def __init__(self, checkpoint=None, predict_fn=None):
"""Initializes the BLEURT model.
Args:
checkpoint: BLEURT checkpoint. Will default to BLEURT-tiny if None.
predict_fn: (optional) prediction function, overrides chkpt_dir. Mostly
used for testing.
Returns:
A BLEURT scorer export.
"""
if not checkpoint:
logging.info("No checkpoint specified, defaulting to BLEURT-tiny.")
checkpoint = _get_default_checkpoint()
logging.info("Reading checkpoint {}.".format(checkpoint))
config = checkpoint_lib.read_bleurt_config(checkpoint)
max_seq_length = config["max_seq_length"]
vocab_file = config["vocab_file"]
do_lower_case = config["do_lower_case"]
logging.info("Creating BLEURT scorer.")
self.tokenizer = tokenization.FullTokenizer(
vocab_file=vocab_file, do_lower_case=do_lower_case)
self.max_seq_length = max_seq_length
if predict_fn:
self.predict_fn = predict_fn
logging.info("BLEURT initialized.")
return
logging.info("Loading model...")
self.chkpt_dir = checkpoint
self.predict_fn = _make_predict_fn_from_checkpoint(checkpoint)
logging.info("BLEURT initialized.")
def test_finetune_and_predict(self):
checkpoint = get_test_checkpoint()
train_file, dev_file = get_test_data()
with tempfile.TemporaryDirectory() as model_dir:
# Sets new flags.
FLAGS.init_checkpoint = os.path.join(checkpoint, "variables",
"variables")
FLAGS.bert_config_file = os.path.join(checkpoint,
"bert_config.json")
FLAGS.vocab_file = os.path.join(checkpoint, "vocab.txt")
FLAGS.do_lower_case = True
FLAGS.dynamic_seq_length = True
FLAGS.max_seq_length = 512
FLAGS.model_dir = model_dir
FLAGS.num_train_steps = 1
FLAGS.learning_rate = 0.00000000001
FLAGS.serialized_train_set = os.path.join(model_dir,
"train.tfrecord")
FLAGS.serialized_dev_set = os.path.join(model_dir, "dev.tfrecord")
# Runs 1 training step.
export = finetune.run_finetuning_pipeline(train_file, dev_file)
# Checks if the pipeline produced a valid BLEURT checkpoint.
self.assertTrue(tf.io.gfile.exists(export))
config = checkpoint_lib.read_bleurt_config(export)
self.assertTrue(type(config), dict)
# Runs a prediction.
scorer = score.LengthBatchingBleurtScorer(export)
scores = scorer.score(references=references, candidates=candidates)
self.assertLen(scores, 2)
self.assertAllClose(scores, ref_scores)
def test_finetune_from_bert(self):
checkpoint = get_test_checkpoint()
train_file, dev_file = get_test_data()
with tempfile.TemporaryDirectory() as model_dir:
# Sets new flags.
FLAGS.model_dir = model_dir
FLAGS.init_checkpoint = os.path.join(checkpoint, "variables",
"variables")
FLAGS.bert_config_file = os.path.join(checkpoint,
"bert_config.json")
FLAGS.vocab_file = os.path.join(checkpoint, "vocab.txt")
FLAGS.do_lower_case = True
FLAGS.max_seq_length = 512
FLAGS.num_train_steps = 1
FLAGS.serialized_train_set = os.path.join(model_dir,
"train.tfrecord")
FLAGS.serialized_dev_set = os.path.join(model_dir, "dev.tfrecord")
# Runs 1 training step.
export = finetune.run_finetuning_pipeline(train_file, dev_file)
# Checks if the pipeline produced a valid BLEURT checkpoint.
self.assertTrue(tf.io.gfile.exists(export))
config = checkpoint_lib.read_bleurt_config(export)
self.assertTrue(type(config), dict)
def __init__(self, checkpoint=None, predict_fn=None):
"""Initializes the BLEURT model.
Args:
checkpoint: BLEURT checkpoint. Will default to BLEURT-tiny if None.
predict_fn: (optional) prediction function, overrides chkpt_dir. Mostly
used for testing.
Returns:
A BLEURT scorer export.
"""
if not checkpoint:
logging.info("No checkpoint specified, defaulting to BLEURT-tiny.")
checkpoint = _get_default_checkpoint()
logging.info("Reading checkpoint {}.".format(checkpoint))
self.config = checkpoint_lib.read_bleurt_config(checkpoint)
max_seq_length = self.config["max_seq_length"]
vocab_file = self.config["vocab_file"]
do_lower_case = self.config["do_lower_case"]
sp_model = self.config["sp_model"]
logging.info("Creating BLEURT scorer.")
self.tokenizer = tokenizers.create_tokenizer(
vocab_file=vocab_file,
do_lower_case=do_lower_case,
sp_model=sp_model)
self.max_seq_length = max_seq_length
self._predictor = _create_predictor(checkpoint, predict_fn)
self._predictor.initialize()
logging.info("BLEURT initialized.")
def bleurt_ops(*args, references=None, candidates=None):
"""Builds computation graph for BLEURT.
Args:
*args: dummy positional arguments.
references: <tf.string>[...] Tensor that contains reference sentences.
candidates: <tf.string>[...] Tensor that contains candidate sentences.
Returns:
A <tf.float>[...] Tensor that contains BLEURT scores.
"""
logging.info("Creating BLEURT TF Ops...")
assert not args, (
"This function does not accept positional arguments. Please "
"specify the name of the arguments explicitly, i.e., "
"`bleurt_ops(references=..., candidates=...`).")
assert references is not None and candidates is not None
logging.info("Reading info from checkpoint {}".format(checkpoint))
config = checkpoint_lib.read_bleurt_config(checkpoint)
max_seq_length = config["max_seq_length"]
vocab_file = config["vocab_file"]
do_lower_case = config["do_lower_case"]
sp_model = config["sp_model"]
logging.info("Creating tokenizer...")
tokenizer = tokenizers.create_tokenizer(vocab_file=vocab_file,
do_lower_case=do_lower_case,
sp_model=sp_model)
logging.info("Tokenizer created")
logging.info("Creating BLEURT Preprocessing Ops...")
bleurt_preprocessing_ops = create_bleurt_preprocessing_ops(
tokenizer, max_seq_length)
logging.info("Preprocessing Ops created.")
logging.info("Loading checkpoint...")
if not bleurt_model_fn:
imported = tf.saved_model.load(checkpoint)
bleurt_model_ops = imported.signatures["serving_default"]
else:
bleurt_model_ops = bleurt_model_fn
logging.info("BLEURT Checkpoint loaded")
input_ids, input_mask, segment_ids = bleurt_preprocessing_ops(
references, candidates)
out = bleurt_model_ops(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids)
logging.info("BLEURT TF Ops created.")
return out
def bleurt_ops(references, candidates):
"""Builds computation graph for BLEURT.
Args:
references: <tf.string>[...] Tensor that contains reference sentences.
candidates: <tf.string>[...] Tensor that contains candidate sentences.
Returns:
A <tf.float>[...] Tensor that contains BLEURT scores.
"""
logging.info("Creating BLEURT TF Ops...")
logging.info("Reading info from checkpoint {}".format(checkpoint))
config = checkpoint_lib.read_bleurt_config(checkpoint)
max_seq_length = config["max_seq_length"]
vocab_file = config["vocab_file"]
do_lower_case = config["do_lower_case"]
logging.info("Creating tokenizer...")
tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file,
do_lower_case=do_lower_case)
logging.info("Tokenizer created")
logging.info("Creating BLEURT Preprocessing Ops...")
bleurt_preprocessing_ops = create_bleurt_preprocessing_ops(
tokenizer, max_seq_length)
logging.info("Preprocessing Ops created.")
logging.info("Loading checkpoint...")
if not bleurt_model_fn:
imported = tf.saved_model.load_v2(checkpoint)
bleurt_model_ops = imported.signatures["serving_default"]
else:
bleurt_model_ops = bleurt_model_fn
logging.info("BLEURT Checkpoint loaded")
input_ids, input_mask, segment_ids = bleurt_preprocessing_ops(
references, candidates)
out = bleurt_model_ops(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids)
logging.info("BLEURT TF Ops created.")
return out
