com.johnsnowlabs.ml.ai.Bert.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.ai.util.PrepareEmbeddings
import com.johnsnowlabs.ml.onnx.{OnnxSession, OnnxWrapper}
import com.johnsnowlabs.ml.openvino.OpenvinoWrapper
import com.johnsnowlabs.ml.tensorflow.sign.{ModelSignatureConstants, ModelSignatureManager}
import com.johnsnowlabs.ml.tensorflow.{TensorResources, TensorflowWrapper}
import com.johnsnowlabs.ml.util.{ModelArch, ONNX, Openvino, TensorFlow}
import com.johnsnowlabs.nlp.annotators.common._
import com.johnsnowlabs.nlp.{Annotation, AnnotatorType}
import org.intel.openvino.Tensor
import org.slf4j.{Logger, LoggerFactory}
import scala.collection.JavaConverters._
/** 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
*
* See
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/BertEmbeddingsTestSpec.scala]]
* for further reference on how to use this API. Sources:
*
* @param tensorflowWrapper
* Bert Model wrapper with TensorFlow Wrapper
* @param onnxWrapper
* Bert Model wrapper with ONNX Wrapper
* @param openvinoWrapper
* Bert Model wrapper with OpenVINO Wrapper
* @param sentenceStartTokenId
* Id of sentence start Token
* @param sentenceEndTokenId
* Id of sentence end Token.
* @param configProtoBytes
* Configuration for TensorFlow session
*
* Paper: [[https://arxiv.org/abs/1810.04805]]
*
* Source: [[https://github.com/google-research/bert]]
*/
private[johnsnowlabs] class Bert(
val tensorflowWrapper: Option[TensorflowWrapper],
val onnxWrapper: Option[OnnxWrapper],
val openvinoWrapper: Option[OpenvinoWrapper],
sentenceStartTokenId: Int,
sentenceEndTokenId: Int,
configProtoBytes: Option[Array[Byte]] = None,
signatures: Option[Map[String, String]] = None,
modelArch: String = ModelArch.wordEmbeddings,
isSBert: Boolean = false)
extends Serializable {
protected val logger: Logger = LoggerFactory.getLogger("Bert")
val _tfBertSignatures: Map[String, String] = signatures.getOrElse(ModelSignatureManager.apply())
val detectedEngine: String =
if (tensorflowWrapper.isDefined) TensorFlow.name
else if (onnxWrapper.isDefined) ONNX.name
else if (openvinoWrapper.isDefined) Openvino.name
else TensorFlow.name
private val onnxSessionOptions: Map[String, String] = new OnnxSession().getSessionOptions
private def sessionWarmup(): Unit = {
val dummyInput =
Array(101, 2292, 1005, 1055, 4010, 6279, 1996, 5219, 2005, 1996, 2034, 28937, 1012, 102)
if (modelArch == ModelArch.wordEmbeddings) {
tag(Seq(dummyInput))
} else if (modelArch == ModelArch.sentenceEmbeddings) {
if (isSBert)
tagSequenceSBert(Seq(dummyInput))
else
tagSequence(Seq(dummyInput))
}
}
sessionWarmup()
def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]] = {
val maxSentenceLength = batch.map(pieceIds => pieceIds.length).max
val batchLength = batch.length
val embeddings = detectedEngine match {
case ONNX.name =>
// [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 segmentTensors =
OnnxTensor.createTensor(env, batch.map(x => Array.fill(maxSentenceLength)(0L)).toArray)
val inputs =
Map(
"input_ids" -> tokenTensors,
"attention_mask" -> maskTensors,
"token_type_ids" -> segmentTensors).asJava
// TODO: A try without a catch or finally is equivalent to putting its body in a block; no exceptions are handled.
try {
val results = runner.run(inputs)
try {
val embeddings = results
.get("last_hidden_state")
.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()
segmentTensors.close()
}
case Openvino.name =>
val shape = Array(batchLength, maxSentenceLength)
val (tokenTensors, maskTensors) =
PrepareEmbeddings.prepareOvLongBatchTensors(batch, maxSentenceLength, batchLength)
val segmentTensors = new Tensor(shape, Array.fill(batchLength * maxSentenceLength)(0L))
val inferRequest = openvinoWrapper.get.getCompiledModel().create_infer_request()
inferRequest.set_tensor("input_ids", tokenTensors)
inferRequest.set_tensor("attention_mask", maskTensors)
inferRequest.set_tensor("token_type_ids", segmentTensors)
inferRequest.infer()
val result = inferRequest.get_tensor("last_hidden_state")
val embeddings = result.data()
embeddings
case _ =>
val tensors = new TensorResources()
val (tokenTensors, maskTensors, segmentTensors) =
PrepareEmbeddings.prepareBatchTensorsWithSegment(
tensors,
batch,
maxSentenceLength,
batchLength)
val runner = tensorflowWrapper.get
.getTFSessionWithSignature(
configProtoBytes = configProtoBytes,
savedSignatures = signatures,
initAllTables = false)
.runner
runner
.feed(
_tfBertSignatures.getOrElse(
ModelSignatureConstants.InputIdsV1.key,
"missing_input_id_key"),
tokenTensors)
.feed(
_tfBertSignatures
.getOrElse(ModelSignatureConstants.AttentionMaskV1.key, "missing_input_mask_key"),
maskTensors)
.feed(
_tfBertSignatures
.getOrElse(ModelSignatureConstants.TokenTypeIdsV1.key, "missing_segment_ids_key"),
segmentTensors)
.fetch(
_tfBertSignatures
.getOrElse(
ModelSignatureConstants.LastHiddenStateV1.key,
"missing_sequence_output_key"))
val outs = runner.run().asScala
val embeddings = TensorResources.extractFloats(outs.head)
tokenTensors.close()
maskTensors.close()
segmentTensors.close()
tensors.clearSession(outs)
tensors.clearTensors()
embeddings
}
PrepareEmbeddings.prepareBatchWordEmbeddings(
batch,
embeddings,
maxSentenceLength,
batchLength)
}
def tagSequence(batch: Seq[Array[Int]]): Array[Array[Float]] = {
val maxSentenceLength = batch.map(pieceIds => pieceIds.length).max
val batchLength = batch.length
val embeddings = detectedEngine match {
case ONNX.name =>
// [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 segmentTensors =
OnnxTensor.createTensor(env, batch.map(x => Array.fill(maxSentenceLength)(0L)).toArray)
val inputs =
Map(
"input_ids" -> tokenTensors,
"attention_mask" -> maskTensors,
"token_type_ids" -> segmentTensors).asJava
try {
val results = runner.run(inputs)
try {
val embeddings = results
.get("last_hidden_state")
.get()
.asInstanceOf[OnnxTensor]
.getFloatBuffer
.array()
tokenTensors.close()
maskTensors.close()
segmentTensors.close()
// runner.close()
// env.close()
//
embeddings
} finally if (results != null) results.close()
} catch {
case e: Exception =>
// Log the exception as a warning
logger.warn("Exception: ", e)
// Rethrow the exception to propagate it further
throw e
}
case Openvino.name =>
val shape = Array(batchLength, maxSentenceLength)
val (tokenTensors, maskTensors) =
PrepareEmbeddings.prepareOvLongBatchTensors(batch, maxSentenceLength, batchLength)
val segmentTensors = new Tensor(shape, Array.fill(batchLength * maxSentenceLength)(0L))
val inferRequest = openvinoWrapper.get.getCompiledModel().create_infer_request()
inferRequest.set_tensor("input_ids", tokenTensors)
inferRequest.set_tensor("attention_mask", maskTensors)
inferRequest.set_tensor("token_type_ids", segmentTensors)
inferRequest.infer()
val result = inferRequest.get_tensor("last_hidden_state")
val embeddings = result.data()
embeddings
case _ =>
val tensors = new TensorResources()
val (tokenTensors, maskTensors, segmentTensors) =
PrepareEmbeddings.prepareBatchTensorsWithSegment(
tensors,
batch,
maxSentenceLength,
batchLength)
val runner = tensorflowWrapper.get
.getTFSessionWithSignature(
configProtoBytes = configProtoBytes,
savedSignatures = signatures,
initAllTables = false)
.runner
runner
.feed(
_tfBertSignatures.getOrElse(
ModelSignatureConstants.InputIdsV1.key,
"missing_input_id_key"),
tokenTensors)
.feed(
_tfBertSignatures
.getOrElse(ModelSignatureConstants.AttentionMaskV1.key, "missing_input_mask_key"),
maskTensors)
.feed(
_tfBertSignatures
.getOrElse(ModelSignatureConstants.TokenTypeIdsV1.key, "missing_segment_ids_key"),
segmentTensors)
.fetch(_tfBertSignatures
.getOrElse(ModelSignatureConstants.PoolerOutput.key, "missing_pooled_output_key"))
val outs = runner.run().asScala
val embeddings = TensorResources.extractFloats(outs.head)
tokenTensors.close()
maskTensors.close()
segmentTensors.close()
tensors.clearSession(outs)
tensors.clearTensors()
embeddings
}
val dim = embeddings.length / batchLength
embeddings.grouped(dim).toArray
}
def tagSequenceSBert(batch: Seq[Array[Int]]): Array[Array[Float]] = {
detectedEngine match {
case Openvino.name =>
tagSequence(batch)
case ONNX.name =>
tagSequence(batch)
case TensorFlow.name =>
val tensors = new TensorResources()
val maxSentenceLength = batch.map(x => x.length).max
val batchLength = batch.length
val tokenBuffers = tensors.createLongBuffer(batchLength * maxSentenceLength)
val maskBuffers = tensors.createLongBuffer(batchLength * maxSentenceLength)
val segmentBuffers = tensors.createLongBuffer(batchLength * maxSentenceLength)
val shape = Array(batchLength.toLong, maxSentenceLength)
batch.zipWithIndex.foreach { case (sentence, idx) =>
val offset = idx * maxSentenceLength
tokenBuffers.offset(offset).write(sentence.map(_.toLong))
maskBuffers.offset(offset).write(sentence.map(x => if (x == 0L) 0L else 1L))
segmentBuffers.offset(offset).write(Array.fill(maxSentenceLength)(0L))
}
val tokenTensors = tensors.createLongBufferTensor(shape, tokenBuffers)
val maskTensors = tensors.createLongBufferTensor(shape, maskBuffers)
val segmentTensors = tensors.createLongBufferTensor(shape, segmentBuffers)
val runner = tensorflowWrapper.get
.getTFSessionWithSignature(
configProtoBytes = configProtoBytes,
savedSignatures = signatures,
initAllTables = false)
.runner
runner
.feed(
_tfBertSignatures.getOrElse(
ModelSignatureConstants.InputIdsV1.key,
"missing_input_id_key"),
tokenTensors)
.feed(
_tfBertSignatures
.getOrElse(ModelSignatureConstants.AttentionMaskV1.key, "missing_input_mask_key"),
maskTensors)
.feed(
_tfBertSignatures
.getOrElse(ModelSignatureConstants.TokenTypeIdsV1.key, "missing_segment_ids_key"),
segmentTensors)
.fetch(_tfBertSignatures
.getOrElse(ModelSignatureConstants.PoolerOutput.key, "missing_pooled_output_key"))
val outs = runner.run().asScala
val embeddings = TensorResources.extractFloats(outs.head)
tokenTensors.close()
maskTensors.close()
segmentTensors.close()
tensors.clearSession(outs)
tensors.clearTensors()
val dim = embeddings.length / batchLength
embeddings.grouped(dim).toArray
}
}
def predict(
sentences: Seq[WordpieceTokenizedSentence],
originalTokenSentences: Seq[TokenizedSentence],
batchSize: Int,
maxSentenceLength: Int,
caseSensitive: Boolean): Seq[WordpieceEmbeddingsSentence] = {
/*Run embeddings calculation by batches*/
sentences.zipWithIndex
.grouped(batchSize)
.flatMap { batch =>
val encoded = PrepareEmbeddings.prepareBatchInputsWithPadding(
batch,
maxSentenceLength,
sentenceStartTokenId,
sentenceEndTokenId)
val vectors = tag(encoded)
/*Combine tokens and calculated embeddings*/
batch.zip(vectors).map { case (sentence, tokenVectors) =>
val tokenLength = sentence._1.tokens.length
/*All wordpiece embeddings*/
val tokenEmbeddings = tokenVectors.slice(1, tokenLength + 1)
val originalIndexedTokens = originalTokenSentences(sentence._2)
/*Word-level and span-level alignment with Tokenizer
https://github.com/google-research/bert#tokenization
### Input
orig_tokens = ["John", "Johanson", "'s", "house"]
labels = ["NNP", "NNP", "POS", "NN"]
# bert_tokens == ["[CLS]", "john", "johan", "##son", "'", "s", "house", "[SEP]"]
# orig_to_tok_map == [1, 2, 4, 6]*/
val tokensWithEmbeddings =
sentence._1.tokens.zip(tokenEmbeddings).flatMap { case (token, tokenEmbedding) =>
val tokenWithEmbeddings = TokenPieceEmbeddings(token, tokenEmbedding)
val originalTokensWithEmbeddings = originalIndexedTokens.indexedTokens
.find(p =>
p.begin == tokenWithEmbeddings.begin && tokenWithEmbeddings.isWordStart)
.map { token =>
val originalTokenWithEmbedding = TokenPieceEmbeddings(
TokenPiece(
wordpiece = tokenWithEmbeddings.wordpiece,
token = if (caseSensitive) token.token else token.token.toLowerCase(),
pieceId = tokenWithEmbeddings.pieceId,
isWordStart = tokenWithEmbeddings.isWordStart,
begin = token.begin,
end = token.end),
tokenEmbedding)
originalTokenWithEmbedding
}
originalTokensWithEmbeddings
}
WordpieceEmbeddingsSentence(tokensWithEmbeddings, originalIndexedTokens.sentenceIndex)
}
}
.toSeq
}
def predictSequence(
tokens: Seq[WordpieceTokenizedSentence],
sentences: Seq[Sentence],
batchSize: Int,
maxSentenceLength: Int,
isLong: Boolean = false): Seq[Annotation] = {
/*Run embeddings calculation by batches*/
tokens
.zip(sentences)
.zipWithIndex
.grouped(batchSize)
.flatMap { batch =>
val tokensBatch = batch.map(x => (x._1._1, x._2))
val sentencesBatch = batch.map(x => x._1._2)
val encoded = PrepareEmbeddings.prepareBatchInputsWithPadding(
tokensBatch,
maxSentenceLength,
sentenceStartTokenId,
sentenceEndTokenId)
val embeddings = if (isLong) {
tagSequenceSBert(encoded)
} else {
tagSequence(encoded)
}
sentencesBatch.zip(embeddings).map { case (sentence, vectors) =>
val metadata = Map(
"sentence" -> sentence.index.toString,
"token" -> sentence.content,
"pieceId" -> "-1",
"isWordStart" -> "true")
val finalMetadata = if (sentence.metadata.isDefined) {
sentence.metadata.getOrElse(Map.empty) ++ metadata
} else {
metadata
}
Annotation(
annotatorType = AnnotatorType.SENTENCE_EMBEDDINGS,
begin = sentence.start,
end = sentence.end,
result = sentence.content,
metadata = finalMetadata,
embeddings = vectors)
}
}
.toSeq
}
}
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