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# coding=utf-8
# Copyright 2018 The Google AI Team Authors.
#
# 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.
# Lint as: python2, python3
"""Utility functions for SQuAD v1.1/v2.0 datasets."""
from __future__ import absolute_import
from __future__ import division
# from __future__ import google_type_annotations
from __future__ import print_function
import collections
import json
import math
import re
import string
import sys
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
import numpy as np
import six
from six.moves import map
from six.moves import range
import tensorflow.compat.v1 as tf
from tensorflow.contrib import data as contrib_data
from tensorflow.contrib import layers as contrib_layers
from tensorflow.contrib import tpu as contrib_tpu
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction",
["feature_index", "start_index", "end_index",
"start_log_prob", "end_log_prob"])
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_log_prob", "end_log_prob"])
RawResult = collections.namedtuple("RawResult",
["unique_id",
"start_log_prob",
"end_log_prob"])
RawResultV2 = collections.namedtuple(
"RawResultV2",
["unique_id", "start_top_log_probs", "start_top_index",
"end_top_log_probs", "end_top_index", "cls_logits"])
class SquadExample(object):
"""A single training/test example for simple sequence classification.
For examples without an answer, the start and end position are -1.
"""
def __init__(self,
qas_id,
question_text,
paragraph_text,
orig_answer_text=None,
start_position=None,
end_position=None,
is_impossible=False):
self.qas_id = qas_id
self.question_text = question_text
self.paragraph_text = paragraph_text
self.orig_answer_text = orig_answer_text
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
def __str__(self):
return self.__repr__()
def __repr__(self):
s = ""
s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
s += ", question_text: %s" % (
tokenization.printable_text(self.question_text))
s += ", paragraph_text: [%s]" % (" ".join(self.paragraph_text))
if self.start_position:
s += ", start_position: %d" % (self.start_position)
if self.start_position:
s += ", end_position: %d" % (self.end_position)
if self.start_position:
s += ", is_impossible: %r" % (self.is_impossible)
return s
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
unique_id,
example_index,
doc_span_index,
tok_start_to_orig_index,
tok_end_to_orig_index,
token_is_max_context,
tokens,
input_ids,
input_mask,
segment_ids,
paragraph_len,
p_mask=None,
start_position=None,
end_position=None,
is_impossible=None):
self.unique_id = unique_id
self.example_index = example_index
self.doc_span_index = doc_span_index
self.tok_start_to_orig_index = tok_start_to_orig_index
self.tok_end_to_orig_index = tok_end_to_orig_index
self.token_is_max_context = token_is_max_context
self.tokens = tokens
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.paragraph_len = paragraph_len
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
self.p_mask = p_mask
def read_squad_examples(input_file, is_training):
"""Read a SQuAD json file into a list of SquadExample."""
with tf.gfile.Open(input_file, "r") as reader:
input_data = json.load(reader)["data"]
examples = []
for entry in input_data:
for paragraph in entry["paragraphs"]:
paragraph_text = paragraph["context"]
for qa in paragraph["qas"]:
qas_id = qa["id"]
question_text = qa["question"]
start_position = None
orig_answer_text = None
is_impossible = False
if is_training:
is_impossible = qa.get("is_impossible", False)
if (len(qa["answers"]) != 1) and (not is_impossible):
raise ValueError(
"For training, each question should have exactly 1 answer.")
if not is_impossible:
answer = qa["answers"][0]
orig_answer_text = answer["text"]
start_position = answer["answer_start"]
else:
start_position = -1
orig_answer_text = ""
example = SquadExample(
qas_id=qas_id,
question_text=question_text,
paragraph_text=paragraph_text,
orig_answer_text=orig_answer_text,
start_position=start_position,
is_impossible=is_impossible)
examples.append(example)
return examples
def _convert_index(index, pos, m=None, is_start=True):
"""Converts index."""
if index[pos] is not None:
return index[pos]
n = len(index)
rear = pos
while rear < n - 1 and index[rear] is None:
rear += 1
front = pos
while front > 0 and index[front] is None:
front -= 1
assert index[front] is not None or index[rear] is not None
if index[front] is None:
if index[rear] >= 1:
if is_start:
return 0
else:
return index[rear] - 1
return index[rear]
if index[rear] is None:
if m is not None and index[front] < m - 1:
if is_start:
return index[front] + 1
else:
return m - 1
return index[front]
if is_start:
if index[rear] > index[front] + 1:
return index[front] + 1
else:
return index[rear]
else:
if index[rear] > index[front] + 1:
return index[rear] - 1
else:
return index[front]
def convert_examples_to_features(examples, tokenizer, max_seq_length,
doc_stride, max_query_length, is_training,
output_fn, do_lower_case):
"""Loads a data file into a list of `InputBatch`s."""
cnt_pos, cnt_neg = 0, 0
unique_id = 1000000000
max_n, max_m = 1024, 1024
f = np.zeros((max_n, max_m), dtype=np.float32)
for (example_index, example) in enumerate(examples):
if example_index % 100 == 0:
tf.logging.info("Converting {}/{} pos {} neg {}".format(
example_index, len(examples), cnt_pos, cnt_neg))
query_tokens = tokenization.encode_ids(
tokenizer.sp_model,
tokenization.preprocess_text(
example.question_text, lower=do_lower_case))
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
paragraph_text = example.paragraph_text
para_tokens = tokenization.encode_pieces(
tokenizer.sp_model,
tokenization.preprocess_text(
example.paragraph_text, lower=do_lower_case),
return_unicode=False)
chartok_to_tok_index = []
tok_start_to_chartok_index = []
tok_end_to_chartok_index = []
char_cnt = 0
para_tokens = [six.ensure_text(token, "utf-8") for token in para_tokens]
for i, token in enumerate(para_tokens):
new_token = six.ensure_text(token).replace(
tokenization.SPIECE_UNDERLINE.decode("utf-8"), " ")
chartok_to_tok_index.extend([i] * len(new_token))
tok_start_to_chartok_index.append(char_cnt)
char_cnt += len(new_token)
tok_end_to_chartok_index.append(char_cnt - 1)
tok_cat_text = "".join(para_tokens).replace(
tokenization.SPIECE_UNDERLINE.decode("utf-8"), " ")
n, m = len(paragraph_text), len(tok_cat_text)
if n > max_n or m > max_m:
max_n = max(n, max_n)
max_m = max(m, max_m)
f = np.zeros((max_n, max_m), dtype=np.float32)
g = {}
def _lcs_match(max_dist, n=n, m=m):
"""Longest-common-substring algorithm."""
f.fill(0)
g.clear()
### longest common sub sequence
# f[i, j] = max(f[i - 1, j], f[i, j - 1], f[i - 1, j - 1] + match(i, j))
for i in range(n):
# note(zhiliny):
# unlike standard LCS, this is specifically optimized for the setting
# because the mismatch between sentence pieces and original text will
# be small
for j in range(i - max_dist, i + max_dist):
if j >= m or j < 0: continue
if i > 0:
g[(i, j)] = 0
f[i, j] = f[i - 1, j]
if j > 0 and f[i, j - 1] > f[i, j]:
g[(i, j)] = 1
f[i, j] = f[i, j - 1]
f_prev = f[i - 1, j - 1] if i > 0 and j > 0 else 0
if (tokenization.preprocess_text(
paragraph_text[i], lower=do_lower_case,
remove_space=False) == tok_cat_text[j]
and f_prev + 1 > f[i, j]):
g[(i, j)] = 2
f[i, j] = f_prev + 1
max_dist = abs(n - m) + 5
for _ in range(2):
_lcs_match(max_dist)
if f[n - 1, m - 1] > 0.8 * n: break
max_dist *= 2
orig_to_chartok_index = [None] * n
chartok_to_orig_index = [None] * m
i, j = n - 1, m - 1
while i >= 0 and j >= 0:
if (i, j) not in g: break
if g[(i, j)] == 2:
orig_to_chartok_index[i] = j
chartok_to_orig_index[j] = i
i, j = i - 1, j - 1
elif g[(i, j)] == 1:
j = j - 1
else:
i = i - 1
if (all(v is None for v in orig_to_chartok_index) or
f[n - 1, m - 1] < 0.8 * n):
tf.logging.info("MISMATCH DETECTED!")
continue
tok_start_to_orig_index = []
tok_end_to_orig_index = []
for i in range(len(para_tokens)):
start_chartok_pos = tok_start_to_chartok_index[i]
end_chartok_pos = tok_end_to_chartok_index[i]
start_orig_pos = _convert_index(chartok_to_orig_index, start_chartok_pos,
n, is_start=True)
end_orig_pos = _convert_index(chartok_to_orig_index, end_chartok_pos,
n, is_start=False)
tok_start_to_orig_index.append(start_orig_pos)
tok_end_to_orig_index.append(end_orig_pos)
if not is_training:
tok_start_position = tok_end_position = None
if is_training and example.is_impossible:
tok_start_position = 0
tok_end_position = 0
if is_training and not example.is_impossible:
start_position = example.start_position
end_position = start_position + len(example.orig_answer_text) - 1
start_chartok_pos = _convert_index(orig_to_chartok_index, start_position,
is_start=True)
tok_start_position = chartok_to_tok_index[start_chartok_pos]
end_chartok_pos = _convert_index(orig_to_chartok_index, end_position,
is_start=False)
tok_end_position = chartok_to_tok_index[end_chartok_pos]
assert tok_start_position <= tok_end_position
def _piece_to_id(x):
if six.PY2 and isinstance(x, six.text_type):
x = six.ensure_binary(x, "utf-8")
return tokenizer.sp_model.PieceToId(x)
all_doc_tokens = list(map(_piece_to_id, para_tokens))
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = 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, doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_is_max_context = {}
segment_ids = []
p_mask = []
cur_tok_start_to_orig_index = []
cur_tok_end_to_orig_index = []
tokens.append(tokenizer.sp_model.PieceToId("[CLS]"))
segment_ids.append(0)
p_mask.append(0)
for token in query_tokens:
tokens.append(token)
segment_ids.append(0)
p_mask.append(1)
tokens.append(tokenizer.sp_model.PieceToId("[SEP]"))
segment_ids.append(0)
p_mask.append(1)
for i in range(doc_span.length):
split_token_index = doc_span.start + i
cur_tok_start_to_orig_index.append(
tok_start_to_orig_index[split_token_index])
cur_tok_end_to_orig_index.append(
tok_end_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)
p_mask.append(0)
tokens.append(tokenizer.sp_model.PieceToId("[SEP]"))
segment_ids.append(1)
p_mask.append(1)
paragraph_len = len(tokens)
input_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) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
p_mask.append(1)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
span_is_impossible = example.is_impossible
start_position = None
end_position = None
if is_training and not span_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:
# continue
start_position = 0
end_position = 0
span_is_impossible = True
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 span_is_impossible:
start_position = 0
end_position = 0
if example_index < 20:
tf.logging.info("*** Example ***")
tf.logging.info("unique_id: %s" % (unique_id))
tf.logging.info("example_index: %s" % (example_index))
tf.logging.info("doc_span_index: %s" % (doc_span_index))
tf.logging.info("tok_start_to_orig_index: %s" % " ".join(
[str(x) for x in cur_tok_start_to_orig_index]))
tf.logging.info("tok_end_to_orig_index: %s" % " ".join(
[str(x) for x in cur_tok_end_to_orig_index]))
tf.logging.info("token_is_max_context: %s" % " ".join([
"%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
]))
tf.logging.info("input_pieces: %s" % " ".join(
[tokenizer.sp_model.IdToPiece(x) for x in tokens]))
tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
tf.logging.info(
"input_mask: %s" % " ".join([str(x) for x in input_mask]))
tf.logging.info(
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
if is_training and span_is_impossible:
tf.logging.info("impossible example span")
if is_training and not span_is_impossible:
pieces = [tokenizer.sp_model.IdToPiece(token) for token in
tokens[start_position: (end_position + 1)]]
answer_text = tokenizer.sp_model.DecodePieces(pieces)
tf.logging.info("start_position: %d" % (start_position))
tf.logging.info("end_position: %d" % (end_position))
tf.logging.info(
"answer: %s" % (tokenization.printable_text(answer_text)))
# note(zhiliny): With multi processing,
# the example_index is actually the index within the current process
# therefore we use example_index=None to avoid being used in the future.
# The current code does not use example_index of training data.
if is_training:
feat_example_index = None
else:
feat_example_index = example_index
feature = InputFeatures(
unique_id=unique_id,
example_index=feat_example_index,
doc_span_index=doc_span_index,
tok_start_to_orig_index=cur_tok_start_to_orig_index,
tok_end_to_orig_index=cur_tok_end_to_orig_index,
token_is_max_context=token_is_max_context,
tokens=[tokenizer.sp_model.IdToPiece(x) for x in tokens],
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
paragraph_len=paragraph_len,
start_position=start_position,
end_position=end_position,
is_impossible=span_is_impossible,
p_mask=p_mask)
# Run callback
output_fn(feature)
unique_id += 1
if span_is_impossible:
cnt_neg += 1
else:
cnt_pos += 1
tf.logging.info("Total number of instances: {} = pos {} neg {}".format(
cnt_pos + cnt_neg, cnt_pos, cnt_neg))
def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
# Because of the sliding window approach taken to scoring documents, a single
# token can appear in multiple documents. E.g.
# Doc: the man went to the store and bought a gallon of milk
# Span A: the man went to the
# Span B: to the store and bought
# Span C: and bought a gallon of
# ...
#
# Now the word 'bought' will have two scores from spans B and C. We only
# want to consider the score with "maximum context", which we define as
# the *minimum* of its left and right context (the *sum* of left and
# right context will always be the same, of course).
#
# In the example the maximum context for 'bought' would be span C since
# it has 1 left context and 3 right context, while span B has 4 left context
# and 0 right context.
best_score = None
best_span_index = None
for (span_index, doc_span) in enumerate(doc_spans):
end = doc_span.start + doc_span.length - 1
if position < doc_span.start:
continue
if position > end:
continue
num_left_context = position - doc_span.start
num_right_context = end - position
score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index
def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indexes.append(index_and_score[i][0])
return best_indexes
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs
class FeatureWriter(object):
"""Writes InputFeature to TF example file."""
def __init__(self, filename, is_training):
self.filename = filename
self.is_training = is_training
self.num_features = 0
self._writer = tf.python_io.TFRecordWriter(filename)
def process_feature(self, feature):
"""Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
self.num_features += 1
def create_int_feature(values):
feature = tf.train.Feature(
int64_list=tf.train.Int64List(value=list(values)))
return feature
features = collections.OrderedDict()
features["unique_ids"] = create_int_feature([feature.unique_id])
features["input_ids"] = create_int_feature(feature.input_ids)
features["input_mask"] = create_int_feature(feature.input_mask)
features["segment_ids"] = create_int_feature(feature.segment_ids)
features["p_mask"] = create_int_feature(feature.p_mask)
if self.is_training:
features["start_positions"] = create_int_feature([feature.start_position])
features["end_positions"] = create_int_feature([feature.end_position])
impossible = 0
if feature.is_impossible:
impossible = 1
features["is_impossible"] = create_int_feature([impossible])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
self._writer.write(tf_example.SerializeToString())
def close(self):
self._writer.close()
def input_fn_builder(input_file, seq_length, is_training,
drop_remainder, use_tpu, bsz, is_v2):
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
name_to_features = {
"unique_ids": tf.FixedLenFeature([], tf.int64),
"input_ids": tf.FixedLenFeature([seq_length], tf.int64),
"input_mask": tf.FixedLenFeature([seq_length], tf.int64),
"segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
}
# p_mask is not required for SQuAD v1.1
if is_v2:
name_to_features["p_mask"] = tf.FixedLenFeature([seq_length], tf.int64)
if is_training:
name_to_features["start_positions"] = tf.FixedLenFeature([], tf.int64)
name_to_features["end_positions"] = tf.FixedLenFeature([], tf.int64)
name_to_features["is_impossible"] = tf.FixedLenFeature([], tf.int64)
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
return example
def input_fn(params):
"""The actual input function."""
if use_tpu:
batch_size = params["batch_size"]
else:
batch_size = bsz
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.TFRecordDataset(input_file)
if is_training:
d = d.repeat()
d = d.shuffle(buffer_size=100)
d = d.apply(
contrib_data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
drop_remainder=drop_remainder))
return d
return input_fn
def create_v1_model(albert_config, is_training, input_ids, input_mask,
segment_ids, use_one_hot_embeddings, use_einsum,
hub_module):
"""Creates a classification model."""
(_, final_hidden) = fine_tuning_utils.create_albert(
albert_config=albert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
use_einsum=use_einsum,
hub_module=hub_module)
final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
batch_size = final_hidden_shape[0]
seq_length = final_hidden_shape[1]
hidden_size = final_hidden_shape[2]
output_weights = tf.get_variable(
"cls/squad/output_weights", [2, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"cls/squad/output_bias", [2], initializer=tf.zeros_initializer())
final_hidden_matrix = tf.reshape(final_hidden,
[batch_size * seq_length, hidden_size])
logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
logits = tf.reshape(logits, [batch_size, seq_length, 2])
logits = tf.transpose(logits, [2, 0, 1])
unstacked_logits = tf.unstack(logits, axis=0)
(start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])
return (start_logits, end_logits)
def v1_model_fn_builder(albert_config, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps, use_tpu,
use_one_hot_embeddings, use_einsum, hub_module):
"""Returns `model_fn` closure for TPUEstimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
tf.logging.info("*** Features ***")
for name in sorted(features.keys()):
tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
if "unique_ids" in features:
unique_ids = features["unique_ids"]
else:
unique_ids = None
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
(start_logits, end_logits) = create_v1_model(
albert_config=albert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
use_einsum=use_einsum,
hub_module=hub_module)
# Assign names to the logits so that we can refer to them as output tensors.
start_logits = tf.identity(start_logits, name="start_logits")
end_logits = tf.identity(end_logits, name="end_logits")
tvars = tf.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
seq_length = modeling.get_shape_list(input_ids)[1]
def compute_loss(logits, positions):
one_hot_positions = tf.one_hot(
positions, depth=seq_length, dtype=tf.float32)
log_probs = tf.nn.log_softmax(logits, axis=-1)
loss = -tf.reduce_mean(
tf.reduce_sum(one_hot_positions * log_probs, axis=-1))
return loss
start_positions = features["start_positions"]
end_positions = features["end_positions"]
start_loss = compute_loss(start_logits, start_positions)
end_loss = compute_loss(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2.0
train_op = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
output_spec = contrib_tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
"start_log_prob": start_logits,
"end_log_prob": end_logits,
}
if unique_ids is not None:
predictions["unique_ids"] = unique_ids
output_spec = contrib_tpu.TPUEstimatorSpec(
mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
else:
raise ValueError(
"Only TRAIN and PREDICT modes are supported: %s" % (mode))
return output_spec
return model_fn
def accumulate_predictions_v1(result_dict, all_examples, all_features,
all_results, n_best_size, max_answer_length):
"""accumulate predictions for each positions in a dictionary."""
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
if example_index not in result_dict:
result_dict[example_index] = {}
features = example_index_to_features[example_index]
prelim_predictions = []
min_null_feature_index = 0 # the paragraph slice with min mull score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for (feature_index, feature) in enumerate(features):
if feature.unique_id not in result_dict[example_index]:
result_dict[example_index][feature.unique_id] = {}
result = unique_id_to_result[feature.unique_id]
start_indexes = _get_best_indexes(result.start_log_prob, n_best_size)
end_indexes = _get_best_indexes(result.end_log_prob, n_best_size)
for start_index in start_indexes:
for end_index in end_indexes:
doc_offset = feature.tokens.index("[SEP]") + 1
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index - doc_offset >= len(feature.tok_start_to_orig_index):
continue
if end_index - doc_offset >= len(feature.tok_end_to_orig_index):
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
start_log_prob = result.start_log_prob[start_index]
end_log_prob = result.end_log_prob[end_index]
start_idx = start_index - doc_offset
end_idx = end_index - doc_offset
if (start_idx, end_idx) not in result_dict[example_index][feature.unique_id]:
result_dict[example_index][feature.unique_id][(start_idx, end_idx)] = []
result_dict[example_index][feature.unique_id][(start_idx, end_idx)].append((start_log_prob, end_log_prob))
def write_predictions_v1(result_dict, all_examples, all_features,
all_results, n_best_size, max_answer_length,
output_prediction_file, output_nbest_file):
"""Write final predictions to the json file and log-odds of null if needed."""
tf.logging.info("Writing predictions to: %s" % (output_prediction_file))
tf.logging.info("Writing nbest to: %s" % (output_nbest_file))
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
min_null_feature_index = 0 # the paragraph slice with min mull score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for (feature_index, feature) in enumerate(features):
for ((start_idx, end_idx), logprobs) in \
result_dict[example_index][feature.unique_id].items():
start_log_prob = 0
end_log_prob = 0
for logprob in logprobs:
start_log_prob += logprob[0]
end_log_prob += logprob[1]
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_idx,
end_index=end_idx,
start_log_prob=start_log_prob / len(logprobs),
end_log_prob=end_log_prob / len(logprobs)))
prelim_predictions = sorted(
prelim_predictions,
key=lambda x: (x.start_log_prob + x.end_log_prob),
reverse=True)
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
if pred.start_index >= 0: # this is a non-null prediction
tok_start_to_orig_index = feature.tok_start_to_orig_index
tok_end_to_orig_index = feature.tok_end_to_orig_index
start_orig_pos = tok_start_to_orig_index[pred.start_index]
end_orig_pos = tok_end_to_orig_index[pred.end_index]
paragraph_text = example.paragraph_text
final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip()
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
else:
final_text = ""
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(
text=final_text,
start_log_prob=pred.start_log_prob,
end_log_prob=pred.end_log_prob))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(
_NbestPrediction(text="empty", start_log_prob=0.0, end_log_prob=0.0))
assert len(nbest) >= 1
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_log_prob + entry.end_log_prob)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_log_prob"] = entry.start_log_prob
output["end_log_prob"] = entry.end_log_prob
nbest_json.append(output)
assert len(nbest_json) >= 1
all_predictions[example.qas_id] = nbest_json[0]["text"]
all_nbest_json[example.qas_id] = nbest_json
with tf.gfile.GFile(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
with tf.gfile.GFile(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
return all_predictions
####### following are from official SQuAD v1.1 evaluation scripts
def normalize_answer_v1(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
return re.sub(r"\b(a|an|the)\b", " ", text)
def white_space_fix(text):
return " ".join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def f1_score(prediction, ground_truth):
prediction_tokens = normalize_answer_v1(prediction).split()
ground_truth_tokens = normalize_answer_v1(ground_truth).split()
common = (
collections.Counter(prediction_tokens)
& collections.Counter(ground_truth_tokens))
num_same = sum(common.values())
if num_same == 0:
return 0
precision = 1.0 * num_same / len(prediction_tokens)
recall = 1.0 * num_same / len(ground_truth_tokens)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def exact_match_score(prediction, ground_truth):
return (normalize_answer_v1(prediction) == normalize_answer_v1(ground_truth))
def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
scores_for_ground_truths = []
for ground_truth in ground_truths:
score = metric_fn(prediction, ground_truth)
scores_for_ground_truths.append(score)
return max(scores_for_ground_truths)
def evaluate_v1(dataset, predictions):
f1 = exact_match = total = 0
for article in dataset:
for paragraph in article["paragraphs"]:
for qa in paragraph["qas"]:
total += 1
if qa["id"] not in predictions:
message = ("Unanswered question " + six.ensure_str(qa["id"]) +
" will receive score 0.")
print(message, file=sys.stderr)
continue
ground_truths = [x["text"] for x in qa["answers"]]
# ground_truths = list(map(lambda x: x["text"], qa["answers"]))
prediction = predictions[qa["id"]]
exact_match += metric_max_over_ground_truths(exact_match_score,
prediction, ground_truths)
f1 += metric_max_over_ground_truths(f1_score, prediction, ground_truths)
exact_match = 100.0 * exact_match / total
f1 = 100.0 * f1 / total
return {"exact_match": exact_match, "f1": f1}
####### above are from official SQuAD v1.1 evaluation scripts
####### following are from official SQuAD v2.0 evaluation scripts
def make_qid_to_has_ans(dataset):
qid_to_has_ans = {}
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
qid_to_has_ans[qa['id']] = bool(qa['answers'])
return qid_to_has_ans
def normalize_answer_v2(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
return re.sub(regex, ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def get_tokens(s):
if not s: return []
return normalize_answer_v2(s).split()
def compute_exact(a_gold, a_pred):
return int(normalize_answer_v2(a_gold) == normalize_answer_v2(a_pred))
def compute_f1(a_gold, a_pred):
gold_toks = get_tokens(a_gold)
pred_toks = get_tokens(a_pred)
common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
num_same = sum(common.values())
if len(gold_toks) == 0 or len(pred_toks) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
precision = 1.0 * num_same / len(pred_toks)
recall = 1.0 * num_same / len(gold_toks)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def get_raw_scores(dataset, preds):
exact_scores = {}
f1_scores = {}
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
qid = qa['id']
gold_answers = [a['text'] for a in qa['answers']
if normalize_answer_v2(a['text'])]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
gold_answers = ['']
if qid not in preds:
print('Missing prediction for %s' % qid)
continue
a_pred = preds[qid]
# Take max over all gold answers
exact_scores[qid] = max(compute_exact(a, a_pred) for a in gold_answers)
f1_scores[qid] = max(compute_f1(a, a_pred) for a in gold_answers)
return exact_scores, f1_scores
def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
new_scores = {}
for qid, s in scores.items():
pred_na = na_probs[qid] > na_prob_thresh
if pred_na:
new_scores[qid] = float(not qid_to_has_ans[qid])
else:
new_scores[qid] = s
return new_scores
def make_eval_dict(exact_scores, f1_scores, qid_list=None):
if not qid_list:
total = len(exact_scores)
return collections.OrderedDict([
('exact', 100.0 * sum(exact_scores.values()) / total),
('f1', 100.0 * sum(f1_scores.values()) / total),
('total', total),
])
else:
total = len(qid_list)
return collections.OrderedDict([
('exact', 100.0 * sum(exact_scores[k] for k in qid_list) / total),
('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total),
('total', total),
])
def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for i, qid in enumerate(qid_list):
if qid not in scores: continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if preds[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
return 100.0 * best_score / len(scores), best_thresh
def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval['best_exact'] = best_exact
main_eval['best_exact_thresh'] = exact_thresh
main_eval['best_f1'] = best_f1
main_eval['best_f1_thresh'] = f1_thresh
def merge_eval(main_eval, new_eval, prefix):
for k in new_eval:
main_eval['%s_%s' % (prefix, k)] = new_eval[k]
####### above are from official SQuAD v2.0 evaluation scripts
def accumulate_predictions_v2(result_dict, cls_dict, all_examples,
all_features, all_results, n_best_size,
max_answer_length, start_n_top, end_n_top):
"""accumulate predictions for each positions in a dictionary."""
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
if example_index not in result_dict:
result_dict[example_index] = {}
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
for (feature_index, feature) in enumerate(features):
if feature.unique_id not in result_dict[example_index]:
result_dict[example_index][feature.unique_id] = {}
result = unique_id_to_result[feature.unique_id]
cur_null_score = result.cls_logits
# if we could have irrelevant answers, get the min score of irrelevant
score_null = min(score_null, cur_null_score)
doc_offset = feature.tokens.index("[SEP]") + 1
for i in range(start_n_top):
for j in range(end_n_top):
start_log_prob = result.start_top_log_probs[i]
start_index = result.start_top_index[i]
j_index = i * end_n_top + j
end_log_prob = result.end_top_log_probs[j_index]
end_index = result.end_top_index[j_index]
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index - doc_offset >= len(feature.tok_start_to_orig_index):
continue
if start_index - doc_offset < 0:
continue
if end_index - doc_offset >= len(feature.tok_end_to_orig_index):
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
start_idx = start_index - doc_offset
end_idx = end_index - doc_offset
if (start_idx, end_idx) not in result_dict[example_index][feature.unique_id]:
result_dict[example_index][feature.unique_id][(start_idx, end_idx)] = []
result_dict[example_index][feature.unique_id][(start_idx, end_idx)].append((start_log_prob, end_log_prob))
if example_index not in cls_dict:
cls_dict[example_index] = []
cls_dict[example_index].append(score_null)
def write_predictions_v2(result_dict, cls_dict, all_examples, all_features,
all_results, n_best_size, max_answer_length,
output_prediction_file,
output_nbest_file, output_null_log_odds_file,
null_score_diff_threshold):
"""Write final predictions to the json file and log-odds of null if needed."""
tf.logging.info("Writing predictions to: %s" % (output_prediction_file))
tf.logging.info("Writing nbest to: %s" % (output_nbest_file))
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
# score_null = 1000000 # large and positive
for (feature_index, feature) in enumerate(features):
for ((start_idx, end_idx), logprobs) in \
result_dict[example_index][feature.unique_id].items():
start_log_prob = 0
end_log_prob = 0
for logprob in logprobs:
start_log_prob += logprob[0]
end_log_prob += logprob[1]
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_idx,
end_index=end_idx,
start_log_prob=start_log_prob / len(logprobs),
end_log_prob=end_log_prob / len(logprobs)))
prelim_predictions = sorted(
prelim_predictions,
key=lambda x: (x.start_log_prob + x.end_log_prob),
reverse=True)
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
tok_start_to_orig_index = feature.tok_start_to_orig_index
tok_end_to_orig_index = feature.tok_end_to_orig_index
start_orig_pos = tok_start_to_orig_index[pred.start_index]
end_orig_pos = tok_end_to_orig_index[pred.end_index]
paragraph_text = example.paragraph_text
final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip()
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(
text=final_text,
start_log_prob=pred.start_log_prob,
end_log_prob=pred.end_log_prob))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(
_NbestPrediction(
text="",
start_log_prob=-1e6,
end_log_prob=-1e6))
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_log_prob + entry.end_log_prob)
if not best_non_null_entry:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_log_prob"] = entry.start_log_prob
output["end_log_prob"] = entry.end_log_prob
nbest_json.append(output)
assert len(nbest_json) >= 1
assert best_non_null_entry is not None
score_diff = sum(cls_dict[example_index]) / len(cls_dict[example_index])
scores_diff_json[example.qas_id] = score_diff
# predict null answers when null threshold is provided
if null_score_diff_threshold is None or score_diff < null_score_diff_threshold:
all_predictions[example.qas_id] = best_non_null_entry.text
else:
all_predictions[example.qas_id] = ""
all_nbest_json[example.qas_id] = nbest_json
assert len(nbest_json) >= 1
with tf.gfile.GFile(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
with tf.gfile.GFile(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
with tf.gfile.GFile(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions, scores_diff_json
def create_v2_model(albert_config, is_training, input_ids, input_mask,
segment_ids, use_one_hot_embeddings, features,
max_seq_length, start_n_top, end_n_top, dropout_prob,
hub_module):
"""Creates a classification model."""
(_, output) = fine_tuning_utils.create_albert(
albert_config=albert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
use_einsum=True,
hub_module=hub_module)
bsz = tf.shape(output)[0]
return_dict = {}
output = tf.transpose(output, [1, 0, 2])
# invalid position mask such as query and special symbols (PAD, SEP, CLS)
p_mask = tf.cast(features["p_mask"], dtype=tf.float32)
# logit of the start position
with tf.variable_scope("start_logits"):
start_logits = tf.layers.dense(
output,
1,
kernel_initializer=modeling.create_initializer(
albert_config.initializer_range))
start_logits = tf.transpose(tf.squeeze(start_logits, -1), [1, 0])
start_logits_masked = start_logits * (1 - p_mask) - 1e30 * p_mask
start_log_probs = tf.nn.log_softmax(start_logits_masked, -1)
# logit of the end position
with tf.variable_scope("end_logits"):
if is_training:
# during training, compute the end logits based on the
# ground truth of the start position
start_positions = tf.reshape(features["start_positions"], [-1])
start_index = tf.one_hot(start_positions, depth=max_seq_length, axis=-1,
dtype=tf.float32)
start_features = tf.einsum("lbh,bl->bh", output, start_index)
start_features = tf.tile(start_features[None], [max_seq_length, 1, 1])
end_logits = tf.layers.dense(
tf.concat([output, start_features], axis=-1),
albert_config.hidden_size,
kernel_initializer=modeling.create_initializer(
albert_config.initializer_range),
activation=tf.tanh,
name="dense_0")
end_logits = contrib_layers.layer_norm(end_logits, begin_norm_axis=-1)
end_logits = tf.layers.dense(
end_logits,
1,
kernel_initializer=modeling.create_initializer(
albert_config.initializer_range),
name="dense_1")
end_logits = tf.transpose(tf.squeeze(end_logits, -1), [1, 0])
end_logits_masked = end_logits * (1 - p_mask) - 1e30 * p_mask
end_log_probs = tf.nn.log_softmax(end_logits_masked, -1)
else:
# during inference, compute the end logits based on beam search
start_top_log_probs, start_top_index = tf.nn.top_k(
start_log_probs, k=start_n_top)
start_index = tf.one_hot(start_top_index,
depth=max_seq_length, axis=-1, dtype=tf.float32)
start_features = tf.einsum("lbh,bkl->bkh", output, start_index)
end_input = tf.tile(output[:, :, None],
[1, 1, start_n_top, 1])
start_features = tf.tile(start_features[None],
[max_seq_length, 1, 1, 1])
end_input = tf.concat([end_input, start_features], axis=-1)
end_logits = tf.layers.dense(
end_input,
albert_config.hidden_size,
kernel_initializer=modeling.create_initializer(
albert_config.initializer_range),
activation=tf.tanh,
name="dense_0")
end_logits = contrib_layers.layer_norm(end_logits, begin_norm_axis=-1)
end_logits = tf.layers.dense(
end_logits,
1,
kernel_initializer=modeling.create_initializer(
albert_config.initializer_range),
name="dense_1")
end_logits = tf.reshape(end_logits, [max_seq_length, -1, start_n_top])
end_logits = tf.transpose(end_logits, [1, 2, 0])
end_logits_masked = end_logits * (
1 - p_mask[:, None]) - 1e30 * p_mask[:, None]
end_log_probs = tf.nn.log_softmax(end_logits_masked, -1)
end_top_log_probs, end_top_index = tf.nn.top_k(
end_log_probs, k=end_n_top)
end_top_log_probs = tf.reshape(
end_top_log_probs,
[-1, start_n_top * end_n_top])
end_top_index = tf.reshape(
end_top_index,
[-1, start_n_top * end_n_top])
if is_training:
return_dict["start_log_probs"] = start_log_probs
return_dict["end_log_probs"] = end_log_probs
else:
return_dict["start_top_log_probs"] = start_top_log_probs
return_dict["start_top_index"] = start_top_index
return_dict["end_top_log_probs"] = end_top_log_probs
return_dict["end_top_index"] = end_top_index
# an additional layer to predict answerability
with tf.variable_scope("answer_class"):
# get the representation of CLS
cls_index = tf.one_hot(tf.zeros([bsz], dtype=tf.int32),
max_seq_length,
axis=-1, dtype=tf.float32)
cls_feature = tf.einsum("lbh,bl->bh", output, cls_index)
# get the representation of START
start_p = tf.nn.softmax(start_logits_masked, axis=-1,
name="softmax_start")
start_feature = tf.einsum("lbh,bl->bh", output, start_p)
# note(zhiliny): no dependency on end_feature so that we can obtain
# one single `cls_logits` for each sample
ans_feature = tf.concat([start_feature, cls_feature], -1)
ans_feature = tf.layers.dense(
ans_feature,
albert_config.hidden_size,
activation=tf.tanh,
kernel_initializer=modeling.create_initializer(
albert_config.initializer_range),
name="dense_0")
ans_feature = tf.layers.dropout(ans_feature, dropout_prob,
training=is_training)
cls_logits = tf.layers.dense(
ans_feature,
1,
kernel_initializer=modeling.create_initializer(
albert_config.initializer_range),
name="dense_1",
use_bias=False)
cls_logits = tf.squeeze(cls_logits, -1)
return_dict["cls_logits"] = cls_logits
return return_dict
def v2_model_fn_builder(albert_config, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps, use_tpu,
use_one_hot_embeddings, max_seq_length, start_n_top,
end_n_top, dropout_prob, hub_module):
"""Returns `model_fn` closure for TPUEstimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
tf.logging.info("*** Features ***")
for name in sorted(features.keys()):
tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
# unique_ids = features["unique_ids"]
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
outputs = create_v2_model(
albert_config=albert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
features=features,
max_seq_length=max_seq_length,
start_n_top=start_n_top,
end_n_top=end_n_top,
dropout_prob=dropout_prob,
hub_module=hub_module)
tvars = tf.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
seq_length = modeling.get_shape_list(input_ids)[1]
def compute_loss(log_probs, positions):
one_hot_positions = tf.one_hot(
positions, depth=seq_length, dtype=tf.float32)
loss = - tf.reduce_sum(one_hot_positions * log_probs, axis=-1)
loss = tf.reduce_mean(loss)
return loss
start_loss = compute_loss(
outputs["start_log_probs"], features["start_positions"])
end_loss = compute_loss(
outputs["end_log_probs"], features["end_positions"])
total_loss = (start_loss + end_loss) * 0.5
cls_logits = outputs["cls_logits"]
is_impossible = tf.reshape(features["is_impossible"], [-1])
regression_loss = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.cast(is_impossible, dtype=tf.float32), logits=cls_logits)
regression_loss = tf.reduce_mean(regression_loss)
# note(zhiliny): by default multiply the loss by 0.5 so that the scale is
# comparable to start_loss and end_loss
total_loss += regression_loss * 0.5
train_op = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
output_spec = contrib_tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
"unique_ids": features["unique_ids"],
"start_top_index": outputs["start_top_index"],
"start_top_log_probs": outputs["start_top_log_probs"],
"end_top_index": outputs["end_top_index"],
"end_top_log_probs": outputs["end_top_log_probs"],
"cls_logits": outputs["cls_logits"]
}
output_spec = contrib_tpu.TPUEstimatorSpec(
mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
else:
raise ValueError(
"Only TRAIN and PREDICT modes are supported: %s" % (mode))
return output_spec
return model_fn
def evaluate_v2(result_dict, cls_dict, prediction_json, eval_examples,
eval_features, all_results, n_best_size, max_answer_length,
output_prediction_file, output_nbest_file,
output_null_log_odds_file):
null_score_diff_threshold = None
predictions, na_probs = write_predictions_v2(
result_dict, cls_dict, eval_examples, eval_features,
all_results, n_best_size, max_answer_length,
output_prediction_file, output_nbest_file,
output_null_log_odds_file, null_score_diff_threshold)
na_prob_thresh = 1.0 # default value taken from the eval script
qid_to_has_ans = make_qid_to_has_ans(prediction_json) # maps qid to True/False
has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
exact_raw, f1_raw = get_raw_scores(prediction_json, predictions)
exact_thresh = apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans,
na_prob_thresh)
f1_thresh = apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans,
na_prob_thresh)
out_eval = make_eval_dict(exact_thresh, f1_thresh)
find_all_best_thresh(out_eval, predictions, exact_raw, f1_raw, na_probs, qid_to_has_ans)
null_score_diff_threshold = out_eval["best_f1_thresh"]
predictions, na_probs = write_predictions_v2(
result_dict, cls_dict,eval_examples, eval_features,
all_results, n_best_size, max_answer_length,
output_prediction_file, output_nbest_file,
output_null_log_odds_file, null_score_diff_threshold)
qid_to_has_ans = make_qid_to_has_ans(prediction_json) # maps qid to True/False
has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
exact_raw, f1_raw = get_raw_scores(prediction_json, predictions)
exact_thresh = apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans,
na_prob_thresh)
f1_thresh = apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans,
na_prob_thresh)
out_eval = make_eval_dict(exact_thresh, f1_thresh)
out_eval["null_score_diff_threshold"] = null_score_diff_threshold
return out_eval
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