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com.johnsnowlabs.ml.ai.DeBertaClassification.scala Maven / Gradle / Ivy
/*
* 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.ml.ai
import ai.onnxruntime.OnnxTensor
import com.johnsnowlabs.ml.onnx.{OnnxSession, OnnxWrapper}
import com.johnsnowlabs.ml.tensorflow.sentencepiece.{SentencePieceWrapper, SentencepieceEncoder}
import com.johnsnowlabs.ml.tensorflow.sign.{ModelSignatureConstants, ModelSignatureManager}
import com.johnsnowlabs.ml.tensorflow.{TensorResources, TensorflowWrapper}
import com.johnsnowlabs.ml.util.{ONNX, TensorFlow}
import com.johnsnowlabs.nlp.annotators.common._
import com.johnsnowlabs.nlp.annotators.tokenizer.wordpiece.BasicTokenizer
import com.johnsnowlabs.nlp.{ActivationFunction, Annotation}
import org.tensorflow.ndarray.buffer
import org.tensorflow.ndarray.buffer.{IntDataBuffer, LongDataBuffer}
import org.slf4j.{Logger, LoggerFactory}
import scala.collection.JavaConverters._
/** @param tensorflowWrapper
* DeBERTa Model v2 & v3 wrapper with TensorFlow Wrapper
* @param spp
* DeBERTa SentencePiece model with SentencePieceWrapper
* @param configProtoBytes
* Configuration for TensorFlow session
* @param tags
* labels which model was trained with in order
* @param signatures
* TF v2 signatures in Spark NLP
*/
private[johnsnowlabs] class DeBertaClassification(
val tensorflowWrapper: Option[TensorflowWrapper],
val onnxWrapper: Option[OnnxWrapper],
val spp: SentencePieceWrapper,
configProtoBytes: Option[Array[Byte]] = None,
tags: Map[String, Int],
signatures: Option[Map[String, String]] = None,
threshold: Float = 0.5f)
extends Serializable
with XXXForClassification {
protected val logger: Logger = LoggerFactory.getLogger("DeBertaClassification")
val _tfDeBertaSignatures: Map[String, String] =
signatures.getOrElse(ModelSignatureManager.apply())
val detectedEngine: String =
if (tensorflowWrapper.isDefined) TensorFlow.name
else if (onnxWrapper.isDefined) ONNX.name
else TensorFlow.name
private val onnxSessionOptions: Map[String, String] = new OnnxSession().getSessionOptions
// keys representing the input and output tensors of the DeBERTa model
protected val sentencePadTokenId: Int = spp.getSppModel.pieceToId("[PAD]")
protected val sentenceStartTokenId: Int = spp.getSppModel.pieceToId("[CLS]")
protected val sentenceEndTokenId: Int = spp.getSppModel.pieceToId("[SEP]")
private val sentencePieceDelimiterId: Int = spp.getSppModel.pieceToId("▁")
protected val sigmoidThreshold: Float = threshold
def tokenizeWithAlignment(
sentences: Seq[TokenizedSentence],
maxSeqLength: Int,
caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] = {
val encoder =
new SentencepieceEncoder(spp, caseSensitive, sentencePieceDelimiterId, pieceIdOffset = 1)
val sentenceTokenPieces = sentences.map { s =>
val trimmedSentence = s.indexedTokens.take(maxSeqLength - 2)
val wordpieceTokens =
trimmedSentence.flatMap(token => encoder.encode(token)).take(maxSeqLength)
WordpieceTokenizedSentence(wordpieceTokens)
}
sentenceTokenPieces
}
def tokenizeSeqString(
candidateLabels: Seq[String],
maxSeqLength: Int,
caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] = {
val basicTokenizer = new BasicTokenizer(caseSensitive)
val encoder =
new SentencepieceEncoder(spp, caseSensitive, sentencePieceDelimiterId, pieceIdOffset = 1)
val labelsToSentences = candidateLabels.map { s => Sentence(s, 0, s.length - 1, 0) }
labelsToSentences.map(label => {
val tokens = basicTokenizer.tokenize(label)
val wordpieceTokens = tokens.flatMap(token => encoder.encode(token)).take(maxSeqLength)
WordpieceTokenizedSentence(wordpieceTokens)
})
}
def tokenizeDocument(
docs: Seq[Annotation],
maxSeqLength: Int,
caseSensitive: Boolean): Seq[WordpieceTokenizedSentence] = {
val encoder =
new SentencepieceEncoder(spp, caseSensitive, sentencePieceDelimiterId, pieceIdOffset = 0)
val sentences = docs.map { s => Sentence(s.result, s.begin, s.end, 0) }
val sentenceTokenPieces = sentences.map { s =>
val wordpieceTokens = encoder.encodeSentence(s, maxLength = maxSeqLength).take(maxSeqLength)
WordpieceTokenizedSentence(wordpieceTokens)
}
sentenceTokenPieces
}
def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]] = {
val maxSentenceLength = batch.map(encodedSentence => encodedSentence.length).max
val batchLength = batch.length
val rawScores = detectedEngine match {
case ONNX.name => getRawScoresWithOnnx(batch)
case _ => getRawScoresWithTF(batch)
}
val dim = rawScores.length / (batchLength * maxSentenceLength)
val batchScores: Array[Array[Array[Float]]] = rawScores
.grouped(dim)
.map(scores => calculateSoftmax(scores))
.toArray
.grouped(maxSentenceLength)
.toArray
batchScores
}
private def getRawScoresWithTF(batch: Seq[Array[Int]]): Array[Float] = {
val tensors = new TensorResources()
val maxSentenceLength = batch.map(encodedSentence => encodedSentence.length).max
val batchLength = batch.length
val tokenBuffers: IntDataBuffer = tensors.createIntBuffer(batchLength * maxSentenceLength)
val maskBuffers: IntDataBuffer = tensors.createIntBuffer(batchLength * maxSentenceLength)
val segmentBuffers: IntDataBuffer = tensors.createIntBuffer(batchLength * maxSentenceLength)
// [nb of encoded sentences , maxSentenceLength]
val shape = Array(batch.length.toLong, maxSentenceLength)
batch.zipWithIndex
.foreach { case (sentence, idx) =>
val offset = idx * maxSentenceLength
tokenBuffers.offset(offset).write(sentence)
maskBuffers
.offset(offset)
.write(sentence.map(x => if (x == sentencePadTokenId) 0 else 1))
segmentBuffers.offset(offset).write(Array.fill(maxSentenceLength)(0))
}
val runner = tensorflowWrapper.get
.getTFSessionWithSignature(configProtoBytes = configProtoBytes, initAllTables = false)
.runner
val tokenTensors = tensors.createIntBufferTensor(shape, tokenBuffers)
val maskTensors = tensors.createIntBufferTensor(shape, maskBuffers)
val segmentTensors = tensors.createIntBufferTensor(shape, segmentBuffers)
runner
.feed(
_tfDeBertaSignatures.getOrElse(
ModelSignatureConstants.InputIds.key,
"missing_input_id_key"),
tokenTensors)
.feed(
_tfDeBertaSignatures
.getOrElse(ModelSignatureConstants.AttentionMask.key, "missing_input_mask_key"),
maskTensors)
.feed(
_tfDeBertaSignatures
.getOrElse(ModelSignatureConstants.TokenTypeIds.key, "missing_segment_ids_key"),
segmentTensors)
.fetch(_tfDeBertaSignatures
.getOrElse(ModelSignatureConstants.LogitsOutput.key, "missing_logits_key"))
val outs = runner.run().asScala
val rawScores = TensorResources.extractFloats(outs.head)
outs.foreach(_.close())
tensors.clearSession(outs)
tensors.clearTensors()
rawScores
}
private def getRawScoresWithOnnx(batch: Seq[Array[Int]]): Array[Float] = {
// [nb of encoded sentences , maxSentenceLength]
val (runner, env) = onnxWrapper.get.getSession(onnxSessionOptions)
val tokenTensors =
OnnxTensor.createTensor(env, batch.map(x => x.map(x => x.toLong)).toArray)
val maskTensors =
OnnxTensor.createTensor(
env,
batch.map(sentence => sentence.map(x => if (x == 0L) 0L else 1L)).toArray)
val inputs =
Map("input_ids" -> tokenTensors, "attention_mask" -> maskTensors).asJava
try {
val results = runner.run(inputs)
try {
val embeddings = results
.get("logits")
.get()
.asInstanceOf[OnnxTensor]
.getFloatBuffer
.array()
embeddings
} finally if (results != null) results.close()
} catch {
case e: Exception =>
// Handle exceptions by logging or other means.
e.printStackTrace()
Array.empty[Float] // Return an empty array or appropriate error handling
} finally {
// Close tensors outside the try-catch to avoid repeated null checks.
// These resources are initialized before the try-catch, so they should be closed here.
tokenTensors.close()
maskTensors.close()
}
}
def tagSequence(batch: Seq[Array[Int]], activation: String): Array[Array[Float]] = {
val batchLength = batch.length
val rawScores = detectedEngine match {
case ONNX.name => getRawScoresWithOnnx(batch)
case _ => getRawScoresWithTF(batch)
}
val dim = rawScores.length / batchLength
val batchScores: Array[Array[Float]] =
rawScores
.grouped(dim)
.map(scores =>
activation match {
case ActivationFunction.softmax => calculateSoftmax(scores)
case ActivationFunction.sigmoid => calculateSigmoid(scores)
case _ => calculateSoftmax(scores)
})
.toArray
batchScores
}
private def padArrayWithZeros(arr: Array[Int], maxLength: Int): Array[Int] = {
if (arr.length >= maxLength) {
arr
} else {
arr ++ Array.fill(maxLength - arr.length)(0)
}
}
def tagZeroShotSequence(
batch: Seq[Array[Int]],
entailmentId: Int,
contradictionId: Int,
activation: String): Array[Array[Float]] = {
val maxSentenceLength = batch.map(encodedSentence => encodedSentence.length).max
val paddedBatch = batch.map(arr => padArrayWithZeros(arr, maxSentenceLength))
val batchLength = paddedBatch.length
val rawScores = detectedEngine match {
case ONNX.name => computeZeroShotLogitsWithONNX(paddedBatch, maxSentenceLength)
case _ => computeZeroShotLogitsWithTF(paddedBatch, maxSentenceLength)
}
val dim = rawScores.length / batchLength
rawScores
.grouped(dim)
.toArray
}
def computeZeroShotLogitsWithONNX(
batch: Seq[Array[Int]],
maxSentenceLength: Int): Array[Float] = {
val (runner, env) = onnxWrapper.get.getSession(onnxSessionOptions)
val tokenTensors =
OnnxTensor.createTensor(env, batch.map(x => x.map(x => x.toLong)).toArray)
val maskTensors =
OnnxTensor.createTensor(
env,
batch.map(sentence => sentence.map(x => if (x == 0L) 0L else 1L)).toArray)
val inputs =
Map("input_ids" -> tokenTensors, "attention_mask" -> maskTensors).asJava
try {
val results = runner.run(inputs)
try {
val embeddings = results
.get("logits")
.get()
.asInstanceOf[OnnxTensor]
.getFloatBuffer
.array()
tokenTensors.close()
maskTensors.close()
embeddings
} finally if (results != null) results.close()
}
}
def computeZeroShotLogitsWithTF(
batch: Seq[Array[Int]],
maxSentenceLength: Int): Array[Float] = {
val tensors = new TensorResources()
val batchLength = batch.length
val tokenBuffers: IntDataBuffer = tensors.createIntBuffer(batchLength * maxSentenceLength)
val maskBuffers: IntDataBuffer = tensors.createIntBuffer(batchLength * maxSentenceLength)
val segmentBuffers: IntDataBuffer = tensors.createIntBuffer(batchLength * maxSentenceLength)
// [nb of encoded sentences , maxSentenceLength]
val shape = Array(batch.length.toLong, maxSentenceLength)
batch.zipWithIndex
.foreach { case (sentence, idx) =>
val offset = idx * maxSentenceLength
tokenBuffers.offset(offset).write(sentence)
maskBuffers.offset(offset).write(sentence.map(x => if (x == 0) 0 else 1))
val sentenceEndTokenIndex = sentence.indexOf(sentenceEndTokenId)
segmentBuffers
.offset(offset)
.write(
sentence.indices
.map(i =>
if (i < sentenceEndTokenIndex) 0
else if (i == sentenceEndTokenIndex) 1
else 1)
.toArray)
}
val session = tensorflowWrapper.get.getTFSessionWithSignature(
configProtoBytes = configProtoBytes,
savedSignatures = signatures,
initAllTables = false)
val runner = session.runner
val tokenTensors = tensors.createIntBufferTensor(shape, tokenBuffers)
val maskTensors = tensors.createIntBufferTensor(shape, maskBuffers)
val segmentTensors = tensors.createIntBufferTensor(shape, segmentBuffers)
runner
.feed(
_tfDeBertaSignatures.getOrElse(
ModelSignatureConstants.InputIds.key,
"missing_input_id_key"),
tokenTensors)
.feed(
_tfDeBertaSignatures
.getOrElse(ModelSignatureConstants.AttentionMask.key, "missing_input_mask_key"),
maskTensors)
.feed(
_tfDeBertaSignatures
.getOrElse(ModelSignatureConstants.TokenTypeIds.key, "missing_segment_ids_key"),
segmentTensors)
.fetch(_tfDeBertaSignatures
.getOrElse(ModelSignatureConstants.LogitsOutput.key, "missing_logits_key"))
val outs = runner.run().asScala
val rawScores = TensorResources.extractFloats(outs.head)
outs.foreach(_.close())
tensors.clearSession(outs)
tensors.clearTensors()
rawScores
}
def tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]]) = {
val batchLength = batch.length
val (startLogits, endLogits) = detectedEngine match {
case ONNX.name => computeLogitsWithOnnx(batch)
case _ => computeLogitsWithTF(batch)
}
val endDim = endLogits.length / batchLength
val endScores: Array[Array[Float]] =
endLogits.grouped(endDim).map(scores => calculateSoftmax(scores)).toArray
val startDim = startLogits.length / batchLength
val startScores: Array[Array[Float]] =
startLogits.grouped(startDim).map(scores => calculateSoftmax(scores)).toArray
(startScores, endScores)
}
private def computeLogitsWithTF(batch: Seq[Array[Int]]): (Array[Float], Array[Float]) = {
val tensors = new TensorResources()
val maxSentenceLength = batch.map(encodedSentence => encodedSentence.length).max
val batchLength = batch.length
val tokenBuffers: IntDataBuffer = tensors.createIntBuffer(batchLength * maxSentenceLength)
val maskBuffers: IntDataBuffer = tensors.createIntBuffer(batchLength * maxSentenceLength)
// [nb of encoded sentences , maxSentenceLength]
val shape = Array(batch.length.toLong, maxSentenceLength)
batch.zipWithIndex
.foreach { case (sentence, idx) =>
val offset = idx * maxSentenceLength
tokenBuffers.offset(offset).write(sentence)
maskBuffers
.offset(offset)
.write(sentence.map(x => if (x == sentencePadTokenId) 0 else 1))
}
val runner = tensorflowWrapper.get
.getTFSessionWithSignature(configProtoBytes = configProtoBytes, initAllTables = false)
.runner
val tokenTensors = tensors.createIntBufferTensor(shape, tokenBuffers)
val maskTensors = tensors.createIntBufferTensor(shape, maskBuffers)
runner
.feed(
_tfDeBertaSignatures
.getOrElse(ModelSignatureConstants.InputIds.key, "missing_input_id_key"),
tokenTensors)
.feed(
_tfDeBertaSignatures
.getOrElse(ModelSignatureConstants.AttentionMask.key, "missing_input_mask_key"),
maskTensors)
.fetch(_tfDeBertaSignatures
.getOrElse(ModelSignatureConstants.EndLogitsOutput.key, "missing_end_logits_key"))
.fetch(_tfDeBertaSignatures
.getOrElse(ModelSignatureConstants.StartLogitsOutput.key, "missing_start_logits_key"))
val outs = runner.run().asScala
val endLogits = TensorResources.extractFloats(outs.head)
val startLogits = TensorResources.extractFloats(outs.last)
outs.foreach(_.close())
tensors.clearSession(outs)
tensors.clearTensors()
(startLogits, endLogits)
}
private def computeLogitsWithOnnx(batch: Seq[Array[Int]]): (Array[Float], Array[Float]) = {
// [nb of encoded sentences]
val (runner, env) = onnxWrapper.get.getSession(onnxSessionOptions)
val tokenTensors =
OnnxTensor.createTensor(env, batch.map(x => x.map(x => x.toLong)).toArray)
val maskTensors =
OnnxTensor.createTensor(
env,
batch.map(sentence => sentence.map(x => if (x == 0L) 0L else 1L)).toArray)
val inputs =
Map("input_ids" -> tokenTensors, "attention_mask" -> maskTensors).asJava
try {
val output = runner.run(inputs)
try {
val startLogits = output
.get("start_logits")
.get()
.asInstanceOf[OnnxTensor]
.getFloatBuffer
.array()
val endLogits = output
.get("end_logits")
.get()
.asInstanceOf[OnnxTensor]
.getFloatBuffer
.array()
tokenTensors.close()
maskTensors.close()
(startLogits, endLogits)
} finally if (output != null) output.close()
} catch {
case e: Exception =>
// Log the exception as a warning
logger.warn("Exception: ", e)
// Rethrow the exception to propagate it further
throw e
}
}
def findIndexedToken(
tokenizedSentences: Seq[TokenizedSentence],
sentence: (WordpieceTokenizedSentence, Int),
tokenPiece: TokenPiece): Option[IndexedToken] = {
tokenizedSentences(sentence._2).indexedTokens.find(p =>
p.begin == tokenPiece.begin && tokenPiece.isWordStart)
}
}
