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Commit 6ce292df authored by Frederick Liu's avatar Frederick Liu Committed by A. Unique TensorFlower
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[tfnlp] Remove nlp/transformer as we moved all usage to legacy/transformer 

please use Seq2SeqTransformer in tfnlp instead.

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official/nlp/transformer/transformer_main_test.py deleted 100644 → 0
View file @ d78ec6ea
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# 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.
"""Test Transformer model."""
import os
import re
import sys
import unittest
from absl import flags
from absl.testing import flagsaver
import tensorflow as tf
from tensorflow.python.eager import context # pylint: disable=ungrouped-imports
from official.nlp.transformer import misc
from official.nlp.transformer import transformer_main
FLAGS = flags.FLAGS
FIXED_TIMESTAMP = 'my_time_stamp'
WEIGHT_PATTERN = re.compile(r'weights-epoch-.+\.hdf5')
def _generate_file(filepath, lines):
with open(filepath, 'w') as f:
for l in lines:
f.write('{}\n'.format(l))
class TransformerTaskTest(tf.test.TestCase):
local_flags = None
def setUp(self): # pylint: disable=g-missing-super-call
temp_dir = self.get_temp_dir()
if TransformerTaskTest.local_flags is None:
misc.define_transformer_flags()
# Loads flags, array cannot be blank.
flags.FLAGS(['foo'])
TransformerTaskTest.local_flags = flagsaver.save_flag_values()
else:
flagsaver.restore_flag_values(TransformerTaskTest.local_flags)
FLAGS.model_dir = os.path.join(temp_dir, FIXED_TIMESTAMP)
FLAGS.param_set = 'tiny'
FLAGS.use_synthetic_data = True
FLAGS.steps_between_evals = 1
FLAGS.train_steps = 1
FLAGS.validation_steps = 1
FLAGS.batch_size = 4
FLAGS.max_length = 1
FLAGS.num_gpus = 1
FLAGS.distribution_strategy = 'off'
FLAGS.dtype = 'fp32'
self.model_dir = FLAGS.model_dir
self.temp_dir = temp_dir
self.vocab_file = os.path.join(temp_dir, 'vocab')
self.vocab_size = misc.get_model_params(FLAGS.param_set, 0)['vocab_size']
self.bleu_source = os.path.join(temp_dir, 'bleu_source')
self.bleu_ref = os.path.join(temp_dir, 'bleu_ref')
self.orig_policy = (
tf.compat.v2.keras.mixed_precision.global_policy())
def tearDown(self): # pylint: disable=g-missing-super-call
tf.compat.v2.keras.mixed_precision.set_global_policy(self.orig_policy)
def _assert_exists(self, filepath):
self.assertTrue(os.path.exists(filepath))
def test_train_no_dist_strat(self):
if context.num_gpus() >= 2:
self.skipTest('No need to test 2+ GPUs without a distribution strategy.')
t = transformer_main.TransformerTask(FLAGS)
t.train()
def test_train_save_full_model(self):
if context.num_gpus() >= 2:
self.skipTest('No need to test 2+ GPUs without a distribution strategy.')
FLAGS.save_weights_only = False
t = transformer_main.TransformerTask(FLAGS)
t.train()
def test_train_static_batch(self):
if context.num_gpus() >= 2:
self.skipTest('No need to test 2+ GPUs without a distribution strategy.')
FLAGS.distribution_strategy = 'one_device'
if tf.test.is_built_with_cuda():
FLAGS.num_gpus = 1
else:
FLAGS.num_gpus = 0
FLAGS.static_batch = True
t = transformer_main.TransformerTask(FLAGS)
t.train()
@unittest.skipUnless(tf.test.is_built_with_cuda(), 'requires GPU')
def test_train_1_gpu_with_dist_strat(self):
FLAGS.distribution_strategy = 'one_device'
t = transformer_main.TransformerTask(FLAGS)
t.train()
@unittest.skipUnless(tf.test.is_built_with_cuda(), 'requires GPU')
def test_train_fp16(self):
FLAGS.distribution_strategy = 'one_device'
FLAGS.dtype = 'fp16'
t = transformer_main.TransformerTask(FLAGS)
t.train()
@unittest.skipUnless(tf.test.is_built_with_cuda(), 'requires GPU')
def test_train_2_gpu(self):
if context.num_gpus() < 2:
self.skipTest(
'{} GPUs are not available for this test. {} GPUs are available'
.format(2, context.num_gpus()))
FLAGS.distribution_strategy = 'mirrored'
FLAGS.num_gpus = 2
FLAGS.param_set = 'base'
t = transformer_main.TransformerTask(FLAGS)
t.train()
@unittest.skipUnless(tf.test.is_built_with_cuda(), 'requires GPU')
def test_train_2_gpu_fp16(self):
if context.num_gpus() < 2:
self.skipTest(
'{} GPUs are not available for this test. {} GPUs are available'
.format(2, context.num_gpus()))
FLAGS.distribution_strategy = 'mirrored'
FLAGS.num_gpus = 2
FLAGS.param_set = 'base'
FLAGS.dtype = 'fp16'
t = transformer_main.TransformerTask(FLAGS)
t.train()
def _prepare_files_and_flags(self, *extra_flags):
# Make log dir.
if not os.path.exists(self.temp_dir):
os.makedirs(self.temp_dir)
# Fake vocab, bleu_source and bleu_ref.
tokens = [
"'<pad>'", "'<EOS>'", "'_'", "'a'", "'b'", "'c'", "'d'", "'a_'", "'b_'",
"'c_'", "'d_'"
]
tokens += ["'{}'".format(i) for i in range(self.vocab_size - len(tokens))]
_generate_file(self.vocab_file, tokens)
_generate_file(self.bleu_source, ['a b', 'c d'])
_generate_file(self.bleu_ref, ['a b', 'd c'])
# Update flags.
update_flags = [
'ignored_program_name',
'--vocab_file={}'.format(self.vocab_file),
'--bleu_source={}'.format(self.bleu_source),
'--bleu_ref={}'.format(self.bleu_ref),
]
if extra_flags:
update_flags.extend(extra_flags)
FLAGS(update_flags)
def test_predict(self):
if context.num_gpus() >= 2:
self.skipTest('No need to test 2+ GPUs without a distribution strategy.')
self._prepare_files_and_flags()
t = transformer_main.TransformerTask(FLAGS)
t.predict()
@unittest.skipUnless(tf.test.is_built_with_cuda(), 'requires GPU')
def test_predict_fp16(self):
if context.num_gpus() >= 2:
self.skipTest('No need to test 2+ GPUs without a distribution strategy.')
self._prepare_files_and_flags('--dtype=fp16')
t = transformer_main.TransformerTask(FLAGS)
t.predict()
def test_eval(self):
if context.num_gpus() >= 2:
self.skipTest('No need to test 2+ GPUs without a distribution strategy.')
if 'test_xla' in sys.argv[0]:
self.skipTest('TODO(xla): Make this test faster under XLA.')
self._prepare_files_and_flags()
t = transformer_main.TransformerTask(FLAGS)
t.eval()
if __name__ == '__main__':
tf.test.main()
official/nlp/transformer/transformer_test.py deleted 100644 → 0
View file @ d78ec6ea
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# 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.
"""Test Transformer model."""
import tensorflow as tf
from official.nlp.transformer import model_params
from official.nlp.transformer import transformer
class TransformerV2Test(tf.test.TestCase):
def setUp(self):
super().setUp()
self.params = params = model_params.TINY_PARAMS
params["batch_size"] = params["default_batch_size"] = 16
params["use_synthetic_data"] = True
params["hidden_size"] = 12
params["num_hidden_layers"] = 2
params["filter_size"] = 14
params["num_heads"] = 2
params["vocab_size"] = 41
params["extra_decode_length"] = 2
params["beam_size"] = 3
params["dtype"] = tf.float32
def test_create_model_train(self):
model = transformer.create_model(self.params, True)
inputs, outputs = model.inputs, model.outputs
self.assertEqual(len(inputs), 2)
self.assertEqual(len(outputs), 1)
self.assertEqual(inputs[0].shape.as_list(), [None, None])
self.assertEqual(inputs[0].dtype, tf.int64)
self.assertEqual(inputs[1].shape.as_list(), [None, None])
self.assertEqual(inputs[1].dtype, tf.int64)
self.assertEqual(outputs[0].shape.as_list(), [None, None, 41])
self.assertEqual(outputs[0].dtype, tf.float32)
def test_create_model_not_train(self):
model = transformer.create_model(self.params, False)
inputs, outputs = model.inputs, model.outputs
self.assertEqual(len(inputs), 1)
self.assertEqual(len(outputs), 2)
self.assertEqual(inputs[0].shape.as_list(), [None, None])
self.assertEqual(inputs[0].dtype, tf.int64)
self.assertEqual(outputs[0].shape.as_list(), [None, None])
self.assertEqual(outputs[0].dtype, tf.int32)
self.assertEqual(outputs[1].shape.as_list(), [None])
self.assertEqual(outputs[1].dtype, tf.float32)
def test_export(self):
model = transformer.Transformer(self.params, name="transformer_v2")
export_dir = self.get_temp_dir()
batch_size = 5
max_length = 6
class SaveModule(tf.Module):
def __init__(self, model):
super(SaveModule, self).__init__()
self.model = model
@tf.function
def serve(self, x):
return self.model.call([x], training=False)
save_module = SaveModule(model)
tensor_shape = (None, None)
sample_input = tf.zeros((batch_size, max_length), dtype=tf.int64)
_ = save_module.serve(sample_input)
signatures = dict(
serving_default=save_module.serve.get_concrete_function(
tf.TensorSpec(shape=tensor_shape, dtype=tf.int64, name="x")))
tf.saved_model.save(save_module, export_dir, signatures=signatures)
imported = tf.saved_model.load(export_dir)
serving_fn = imported.signatures["serving_default"]
all_outputs = serving_fn(sample_input)
output = all_outputs["outputs"]
output_shapes = output.shape.as_list()
self.assertEqual(output_shapes[0], batch_size)
self.assertEqual(output_shapes[1],
max_length + model.params["extra_decode_length"])
if __name__ == "__main__":
tf.test.main()
official/nlp/transformer/translate.py deleted 100644 → 0
View file @ d78ec6ea
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# 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.
"""Translate text or files using trained transformer model."""
# Import libraries
from absl import logging
import numpy as np
import tensorflow as tf
from official.nlp.transformer.utils import tokenizer
_EXTRA_DECODE_LENGTH = 100
_BEAM_SIZE = 4
_ALPHA = 0.6
def _get_sorted_inputs(filename):
"""Read and sort lines from the file sorted by decreasing length.
Args:
filename: String name of file to read inputs from.
Returns:
Sorted list of inputs, and dictionary mapping original index->sorted index
of each element.
"""
with tf.io.gfile.GFile(filename) as f:
records = f.read().split("\n")
inputs = [record.strip() for record in records]
if not inputs[-1]:
inputs.pop()
input_lens = [(i, len(line.split())) for i, line in enumerate(inputs)]
sorted_input_lens = sorted(input_lens, key=lambda x: x[1], reverse=True)
sorted_inputs = [None] * len(sorted_input_lens)
sorted_keys = [0] * len(sorted_input_lens)
for i, (index, _) in enumerate(sorted_input_lens):
sorted_inputs[i] = inputs[index]
sorted_keys[index] = i
return sorted_inputs, sorted_keys
def _encode_and_add_eos(line, subtokenizer):
"""Encode line with subtokenizer, and add EOS id to the end."""
return subtokenizer.encode(line) + [tokenizer.EOS_ID]
def _trim_and_decode(ids, subtokenizer):
"""Trim EOS and PAD tokens from ids, and decode to return a string."""
try:
index = list(ids).index(tokenizer.EOS_ID)
return subtokenizer.decode(ids[:index])
except ValueError: # No EOS found in sequence
return subtokenizer.decode(ids)
def translate_file(model,
params,
subtokenizer,
input_file,
output_file=None,
print_all_translations=True,
distribution_strategy=None):
"""Translate lines in file, and save to output file if specified.
Args:
model: A Keras model, used to generate the translations.
params: A dictionary, containing the translation related parameters.
subtokenizer: A subtokenizer object, used for encoding and decoding source
and translated lines.
input_file: A file containing lines to translate.
output_file: A file that stores the generated translations.
print_all_translations: A bool. If true, all translations are printed to
stdout.
distribution_strategy: A distribution strategy, used to perform inference
directly with tf.function instead of Keras model.predict().
Raises:
ValueError: if output file is invalid.
"""
batch_size = params["decode_batch_size"]
# Read and sort inputs by length. Keep dictionary (original index-->new index
# in sorted list) to write translations in the original order.
sorted_inputs, sorted_keys = _get_sorted_inputs(input_file)
total_samples = len(sorted_inputs)
num_decode_batches = (total_samples - 1) // batch_size + 1
def input_generator():
"""Yield encoded strings from sorted_inputs."""
for i in range(num_decode_batches):
lines = [
sorted_inputs[j + i * batch_size]
for j in range(batch_size)
if j + i * batch_size < total_samples
]
lines = [_encode_and_add_eos(l, subtokenizer) for l in lines]
if distribution_strategy:
for j in range(batch_size - len(lines)):
lines.append([tokenizer.EOS_ID])
batch = tf.keras.preprocessing.sequence.pad_sequences(
lines,
maxlen=params["decode_max_length"],
dtype="int32",
padding="post")
logging.info("Decoding batch %d out of %d.", i, num_decode_batches)
yield batch
@tf.function
def predict_step(inputs):
"""Decoding step function for TPU runs."""
def _step_fn(inputs):
"""Per replica step function."""
tag = inputs[0]
val_inputs = inputs[1]
val_outputs, _ = model([val_inputs], training=False)
return tag, val_outputs
return distribution_strategy.run(_step_fn, args=(inputs,))
translations = []
if distribution_strategy:
num_replicas = distribution_strategy.num_replicas_in_sync
local_batch_size = params["decode_batch_size"] // num_replicas
for i, text in enumerate(input_generator()):
if distribution_strategy:
text = np.reshape(text, [num_replicas, local_batch_size, -1])
# Add tag to the input of each replica with the reordering logic after
# outputs, to ensure the output order matches the input order.
text = tf.constant(text)
@tf.function
def text_as_per_replica():
replica_context = tf.distribute.get_replica_context()
replica_id = replica_context.replica_id_in_sync_group
return replica_id, text[replica_id] # pylint: disable=cell-var-from-loop
text = distribution_strategy.run(text_as_per_replica)
outputs = distribution_strategy.experimental_local_results(
predict_step(text))
val_outputs = [output for _, output in outputs]
val_outputs = np.reshape(val_outputs, [params["decode_batch_size"], -1])
else:
val_outputs, _ = model.predict(text)
length = len(val_outputs)
for j in range(length):
if j + i * batch_size < total_samples:
translation = _trim_and_decode(val_outputs[j], subtokenizer)
translations.append(translation)
if print_all_translations:
logging.info("Translating:\n\tInput: %s\n\tOutput: %s",
sorted_inputs[j + i * batch_size], translation)
# Write translations in the order they appeared in the original file.
if output_file is not None:
if tf.io.gfile.isdir(output_file):
raise ValueError("File output is a directory, will not save outputs to "
"file.")
logging.info("Writing to file %s", output_file)
with tf.io.gfile.GFile(output_file, "w") as f:
for i in sorted_keys:
f.write("%s\n" % translations[i])
def translate_from_text(model, subtokenizer, txt):
encoded_txt = _encode_and_add_eos(txt, subtokenizer)
result = model.predict(encoded_txt)
outputs = result["outputs"]
logging.info("Original: \"%s\"", txt)
translate_from_input(outputs, subtokenizer)
def translate_from_input(outputs, subtokenizer):
translation = _trim_and_decode(outputs, subtokenizer)
logging.info("Translation: \"%s\"", translation)
official/nlp/transformer/utils/__init__.py deleted 100644 → 0
View file @ d78ec6ea
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# 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.
official/nlp/transformer/utils/metrics.py deleted 100644 → 0
View file @ d78ec6ea
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# 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.
"""Functions for calculating loss, accuracy, and other model metrics.
Metrics:
- Padded loss, accuracy, and negative log perplexity. Source:
https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/metrics.py
- BLEU approximation. Source:
https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/bleu_hook.py
- ROUGE score. Source:
https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/rouge.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import math
import numpy as np
import six
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow.compat.v1 as tf
def _pad_tensors_to_same_length(x, y):
"""Pad x and y so that the results have the same length (second dimension)."""
with tf.name_scope("pad_to_same_length"):
x_length = tf.shape(x)[1]
y_length = tf.shape(y)[1]
max_length = tf.maximum(x_length, y_length)
x = tf.pad(x, [[0, 0], [0, max_length - x_length], [0, 0]])
y = tf.pad(y, [[0, 0], [0, max_length - y_length]])
return x, y
def padded_cross_entropy_loss(logits, labels, smoothing, vocab_size):
"""Calculate cross entropy loss while ignoring padding.
Args:
logits: Tensor of size [batch_size, length_logits, vocab_size]
labels: Tensor of size [batch_size, length_labels]
smoothing: Label smoothing constant, used to determine the on and off values
vocab_size: int size of the vocabulary
Returns:
Returns the cross entropy loss and weight tensors: float32 tensors with
shape [batch_size, max(length_logits, length_labels)]
"""
with tf.name_scope("loss", values=[logits, labels]):
logits, labels = _pad_tensors_to_same_length(logits, labels)
# Calculate smoothing cross entropy
with tf.name_scope("smoothing_cross_entropy", values=[logits, labels]):
confidence = 1.0 - smoothing
low_confidence = (1.0 - confidence) / tf.cast(vocab_size - 1, tf.float32)
soft_targets = tf.one_hot(
tf.cast(labels, tf.int32),
depth=vocab_size,
on_value=confidence,
off_value=low_confidence)
xentropy = tf.nn.softmax_cross_entropy_with_logits_v2(
logits=logits, labels=soft_targets)
# Calculate the best (lowest) possible value of cross entropy, and
# subtract from the cross entropy loss.
normalizing_constant = -(
confidence * tf.log(confidence) + tf.cast(vocab_size - 1, tf.float32)
* low_confidence * tf.log(low_confidence + 1e-20))
xentropy -= normalizing_constant
weights = tf.cast(tf.not_equal(labels, 0), tf.float32)
return xentropy * weights, weights
def _convert_to_eval_metric(metric_fn):
"""Wrap a metric fn that returns scores and weights as an eval metric fn.
The input metric_fn returns values for the current batch. The wrapper
aggregates the return values collected over all of the batches evaluated.
Args:
metric_fn: function that returns scores and weights for the current batch's
logits and predicted labels.
Returns:
function that aggregates the scores and weights from metric_fn.
"""
def problem_metric_fn(*args):
"""Returns an aggregation of the metric_fn's returned values."""
(scores, weights) = metric_fn(*args)
# The tf.metrics.mean function assures correct aggregation.
return tf.metrics.mean(scores, weights)
return problem_metric_fn
def get_eval_metrics(logits, labels, params):
"""Return dictionary of model evaluation metrics."""
metrics = {
"accuracy": _convert_to_eval_metric(padded_accuracy)(logits, labels),
"accuracy_top5": _convert_to_eval_metric(padded_accuracy_top5)(
logits, labels),
"accuracy_per_sequence": _convert_to_eval_metric(
padded_sequence_accuracy)(logits, labels),
"neg_log_perplexity": _convert_to_eval_metric(padded_neg_log_perplexity)(
logits, labels, params["vocab_size"]),
}
if not params["use_tpu"]:
# TPU does not support tf.py_func
metrics.update({
"approx_bleu_score": _convert_to_eval_metric(
bleu_score)(logits, labels),
"rouge_2_fscore": _convert_to_eval_metric(
rouge_2_fscore)(logits, labels),
"rouge_L_fscore": _convert_to_eval_metric(
rouge_l_fscore)(logits, labels),
})
# Prefix each of the metric names with "metrics/". This allows the metric
# graphs to display under the "metrics" category in TensorBoard.
metrics = {"metrics/%s" % k: v for k, v in six.iteritems(metrics)}
return metrics
def padded_accuracy(logits, labels):
"""Percentage of times that predictions matches labels on non-0s."""
with tf.variable_scope("padded_accuracy", values=[logits, labels]):
logits, labels = _pad_tensors_to_same_length(logits, labels)
weights = tf.cast(tf.not_equal(labels, 0), tf.float32)
outputs = tf.cast(tf.argmax(logits, axis=-1), tf.int32)
padded_labels = tf.cast(labels, tf.int32)
return tf.cast(tf.equal(outputs, padded_labels), tf.float32), weights
def padded_accuracy_topk(logits, labels, k):
"""Percentage of times that top-k predictions matches labels on non-0s."""
with tf.variable_scope("padded_accuracy_topk", values=[logits, labels]):
logits, labels = _pad_tensors_to_same_length(logits, labels)
weights = tf.cast(tf.not_equal(labels, 0), tf.float32)
effective_k = tf.minimum(k, tf.shape(logits)[-1])
_, outputs = tf.nn.top_k(logits, k=effective_k)
outputs = tf.cast(outputs, tf.int32)
padded_labels = tf.cast(labels, tf.int32)
padded_labels = tf.expand_dims(padded_labels, axis=-1)
padded_labels += tf.zeros_like(outputs) # Pad to same shape.
same = tf.cast(tf.equal(outputs, padded_labels), tf.float32)
same_topk = tf.reduce_sum(same, axis=-1)
return same_topk, weights
def padded_accuracy_top5(logits, labels):
return padded_accuracy_topk(logits, labels, 5)
def padded_sequence_accuracy(logits, labels):
"""Percentage of times that predictions matches labels everywhere (non-0)."""
with tf.variable_scope("padded_sequence_accuracy", values=[logits, labels]):
logits, labels = _pad_tensors_to_same_length(logits, labels)
weights = tf.cast(tf.not_equal(labels, 0), tf.float32)
outputs = tf.cast(tf.argmax(logits, axis=-1), tf.int32)
padded_labels = tf.cast(labels, tf.int32)
not_correct = (tf.cast(tf.not_equal(outputs, padded_labels), tf.float32) *
weights)
axis = list(range(1, len(outputs.get_shape())))
correct_seq = 1.0 - tf.minimum(1.0, tf.reduce_sum(not_correct, axis=axis))
return correct_seq, tf.constant(1.0)
def padded_neg_log_perplexity(logits, labels, vocab_size):
"""Average log-perplexity excluding padding 0s. No smoothing."""
num, den = padded_cross_entropy_loss(logits, labels, 0, vocab_size)
return -num, den
def bleu_score(logits, labels):
"""Approximate BLEU score computation between labels and predictions.
An approximate BLEU scoring method since we do not glue word pieces or
decode the ids and tokenize the output. By default, we use ngram order of 4
and use brevity penalty. Also, this does not have beam search.
Args:
logits: Tensor of size [batch_size, length_logits, vocab_size]
labels: Tensor of size [batch-size, length_labels]
Returns:
bleu: int, approx bleu score
"""
predictions = tf.cast(tf.argmax(logits, axis=-1), tf.int32)
# TODO: Look into removing use of py_func # pylint: disable=g-bad-todo
bleu = tf.py_func(compute_bleu, (labels, predictions), tf.float32)
return bleu, tf.constant(1.0)
def _get_ngrams_with_counter(segment, max_order):
"""Extracts all n-grams up to a given maximum order from an input segment.
Args:
segment: text segment from which n-grams will be extracted.
max_order: maximum length in tokens of the n-grams returned by this
methods.
Returns:
The Counter containing all n-grams upto max_order in segment
with a count of how many times each n-gram occurred.
"""
ngram_counts = collections.Counter()
for order in xrange(1, max_order + 1):
for i in xrange(0, len(segment) - order + 1):
ngram = tuple(segment[i:i + order])
ngram_counts[ngram] += 1
return ngram_counts
def compute_bleu(reference_corpus, translation_corpus, max_order=4,
use_bp=True):
"""Computes BLEU score of translated segments against one or more references.
Args:
reference_corpus: list of references for each translation. Each
reference should be tokenized into a list of tokens.
translation_corpus: list of translations to score. Each translation
should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
use_bp: boolean, whether to apply brevity penalty.
Returns:
BLEU score.
"""
reference_length = 0
translation_length = 0
bp = 1.0
geo_mean = 0
matches_by_order = [0] * max_order
possible_matches_by_order = [0] * max_order
precisions = []
for (references, translations) in zip(reference_corpus, translation_corpus):
reference_length += len(references)
translation_length += len(translations)
ref_ngram_counts = _get_ngrams_with_counter(references, max_order)
translation_ngram_counts = _get_ngrams_with_counter(translations, max_order)
overlap = dict((ngram,
min(count, translation_ngram_counts[ngram]))
for ngram, count in ref_ngram_counts.items())
for ngram in overlap:
matches_by_order[len(ngram) - 1] += overlap[ngram]
for ngram in translation_ngram_counts:
possible_matches_by_order[len(ngram) - 1] += translation_ngram_counts[
ngram]
precisions = [0] * max_order
smooth = 1.0
for i in xrange(0, max_order):
if possible_matches_by_order[i] > 0:
precisions[i] = float(matches_by_order[i]) / possible_matches_by_order[i]
if matches_by_order[i] > 0:
precisions[i] = float(matches_by_order[i]) / possible_matches_by_order[
i]
else:
smooth *= 2
precisions[i] = 1.0 / (smooth * possible_matches_by_order[i])
else:
precisions[i] = 0.0
if max(precisions) > 0:
p_log_sum = sum(math.log(p) for p in precisions if p)
geo_mean = math.exp(p_log_sum / max_order)
if use_bp:
ratio = translation_length / reference_length
bp = math.exp(1 - 1. / ratio) if ratio < 1.0 else 1.0
bleu = geo_mean * bp
return np.float32(bleu)
def rouge_2_fscore(logits, labels):
"""ROUGE-2 F1 score computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
logits: tensor, model predictions
labels: tensor, gold output.
Returns:
rouge2_fscore: approx rouge-2 f1 score.
"""
predictions = tf.cast(tf.argmax(logits, axis=-1), tf.int32)
# TODO: Look into removing use of py_func # pylint: disable=g-bad-todo
rouge_2_f_score = tf.py_func(rouge_n, (predictions, labels), tf.float32)
return rouge_2_f_score, tf.constant(1.0)
def _get_ngrams(n, text):
"""Calculates n-grams.
Args:
n: which n-grams to calculate
text: An array of tokens
Returns:
A set of n-grams
"""
ngram_set = set()
text_length = len(text)
max_index_ngram_start = text_length - n
for i in range(max_index_ngram_start + 1):
ngram_set.add(tuple(text[i:i + n]))
return ngram_set
def rouge_n(eval_sentences, ref_sentences, n=2):
"""Computes ROUGE-N f1 score of two text collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
Args:
eval_sentences: Predicted sentences.
ref_sentences: Sentences from the reference set
n: Size of ngram. Defaults to 2.
Returns:
f1 score for ROUGE-N
"""
f1_scores = []
for eval_sentence, ref_sentence in zip(eval_sentences, ref_sentences):
eval_ngrams = _get_ngrams(n, eval_sentence)
ref_ngrams = _get_ngrams(n, ref_sentence)
ref_count = len(ref_ngrams)
eval_count = len(eval_ngrams)
# Count the overlapping ngrams between evaluated and reference
overlapping_ngrams = eval_ngrams.intersection(ref_ngrams)
overlapping_count = len(overlapping_ngrams)
# Handle edge case. This isn't mathematically correct, but it's good enough
if eval_count == 0:
precision = 0.0
else:
precision = float(overlapping_count) / eval_count
if ref_count == 0:
recall = 0.0
else:
recall = float(overlapping_count) / ref_count
f1_scores.append(2.0 * ((precision * recall) / (precision + recall + 1e-8)))
# return overlapping_count / reference_count
return np.mean(f1_scores, dtype=np.float32)
def rouge_l_fscore(predictions, labels):
"""ROUGE scores computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor, gold output.
Returns:
rouge_l_fscore: approx rouge-l f1 score.
"""
outputs = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)
rouge_l_f_score = tf.py_func(rouge_l_sentence_level, (outputs, labels),
tf.float32)
return rouge_l_f_score, tf.constant(1.0)
def rouge_l_sentence_level(eval_sentences, ref_sentences):
"""Computes ROUGE-L (sentence level) of two collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
Calculated according to:
R_lcs = LCS(X,Y)/m
P_lcs = LCS(X,Y)/n
F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)
where:
X = reference summary
Y = Candidate summary
m = length of reference summary
n = length of candidate summary
Args:
eval_sentences: The sentences that have been picked by the summarizer
ref_sentences: The sentences from the reference set
Returns:
A float: F_lcs
"""
f1_scores = []
for eval_sentence, ref_sentence in zip(eval_sentences, ref_sentences):
m = float(len(ref_sentence))
n = float(len(eval_sentence))
lcs = _len_lcs(eval_sentence, ref_sentence)
f1_scores.append(_f_lcs(lcs, m, n))
return np.mean(f1_scores, dtype=np.float32)
def _len_lcs(x, y):
"""Returns the length of the Longest Common Subsequence between two seqs.
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: sequence of words
y: sequence of words
Returns
integer: Length of LCS between x and y
"""
table = _lcs(x, y)
n, m = len(x), len(y)
return table[n, m]
def _lcs(x, y):
"""Computes the length of the LCS between two seqs.
The implementation below uses a DP programming algorithm and runs
in O(nm) time where n = len(x) and m = len(y).
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: collection of words
y: collection of words
Returns:
Table of dictionary of coord and len lcs
"""
n, m = len(x), len(y)
table = dict()
for i in range(n + 1):
for j in range(m + 1):
if i == 0 or j == 0:
table[i, j] = 0
elif x[i - 1] == y[j - 1]:
table[i, j] = table[i - 1, j - 1] + 1
else:
table[i, j] = max(table[i - 1, j], table[i, j - 1])
return table
def _f_lcs(llcs, m, n):
"""Computes the LCS-based F-measure score.
Source: http://research.microsoft.com/en-us/um/people/cyl/download/papers/
rouge-working-note-v1.3.1.pdf
Args:
llcs: Length of LCS
m: number of words in reference summary
n: number of words in candidate summary
Returns:
Float. LCS-based F-measure score
"""
r_lcs = llcs / m
p_lcs = llcs / n
beta = p_lcs / (r_lcs + 1e-12)
num = (1 + (beta ** 2)) * r_lcs * p_lcs
denom = r_lcs + ((beta ** 2) * p_lcs)
f_lcs = num / (denom + 1e-12)
return f_lcs
official/nlp/transformer/utils/tokenizer.py deleted 100644 → 0
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official/nlp/transformer/utils/tokenizer_test.py deleted 100644 → 0
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# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# 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.
"""Test Subtokenizer and string helper methods."""
import collections
import tempfile
import tensorflow as tf
from official.nlp.transformer.utils import tokenizer
class SubtokenizerTest(tf.test.TestCase):
def _init_subtokenizer(self, vocab_list):
temp_file = tempfile.NamedTemporaryFile(delete=False)
with tf.io.gfile.GFile(temp_file.name, "w") as w:
for subtoken in vocab_list:
w.write("'%s'" % subtoken)
w.write("\n")
return tokenizer.Subtokenizer(temp_file.name, reserved_tokens=[])
def test_encode(self):
vocab_list = ["123_", "test", "ing_"]
subtokenizer = self._init_subtokenizer(vocab_list)
s = "testing 123"
encoded_list = subtokenizer.encode(s)
self.assertEqual([1, 2, 0], encoded_list)
def test_decode(self):
vocab_list = ["123_", "test", "ing_"]
subtokenizer = self._init_subtokenizer(vocab_list)
encoded_list = [1, 2, 0] # testing 123
decoded_str = subtokenizer.decode(encoded_list)
self.assertEqual("testing 123", decoded_str)
def test_subtoken_ids_to_tokens(self):
vocab_list = ["123_", "test", "ing_"]
subtokenizer = self._init_subtokenizer(vocab_list)
encoded_list = [1, 2, 0] # testing 123
token_list = subtokenizer._subtoken_ids_to_tokens(encoded_list)
self.assertEqual([u"testing", u"123"], token_list)
class StringHelperTest(tf.test.TestCase):
def test_split_string_to_tokens(self):
text = "test? testing 123."
tokens = tokenizer._split_string_to_tokens(text,
tokenizer._ALPHANUMERIC_CHAR_SET)
self.assertEqual(["test", "? ", "testing", "123", "."], tokens)
def test_join_tokens_to_string(self):
tokens = ["test", "? ", "testing", "123", "."]
s = tokenizer._join_tokens_to_string(tokens,
tokenizer._ALPHANUMERIC_CHAR_SET)
self.assertEqual("test? testing 123.", s)
def test_escape_token(self):
token = u"abc_\\4"
alphabet = set("abc_\\u;")
escaped_token = tokenizer._escape_token(token, alphabet)
self.assertEqual("abc\\u\\\\\\52;_", escaped_token)
def test_unescape_token(self):
escaped_token = u"Underline: \\u, Backslash: \\\\, Unicode: \\52;"
unescaped_token = tokenizer._unescape_token(escaped_token)
self.assertEqual("Underline: _, Backslash: \\, Unicode: 4", unescaped_token)
def test_list_to_index_dict(self):
lst = ["test", "strings"]
d = tokenizer._list_to_index_dict(lst)
self.assertDictEqual({"test": 0, "strings": 1}, d)
def test_split_token_to_subtokens(self):
token = "abc"
subtoken_dict = {"a": 0, "b": 1, "c": 2, "ab": 3}
max_subtoken_length = 2
subtokens = tokenizer._split_token_to_subtokens(token, subtoken_dict,
max_subtoken_length)
self.assertEqual(["ab", "c"], subtokens)
def test_generate_alphabet_dict(self):
s = ["testing", "123"]
reserved_tokens = ["???"]
alphabet = tokenizer._generate_alphabet_dict(s, reserved_tokens)
self.assertIn("?", alphabet)
self.assertIn("t", alphabet)
self.assertIn("e", alphabet)
self.assertIn("s", alphabet)
self.assertIn("i", alphabet)
self.assertIn("n", alphabet)
self.assertIn("g", alphabet)
self.assertIn("1", alphabet)
self.assertIn("2", alphabet)
self.assertIn("3", alphabet)
def test_count_and_gen_subtokens(self):
token_counts = {"abc": 5}
alphabet = set("abc_")
subtoken_dict = {"a": 0, "b": 1, "c": 2, "_": 3}
max_subtoken_length = 2
subtoken_counts = tokenizer._count_and_gen_subtokens(
token_counts, alphabet, subtoken_dict, max_subtoken_length)
self.assertIsInstance(subtoken_counts, collections.defaultdict)
self.assertDictEqual(
{
"a": 5,
"b": 5,
"c": 5,
"_": 5,
"ab": 5,
"bc": 5,
"c_": 5,
"abc": 5,
"bc_": 5,
"abc_": 5
}, subtoken_counts)
def test_filter_and_bucket_subtokens(self):
subtoken_counts = collections.defaultdict(int, {
"a": 2,
"b": 4,
"c": 1,
"ab": 6,
"ac": 3,
"abbc": 5
})
min_count = 3
subtoken_buckets = tokenizer._filter_and_bucket_subtokens(
subtoken_counts, min_count)
self.assertEqual(len(subtoken_buckets[0]), 0)
self.assertEqual(set("b"), subtoken_buckets[1])
self.assertEqual(set(["ab", "ac"]), subtoken_buckets[2])
self.assertEqual(len(subtoken_buckets[3]), 0)
self.assertEqual(set(["abbc"]), subtoken_buckets[4])
def test_gen_new_subtoken_list(self):
subtoken_counts = collections.defaultdict(int, {
"translate": 10,
"t": 40,
"tr": 16,
"tra": 12
})
min_count = 5
alphabet = set("translate")
reserved_tokens = ["reserved", "tokens"]
subtoken_list, max_token_length = tokenizer._gen_new_subtoken_list(
subtoken_counts, min_count, alphabet, reserved_tokens)
# Check that "tra" isn"t in the list (its count should be decremented to 2,
# so it should not be added to the canddiate list).
self.assertNotIn("tra", subtoken_list)
self.assertIn("tr", subtoken_list)
self.assertIn("t", subtoken_list)
self.assertEqual(len("translate"), max_token_length)
def test_generate_subtokens(self):
token_counts = {"ab": 1, "bc": 3, "abc": 5}
alphabet = set("abc_")
min_count = 100
num_iterations = 1
reserved_tokens = ["reserved", "tokens"]
vocab_list = tokenizer._generate_subtokens(token_counts, alphabet,
min_count, num_iterations,
reserved_tokens)
# Check that reserved tokens are at the front of the list
self.assertEqual(vocab_list[:2], reserved_tokens)
# Check that each character in alphabet is in the vocab list
for c in alphabet:
self.assertIn(c, vocab_list)
if __name__ == "__main__":
tf.test.main()
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