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2 changes: 1 addition & 1 deletion
2
...ormers/encoder/apps/BlasInstanceApp.scala → ...ansformers/apps/BlasInstanceApp.scala.txt
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130 changes: 130 additions & 0 deletions
130
apps/src/main/scala/org/clulab/scala_transformers/apps/TokenClassifierTimerApp.scala
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,130 @@ | ||
| package org.clulab.scala_transformers.apps | ||
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| import org.clulab.scala_transformers.common.Timers | ||
| import org.clulab.scala_transformers.encoder.{EncoderMaxTokensRuntimeException, TokenClassifier} | ||
| import org.clulab.scala_transformers.tokenizer.LongTokenization | ||
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| import scala.io.Source | ||
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| object TokenClassifierTimerApp extends App { | ||
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| class TimedTokenClassifier(tokenClassifier: TokenClassifier) extends TokenClassifier( | ||
| tokenClassifier.encoder, tokenClassifier.maxTokens, tokenClassifier.tasks, tokenClassifier.tokenizer | ||
| ) { | ||
| val tokenizeTimer = Timers.getOrNew("Tokenizer") | ||
| val forwardTimer = Timers.getOrNew("Encoder.forward") | ||
| val predictTimers = tokenClassifier.tasks.indices.map { index => | ||
| val name = tasks(index).name | ||
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| Timers.getOrNew(s"Encoder.predict $index\t$name") | ||
| } | ||
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| // NOTE: This should be copied from the base class and then instrumented with timers. | ||
| override def predictWithScores(words: Seq[String], headTaskName: String = "Deps Head"): Array[Array[Array[(String, Float)]]] = { | ||
| // This condition must be met in order for allLabels to be filled properly without nulls. | ||
| // The condition is not checked at runtime! | ||
| // if (tasks.exists(_.dual)) | ||
| // require(tasks.count(task => !task.dual && task.name == headTaskName) == 1) | ||
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| // tokenize to subword tokens | ||
| val tokenization = tokenizeTimer.time { | ||
| LongTokenization(tokenizer.tokenize(words.toArray)) | ||
| } | ||
| val inputIds = tokenization.tokenIds | ||
| val wordIds = tokenization.wordIds | ||
| val tokens = tokenization.tokens | ||
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| if (inputIds.length > maxTokens) { | ||
| throw new EncoderMaxTokensRuntimeException(s"Encoder error: the following text contains more tokens than the maximum number accepted by this encoder ($maxTokens): ${tokens.mkString(", ")}") | ||
| } | ||
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| // run the sentence through the transformer encoder | ||
| val encOutput = forwardTimer.time { | ||
| encoder.forward(inputIds) | ||
| } | ||
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| // outputs for all tasks stored here: task x tokens in sentence x scores per token | ||
| val allLabels = new Array[Array[Array[(String, Float)]]](tasks.length) | ||
| // all heads predicted for every token | ||
| // dimensions: token x heads | ||
| var heads: Option[Array[Array[Int]]] = None | ||
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| // now generate token label predictions for all primary tasks (not dual!) | ||
| for (i <- tasks.indices) { | ||
| if (!tasks(i).dual) { | ||
| val tokenLabels = predictTimers(i).time { | ||
| tasks(i).predictWithScores(encOutput, None, None) | ||
| } | ||
| val wordLabels = TokenClassifier.mapTokenLabelsAndScoresToWords(tokenLabels, tokenization.wordIds) | ||
| allLabels(i) = wordLabels | ||
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| // if this is the task that predicts head positions, then save them for the dual tasks | ||
| // we save all the heads predicted for each token | ||
| if (tasks(i).name == headTaskName) { | ||
| heads = Some(tokenLabels.map(_.map(_._1.toInt))) | ||
| } | ||
| } | ||
| } | ||
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| // generate outputs for the dual tasks, if heads were predicted by one of the primary tasks | ||
| // the dual task(s) must be aligned with the heads. | ||
| // that is, we predict the top label for each of the head candidates | ||
| if (heads.isDefined) { | ||
| //println("Tokens: " + tokens.mkString(", ")) | ||
| //println("Heads:\n\t" + heads.get.map(_.slice(0, 3).mkString(", ")).mkString("\n\t")) | ||
| //println("Masks: " + TokenClassifier.mkTokenMask(wordIds).mkString(", ")) | ||
| val masks = Some(TokenClassifier.mkTokenMask(wordIds)) | ||
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| for (i <- tasks.indices) { | ||
| if (tasks(i).dual) { | ||
| val tokenLabels = predictTimers(i).time { | ||
| tasks(i).predictWithScores(encOutput, heads, masks) | ||
| } | ||
| val wordLabels = TokenClassifier.mapTokenLabelsAndScoresToWords(tokenLabels, tokenization.wordIds) | ||
| allLabels(i) = wordLabels | ||
| } | ||
| } | ||
| } | ||
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| allLabels | ||
| } | ||
| } | ||
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| val verbose = false | ||
| val fileName = args.lift(0).getOrElse("../corpora/sentences/sentences.txt") | ||
| // Choose one of these. | ||
| val untimedTokenClassifier = TokenClassifier.fromFiles("../models/microsoft_deberta_v3_base_mtl/avg_export") | ||
| // val untimedTokenClassifier = TokenClassifier.fromFiles("../models/google_electra_small_discriminator_mtl/avg_export") | ||
| // val untimedTokenClassifier = TokenClassifier.fromFiles("../models/roberta_base_mtl/avg_export") | ||
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| val tokenClassifier = new TimedTokenClassifier(untimedTokenClassifier) | ||
| val lines = { | ||
| val source = Source.fromFile(fileName) | ||
| val lines = source.getLines().take(100).toArray | ||
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| source.close | ||
| lines | ||
| } | ||
| val elapsedTimer = Timers.getOrNew("Elapsed") | ||
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| elapsedTimer.time { | ||
| lines.zipWithIndex/*.par*/.foreach { case (line, index) => | ||
| println(s"$index $line") | ||
| if (index != 1382) { | ||
| val words = line.split(" ").toSeq | ||
| val allLabelSeqs = tokenClassifier.predictWithScores(words) | ||
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| if (verbose) { | ||
| println(s"Words: ${words.mkString(", ")}") | ||
| for (layer <- allLabelSeqs) { | ||
| val words = layer.map(_.head) // Collapse the next layer by just taking the head. | ||
| val wordLabels = words.map(_._1) | ||
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| println(s"Labels: ${wordLabels.mkString(", ")}") | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
| Timers.summarize() | ||
| } |
2 changes: 1 addition & 1 deletion
2
...la_transformers/encoder/timer/Timer.scala → ...lab/scala_transformers/common/Timer.scala
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6 changes: 3 additions & 3 deletions
6
...transformers/encoder/BreezeExamples.scala → ...sformers/encoder/BreezeExamples.scala.txt
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33 changes: 0 additions & 33 deletions
33
encoder/src/main/scala/org/clulab/scala_transformers/encoder/BreezeUtils.scala
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