def __init__(self, config, source):
self.config = config
self.source = source
self.secondSource = source.clone()
self.secondSource.shuffleDocuments()
self.random = numpy.random.RandomState(seed=self.getSeed())
self.vocab = Vocab(config)
def getOrLoadModel(self):
import os
self.vocab = Vocab(self.config)
shouldCreate = not os.path.exists(
self.checkpointer.getModelDirectory()) or self.getShouldCreateModel()
if shouldCreate:
self.createModel()
else:
self.load()
def getOrLoadModel(self):
"""Returns a linear model.
If specified, create a new model else load an already existing model.
"""
self.vocab = Vocab(self.config)
shouldCreate = not os.path.exists(self.checkpointer.getModelDirectory(
)) or self.getShouldCreateModel()
if shouldCreate:
self.createModel()
else:
self.loadModel()
def maskOffTokens(self, labels):
inputs = list(labels)
for i in range(1, len(labels)):
if self.random.binomial(1, 0.15):
if self.random.binomial(1, 0.8):
inputs[i] = Vocab.getMaskToken()
else:
if self.random.binomial(1, 0.5):
inputs[i] = self.random.randint(Vocab.getVocabOffset(),
self.vocab.getSize())
inputs[0] = Vocab.getClassLabelToken()
return inputs
class PerTokenEvaluator:
def __init__(self, config):
self.config = config
self.vocab = Vocab(config)
def initialize(self):
self.entropy = 0.0
self.totalBytes = 0
def evaluate(self, inputs, labels, predictions):
import math
batchSize = predictions.shape[0]
sequenceLength = predictions.shape[1]
for batch in range(batchSize):
for token in range(sequenceLength):
p = predictions[batch, token, 0]
tokenBytes = self.vocab.getTokenBytes(token)
self.entropy += (-math.log(p)) / tokenBytes
self.totalBytes += tokenBytes
def getRequestedPredictions(self, inputs, labels):
import numpy
return numpy.expand_dims(labels, axis=2)
def finalize(self):
return 2**(self.entropy / self.totalBytes)
def saveVocab(dataset, size, directory):
import os
import time
vocab = createInitialVocab()
if os.path.isdir(directory):
outputPath = os.path.join(directory, "vocab.txt")
if not os.path.exists(directory):
os.makedirs(directory)
else:
outputPath = directory
previousVocabSize = 0
start = time.time()
totalTokens = 0
while True:
string = dataset.next()
if len(string) == 0:
break
if not string in vocab:
vocab[string] = 0
totalTokens += 1
vocab[string] += 1
if len(vocab) + Vocab.getVocabOffset(
) >= previousVocabSize + size * 0.01:
previousVocabSize = len(vocab) + Vocab.getVocabOffset()
logger.debug("Vocab size is " + str(previousVocabSize) +
" time so far: " + str(time.time() - start) +
" total tokens: " + str(totalTokens))
if len(vocab) + Vocab.getVocabOffset() >= size:
break
with open(outputPath, "w", encoding='utf-8') as outputFile:
for token, count in reversed(sorted(vocab.items(),
key=lambda x: x[1])):
if token[-1] != '\n':
token += '\n'
outputFile.write(token)
def next(self):
tokenString = self.source.next()
if self.vocab.contains(tokenString):
token = self.vocab.getToken(tokenString)
else:
token = Vocab.getUnkToken()
return token
class RandomModel:
def __init__(self, config):
self.config = config
self.vocab = Vocab(config)
def train(self):
# no training happens in this model
pass
def predict(self, inputs):
# output is [batch-size, sequence-length, vocab-size] of 1.0/vocab-size
batchSize = inputs.shape[0]
sequenceLength = inputs.shape[1]
vocabSize = self.getVocabSize()
return numpy.full([batchSize, sequenceLength, vocabSize],
1.0 / vocabSize)
def getVocabSize(self):
return self.vocab.getSize()
def getVocab(self):
return self.vocab
class ClassTransformerModel:
def __init__(self, config, trainingDataSource, validationDataSource):
"""Initializes the model.
Attributes:
config: The configuration for the model.
trainingDataSource: list of training samples and labels
validationDataSource: list of validation samples and labels
"""
self.config = config
self.trainingDataSource = trainingDataSource
self.validationDataSource = validationDataSource
self.graph = tf.Graph()
self.session = tf.Session(graph=self.graph)
self.checkpointer = ModelDescriptionCheckpointer(config, self.__class__.__name__)
self.isLoaded = False
self.bestValidationLoss = None
def train(self):
"""Trains the model.
Trains the model for epochs specified in the config.
Runs the validation dataset on the model if specified in the config.
"""
with self.graph.as_default():
self.getOrLoadModel()
for epoch in range(self.getEpochs()):
self.runOnTrainingDataset(epoch)
if self.shouldRunValidation():
self.runOnValidationDataset(epoch)
self.validationDataSource.reset()
self.checkpointBestModel()
self.trainingDataSource.reset()
def checkpointBestModel(self):
if self.bestValidationLoss is None:
self.checkpoint("best")
return
if self.totalLoss < self.bestValidationLoss:
logger.info("Updating best model with loss: " + str(self.totalLoss))
self.bestValidationLoss = self.totalLoss
self.checkpoint("best")
else:
self.checkpoint("checkpoint")
def predict(self, inputs, requestedPredictions):
with self.graph.as_default():
self.getOrLoadModel()
assert False, "Not Implemented"
inputs = numpy.array(inputs)
predictions = self.session.run(self.outputProbabilities,
feed_dict={self.inputTokens : inputs})
batchSize = requestedPredictions.shape[0]
length = requestedPredictions.shape[1]
outputPredictions = numpy.zeros(requestedPredictions.shape)
for b in range(batchSize):
for l in range(length):
outputPredictions[b,l,:] = \
predictions[b,l,requestedPredictions[b,l,:]]
return outputPredictions
def getFeatures(self, inputs, secondInputs):
with self.graph.as_default():
self.getOrLoadModel()
inputs = numpy.expand_dims(numpy.array(inputs), axis=2)
secondInputs = numpy.expand_dims(numpy.array(secondInputs), axis=2)
inputs = numpy.concatenate([inputs, secondInputs], axis=2)
# (batch, sequence, 2, embedding-size)
predictions = self.session.run(self.features,
feed_dict={self.inputTokens : inputs})
return predictions[:, :, 0, :]
def getOrLoadModel(self):
"""Returns a linear model.
If specified, create a new model else load an already existing model.
"""
if self.isLoaded:
return
self.vocab = Vocab(self.config)
shouldCreate = not os.path.exists(
self.checkpointer.getModelLoadDirectory()) or self.getShouldCreateModel()
if shouldCreate:
self.createModel()
else:
self.loadModel()
self.logModel()
def logModel(self):
totalParameters = 0
for variable in tf.trainable_variables():
shape = variable.get_shape()
variableParameters = 1
for dim in shape:
variableParameters *= dim.value
totalParameters += variableParameters
logger.debug("Variable '" + variable.name + "' " +
str(humanize.naturalsize(variableParameters)) + " (params) " +
str(shape) + " (dims)")
logger.debug("Total #params '" + str(humanize.naturalsize(totalParameters)) + "' ")
def loadModel(self):
"""Loads an already existing model from the specified path """
self.checkpointer.load()
directory = self.checkpointer.getModelLoadDirectory()
logger.debug("Loading checkpoint from: " + str(directory))
tf.saved_model.loader.load(
self.session,
["serve"],
directory
)
self.setOperationsByName()
self.isLoaded = True
def setOperationsByName(self):
self.inputTokens = self.graph.get_tensor_by_name("input-tokens:0")
self.labels = self.graph.get_tensor_by_name("output-labels:0")
self.features = self.graph.get_tensor_by_name("features:0")
self.vocabLoss = self.graph.get_tensor_by_name("vocab-loss:0")
self.classificationLoss = self.graph.get_tensor_by_name("classification-loss:0")
self.classLoss = self.graph.get_tensor_by_name("class-loss:0")
self.outputProbabilities = self.graph.get_tensor_by_name("output-probabilities:0")
self.outputDocumentClass = self.graph.get_tensor_by_name("output-document-class:0")
self.loss = self.graph.get_tensor_by_name("loss:0")
self.optimizerStep = self.graph.get_operation_by_name("optimizer-step")
def createModel(self):
# inputs (batch, sequence-length, 2)
self.inputTokens = tf.placeholder(tf.int32, shape=(None, None, 2),
name="input-tokens")
# labels (batch, sequence-length, 2)
self.labels = tf.placeholder(tf.int32, shape=(None, None, 2),
name="output-labels")
self.createClassMappings()
# convert to classes (batch, sequence-length, 2, assignments)
self.inputClasses = self.convertToClasses(self.inputTokens)
self.classLabels = self.convertToClasses(self.labels)
# class logits (batch, sequence-length, 2, assignmets, class-size)
classLogits = self.runClassModel(self.inputClasses)
# classification logits (batch, sequence-length, 2, assignments, 2)
classificationLogits = self.runClassificationModel()
# document classification logits (batch, sequence-length, 2, assignments, 2)
documentClassificationLogits = self.runDocumentClassificationModel()
# compute the losses
self.clusterLoss = tf.identity(self.evaluateClusteringLoss(
self.features, self.classLabels), name="clustering-loss")
self.classificationLoss = tf.identity(self.evaluateClassificationLoss(
classificationLogits, self.classLabels), name="classification-loss")
self.documentClassificationLoss = tf.identity(self.evaluateDocumentClassificationLoss(
documentClassificationLogits, self.classLabels), name="document-classification-loss")
self.classLoss = tf.identity(self.evaluateLoss(classLogits[:, 1:, :, :, :],
self.classLabels[:, 1:, :, :]), name="class-loss")
self.vocabLoss = tf.identity(self.evaluateVocabLoss(classLogits[:, 1:, :, :, :],
self.labels[:, 1:, :]), name="vocab-loss")
self.loss = tf.identity(self.classLoss +
self.classificationLoss +
self.clusterLoss +
self.vocabLoss,
name="loss")
# convert to vocab logits (batch, sequence-length, vocab-size)
vocabLogits = self.expandClassLogitsToVocab(classLogits)
self.outputProbabilities = tf.nn.softmax(vocabLogits,
name="output-probabilities")
self.outputDocumentClass = tf.reduce_max(documentClassificationLogits, axis=3)
# optimizer
self.optimizerStep = self.createOptimizerStep(self.loss, "")
self.documentOptimizerStep = self.createOptimizerStep(self.documentClassificationLoss,
"document")
# initializers
self.globalInitializer = tf.global_variables_initializer()
self.localInitializer = tf.local_variables_initializer()
# summaries
self.setupSummaries()
# do the initialization
self.initializeModel()
def createClassMappings(self):
mappings = numpy.zeros([self.getAssignmentCount(), self.vocab.getSize()],
dtype=numpy.int32)
weights = numpy.zeros([self.getAssignmentCount(), self.vocab.getSize()],
dtype=numpy.float32)
for assignment in range(self.getAssignmentCount()):
mappings[assignment, :], weights[assignment, :] = self.createMapping(assignment)
self.classMappingsHost = mappings
self.classMappings = tf.constant(mappings)
self.classWeights = tf.constant(weights)
def logAdd(self, left, right):
if left is None:
return right
if left == float("-inf"):
return right
if right == float("-inf"):
return left
return max(left, right) + math.log1p(math.exp( -math.fabs(left - right)))
def logSumArray(self, array):
from functools import reduce
return reduce(lambda x, y : self.logAdd(x, y), array)
def logSubtract(self, left, right):
if left <= right:
assert False, "log of negative number in subtraction " + str(left) + " - " + str(right)
if right == float("-inf"):
return left
return left + math.log1p(-math.exp(right - left))
def createMapping(self, assignment):
assert self.getNumberOfDirectClasses() <= self.getNumberOfClasses()
assert self.getNumberOfDirectClasses() <= self.vocab.getSize()
vocabSize = self.vocab.getSize() - self.getNumberOfDirectClasses()
numberOfClasses = self.getNumberOfClasses() - self.getNumberOfDirectClasses()
directMapping = numpy.arange(self.getNumberOfDirectClasses(), dtype=numpy.int32)
directWeights = numpy.ones(self.getNumberOfDirectClasses(), dtype=numpy.float32)
mapping, weights = self.createLogMapping(assignment, vocabSize, numberOfClasses)
return (numpy.concatenate([directMapping, self.getNumberOfDirectClasses() + mapping]),
numpy.concatenate([directWeights, weights]))
def createLogMapping(self, assignment, vocabSize, numberOfClasses):
generator = numpy.random.RandomState(seed=assignment)
wordCounts = reversed([i * self.getWordFrequencyPowerLawExponent()
for i in range(vocabSize)])
wordCountsPlusRandom = [i + math.log(generator.uniform(0.0, 1000.0)) for i in wordCounts]
logTotalCount = self.logSumArray(wordCountsPlusRandom)
sortedWordCounts = sorted(enumerate(wordCountsPlusRandom), key=lambda x: x[1], reverse=True)
logClassSize = logTotalCount - math.log(numberOfClasses)
mapping = numpy.zeros([vocabSize], dtype=numpy.int32)
weights = numpy.zeros([vocabSize], dtype=numpy.float32)
currentClass = 0
wordsInCurrentClass = 0
logCurrentCount = None
for wordIndex, logWordCount in sortedWordCounts:
assert currentClass < numberOfClasses
mapping[wordIndex] = currentClass
wordsInCurrentClass += 1
logCurrentCount = self.logAdd(logCurrentCount, logWordCount)
if logCurrentCount >= logClassSize and currentClass + 1 != numberOfClasses:
#print(logCurrentCount, logWordCount, currentClass, logClassSize)
logCurrentCount = self.logSubtract(logCurrentCount, logClassSize)
wordsInCurrentClass = 0
currentClass += 1
currentClass = 0
currentClassSize = 0
currentClassMembers = []
for i, wordCountAndIndex in enumerate(sortedWordCounts):
wordIndex, wordCount = wordCountAndIndex
currentClassMembers.append(wordIndex)
currentClassSize += 1
# if end of current class
if ((1 + i) == len(sortedWordCounts) or
mapping[sortedWordCounts[1 + i][0]] != currentClass):
for memberIndex in currentClassMembers:
weights[memberIndex] = 1.0 / currentClassSize
if currentClass == 0 or i == (len(sortedWordCounts) - 1):
logger.info("current class " + str(currentClass) +
" members " + str(len(currentClassMembers)))
currentClass += 1
currentClassSize = 0
currentClassMembers = []
return mapping, weights
def initializeModel(self):
self.session.run(self.globalInitializer)
self.session.run(self.localInitializer)
def runOnTrainingDataset(self, epoch):
"""Trains the linear model on the training dataset for one epoch."""
trainStart = time.time()
totalLoss = 0.0
message = None
for step in range(self.getStepsPerEpoch()):
generatorStart = time.time()
try:
inputs, labels, secondInputs, secondLabels = self.trainingDataSource.next()
except Exception as e:
if message is None:
message = str(e)
break
generatorEnd = time.time()
trainStepStart = time.time()
loss, gradNorm = self.trainingStep(inputs, labels, secondInputs, secondLabels,
step, epoch)
trainStepEnd = time.time()
totalLoss += loss
message = ("Epoch (" + str(epoch) + " / " + str(self.getEpochs()) +
"), Step (" + str(step) + " / " + str(self.getStepsPerEpoch()) +
"), Generator time: " + ("%.2f" % (generatorEnd - generatorStart)) +
", training step time: " + ("%.2f" % (trainStepEnd -
trainStepStart) +
", loss: " + str("%.2f" % loss) +
", grad norm: " + str("%.2f" % gradNorm)) +
", avg-loss: " + str("%.2f" % (totalLoss / (step + 1))))
print(message, end="\r", flush=True)
trainEnd = time.time()
print(message)
logger.debug(" Training took: " + (str(trainEnd - trainStart)) + " seconds...")
def trainingStep(self, inputs, labels, secondInputs, secondLabels, step, epoch):
"""Training step for one minibatch of training data."""
inputs = numpy.expand_dims(numpy.array(inputs), axis=2)
labels = numpy.expand_dims(numpy.array(labels), axis=2)
secondInputs = numpy.expand_dims(numpy.array(secondInputs), axis=2)
secondLabels = numpy.expand_dims(numpy.array(secondLabels), axis=2)
inputs = numpy.concatenate([inputs, secondInputs], axis=2)
labels = numpy.concatenate([labels, secondLabels], axis=2)
if self.getShouldClassifyDocument():
optimizerStep = self.documentOptimizerStep
loss = self.documentClassificationLoss
else:
optimizerStep = self.optimizerStep
loss = self.loss
trainingLoss, gradNorm, summaries, _ = self.session.run([loss,
self.gradientNorm, self.mergedSummary, optimizerStep],
feed_dict={self.inputTokens : inputs, self.labels : labels })
if step % self.getStepsPerTensorboardLog():
self.trainingSummaryWriter.add_summary(summaries,
step + epoch * self.getStepsPerEpoch())
return trainingLoss, gradNorm
def runOnValidationDataset(self, epoch):
"""Runs the linear model on the validation dataset for one epoch."""
validationStart = time.time()
self.totalLoss = 0.0
self.totalVocabLoss = 0.0
message = None
for step in range(self.getValidationStepsPerEpoch()):
generatorStart = time.time()
try:
inputs, labels, secondInputs, secondLabels = self.validationDataSource.next()
except Exception as e:
if message is None:
message = str(e)
break
generatorEnd = time.time()
validationStepStart = time.time()
loss, vocabLoss = self.validationStep(inputs, labels, secondInputs, secondLabels)
validationStepEnd = time.time()
self.totalLoss += loss
self.totalVocabLoss += vocabLoss
message = ("Validation Step (" + str(step) + " / " +
str(self.getValidationStepsPerEpoch()) +
"), Generator time: " + ("%.2f" % (generatorEnd - generatorStart)) +
", validation step time: " + ("%.2f" % (validationStepEnd - validationStepStart)) +
", avg-loss: " + ("%.2f" % (self.totalLoss/(step + 1))))
print(message, end="\r", flush=True)
validationEnd = time.time()
print(message)
logger.debug(" Validation took: " + (str(validationEnd - validationStart)) + " seconds...")
self.addValidationSummaries(self.totalLoss, self.totalVocabLoss, epoch)
def addValidationSummaries(self, totalLoss, vocabLoss, epoch):
averageLoss = totalLoss / self.getValidationStepsPerEpoch()
summary = tf.Summary(value=[
tf.Summary.Value(tag="validation-loss", simple_value=averageLoss),
])
self.trainingSummaryWriter.add_summary(summary, epoch)
averageVocabLoss = vocabLoss / self.getValidationStepsPerEpoch()
summary = tf.Summary(value=[
tf.Summary.Value(tag="validation-vocab-cross-entropy", simple_value=averageVocabLoss),
])
self.trainingSummaryWriter.add_summary(summary, epoch)
def validationStep(self, inputs, labels, secondInputs, secondLabels):
"""One minibatch of validation data processed by the model."""
inputs = numpy.expand_dims(numpy.array(inputs), axis=2)
labels = numpy.expand_dims(numpy.array(labels), axis=2)
secondInputs = numpy.expand_dims(numpy.array(secondInputs), axis=2)
secondLabels = numpy.expand_dims(numpy.array(secondLabels), axis=2)
inputs = numpy.concatenate([inputs, secondInputs], axis=2)
labels = numpy.concatenate([labels, secondLabels], axis=2)
if self.getShouldClassifyDocument():
loss = self.documentClassificationLoss
else:
loss = self.loss
validationLoss, vocabLoss = self.session.run([loss, self.vocabLoss],
feed_dict={self.inputTokens : inputs,
self.labels : labels})
return validationLoss, vocabLoss
def createOptimizerStep(self, loss, name):
"""One step of backprop."""
optimizer = tf.train.AdamOptimizer(
learning_rate=float(self.config["model"]["learning-rate"]),
beta1=0.9,
beta2=0.98,
epsilon=10e-9,
name=name+"optimizer-step")
gradients, variables = zip(*optimizer.compute_gradients(loss))
gradients, _ = tf.clip_by_global_norm(gradients,
self.config["model"]["gradient-clipping-factor"])
self.gradientNorm = tf.global_norm(gradients, name="gradient-norm")
return optimizer.apply_gradients(zip(gradients, variables))
def setupSummaries(self):
tf.summary.scalar('total-loss', self.loss)
if self.getShouldClassifyDocument():
tf.summary.scalar('document-class-cross-entropy', self.documentClassificationLoss)
else:
tf.summary.scalar('document-match-cross-entropy', self.classificationLoss)
tf.summary.scalar('vocab-cross-entropy', self.vocabLoss)
tf.summary.scalar('class-cross-entropy', self.classLoss)
tf.summary.scalar('cluster-loss', self.clusterLoss)
tf.summary.scalar('gradient-norm', self.gradientNorm)
self.mergedSummary = tf.summary.merge_all()
self.trainingSummaryWriter = tf.summary.FileWriter(
os.path.join(self.getExperimentDirectory(), 'training-summaries'),
self.graph)
#if self.shouldRunValidation():
# self.validationSummaryWriter = tf.summary.FileWriter(
# os.path.join(self.getExperimentDirectory(), 'validation-summaries'),
# self.graph)
def evaluateClusteringLoss(self, features, classLabels):
# features is [batch, sequence, 2, assignments, feature-dimension]
# class labels is [batch, sequence, 2, assignments]
assignmentLosses = []
batchSize = tf.shape(features)[0]
sequenceLength = tf.shape(features)[1]
features = tf.reshape(self.features, (batchSize, sequenceLength,
2, self.getAssignmentCount(), self.getEmbeddingSize()))
for i in range(self.getAssignmentCount()):
assignmentLosses.append(self.evaluatePerAssignmentClusterLoss(
features[:, :, :, i, :], classLabels[:, :, :, i]))
return sum(assignmentLosses) / (tf.multiply(tf.cast(batchSize, dtype=tf.float32),
2.0 * self.getAssignmentCount()))
def evaluatePerAssignmentClusterLoss(self, features, labels):
# features is [batch, sequence, 2, feature-dim]
# labels is [batch, sequence, 2]
wordFeatures = tf.reshape(features[:, 0, :, :], (-1, self.getEmbeddingSize()))
tripletLabels = tf.reshape(labels[:, 0, :], (-1, ))
return self.tripletLoss(wordFeatures, tripletLabels)
def tripletLoss(self, features, labels):
return tf.contrib.losses.metric_learning.triplet_semihard_loss(labels, features)
def evaluateClassificationLoss(self, batchOutputs, labels):
# batch outputs is [batch, assignments, 2]
# labels is [batch, sequence, 2, assignments, 1]
labels = tf.cast(tf.equal(labels[:, 0, 0, :, 0], labels[:, 0, 1, :, 0]), tf.int32)
return tf.losses.sparse_softmax_cross_entropy(
labels=labels,
logits=batchOutputs)
def evaluateDocumentClassificationLoss(self, batchOutputs, labels):
# batch outputs is [batch, 2, assignments, 2]
# labels is [batch, sequence, 2, assignments, 1]
labels = labels[:,0,:,:,0]
return tf.losses.sparse_softmax_cross_entropy(
labels=labels,
logits=batchOutputs)
def evaluateLoss(self, batchOutputs, labels):
return tf.losses.sparse_softmax_cross_entropy(
labels=labels,
logits=batchOutputs)
def klDivergence(self, a, b):
a = tf.distributions.Categorical(probs=a + numpy.finfo(float).eps)
b = tf.distributions.Categorical(probs=tf.nn.softmax(b) + numpy.finfo(float).eps)
return tf.reduce_mean(tf.distributions.kl_divergence(a, b, allow_nan_stats=False))
def convertToClasses(self, inputs):
# inputs is (batch, sequence, 2)
# class mappings is (assignments, vocab size)
# outputs is (batch, sequence, 2, assignments)
batchSize = tf.shape(inputs)[0]
sequenceLength = tf.shape(inputs)[1]
classes = tf.concat([tf.reshape(tf.gather(self.classMappings[i, :], inputs),
(batchSize, sequenceLength, 2, 1))
for i in range(self.getAssignmentCount())], axis=3)
return tf.reshape(classes, (batchSize, sequenceLength, 2, self.getAssignmentCount(), 1))
def expandClassLogitsToVocab(self, classLogits):
# class logits is (batch size, sequence-length, 2, assignments, class-size)
# class mappings is (class-assignments, vocab-size)
# class weights is (class-assignments, vocab-size)
# output is (batch-size, sequence-length, 2, vocab-size)
batchSize = tf.shape(classLogits)[0]
sequenceLength = tf.shape(classLogits)[1]
gatheredLogits = tf.concat([tf.reshape(tf.gather(classLogits[:,:,:,i,:], self.classMappings[i, :], axis=3),
(batchSize, sequenceLength, 2, 1, self.vocab.getSize()))
for i in range(self.getAssignmentCount())], axis=3)
return tf.reduce_mean(tf.multiply(gatheredLogits, self.classWeights), axis=3)
def evaluateVocabLoss(self, classLogits, vocabLabels):
# labels is (batch size, sequence-length, 2)
batchSize = tf.shape(classLogits)[0]
sequenceLength = tf.shape(classLogits)[1]
sampleCount = self.getSoftmaxSampleCount()
samples = self.generateSamples(sampleCount)
sampledLabels = tf.zeros((batchSize, sequenceLength, 2), dtype=tf.int32)
# sampled mappings is (assignment count, sample count)
sampledMappings = self.sample(self.classMappings, samples, sampleCount)
# sampled weights is (assignment count, sample count)
sampledWeights = self.sample(self.classWeights, samples, sampleCount)
# gathered logits is (batch size, sequence length, assignment count, sample count)
gatheredLogits = tf.concat([tf.reshape(tf.gather(classLogits[:,:,:,i,:], sampledMappings[i,:], axis=3),
(batchSize, sequenceLength, 2, 1, sampleCount))
for i in range(self.getAssignmentCount())], axis=3)
# gathered weights is (batch size, sequence length, 2, assignment count, sample count)
gatheredWeights = self.broadcastToExpandedDimension(sampledWeights, batchSize, sequenceLength)
# gathered logits and weights is (batch size, sequence length, 2, assignment count, sample count + 1)
gatheredLogits = self.extendLogits(gatheredLogits, classLogits, vocabLabels)
gatheredWeights = self.extendWeights(gatheredWeights, vocabLabels)
# weighted logits is (batch size, sequence length, 2, assignments, sample count + 1)
weightedLogits = tf.multiply(gatheredLogits, gatheredWeights)
# vocab logits is (batch size, sequence length, 2, sample count + 1)
vocabLogits = tf.reduce_mean(weightedLogits, axis=3)
return self.evaluateLoss(vocabLogits[:, 1:, :, :], sampledLabels[:, 1:, :])
def generateSamples(self, sampleCount):
samplesPerAssignment = []
# TODO: BUG: Dont sample the label
for assignment in range(self.getAssignmentCount()):
samples, _, _ = tf.random.uniform_candidate_sampler(
true_classes=tf.broadcast_to(tf.range(self.vocab.getSize(), dtype=tf.int64),
(1, self.vocab.getSize())),
num_true=self.vocab.getSize(),
num_sampled=sampleCount,
range_max=self.vocab.getSize(),
unique=True)
samplesPerAssignment.append(tf.reshape(samples, (1, -1)))
return tf.concat(samplesPerAssignment, axis=0)
def extendLogits(self, vocabLogits, classLogits, labels):
# class logits is (batch size, sequence length, 2, assignment count, sample count)
# map is (assignment count, vocab size)
# labels is (batch size, sequence length, 2)
batchSize = tf.shape(classLogits)[0]
sequenceLength = tf.shape(classLogits)[1]
# labelClasses is (batch size, sequence length, 2, assignment count, 1)
labelClasses = tf.concat(
[tf.reshape(tf.gather(self.classMappings[i, :], labels),
(batchSize, sequenceLength, 2, 1, 1)) for i in range(self.getAssignmentCount())],
axis=3)
# gathered logits is (batch size, sequence length, 2, assignment count, 1)
gatheredLogits = tf.batch_gather(classLogits, labelClasses)
return tf.concat([gatheredLogits, vocabLogits], axis=4)
def extendWeights(self, vocabWeights, labels):
# vocab weights is (batch size, sequence length, 2, assignment count, sample count)
# labels is (batch size, sequence length)
batchSize = tf.shape(vocabWeights)[0]
sequenceLength = tf.shape(vocabWeights)[1]
# labelWeights is (batch size, sequence length, 2, assignment count, 1)
labelWeights = tf.concat(
[tf.reshape(tf.gather(self.classWeights[i, :], labels),
(batchSize, sequenceLength, 2, 1, 1)) for i in range(self.getAssignmentCount())],
axis=3)
return tf.concat([labelWeights, vocabWeights], axis=4)
def sample(self, mappings, samples, sampleCount):
assignments = []
for i in range(self.getAssignmentCount()):
assignments.append(tf.reshape(tf.gather(mappings[i, :], samples[i,:]), (1, sampleCount)))
return tf.concat(assignments, axis=0)
def broadcastToExpandedDimension(self, tensor, batchSize, sequenceLength):
classAssignments = tensor.shape[0]
vocabSize = tensor.shape[1]
newShape = (batchSize, sequenceLength, 2, classAssignments, vocabSize)
expandedTensor = tf.broadcast_to(tensor, newShape)
#print(expandedTensor.shape)
reshapedTensor = tf.reshape(expandedTensor, newShape)
#print(reshapedTensor.shape)
return reshapedTensor
def runClassModel(self, inputs):
#print("inputs", inputs.shape)
inputEmbeddings = self.convertToEmbeddings(inputs)
#print("inputEmbeddings", inputEmbeddings.shape)
# run encoder (logits is (batch-size, sequence-length, assignments, class-count))
encodedEmbeddings = self.runEncoder(inputEmbeddings)
logits = self.runDecoder(encodedEmbeddings)
#print("logits", logits.shape)
return logits
def runClassificationModel(self):
batchSize = tf.shape(self.features)[0]
sequenceLength = tf.shape(self.features)[1]
features = tf.reshape(self.features, (batchSize, sequenceLength, 2,
self.getAssignmentCount(), self.getEmbeddingSize()))
features = self.multiheadedAttention(features)
# features is (batch-size, sequence-length, 2, assignments, embedding-size)
reducedFeatures = tf.reduce_max(features, axis=1)
# reducedFeatures is (batch size, 2, assignments, embedding-size)
transposedFeatures = tf.transpose(reducedFeatures, [0,2,1,3])
# transposedFeatures is (batch size, assignments, 2, embedding-size)
reshapedFeatures = tf.reshape(transposedFeatures, (-1, self.getAssignmentCount(),
2 * self.getEmbeddingSize()))
return tf.layers.dense(reshapedFeatures, units=2)
def runDocumentClassificationModel(self):
batchSize = tf.shape(self.features)[0]
sequenceLength = tf.shape(self.features)[1]
features = tf.reshape(self.features, (batchSize, sequenceLength, 2,
self.getAssignmentCount(), self.getEmbeddingSize()))
features = self.multiheadedAttention(features)
# features is (batch-size, sequence-length, 2, assignments, embedding-size)
reducedFeatures = tf.reduce_max(features, axis=1)
# transposedFeatures is (batch size, assignments, 2, embedding-size)
reshapedFeatures = tf.reshape(reducedFeatures, (-1, 2, self.getAssignmentCount(),
self.getEmbeddingSize()))
return tf.layers.dense(reshapedFeatures, units=2)
def convertToEmbeddings(self, sequenceIds):
assignments = []
for assignment in range(self.getAssignmentCount()):
assignments.append(self.convertToClassEmbeddings(sequenceIds, assignment))
return tf.concat(assignments, axis = 3)
def convertToClassEmbeddings(self, ids, assignment):
with tf.variable_scope("linear-embeddings", reuse=tf.AUTO_REUSE):
wordEmbeddingsGlobal = tf.get_variable('class-embeddings-' + str(assignment), \
[self.getNumberOfClasses(), self.getEmbeddingSize()])
wordEmbeddings = tf.nn.embedding_lookup(wordEmbeddingsGlobal, ids[:, :, :, assignment, :])
return wordEmbeddings
def runEncoder(self, embeddings):
return self.multiheadedAttentionStack(embeddings)
def runDecoder(self, embeddings):
batchSize = tf.shape(embeddings)[0]
sequenceLength = tf.shape(embeddings)[1]
# embeddings is (batch size, sequence length, 2, assignments, classes)
return tf.concat([tf.reshape(tf.layers.dense(embeddings[:,:,:,i,:], units=self.getNumberOfClasses()),
(batchSize, sequenceLength, 2, 1, self.getNumberOfClasses()))
for i in range(self.getAssignmentCount())], axis=3)
def multiheadedAttentionStack(self, embeddings):
embeddings = self.addPositions(embeddings)
# embeddings (batch-size, sequence-length, 2, assignments, hidden-dimension)
for layer in range(self.getNumberOfLayers()):
embeddings = self.multiheadedAttention(embeddings)
if self.isMiddleLayer(layer):
batchSize = tf.shape(embeddings)[0]
sequenceLength = tf.shape(embeddings)[1]
self.features = tf.identity(tf.reshape(embeddings, (batchSize, sequenceLength, 2,
self.getAssignmentCount() * self.getEmbeddingSize())), name="features")
return embeddings
def addPositions(self, embeddings):
batchSize = tf.shape(embeddings)[0]
sequenceLength = tf.shape(embeddings)[1]
halfSequenceLength = (sequenceLength + 1) // 2
positions = tf.cast(tf.reshape(tf.range(halfSequenceLength),
(1, halfSequenceLength, 1, 1, 1)), dtype=tf.float32)
dimensions = tf.cast(tf.reshape(tf.range(self.getEmbeddingSize()),
(1, 1, 1, 1, self.getEmbeddingSize())), dtype=tf.float32)
angles = positions / tf.pow(2.0 * tf.cast(halfSequenceLength, dtype=tf.float32),
2.0 * dimensions / self.getEmbeddingSize())
evenPositionEmbeddings = tf.reshape(tf.sin(angles),
(1, halfSequenceLength, 1, 1, 1, self.getEmbeddingSize()))
oddPositionEmbeddings = tf.reshape(tf.cos(angles),
(1, halfSequenceLength, 1, 1, 1, self.getEmbeddingSize()))
# merge them
positionEmbeddings = tf.concat([evenPositionEmbeddings, oddPositionEmbeddings], axis=2)
positionEmbeddings = tf.reshape(positionEmbeddings,
(1, 2 * halfSequenceLength, 1, 1, self.getEmbeddingSize()))
positionEmbeddings = positionEmbeddings[:, 0:sequenceLength, :, :, :]
return embeddings + positionEmbeddings
def isMiddleLayer(self, layer):
if self.getNumberOfLayers() > 1:
return layer == (self.getNumberOfLayers() - 2)
return layer == (self.getNumberOfLayers() - 1)
def multiheadedAttention(self, embeddings):
# embeddings (batch-size, sequence-length, assignments, hidden-dimension)
projectedEmbeddings = self.projectEmbeddings(embeddings)
# proj-embeddings (batch-size, sequence-length, assignments, QKV, attention-heads, hidden-dimension)
attentionOutput = self.runAttention(projectedEmbeddings)
# project back
outputEmbeddings = self.projectBackEmbeddings(attentionOutput)
# add and norm
embeddings = self.addAndNorm(outputEmbeddings, embeddings)
# dense layer
denseOutput = tf.layers.dense(embeddings,
self.getEmbeddingSize(), activation="relu")
# add and norm
denseOutput = self.addAndNorm(denseOutput, embeddings)
return denseOutput
def projectEmbeddings(self, embeddings):
output = tf.layers.dense(embeddings,
embeddings.shape[-1] * 3 * self.getNumberOfAttentionHeads())
batchSize = tf.shape(embeddings)[0]
sequenceLength = tf.shape(embeddings)[1]
assignments = embeddings.shape[3]
return tf.reshape(output,
(batchSize, sequenceLength, 2, assignments, 3,
self.getNumberOfAttentionHeads(), embeddings.shape[-1]))
def projectBackEmbeddings(self, embeddings):
# embeddings are (batch-size, sequence-length, 2, assignments, attention-heads, embedding-size)
# project to (batch-size, sequece-length, 2, assignments, embedding-size)
batchSize = tf.shape(embeddings)[0]
sequenceLength = tf.shape(embeddings)[1]
assignments = embeddings.shape[3]
reshapedEmbeddings = tf.reshape(embeddings, (batchSize, sequenceLength, 2, assignments,
embeddings.shape[-1] * embeddings.shape[-2]))
projectedEmbeddings = tf.layers.dense(reshapedEmbeddings, self.getEmbeddingSize())
return projectedEmbeddings
def addAndNorm(self, left, right):
return tf.contrib.layers.layer_norm(tf.add(left, right))
def runAttention(self, embeddings):
# Q,K,V (batch-size, sequence-length, 2, assignments, attention-heads, hidden-dimension)
Q = embeddings[:,:,:,:,0,:,:]
K = embeddings[:,:,:,:,1,:,:]
V = embeddings[:,:,:,:,2,:,:]
readOn = tf.matmul(Q, K, transpose_b=True)
scale = math.sqrt(self.getEmbeddingSize())
scaledReadOn = readOn / scale
contribution = tf.nn.softmax(scaledReadOn, axis=1)
result = tf.matmul(contribution, V)
return result
def checkpoint(self, prefix):
"""Creates a checkpoint of the current model and saves to model
directory.
"""
self.checkpointer.setPrefix(prefix)
directory = self.checkpointer.getModelSaveDirectory()
logger.debug("Saving checkpoint to: " + str(directory))
self.checkpointer.checkpoint()
with self.graph.as_default():
tf.saved_model.simple_save(self.session,
directory,
inputs={"input_text" : self.inputTokens},
outputs={"outputs" : self.outputDocumentClass})
self.checkpointer.cleanup()
"""Functions to load configuration parameters."""
def getEmbeddingSize(self):
return int(self.config["model"]["embedding-size"])
def getAssignmentCount(self):
return int(self.config["model"]["assignment-count"])
def getSoftmaxSampleCount(self):
return int(self.config["model"]["softmax-sample-count"])
def getNumberOfClasses(self):
return int(self.config["model"]["number-of-classes"])
def getNumberOfDirectClasses(self):
return int(self.config["model"]["number-of-direct-classes"])
def getNumberOfLayers(self):
return int(self.config["model"]["number-of-layers"])
def getNumberOfAttentionHeads(self):
return int(self.config["model"]["number-of-attention-heads"])
def getWordFrequencyPowerLawExponent(self):
return float(self.config["model"]["word-frequency-power-law-exponent"])
def shouldRunValidation(self):
return self.config["model"]["run-validation"]
def getEpochs(self):
return int(self.config["model"]["epochs"])
def getShouldCreateModel(self):
if not "create-new-model" in self.config["model"]:
return False
return bool(self.config["model"]["create-new-model"])
def getShouldClassifyDocument(self):
if not "classify-document" in self.config["model"]:
return False
return bool(self.config["model"]["classify-document"])
def getStepsPerEpoch(self):
return int(self.config["model"]["steps-per-epoch"])
def getStepsPerTensorboardLog(self):
return int(self.config["model"]["steps-per-tensorboard-log"])
def getValidationStepsPerEpoch(self):
return int(self.config["model"]["validation-steps-per-epoch"])
def getExperimentDirectory(self):
return self.config["model"]["directory"]
import sys
sys.path.append('source')
from models.Vocab import Vocab
from sklearn.cluster import MiniBatchKMeans as MiniBatchKMeans
import os
import numpy
directory = 'output-features-16k-classes-2-layers-200MB-3'
numberOfClusters = 16
vocab = Vocab({"model": {"vocab": os.path.join(directory, 'vocab.txt')}})
embeddings = numpy.load(os.path.join(directory, 'features.npy'))
inputs = numpy.load(os.path.join(directory, 'inputs.npy'))
labels = numpy.load(os.path.join(directory, 'labels.npy'))
chunkCount = embeddings.shape[0]
chunkLength = embeddings.shape[1]
clusters = numpy.reshape(
KMeans(n_clusters=numberOfClusters).fit_predict(
numpy.reshape(embeddings, (-1, embeddings.shape[-1]))),
(chunkCount, chunkLength))
clusterMap = {i: [] for i in range(numberOfClusters)}
for chunk in range(chunkCount):
chunkString = [
vocab.getTokenString(labels[chunk, word])
def __init__(self, config):
self.config = config
self.vocab = Vocab(config)
def rewriteSplitTokens(self, inputs, labels, predictions):
from functools import reduce
newInputs = []
newPredictions = []
newVocabProbabilities = []
batchSize = predictions.shape[0]
sequenceLength = predictions.shape[1]
# collapse expanded tokens
for batch in range(batchSize):
inputString = "".join([
self.vocab.getTokenString(token) for token in labels[batch, :]
if not Vocab.isReservedToken(token)
])
reservedIndices = set([
index for index, token in enumerate(labels[batch, :])
if Vocab.isReservedToken(token)
])
tokenizer = UnlimitedVocabTokenizerAdaptor(
StringDataSource(inputString))
completeTokens = [
tokenizer.next() for i in range(tokenizer.size())
]
logger.debug("Reformed input string: '" + str([
self.vocab.getTokenString(token) for token in labels[batch, :]
if not Vocab.isReservedToken(token)
]))
logger.debug("' tokenized to: " + str(completeTokens))
logger.debug(
" tokens: " +
str([self.vocab.getToken(token) for token in completeTokens]))
index = 0
completeTokenIndex = 0
newBatchInputs = []
newBatchPredictions = []
newBatchVocabProbabilities = []
while index < sequenceLength:
token = labels[batch, index]
completeToken = completeTokens[completeTokenIndex]
# get token end
tokenEndIndex = index + 1
if self.vocab.getToken(
completeToken
) != token and not index in reservedIndices:
while tokenEndIndex < sequenceLength:
possibleToken = labels[batch, tokenEndIndex]
if (completeTokenIndex + 1) < len(completeTokens):
if self.vocab.getToken(
completeTokens[completeTokenIndex +
1]) == possibleToken:
break
tokenEndIndex += 1
# add token
newBatchInputs.append([index, tokenEndIndex])
newBatchVocabProbabilities.append(
list(predictions[batch, index, :]))
newBatchVocabProbabilities[-1][0] = 0.0
# compute new probabilities for the merged token
predictionValues = predictions[batch, index:tokenEndIndex, 0]
newBatchPredictions.append(
reduce(lambda x, y: x * y, predictionValues))
if tokenEndIndex > (index + 1):
logger.debug("Reformed split tokens: " + str([
self.vocab.getTokenString(token)
for token in labels[batch, index:tokenEndIndex]
]) + (" with prob: %.4f" % newBatchPredictions[-1]))
if not index in reservedIndices:
completeTokenIndex += 1
index = tokenEndIndex
newInputs.append(newBatchInputs)
newPredictions.append(newBatchPredictions)
newVocabProbabilities.append(newBatchVocabProbabilities)
# pad
maxLength = max([len(tokens) for tokens in newInputs])
newInputs = [
inputs +
[self.getPadToken() for i in range(maxLength - len(inputs))]
for inputs in newInputs
]
newPredictions = [
predictions + [0.0 for i in range(maxLength - len(predictions))]
for predictions in newPredictions
]
newVocabProbabilties = [
predictions + [0.0 for i in range(maxLength - len(predictions))]
for predictions in newVocabProbabilities
]
return numpy.array(newInputs), numpy.array(
newPredictions), numpy.array(newVocabProbabilities)
def runLocally(arguments):
import numpy
numpy.set_printoptions(precision=3, linewidth=150)
device = getDevice()
with tf.device(device):
for scope in arguments["enable_logger"]:
logger = logging.getLogger(scope)
logger.setLevel(logging.DEBUG)
config = loadConfig(arguments)
overrideConfig(config, arguments)
if arguments["predict"]:
if not "predictor" in config:
config["predictor"] = {}
validationData = getValidationData(config)
predictor = getPredictor(config, validationData)
perplexity = predictor.predict()
print("Perplexity " + str(perplexity))
elif arguments["make_clusters"]:
validationData = getValidationData(config)
clusterer = getClusterer(config, validationData,
arguments["output_directory"],
int(arguments["cluster_count"]))
clusterer.groupDataIntoClusters()
elif arguments["make_test_set"]:
validationData = getValidationData(config)
if int(arguments["test_set_size"]) > 0:
saveData(validationData, int(arguments["test_set_size"]),
arguments["output_directory"], Vocab(config))
else:
assert int(arguments["test_set_size_bytes"]) > 0
saveDataBytes(validationData,
int(arguments["test_set_size_bytes"]),
arguments["output_directory"], Vocab(config))
elif arguments["make_vocab"]:
validationData = getValidationData(config)
saveVocab(validationData, int(arguments["vocab_size"]),
arguments["output_directory"])
else:
config["model"]["directory"] = nameDirectory(
arguments["experiment_name"])
makeExperiment(config)
trainingData = getTrainingData(config)
validationData = getValidationData(config)
model = getModel(config, trainingData, validationData)
model.train()
def loadVocab(self):
return Vocab(self.config)
class LinearModel:
def __init__(self, config, trainingDataSource, validationDataSource):
"""Initializes the linear model object.
Attributes:
config: The configuration for the model.
trainingDataSource: list of training samples and labels
validationDataSource: list of validation samples and labels
"""
self.config = config
self.trainingDataSource = trainingDataSource
self.validationDataSource = validationDataSource
self.graph = tf.Graph()
self.session = tf.Session(graph=self.graph)
self.checkpointer = ModelDescriptionCheckpointer(config, "LinearModel")
self.isLoaded = False
def train(self):
"""Trains the linear model.
Trains the model for epochs specified in the config.
Runs the validation dataset on the model if specified in the config.
"""
with self.graph.as_default():
self.getOrLoadModel()
for epoch in range(self.getEpochs()):
self.runOnTrainingDataset(epoch)
if self.shouldRunValidation():
self.runOnValidationDataset(epoch)
self.checkpoint()
def predict(self, inputs, requestedPredictions):
with self.graph.as_default():
self.getOrLoadModel()
inputs = numpy.array(inputs)
predictions = self.session.run(self.outputProbabilities,
feed_dict={self.inputTokens: inputs})
batchSize = requestedPredictions.shape[0]
length = requestedPredictions.shape[1]
outputPredictions = numpy.zeros(requestedPredictions.shape)
for b in range(batchSize):
for l in range(length):
outputPredictions[b,l,:] = \
predictions[b,l,requestedPredictions[b,l,:]]
return outputPredictions
def getOrLoadModel(self):
"""Returns a linear model.
If specified, create a new model else load an already existing model.
"""
self.vocab = Vocab(self.config)
shouldCreate = not os.path.exists(self.checkpointer.getModelDirectory(
)) or self.getShouldCreateModel()
if shouldCreate:
self.createModel()
else:
self.loadModel()
def loadModel(self):
"""Loads an already existing model from the specified path """
if self.isLoaded:
return
self.checkpointer.load()
directory = self.checkpointer.getModelDirectory()
logger.debug("Loading checkpoint from: " + str(directory))
tf.saved_model.loader.load(self.session, ["serve"], directory)
self.setOperationsByName()
self.isLoaded = True
def setOperationsByName(self):
self.inputTokens = self.graph.get_tensor_by_name("input-tokens:0")
self.labels = self.graph.get_tensor_by_name("output-labels:0")
self.outputProbabilities = \
self.graph.get_tensor_by_name("output-probabilities:0")
self.loss = self.graph.get_tensor_by_name("loss:0")
self.optimizerStep = self.graph.get_operation_by_name("optimizer-step")
def createModel(self):
## (batch, sequence-length, 1)
self.inputTokens = tf.placeholder(tf.int32,
shape=(None, None),
name="input-tokens")
self.labels = tf.placeholder(tf.int32,
shape=(None, None),
name="output-labels")
predictedLogits = self.processInputMiniBatch(self.inputTokens)
self.loss = self.evaluateLoss(predictedLogits, self.labels)
self.outputProbabilities = tf.nn.softmax(predictedLogits,
name="output-probabilities")
# optimizer
self.optimizerStep = self.createOptimizerStep(self.loss)
# initializers
self.globalInitializer = tf.global_variables_initializer()
self.localInitializer = tf.local_variables_initializer()
# summaries
self.setupSummaries()
# do the initialization
self.initializeModel()
def initializeModel(self):
self.session.run(self.globalInitializer)
self.session.run(self.localInitializer)
def runOnTrainingDataset(self, epoch):
"""Trains the linear model on the training dataset for one epoch."""
trainStart = time.time()
for step in range(self.getStepsPerEpoch()):
generatorStart = time.time()
inputs, labels = self.trainingDataSource.next()
generatorEnd = time.time()
trainStepStart = time.time()
loss, gradNorm = self.trainingStep(inputs, labels, step)
trainStepEnd = time.time()
message = ("Epoch (" + str(epoch) + " / " + str(self.getEpochs()) +
"), Step (" + str(step) + " / " +
str(self.getStepsPerEpoch()) + "), Generator time: " +
("%.2f" % (generatorEnd - generatorStart)) +
", training step time: " +
("%.2f" % (trainStepEnd - trainStepStart) + ", loss: " +
str("%.2f" % loss) + ", grad norm: " +
str("%.2f" % gradNorm)))
print(message, end="\r", flush=True)
trainEnd = time.time()
print(message)
logger.debug(" Training took: " + (str(trainEnd - trainStart)) +
" seconds...")
def trainingStep(self, inputs, labels, step):
"""Training step for one minibatch of training data."""
inputs = numpy.array(inputs)
labels = numpy.array(labels)
trainingLoss, gradNorm, summaries, _ = self.session.run(
[
self.loss, self.gradientNorm, self.mergedSummary,
self.optimizerStep
],
feed_dict={
self.inputTokens: inputs,
self.labels: labels
})
self.trainingSummaryWriter.add_summary(summaries, step)
return trainingLoss, gradNorm
def runOnValidationDataset(self, epoch):
"""Runs the linear model on the validation dataset for one epoch."""
validationStart = time.time()
for step in range(self.getValidationStepsPerEpoch()):
generatorStart = time.time()
inputs, labels = self.validationDataSource.next()
generatorEnd = time.time()
validationStepStart = time.time()
loss, summary = self.validationStep(inputs, labels)
validationStepEnd = time.time()
message = ("Validation Step (" + str(step) + " / " +
str(self.getValidationStepsPerEpoch()) +
"), Generator time: " +
("%.2f" % (generatorEnd - generatorStart)) +
", validation step time: " +
("%.2f" % (validationStepEnd - validationStepStart) +
", loss: " + str(loss)))
print(message, end="\r", flush=True)
validationEnd = time.time()
print(message)
logger.debug(" Validation took: " +
(str(validationEnd - validationStart)) + " seconds...")
def validationStep(self, inputs, labels):
"""One minibatch of validation data processed by the model."""
inputs = numpy.array(inputs)
labels = numpy.array(labels)
validationLoss, summaries = self.session.run(
[self.loss, self.mergedSummary],
feed_dict={
self.inputTokens: inputs,
self.labels: labels
})
return validationLoss, summaries
def createOptimizerStep(self, loss):
"""One step of backprop."""
optimizer = tf.train.AdamOptimizer(learning_rate=float(
self.config["model"]["learningRate"]),
beta1=0.9,
beta2=0.999,
epsilon=numpy.finfo(float).eps,
name="optimizer-step")
gradients, variables = zip(*optimizer.compute_gradients(loss))
gradients, _ = tf.clip_by_global_norm(
gradients, self.config["model"]["gradientClippingFactor"])
self.gradientNorm = tf.global_norm(gradients, name="gradient-norm")
return optimizer.apply_gradients(zip(gradients, variables))
def setupSummaries(self):
tf.summary.scalar('cross-entropy', self.loss)
tf.summary.scalar('gradient-norm', self.gradientNorm)
self.mergedSummary = tf.summary.merge_all()
self.trainingSummaryWriter = tf.summary.FileWriter(
os.path.join(self.getExperimentDirectory(), 'training-summaries'),
self.graph)
if self.shouldRunValidation():
self.validationSummaryWriter = tf.summary.FileWriter(
os.path.join(self.getExperimentDirectory(),
'validation-summaries'), self.graph)
def evaluateLoss(self, batchOutputs, labels):
return tf.identity(tf.losses.sparse_softmax_cross_entropy(
labels=labels, logits=batchOutputs),
name="loss")
def processInputMiniBatch(self, inputs):
return self.runEncoderDecoder(inputs, inputs)
def runEncoderDecoder(self, inputSequence, historicSequence):
# convert sequences to embeddings (output embeddings are Tensor(batch-size, sequence-length, hidden))
inputEmbeddings = self.convertToEmbeddings(inputSequence)
historicEmbeddings = self.convertToEmbeddings(historicSequence)
# run encoder (encodedEmbeddings is (batch-size, sequence-length, hidden))
encodedEmbeddings = self.runEncoder(inputEmbeddings)
# run decoder (logits is Tensor(batch-size, sequence-length, vocab-size)
logits = self.runDecoder(encodedEmbeddings, historicEmbeddings)
return logits
def convertToEmbeddings(self, sequenceIds):
with tf.variable_scope("linear-embeddings", reuse=tf.AUTO_REUSE):
wordEmbeddingsGlobal = tf.get_variable('word-embeddings', \
[self.vocab.getSize(), self.getEmbeddingSize()])
wordEmbeddings = tf.nn.embedding_lookup(wordEmbeddingsGlobal,
sequenceIds)
return wordEmbeddings
def runEncoder(self, embeddings):
return tf.layers.dense(embeddings,
self.getEmbeddingSize(),
activation="relu")
def runDecoder(self, inputEmbeddings, historicEmbeddings):
return tf.layers.dense(
tf.concat([inputEmbeddings, historicEmbeddings], axis=2),
self.vocab.getSize())
def checkpoint(self):
"""Creates a checkpoint of current model and saves to model
directory.
"""
directory = self.checkpointer.getModelDirectory()
logger.debug("Saving checkpoint to: " + str(directory))
self.checkpointer.checkpoint()
exists = os.path.exists(directory)
if exists:
tempDirectory = directory + "-temp"
shutil.move(directory, tempDirectory)
with self.graph.as_default():
tf.saved_model.simple_save(
self.session,
directory,
inputs={"input-tokens": self.inputTokens},
outputs={"output-probabilities": self.outputProbabilities})
if exists:
shutil.rmtree(tempDirectory)
"""Functions to load configuration parameters."""
def getEmbeddingSize(self):
return int(self.config["model"]["embeddingSize"])
def shouldRunValidation(self):
return self.config["model"]["runValidation"]
def getEpochs(self):
return int(self.config["model"]["epochs"])
def getShouldCreateModel(self):
if not "createNewModel" in self.config["model"]:
return False
return bool(self.config["model"]["createNewModel"])
def getStepsPerEpoch(self):
return int(self.config["model"]["stepsPerEpoch"])
def getValidationStepsPerEpoch(self):
return int(self.config["model"]["validationStepsPerEpoch"])
def getExperimentDirectory(self):
return self.config["model"]["directory"]
class LabelAdaptor:
def __init__(self, config, source):
self.config = config
self.source = source
self.secondSource = source.clone()
self.secondSource.shuffleDocuments()
self.random = numpy.random.RandomState(seed=self.getSeed())
self.vocab = Vocab(config)
def next(self):
chunk = self.source.next()
isFromSameSource = self.random.binomial(1, 0.5)
if isFromSameSource:
secondChunk = self.source.next()
else:
secondChunk = self.secondSource.next()
chunk, documentId = zip(*chunk)
secondChunk, secondDocumentId = zip(*secondChunk)
labels = self.addTokenLabels(chunk, documentId)
inputs = self.maskOffTokens(labels)
secondLabels = self.addTokenLabels(secondChunk, secondDocumentId)
secondInputs = self.maskOffTokens(secondLabels)
return inputs, labels, secondInputs, secondLabels
def addTokenLabels(self, chunk, documentIds):
return [documentIds[0]] + list(chunk)
def maskOffTokens(self, labels):
inputs = list(labels)
for i in range(1, len(labels)):
if self.random.binomial(1, 0.15):
if self.random.binomial(1, 0.8):
inputs[i] = Vocab.getMaskToken()
else:
if self.random.binomial(1, 0.5):
inputs[i] = self.random.randint(Vocab.getVocabOffset(),
self.vocab.getSize())
inputs[0] = Vocab.getClassLabelToken()
return inputs
def getSeed(self):
if not "size" in self.config["adaptor"]["labels"]:
return 122
return int(self.config["adaptor"]["labels"]["seed"])
def reset(self):
self.random = numpy.random.RandomState(seed=self.getSeed())
self.source.reset()
self.secondSource.reset()
self.secondSource.shuffleDocuments()
def size(self):
return self.source.size()
def setMaximumSize(self, size):
self.source.setMaximumSize(size)
class FallbackTokenEvaluator:
def __init__(self, config):
self.config = config
self.vocab = Vocab(config)
def initialize(self):
self.perplexityStates = self.createPerplexityStates(
self.getBatchSize())
def evaluate(self, inputs, labels, predictions):
inputIndices, predictions, vocabProbabilities = self.rewriteSplitTokens(
inputs, labels, predictions)
self.recordPredictions(predictions, vocabProbabilities, inputIndices,
inputs)
def getRequestedPredictions(self, inputs, labels):
return numpy.expand_dims(labels, axis=2)
def finalize(self):
return self.getPerplexity()
def getBatchSize(self):
if not "adaptor" in self.config:
return 1
if not "batching" in self.config["adaptor"]:
return 1
if not "size" in self.config["adaptor"]["batching"]:
return 1
return int(self.config["adaptor"]["batching"]["size"])
def createPerplexityStates(self, count):
return [PerplexityState(self.vocab) for i in range(count)]
def getPerplexity(self):
byteCount = sum(
[state.getByteCount() for state in self.perplexityStates])
totalEntropy = sum(
[state.getEntropy() for state in self.perplexityStates])
return 2.0**(totalEntropy / byteCount)
def recordPredictions(self, predictions, vocabProbabilities, inputIndices,
inputs):
# predictions is Tensor(batch-size, sequence-length, vocab-size)
# inputs is Tensor(batch-size, sequence-length)
batchSize = predictions.shape[0]
sequenceLength = predictions.shape[1]
# TODO: replace with something like batch gather
for batch in range(batchSize):
for element in range(sequenceLength):
labelPrediction = predictions[batch, element]
self.perplexityStates[batch].addPrediction(
inputs[batch, :], inputIndices[batch, element],
labelPrediction, vocabProbabilities[batch, element, :])
def rewriteSplitTokens(self, inputs, labels, predictions):
from functools import reduce
newInputs = []
newPredictions = []
newVocabProbabilities = []
batchSize = predictions.shape[0]
sequenceLength = predictions.shape[1]
# collapse expanded tokens
for batch in range(batchSize):
inputString = "".join([
self.vocab.getTokenString(token) for token in labels[batch, :]
if not Vocab.isReservedToken(token)
])
reservedIndices = set([
index for index, token in enumerate(labels[batch, :])
if Vocab.isReservedToken(token)
])
tokenizer = UnlimitedVocabTokenizerAdaptor(
StringDataSource(inputString))
completeTokens = [
tokenizer.next() for i in range(tokenizer.size())
]
logger.debug("Reformed input string: '" + str([
self.vocab.getTokenString(token) for token in labels[batch, :]
if not Vocab.isReservedToken(token)
]))
logger.debug("' tokenized to: " + str(completeTokens))
logger.debug(
" tokens: " +
str([self.vocab.getToken(token) for token in completeTokens]))
index = 0
completeTokenIndex = 0
newBatchInputs = []
newBatchPredictions = []
newBatchVocabProbabilities = []
while index < sequenceLength:
token = labels[batch, index]
completeToken = completeTokens[completeTokenIndex]
# get token end
tokenEndIndex = index + 1
if self.vocab.getToken(
completeToken
) != token and not index in reservedIndices:
while tokenEndIndex < sequenceLength:
possibleToken = labels[batch, tokenEndIndex]
if (completeTokenIndex + 1) < len(completeTokens):
if self.vocab.getToken(
completeTokens[completeTokenIndex +
1]) == possibleToken:
break
tokenEndIndex += 1
# add token
newBatchInputs.append([index, tokenEndIndex])
newBatchVocabProbabilities.append(
list(predictions[batch, index, :]))
newBatchVocabProbabilities[-1][0] = 0.0
# compute new probabilities for the merged token
predictionValues = predictions[batch, index:tokenEndIndex, 0]
newBatchPredictions.append(
reduce(lambda x, y: x * y, predictionValues))
if tokenEndIndex > (index + 1):
logger.debug("Reformed split tokens: " + str([
self.vocab.getTokenString(token)
for token in labels[batch, index:tokenEndIndex]
]) + (" with prob: %.4f" % newBatchPredictions[-1]))
if not index in reservedIndices:
completeTokenIndex += 1
index = tokenEndIndex
newInputs.append(newBatchInputs)
newPredictions.append(newBatchPredictions)
newVocabProbabilities.append(newBatchVocabProbabilities)
# pad
maxLength = max([len(tokens) for tokens in newInputs])
newInputs = [
inputs +
[self.getPadToken() for i in range(maxLength - len(inputs))]
for inputs in newInputs
]
newPredictions = [
predictions + [0.0 for i in range(maxLength - len(predictions))]
for predictions in newPredictions
]
newVocabProbabilties = [
predictions + [0.0 for i in range(maxLength - len(predictions))]
for predictions in newVocabProbabilities
]
return numpy.array(newInputs), numpy.array(
newPredictions), numpy.array(newVocabProbabilities)
class UnigramModel:
def __init__(self, config, trainingData, validationData):
self.config = config
self.trainingData = trainingData
self.validationData = validationData
self.checkpointer = ModelDescriptionCheckpointer(config, "UnigramModel")
if not self.trainingData is None:
self.trainingData.setMaximumSize(int(self.config["model"]["stepsPerEpoch"]))
if not self.validationData is None:
self.validationData.setMaximumSize(int(self.config["model"]["validationStepsPerEpoch"]))
self.getOrLoadModel()
def train(self):
for epoch in range(self.getEpochs()):
self.trainingData.reset()
self.runOnTrainingDataset(epoch)
if self.shouldRunValidation():
self.trainingData.reset()
self.runOnValidationDataset(epoch)
self.checkpoint()
def runOnTrainingDataset(self, epoch):
import time
trainStart = time.time()
for step in range(self.getStepsPerEpoch()):
generatorStart = time.time()
inputs, labels = self.trainingData.next()
generatorEnd = time.time()
trainStepStart = time.time()
self.trainingStep(inputs, labels)
trainStepEnd = time.time()
message = ("Epoch (" + str(epoch) + " / " + str(self.getEpochs()) +
"), Step (" + str(step) + " / " + str(self.getStepsPerEpoch()) +
"), Generator time: " + ("%.2f" % (generatorEnd - generatorStart)) +
", training step time: " + ("%.2f" % (trainStepEnd - trainStepStart)))
print(message, end="\r", flush=True)
trainEnd = time.time()
print(message)
logger.debug(" Training took: " + (str(trainEnd - trainStart)) + " seconds...")
def trainingStep(self, inputs, labels):
# just consider the labels
for batch in range(labels.shape[0]):
self.totalTokens += labels.shape[1]
for token in range(labels.shape[1]):
self.tokenCounts[labels[batch, token]] += 1
def runOnValidationDataset(self, epoch):
import time
start = time.time()
totalCrossEntropy = 0.0
totalBytes = 0
for step in range(self.getValidationStepsPerEpoch()):
generatorStart = time.time()
inputs, labels = self.validationData.next()
generatorEnd = time.time()
stepStart = time.time()
crossEntropy, byteCount = self.validationStep(inputs, labels)
stepEnd = time.time()
message = ("Epoch (" + str(epoch) + " / " + str(self.getEpochs()) +
"), Step (" + str(step) + " / " + str(self.getValidationStepsPerEpoch()) +
"), Generator time: " + ("%.2f" % (generatorEnd - generatorStart)) +
", validation step time: " + ("%.2f" % (stepEnd - stepStart)) +
", loss is " + str(crossEntropy/tokens))
print(message, end="\r", flush=True)
totalCrossEntropy += crossEntropy
totalBytes += byteCount
end = time.time()
print(message)
logger.debug(" Validation took: " + (str(end - start)) + " seconds... cross entropy is " +
str(totalCrossEntropy/totalBytes))
def validationStep(self, inputs, labels):
import math
crossEntropy = 0.0
byteCount = 0
for batch in range(labels.shape[0]):
for index in range(labels.shape[1]):
token = labels[batch, token]
tokenProbability = self.getTokenProbability(token)
crossEntropy += -math.log(tokenProbability)
byteCount = self.vocab.getTokenBytes()
return crossEntropy, byteCount
def getTokenProbability(self, token):
count = self.tokenCounts[token]
# TODO: Implement kneser ney smoothing
return (count + 1.0) / (self.totalTokens + 1.0)
def getOrLoadModel(self):
import os
self.vocab = Vocab(self.config)
shouldCreate = not os.path.exists(
self.checkpointer.getModelDirectory()) or self.getShouldCreateModel()
if shouldCreate:
self.createModel()
else:
self.load()
def createModel(self):
self.tokenCounts = numpy.zeros(self.vocab.getSize())
self.totalTokens = 0
def checkpoint(self):
import json
import os
import shutil
directory = self.checkpointer.getModelDirectory()
logger.debug("Saving checkpoint to: " + str(directory))
self.checkpointer.checkpoint()
exists = os.path.exists(directory)
if exists:
tempDirectory = directory + "-temp"
shutil.move(directory, tempDirectory)
os.makedirs(directory)
with open(os.path.join(directory, "unigram-statistics.json"), "w") as jsonFile:
json.dump([self.totalTokens, [i for i in self.tokenCounts]], jsonFile)
if exists:
shutil.rmtree(tempDirectory)
def predict(self, inputs):
batchSize = inputs.shape[0]
length = inputs.shape[1]
vocab = self.getVocab().getSize()
probs = [self.getTokenProbability(token) for token in range(vocab)]
return numpy.broadcast_to(numpy.array(probs), [batchSize, length, vocab])
def load(self):
import os
import json
self.checkpointer.load()
directory = self.checkpointer.getModelDirectory()
logger.debug("Loading checkpoint from: " + str(directory))
with open(os.path.join(directory, "unigram-statistics.json"), "r") as jsonFile:
self.totalTokens, counts = json.load(jsonFile)
self.tokenCounts = numpy.array(counts)
def getVocab(self):
return self.vocab
def getEpochs(self):
return int(self.config["model"]["epochs"])
def getShouldCreateModel(self):
if not "createNewModel" in self.config["model"]:
return False
return bool(self.config["model"]["createNewModel"])
def getStepsPerEpoch(self):
return min(int(self.config["model"]["stepsPerEpoch"]), self.trainingData.size())
def getValidationStepsPerEpoch(self):
return min(int(self.config["model"]["validationStepsPerEpoch"]), self.validationData.size())
def shouldRunValidation(self):
if not "runValidation" in self.config["model"]:
return True
return bool(self.config["model"]["runValidation"])
def isPredictedToken(self, token):
return token == Vocab.getMaskToken() or token == Vocab.getVocabOffset()
def groupDataIntoClusters(self):
kmeans = MiniBatchKMeans(n_clusters=self.numberOfClusters)
featurizer = Featurizer(self.config, self.validationDataset)
vocab = Vocab(self.config)
if self.usePCA():
pca = IncrementalPCA(n_components=32)
logger.info("Reducing dimensionality...")
# fit the pca model
if self.usePCA():
for iteration in range(self.getIterations()):
if iteration % 10 == 0:
logger.info(" " + str(iteration) + " / " +
str(self.getIterations()))
inputs, labels, embeddings = featurizer.featurizeOneBatch()
pca.partial_fit(
numpy.reshape(embeddings, (-1, embeddings.shape[-1])))
self.validationDataset.reset()
logger.info("Fitting model...")
# fit the kmeans model
for iteration in range(self.getIterations()):
if iteration % 10 == 0:
inputs, labels, embeddings, dataTime, modelTime = featurizer.featurizeOneBatch(
reportTime=True)
logger.info(" " + str(iteration) + " / " +
str(self.getIterations()) + " data load time: " +
str(dataTime) + " model eval time: " +
str(modelTime))
else:
inputs, labels, embeddings = featurizer.featurizeOneBatch()
if self.usePCA():
embeddings = pca.transform(
numpy.reshape(embeddings, (-1, embeddings.shape[-1])))
kmeans.partial_fit(
numpy.reshape(embeddings, (-1, embeddings.shape[-1])))
self.validationDataset.reset()
# group into clusters
# create a histogram of word frequencies per cluster
clusterHistogram = {i: {} for i in range(self.numberOfClusters)}
clusterWins = {i: 0 for i in range(self.numberOfClusters)}
documentMap = {}
logger.info("Clustering data...")
for iteration in range(self.getIterations()):
if iteration % 10 == 0:
inputs, labels, embeddings, dataTime, modelTime = featurizer.featurizeOneBatch(
reportTime=True)
logger.info(" " + str(iteration) + " / " +
str(self.getIterations()) + " data load time: " +
str(dataTime) + " model eval time: " +
str(modelTime))
else:
inputs, labels, embeddings = featurizer.featurizeOneBatch()
chunkLength = embeddings.shape[1]
batchSize = embeddings.shape[0]
if self.usePCA():
embeddings = pca.transform(
numpy.reshape(embeddings, (-1, embeddings.shape[-1])))
clusters = numpy.reshape(
kmeans.predict(
numpy.reshape(embeddings, (-1, embeddings.shape[-1]))),
(batchSize, chunkLength))
for batch in range(batchSize):
documentId = labels[batch, 0]
if not documentId in documentMap:
documentMap[documentId] = []
clusterIds = []
for wordIndex in range(1, chunkLength):
word = vocab.getTokenString(labels[batch, wordIndex])
cluster = clusters[batch, wordIndex]
clusterIds.append(cluster)
if not labels[batch,
wordIndex] in clusterHistogram[cluster]:
clusterHistogram[cluster][labels[batch, wordIndex]] = 0
clusterHistogram[cluster][labels[batch, wordIndex]] += 1
clusterWins[cluster] += 1
documentMap[documentId].extend(clusterIds)
if not os.path.exists(self.outputDirectory):
os.makedirs(self.outputDirectory)
# write histograms
with open(self.getOutputHistogramFileName(), "w") as log:
for clusterId, clusterCount in sorted(clusterWins.items(),
key=lambda x: x[1],
reverse=True):
words = clusterHistogram[clusterId]
log.write("Cluster, " + str(clusterId) + " (" +
str(clusterCount) + ")\n")
for wordIndex, count in sorted(words.items(),
key=lambda x: x[1],
reverse=True):
log.write(" '" + vocab.getTokenString(wordIndex) +
"' " + str(count) + "\n")
# write document clusters
for documentId, clusters in documentMap.items():
histogram = {}
for cluster in clusters:
if not cluster in histogram:
histogram[cluster] = 0
histogram[cluster] += 1
with open(self.getOutputDocumentClusterFileName(documentId),
"w") as log:
for cluster, count in sorted(histogram.items(),
key=lambda x: x[1],
reverse=True):
words = clusterHistogram[cluster]
topWord = vocab.getTokenString(
sorted(words.items(), key=lambda x: x[1],
reverse=True)[0][0])
log.write("Cluster, " + str(cluster) + ", " + topWord +
", " + str(count) + "\n")
class BERTModel:
def __init__(self, config, trainingDataSource, validationDataSource):
"""Initializes the linear model object.
Attributes:
config: The configuration for the model.
trainingDataSource: list of training samples and labels
validationDataSource: list of validation samples and labels
"""
self.config = config
self.trainingDataSource = trainingDataSource
self.validationDataSource = validationDataSource
self.graph = tf.Graph()
self.session = tf.Session(graph=self.graph)
self.checkpointer = ModelDescriptionCheckpointer(config, "BERTModel")
self.isLoaded = False
def train(self):
"""Trains the linear model.
Trains the model for epochs specified in the config.
Runs the validation dataset on the model if specified in the config.
"""
with self.graph.as_default():
self.getOrLoadModel()
for epoch in range(self.getEpochs()):
self.runOnTrainingDataset(epoch)
if self.shouldRunValidation():
self.runOnValidationDataset(epoch)
self.checkpoint()
def predict(self, inputs, requestedPredictions):
with self.graph.as_default():
self.getOrLoadModel()
inputs = numpy.array(inputs)
predictions = self.session.run(self.outputProbabilities,
feed_dict={self.inputTokens: inputs})
batchSize = requestedPredictions.shape[0]
length = requestedPredictions.shape[1]
outputPredictions = numpy.zeros(requestedPredictions.shape)
for b in range(batchSize):
for l in range(length):
outputPredictions[b,l,:] = \
predictions[b,l,requestedPredictions[b,l,:]]
return outputPredictions
def getOrLoadModel(self):
"""Returns a linear model.
If specified, create a new model else load an already existing model.
"""
self.vocab = Vocab(self.config)
shouldCreate = not os.path.exists(self.checkpointer.getModelDirectory(
)) or self.getShouldCreateModel()
if shouldCreate:
self.createModel()
else:
self.loadModel()
def loadModel(self):
"""Loads an already existing model from the specified path """
if self.isLoaded:
return
self.checkpointer.load()
directory = self.checkpointer.getModelDirectory()
logger.debug("Loading checkpoint from: " + str(directory))
tf.saved_model.loader.load(self.session, ["serve"], directory)
self.setOperationsByName()
self.isLoaded = True
def setOperationsByName(self):
self.inputTokens = self.graph.get_tensor_by_name("input-tokens:0")
self.labels = self.graph.get_tensor_by_name("output-labels:0")
self.outputProbabilities = \
self.graph.get_tensor_by_name("output-probabilities:0")
self.loss = self.graph.get_tensor_by_name("loss:0")
self.optimizerStep = self.graph.get_operation_by_name("optimizer-step")
def createModel(self):
## (batch, sequence-length, 1)
self.inputTokens = tf.placeholder(tf.int32,
shape=(None, None),
name="input-tokens")
self.labels = tf.placeholder(tf.int32,
shape=(None, None),
name="output-labels")
predictedLogits = self.processInputMiniBatch(self.inputTokens)
self.loss = self.evaluateLoss(predictedLogits, self.labels)
self.outputProbabilities = tf.nn.softmax(predictedLogits,
name="output-probabilities")
# optimizer
self.optimizerStep = self.createOptimizerStep(self.loss)
# initializers
self.globalInitializer = tf.global_variables_initializer()
self.localInitializer = tf.local_variables_initializer()
# summaries
self.setupSummaries()
# do the initialization
self.initializeModel()
def initializeModel(self):
self.session.run(self.globalInitializer)
self.session.run(self.localInitializer)
def runOnTrainingDataset(self, epoch):
"""Trains the linear model on the training dataset for one epoch."""
trainStart = time.time()
for step in range(self.getStepsPerEpoch()):
generatorStart = time.time()
inputs, labels = self.trainingDataSource.next()
generatorEnd = time.time()
trainStepStart = time.time()
loss, gradNorm = self.trainingStep(inputs, labels, step)
trainStepEnd = time.time()
message = ("Epoch (" + str(epoch) + " / " + str(self.getEpochs()) +
"), Step (" + str(step) + " / " +
str(self.getStepsPerEpoch()) + "), Generator time: " +
("%.2f" % (generatorEnd - generatorStart)) +
", training step time: " +
("%.2f" % (trainStepEnd - trainStepStart) + ", loss: " +
str("%.2f" % loss) + ", grad norm: " +
str("%.2f" % gradNorm)))
print(message, end="\r", flush=True)
trainEnd = time.time()
print(message)
logger.debug(" Training took: " + (str(trainEnd - trainStart)) +
" seconds...")
def trainingStep(self, inputs, labels, step):
"""Training step for one minibatch of training data."""
inputs = numpy.array(inputs)
labels = numpy.array(labels)
trainingLoss, gradNorm, summaries, _ = self.session.run(
[
self.loss, self.gradientNorm, self.mergedSummary,
self.optimizerStep
],
feed_dict={
self.inputTokens: inputs,
self.labels: labels
})
self.trainingSummaryWriter.add_summary(summaries, step)
return trainingLoss, gradNorm
def runOnValidationDataset(self, epoch):
"""Runs the linear model on the validation dataset for one epoch."""
validationStart = time.time()
for step in range(self.getValidationStepsPerEpoch()):
generatorStart = time.time()
inputs, labels = self.validationDataSource.next()
generatorEnd = time.time()
validationStepStart = time.time()
loss, summary = self.validationStep(inputs, labels)
validationStepEnd = time.time()
message = ("Validation Step (" + str(step) + " / " +
str(self.getValidationStepsPerEpoch()) +
"), Generator time: " +
("%.2f" % (generatorEnd - generatorStart)) +
", validation step time: " +
("%.2f" % (validationStepEnd - validationStepStart) +
", loss: " + str(loss)))
print(message, end="\r", flush=True)
validationEnd = time.time()
print(message)
logger.debug(" Validation took: " +
(str(validationEnd - validationStart)) + " seconds...")
def validationStep(self, inputs, labels):
"""One minibatch of validation data processed by the model."""
inputs = numpy.array(inputs)
labels = numpy.array(labels)
validationLoss, summaries = self.session.run(
[self.loss, self.mergedSummary],
feed_dict={
self.inputTokens: inputs,
self.labels: labels
})
return validationLoss, summaries
def createOptimizerStep(self, loss):
"""One step of backprop."""
optimizer = tf.train.AdamOptimizer(learning_rate=float(
self.config["model"]["learningRate"]),
beta1=0.9,
beta2=0.999,
epsilon=numpy.finfo(float).eps,
name="optimizer-step")
gradients, variables = zip(*optimizer.compute_gradients(loss))
gradients, _ = tf.clip_by_global_norm(
gradients, self.config["model"]["gradientClippingFactor"])
self.gradientNorm = tf.global_norm(gradients, name="gradient-norm")
return optimizer.apply_gradients(zip(gradients, variables))
def setupSummaries(self):
tf.summary.scalar('cross-entropy', self.loss)
tf.summary.scalar('gradient-norm', self.gradientNorm)
self.mergedSummary = tf.summary.merge_all()
self.trainingSummaryWriter = tf.summary.FileWriter(
os.path.join(self.getExperimentDirectory(), 'training-summaries'),
self.graph)
if self.shouldRunValidation():
self.validationSummaryWriter = tf.summary.FileWriter(
os.path.join(self.getExperimentDirectory(),
'validation-summaries'), self.graph)
def evaluateLoss(self, batchOutputs, labels):
return tf.identity(tf.losses.sparse_softmax_cross_entropy(
labels=labels, logits=batchOutputs),
name="loss")
def processInputMiniBatch(self, inputs):
return self.runBERT(inputs, inputs)
def runBERT(self, inputSequence, historicSequence):
# convert sequences to embeddings (output embeddings are Tensor(batch-size, sequence-length, hidden))
inputEmbeddings = self.convertToEmbeddings(inputSequence)
inputEmbeddingsPositionallyEncoded = self.getPositionalEncodings(
inputEmbeddings)
# run encoder (encodedEmbeddings is (batch-size, sequence-length, hidden))
encodedEmbeddings = self.runEncoder(inputEmbeddingsPositionallyEncoded)
return tf.layers.dense(encodedEmbeddings, units=self.vocab.getSize())
def convertToEmbeddings(self, sequenceIds):
with tf.variable_scope("linear-embeddings", reuse=tf.AUTO_REUSE):
wordEmbeddingsGlobal = tf.get_variable('word-embeddings', \
[self.vocab.getSize(), self.getEmbeddingSize()])
wordEmbeddings = tf.nn.embedding_lookup(wordEmbeddingsGlobal,
sequenceIds)
return wordEmbeddings
def getPositionalEncodings(self, inputEmbeddings):
#PE(pos,2i)=sin(pos/100002i/dmodel)PE(pos,2i)=sin(pos/100002i/dmodel)
#PE(pos,2i+1)=cos(pos/100002i/dmodel)PE(pos,2i+1)=cos(pos/100002i/dmodel) where pospos is the position and ii is the dimension.
batchSize = tf.shape(inputEmbeddings)[0]
sequenceLength = tf.shape(inputEmbeddings)[1]
hiddenDimension = tf.shape(inputEmbeddings)[2]
sequenceRange = tf.reshape(
tf.range(tf.cast(sequenceLength, tf.float32)),
(1, sequenceLength, 1))
hiddenRange = tf.reshape(
tf.range(tf.cast(hiddenDimension, tf.float32)),
(1, 1, hiddenDimension))
rawPE = sequenceRange / (tf.pow(
10000.0, 2.0 * hiddenRange / tf.cast(hiddenDimension, tf.float32)))
PE_cos = tf.cos(rawPE[:, 0::2, :])
PE_sin = tf.sin(rawPE[:, 1::2, :])
PE_cos = tf.reshape(
PE_cos, (batchSize, tf.shape(PE_cos)[1], 1, hiddenDimension))
PE_sin = tf.reshape(
PE_sin, (batchSize, tf.shape(PE_sin)[1], 1, hiddenDimension))
PE = tf.concat([PE_cos, PE_sin], axis=2)
return inputEmbeddings + tf.reshape(
PE, (batchSize, sequenceLength, hiddenDimension))
def runEncoder(self, embeddings):
for i in range(self.getLayerCount()):
right = self.multiHeadedAttention(embeddings)
left = tf.layers.dense(right, units=embeddings.shape[-1])
embeddings = self.addAndNorm(left, right)
return embeddings
def multiHeadedAttention(self, embeddings):
# Q,K,V are all -> projected embeddings
projectedEmbeddings = self.projectEmbeddings(embeddings)
attentionResults = self.attention(projectedEmbeddings)
left = self.projectAttentionOutput(attentionResults)
return self.addAndNorm(left, embeddings)
def projectEmbeddings(self, embeddings):
#input -> m, seqL, embedding size
#output -> m, seqL, 3 * numberOfAttentionHeads * embedding size
retVal = tf.layers.dense(embeddings,
units=3 * self.getAttentionHeads() *
embeddings.shape[-1])
return tf.reshape(retVal, [
tf.shape(retVal)[0],
tf.shape(retVal)[1], 3,
self.getAttentionHeads(), embeddings.shape[-1]
])
def attention(self, projectedEmbeddings):
#m, seqL, (Q, K, V), attentionHeads, embedding size
Q = projectedEmbeddings[:, :, 0, :, :]
K = projectedEmbeddings[:, :, 1, :, :]
V = projectedEmbeddings[:, :, 2, :, :]
d_k = int(projectedEmbeddings.shape[-1])
m1 = tf.matmul(Q, K, transpose_b=True) / math.sqrt(d_k)
smx = tf.nn.softmax(m1)
return tf.matmul(smx, V)
def projectAttentionOutput(self, attentionResults):
#attentionResults -> m, seqL, attentionHeads, embedding size
#new shape is (batch, sequence length, heads * embedding-size)
batchSize = tf.shape(attentionResults)[0]
sequenceLength = tf.shape(attentionResults)[1]
reshapedEmbeddings = tf.reshape(
attentionResults,
(batchSize, sequenceLength,
attentionResults.shape[-1] * attentionResults.shape[-2]))
return tf.layers.dense(reshapedEmbeddings,
units=attentionResults.shape[-1])
def addAndNorm(self, left, right):
normalizedLeft = tf.contrib.layers.layer_norm(left)
return tf.add(normalizedLeft, right)
def checkpoint(self):
"""Creates a checkpoint of current model and saves to model
directory.
"""
directory = self.checkpointer.getModelDirectory()
logger.debug("Saving checkpoint to: " + str(directory))
self.checkpointer.checkpoint()
exists = os.path.exists(directory)
if exists:
tempDirectory = directory + "-temp"
shutil.move(directory, tempDirectory)
with self.graph.as_default():
tf.saved_model.simple_save(
self.session,
directory,
inputs={"input-tokens": self.inputTokens},
outputs={"output-probabilities": self.outputProbabilities})
if exists:
shutil.rmtree(tempDirectory)
"""Functions to load configuration parameters."""
def getEmbeddingSize(self):
return int(self.config["model"]["embeddingSize"])
def shouldRunValidation(self):
return self.config["model"]["runValidation"]
def getEpochs(self):
return int(self.config["model"]["epochs"])
def getShouldCreateModel(self):
if not "createNewModel" in self.config["model"]:
return False
return bool(self.config["model"]["createNewModel"])
def getStepsPerEpoch(self):
return int(self.config["model"]["stepsPerEpoch"])
def getValidationStepsPerEpoch(self):
return int(self.config["model"]["validationStepsPerEpoch"])
def getLayerCount(self):
return self.config["model"]["layerCount"]
def getAttentionHeads(self):
return self.config["model"]["attentionHeads"]
def getExperimentDirectory(self):
return self.config["model"]["directory"]
