
configProtoBytes = getConfigProtoBytes,
signatures = getSignatures)))
}
this
}
/** 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 = {
if (get(dimension).isEmpty)
set(this.dimension, value)
this
}
/** Whether to lowercase tokens or not
*
* @group setParam
*/
override def setCaseSensitive(value: Boolean): this.type = {
if (get(caseSensitive).isEmpty)
set(this.caseSensitive, value)
this
}
setDefault(
dimension -> 768,
batchSize -> 8,
maxSentenceLength -> 128,
caseSensitive -> false,
instruction -> "")
/** 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]] = {
val allAnnotations = batchedAnnotations
.filter(_.nonEmpty)
.zipWithIndex
.flatMap { case (annotations, i) =>
annotations.filter(_.result.nonEmpty).map(x => (x, i))
}
val processedAnnotations = if (allAnnotations.nonEmpty) {
this.getModelIfNotSet.predict(
sentences = allAnnotations.map(_._1),
batchSize = $(batchSize),
maxSentenceLength = $(maxSentenceLength),
instruction = $(instruction))
} else {
Seq()
}
// Group resulting annotations by rows. If there are not sentences in a given row, return empty sequence
batchedAnnotations.indices.map(rowIndex => {
val rowAnnotations = processedAnnotations
// zip each annotation with its corresponding row index
.zip(allAnnotations)
// select the sentences belonging to the current row
.filter(_._2._2 == rowIndex)
// leave the annotation only
.map(_._1)
if (rowAnnotations.nonEmpty)
rowAnnotations
else
Seq.empty[Annotation]
})
}
/** @group getParam */
def getModelIfNotSet: Instructor = _model.get.value
override def onWrite(path: String, spark: SparkSession): Unit = {
super.onWrite(path, spark)
getEngine match {
case TensorFlow.name =>
writeTensorflowModelV2(
path,
spark,
getModelIfNotSet.tensorflowWrapper.get,
"_instructor",
InstructorEmbeddings.tfFile,
configProtoBytes = getConfigProtoBytes,
savedSignatures = getSignatures)
case ONNX.name =>
writeOnnxModel(
path,
spark,
getModelIfNotSet.onnxWrapper.get,
"_instructor",
InstructorEmbeddings.onnxFile)
}
writeSentencePieceModel(
path,
spark,
getModelIfNotSet.spp,
"_instructor",
InstructorEmbeddings.sppFile)
}
/** @group getParam */
def getConfigProtoBytes: Option[Array[Byte]] = get(this.configProtoBytes).map(_.map(_.toByte))
/** @group getParam */
def getSignatures: Option[Map[String, String]] = get(this.signatures)
override protected def afterAnnotate(dataset: DataFrame): DataFrame = {
dataset.withColumn(
getOutputCol,
wrapSentenceEmbeddingsMetadata(
dataset.col(getOutputCol),
$(dimension),
Some($(storageRef))))
}
}
trait ReadablePretrainedInstructorModel
extends ParamsAndFeaturesReadable[InstructorEmbeddings]
with HasPretrained[InstructorEmbeddings] {
override val defaultModelName: Some[String] = Some("instructor_base")
/** Java compliant-overrides */
override def pretrained(): InstructorEmbeddings = super.pretrained()
override def pretrained(name: String): InstructorEmbeddings = super.pretrained(name)
override def pretrained(name: String, lang: String): InstructorEmbeddings =
super.pretrained(name, lang)
override def pretrained(name: String, lang: String, remoteLoc: String): InstructorEmbeddings =
super.pretrained(name, lang, remoteLoc)
}
trait ReadInstructorDLModel
extends ReadTensorflowModel
with ReadSentencePieceModel
with ReadOnnxModel {
this: ParamsAndFeaturesReadable[InstructorEmbeddings] =>
override val tfFile: String = "instructor_tensorflow"
override val sppFile: String = "instructor_spp"
override val onnxFile: String = "instructor_onnx"
def readModel(instance: InstructorEmbeddings, path: String, spark: SparkSession): Unit = {
val spp = readSentencePieceModel(path, spark, "_instructor_spp", sppFile)
instance.getEngine match {
case TensorFlow.name =>
val tf = readTensorflowModel(
path,
spark,
"_instructor_tf",
savedSignatures = instance.getSignatures,
initAllTables = false)
instance.setModelIfNotSet(spark, Some(tf), None, spp)
case ONNX.name =>
val onnxWrapper =
readOnnxModel(path, spark, "_instructor_onnx", zipped = true, useBundle = false, None)
instance.setModelIfNotSet(spark, None, Some(onnxWrapper), spp)
}
}
addReader(readModel)
def loadSavedModel(modelPath: String, spark: SparkSession): InstructorEmbeddings = {
val (localModelPath, detectedEngine) = modelSanityCheck(modelPath)
/*Universal parameters for all engines*/
val annotatorModel = new InstructorEmbeddings()
annotatorModel.set(annotatorModel.engine, detectedEngine)
val spModel = loadSentencePieceAsset(localModelPath, "spiece.model")
detectedEngine match {
case TensorFlow.name =>
val (tfwrapper, signatures) = TensorflowWrapper.read(
localModelPath,
zipped = false,
useBundle = true,
tags = Array("serve"),
initAllTables = false)
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, spModel)
case ONNX.name =>
val onnxWrapper =
OnnxWrapper.read(spark, localModelPath, zipped = false, useBundle = true)
annotatorModel
.setModelIfNotSet(spark, None, Some(onnxWrapper), spModel)
case _ =>
throw new Exception(notSupportedEngineError)
}
annotatorModel
}
}
/** This is the companion object of [[InstructorEmbeddings]]. Please refer to that class for the
* documentation.
*/
object InstructorEmbeddings
extends ReadablePretrainedInstructorModel
with ReadInstructorDLModel
with ReadSentencePieceModel {
private[InstructorEmbeddings] val logger: Logger =
LoggerFactory.getLogger("InstructorEmbeddings")
}
