def _serialize_dataset(self, tasks, is_training, split):
"""Write out the dataset as tfrecords."""
dataset_name = "_".join(sorted([task.name for task in tasks]))
dataset_name += "_" + split
dataset_prefix = os.path.join(
self._config.preprocessed_data_dir, 'tfrecord')
tfrecords_path = dataset_prefix + ".tfrecord"
metadata_path = dataset_prefix + ".metadata"
batch_size = (self._config.train_batch_size if is_training else
self._config.eval_batch_size)
utils.log("Loading dataset")
n_examples = None
if n_examples is None:
utils.log("Existing tfrecords not found so creating")
examples = []
for task in tasks:
task_examples = task.get_examples(split)
examples += task_examples
utils.mkdir(tfrecords_path.rsplit("/", 1)[0])
n_examples = self.serialize_examples(
examples, is_training, tfrecords_path, batch_size)
utils.write_json({"n_examples": n_examples}, metadata_path)
input_fn = self._input_fn_builder(tfrecords_path, is_training)
if is_training:
steps = int(n_examples // batch_size * self._config.num_train_epochs)
else:
steps = n_examples // batch_size
return input_fn, steps
def log(self, run_values=None):
step = self._global_step if self._is_training else self._steps_run_so_far
if step % self._log_every != 0:
return
msg = "{:}/{:} = {:.1f}%".format(step, self._n_steps,
100.0 * step / self._n_steps)
time_elapsed = time.time() - self._start_time
time_per_step = time_elapsed / (
(step - self._start_step) if self._is_training else step)
msg += ", SPS: {:.1f}".format(1 / time_per_step)
msg += ", ELAP: " + secs_to_str(time_elapsed)
msg += ", ETA: " + secs_to_str((self._n_steps - step) * time_per_step)
if run_values is not None:
for tag, value in run_values.results.items():
msg += " - " + str(tag) + (": {:.4f}".format(value))
utils.log(msg)
def serialize_examples(self, examples, is_training, output_file, batch_size):
"""Convert a set of `InputExample`s to a TFRecord file."""
n_examples = 0
with tf.io.TFRecordWriter(output_file) as writer:
for (ex_index, example) in enumerate(examples):
if ex_index % 2000 == 0:
utils.log("Writing example {:} of {:}".format(
ex_index, len(examples)))
for tf_example in self._example_to_tf_example(
example, is_training,
log=self._config.log_examples and ex_index < 1):
writer.write(tf_example.SerializeToString())
n_examples += 1
# add padding so the dataset is a multiple of batch_size
while n_examples % batch_size != 0:
writer.write(self._make_tf_example(task_id=len(self._config.task_names))
.SerializeToString())
n_examples += 1
return n_examples
def get_examples(self, split):
if split in self._examples:
return self._examples[split]
path = os.path.join(self.config.raw_data_dir, split + ".json")
# path = self.config.raw_data_dir
# print(path)
# input()
with tf.io.gfile.GFile(path, "r") as f:
input_data = json.load(f)["data"]
examples = []
example_failures = [0]
for entry in input_data:
for paragraph in entry["paragraphs"]:
self._add_examples(examples, example_failures, paragraph,
split)
self._examples[split] = examples
utils.log("{:} examples created, {:} failures".format(
len(examples), example_failures[0]))
return examples
def model_fn(features, labels, mode, params):
"""The `model_fn` for TPUEstimator."""
utils.log("Building model...")
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model = FinetuningModel(config, tasks, is_training, features,
num_train_steps)
if pretraining_config is not None:
# init_checkpoint = tf.train.latest_checkpoint(pretraining_config.model_dir)
init_checkpoint = pretraining_config['checkpoint']
utils.log("Using checkpoint", init_checkpoint)
tvars = tf.trainable_variables()
scaffold_fn = None
if init_checkpoint:
assignment_map, _ = modeling.get_assignment_map_from_checkpoint(
tvars, init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
# Build model for training or prediction
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(
model.loss,
config.learning_rate,
num_train_steps,
weight_decay_rate=config.weight_decay_rate,
use_tpu=config.use_tpu,
warmup_proportion=config.warmup_proportion,
layerwise_lr_decay_power=config.layerwise_lr_decay,
n_transformer_layers=model.bert_config.num_hidden_layers)
output_spec = tf.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=model.loss,
train_op=train_op,
scaffold_fn=scaffold_fn,
training_hooks=[
training_utils.ETAHook(
{} if config.use_tpu else dict(loss=model.loss),
num_train_steps, config.iterations_per_loop,
config.use_tpu, 10)
])
else:
assert mode == tf.estimator.ModeKeys.PREDICT
output_spec = tf.estimator.tpu.TPUEstimatorSpec(
mode=mode,
predictions=utils.flatten_dict(model.outputs),
scaffold_fn=scaffold_fn)
utils.log("Building complete")
return output_spec
def get_final_text(config: electra.configure_finetuning.FinetuningConfig, pred_text,
orig_text):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the span of our original text corresponding to the
# span that we predicted.
#
# However, `orig_text` may contain extra characters that we don't want in
# our prediction.
#
# For example, let's say:
# pred_text = steve smith
# orig_text = Steve Smith's
#
# We don't want to return `orig_text` because it contains the extra "'s".
#
# We don't want to return `pred_text` because it's already been normalized
# (the SQuAD eval script also does punctuation stripping/lower casing but
# our tokenizer does additional normalization like stripping accent
# characters).
#
# What we really want to return is "Steve Smith".
#
# Therefore, we have to apply a semi-complicated alignment heruistic between
# `pred_text` and `orig_text` to get a character-to-charcter alignment. This
# can fail in certain cases in which case we just return `orig_text`.
def _strip_spaces(text):
ns_chars = []
ns_to_s_map = collections.OrderedDict()
for i, c in enumerate(text):
if c == " ":
continue
ns_to_s_map[len(ns_chars)] = i
ns_chars.append(c)
ns_text = "".join(ns_chars)
return ns_text, dict(ns_to_s_map)
# We first tokenize `orig_text`, strip whitespace from the result
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = tokenization.BasicTokenizer(do_lower_case=config.do_lower_case)
tok_text = " ".join(tokenizer.tokenize(orig_text))
start_position = tok_text.find(pred_text)
if start_position == -1:
if config.debug:
utils.log(
"Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
return orig_text
end_position = start_position + len(pred_text) - 1
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
if len(orig_ns_text) != len(tok_ns_text):
if config.debug:
utils.log("Length not equal after stripping spaces: '%s' vs '%s'",
orig_ns_text, tok_ns_text)
return orig_text
# We then project the characters in `pred_text` back to `orig_text` using
# the character-to-character alignment.
tok_s_to_ns_map = {}
for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
tok_s_to_ns_map[tok_index] = i
orig_start_position = None
if start_position in tok_s_to_ns_map:
ns_start_position = tok_s_to_ns_map[start_position]
if ns_start_position in orig_ns_to_s_map:
orig_start_position = orig_ns_to_s_map[ns_start_position]
if orig_start_position is None:
if config.debug:
utils.log("Couldn't map start position")
return orig_text
orig_end_position = None
if end_position in tok_s_to_ns_map:
ns_end_position = tok_s_to_ns_map[end_position]
if ns_end_position in orig_ns_to_s_map:
orig_end_position = orig_ns_to_s_map[ns_end_position]
if orig_end_position is None:
if config.debug:
utils.log("Couldn't map end position")
return orig_text
output_text = orig_text[orig_start_position:(orig_end_position + 1)]
return output_text
def featurize(self,
example: QAExample,
is_training,
log=False,
for_eval=False):
all_features = []
query_tokens = self._tokenizer.tokenize(example.question_text)
if len(query_tokens) > self.config.max_query_length:
query_tokens = query_tokens[0:self.config.max_query_length]
tok_to_orig_index = []
orig_to_tok_index = []
all_doc_tokens = []
for (i, token) in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
sub_tokens = self._tokenizer.tokenize(token)
for sub_token in sub_tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
tok_start_position = None
tok_end_position = None
if is_training and example.is_impossible:
tok_start_position = -1
tok_end_position = -1
if is_training and not example.is_impossible:
tok_start_position = orig_to_tok_index[example.start_position]
if example.end_position < len(example.doc_tokens) - 1:
tok_end_position = orig_to_tok_index[example.end_position +
1] - 1
else:
tok_end_position = len(all_doc_tokens) - 1
(tok_start_position, tok_end_position) = _improve_answer_span(
all_doc_tokens, tok_start_position, tok_end_position,
self._tokenizer, example.orig_answer_text)
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = self.config.max_seq_length - len(query_tokens) - 3
# We can have documents that are longer than the maximum sequence length.
# To deal with this we do a sliding window approach, where we take chunks
# of the up to our max length with a stride of `doc_stride`.
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
"DocSpan", ["start", "length"])
doc_spans = []
start_offset = 0
while start_offset < len(all_doc_tokens):
length = len(all_doc_tokens) - start_offset
if length > max_tokens_for_doc:
length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_offset, length=length))
if start_offset + length == len(all_doc_tokens):
break
start_offset += min(length, self.config.doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_to_orig_map = {}
token_is_max_context = {}
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in query_tokens:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
for i in range(doc_span.length):
split_token_index = doc_span.start + i
token_to_orig_map[len(
tokens)] = tok_to_orig_index[split_token_index]
is_max_context = _check_is_max_context(doc_spans,
doc_span_index,
split_token_index)
token_is_max_context[len(tokens)] = is_max_context
tokens.append(all_doc_tokens[split_token_index])
segment_ids.append(1)
tokens.append("[SEP]")
segment_ids.append(1)
input_ids = self._tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < self.config.max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == self.config.max_seq_length
assert len(input_mask) == self.config.max_seq_length
assert len(segment_ids) == self.config.max_seq_length
start_position = None
end_position = None
if is_training and not example.is_impossible:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = doc_span.start
doc_end = doc_span.start + doc_span.length - 1
out_of_span = False
if not (tok_start_position >= doc_start
and tok_end_position <= doc_end):
out_of_span = True
if out_of_span:
start_position = 0
end_position = 0
else:
doc_offset = len(query_tokens) + 2
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
if is_training and example.is_impossible:
start_position = 0
end_position = 0
if log:
utils.log("*** Example ***")
utils.log("doc_span_index: %s" % doc_span_index)
utils.log(
"tokens: %s" %
" ".join([tokenization.printable_text(x) for x in tokens]))
utils.log("token_to_orig_map: %s" % " ".join([
"%d:%d" % (x, y)
for (x, y) in six.iteritems(token_to_orig_map)
]))
utils.log("token_is_max_context: %s" % " ".join([
"%d:%s" % (x, y)
for (x, y) in six.iteritems(token_is_max_context)
]))
utils.log("input_ids: %s" %
" ".join([str(x) for x in input_ids]))
utils.log("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
utils.log("segment_ids: %s" %
" ".join([str(x) for x in segment_ids]))
if is_training and example.is_impossible:
utils.log("impossible example")
if is_training and not example.is_impossible:
answer_text = " ".join(
tokens[start_position:(end_position + 1)])
utils.log("start_position: %d" % start_position)
utils.log("end_position: %d" % end_position)
utils.log("answer: %s" %
(tokenization.printable_text(answer_text)))
features = {
"task_id": self.config.task_names.index(self.name),
self.name + "_eid": (1000 * example.eid) + doc_span_index,
"input_ids": input_ids,
"input_mask": input_mask,
"segment_ids": segment_ids,
}
if for_eval:
features.update({
self.name + "_doc_span_index":
doc_span_index,
self.name + "_tokens":
tokens,
self.name + "_token_to_orig_map":
token_to_orig_map,
self.name + "_token_is_max_context":
token_is_max_context,
})
if is_training:
features.update({
self.name + "_start_positions":
start_position,
self.name + "_end_positions":
end_position,
self.name + "_is_impossible":
example.is_impossible
})
all_features.append(features)
return all_features
def _add_examples(self, examples, example_failures, paragraph, split):
paragraph_text = paragraph["context"]
doc_tokens = []
char_to_word_offset = []
prev_is_whitespace = True
for c in paragraph_text:
if is_whitespace(c):
prev_is_whitespace = True
else:
if prev_is_whitespace:
doc_tokens.append(c)
else:
doc_tokens[-1] += c
prev_is_whitespace = False
char_to_word_offset.append(len(doc_tokens) - 1)
for qa in paragraph["qas"]:
qas_id = qa["id"] if "id" in qa else None
qid = qa["qid"] if "qid" in qa else None
question_text = qa["question"]
start_position = None
end_position = None
orig_answer_text = None
is_impossible = False
if split == "train":
if self.v2:
is_impossible = qa["is_impossible"]
if not is_impossible:
if "detected_answers" in qa: # MRQA format
answer = qa["detected_answers"][0]
answer_offset = answer["char_spans"][0][0]
else: # SQuAD format
answer = qa["answers"][0]
answer_offset = answer["answer_start"]
orig_answer_text = answer["text"]
answer_length = len(orig_answer_text)
start_position = char_to_word_offset[answer_offset]
if answer_offset + answer_length - 1 >= len(
char_to_word_offset):
utils.log("End position is out of document!")
example_failures[0] += 1
continue
end_position = char_to_word_offset[answer_offset +
answer_length - 1]
# Only add answers where the text can be exactly recovered from the
# document. If this CAN'T happen it's likely due to weird Unicode
# stuff so we will just skip the example.
#
# Note that this means for training mode, every example is NOT
# guaranteed to be preserved.
actual_text = " ".join(
doc_tokens[start_position:(end_position + 1)])
cleaned_answer_text = " ".join(
tokenization.whitespace_tokenize(orig_answer_text))
actual_text = actual_text.lower()
cleaned_answer_text = cleaned_answer_text.lower()
if actual_text.find(cleaned_answer_text) == -1:
utils.log("Could not find answer: '{:}' in doc vs. "
"'{:}' in provided answer".format(
tokenization.printable_text(actual_text),
tokenization.printable_text(
cleaned_answer_text)))
example_failures[0] += 1
continue
else:
start_position = -1
end_position = -1
orig_answer_text = ""
# print('======= QA_TASK =========')
# print(self.name)
example = QAExample(task_name=self.name,
eid=len(examples),
qas_id=qas_id,
qid=qid,
question_text=question_text,
doc_tokens=doc_tokens,
orig_answer_text=orig_answer_text,
start_position=start_position,
end_position=end_position,
is_impossible=is_impossible)
# print(example)
examples.append(example)
