def pipeline(root):
example_packer = beam_utils.PriorityExamplePacker(
priority_feature='token_ids',
max_lengths=dict(token_ids=10, sentence_ids=10, global_token_ids=5),
breakpoint_features=dict(
token_ids='long_breakpoints',
global_token_ids='global_breakpoints'),
cumulative_features=['sentence_ids'],
min_packing_fraction=0.75,
max_cache_len=5)
result = (
root | beam.Create(input_examples)
| beam_utils.PackExamples(example_packer)
| beam.Map(str))
beam_testing.assert_that(
result,
beam_testing.equal_to([str(x) for x in expected_packed_examples]))
def pipeline(root):
question_answers = read_question_answer_csv(input_qaps,
input_documents,
data_split)
question_answers = (
root | 'CreateQuestionAnswers' >> beam.Create(question_answers)
| 'ShuffleAfterCreatingQA' >> beam.Reshuffle())
read_outputs = (
question_answers
| 'ReadEvidence' >> beam.ParDo(
ReadEvidence(stories_dir=stories_dir,
summaries_path=summaries_path)).with_outputs())
_ = (read_outputs[ReadEvidenceOutput.NO_STAR_END_CONTENT]
| 'ShuffleNoStarEndContent' >> beam.Reshuffle()
| 'WriteNoStarEndContent' >> write_to_file_fn(
output_prefix, 'no_star_end_content.txt'))
_ = (read_outputs[ReadEvidenceOutput.TOO_SHORT_CONTENT]
| 'ShuffleTooShortContent' >> beam.Reshuffle()
| 'WriteTooShortContent' >> write_to_file_fn(
output_prefix, 'too_short_content.txt'))
outputs = (
read_outputs[ReadEvidenceOutput.SUCCESS]
| 'ShuffleBeforeMakeExamples' >> beam.Reshuffle()
| 'MakeExamples' >> beam.ParDo(
MakeExamples(
spm_model_path=spm_model_path,
num_blocks_per_example=num_blocks_per_example,
block_overlap_length=block_overlap_length,
block_length=block_length,
max_num_annotations_per_block=max_num_annotations_per_block,
padding_token_id=padding_token_id,
cls_token_id=cls_token_id,
sep_token_id=sep_token_id,
generate_answers=generate_answers,
generate_summaries=generate_summaries,
min_rouge_l_oracle_score=min_rouge_l_oracle_score,
nltk_data_path=nltk_data_path)).with_outputs())
# if generate_answers:
# # Write failure cases, when no answer was found
# _ = (
# outputs[MakeExampleOutput.NO_ANSWER]
# | 'ShuffleNoAnswer' >> beam.Reshuffle()
# | 'SampleNoAnswer' >>
# beam.combiners.Sample.FixedSizeGlobally(SAMPLE_NO_ANSWER_QUESTIONS)
# | 'WriteNoAnswer' >> write_to_file_fn('no_answer.jsonl'))
# _ = (
# outputs[MakeExampleOutput.NO_ANSWER_TOKENIZED]
# | 'ShuffleNoAnswerTokenized' >> beam.Reshuffle()
# | 'SampleNoAnswerTokenized' >>
# beam.combiners.Sample.FixedSizeGlobally(SAMPLE_NO_ANSWER_QUESTIONS)
# | 'WriteNoAnswerTokenized' >>
# write_to_file_fn('no_answer_tokenized.jsonl'))
# # Write annotations that have been filtered out after tokenization
# _ = (
# outputs[MakeExampleOutput.SUCCESS_FILTERED_ANNOTATIONS]
# | 'ShuffleSuccessFilteredAnnotations' >> beam.Reshuffle()
# | 'FlattenSuccessFilteredAnnotations' >> beam.FlatMap(lambda x: x)
# | 'WriteSuccessFilteredAnnotations' >>
# write_to_file_fn('success.filtered_annotations.txt'))
# _ = (
# outputs[
# MakeExampleOutput.NO_ANSWER_TOKENIZED_FILTERED_ANNOTATIONS]
# |
# 'ShuffleNoAnswerTokenizedFilteredAnnotations' >> beam.Reshuffle()
# | 'FlattenNoAnswerTokenizedFilteredAnnotations' >>
# beam.FlatMap(lambda x: x)
# | 'WriteNoAnswerTokenizedFilteredAnnotations' >>
# write_to_file_fn('no_answer_tokenized.filtered_annotations.txt'))
# # Write cases where the too many answer spans were found
# _ = (
# outputs[MakeExampleOutput.TOO_MANY_ANSWERS]
# | 'ShuffleTooManyAnswers' >> beam.Reshuffle()
# | ('WriteTooManyAnswers' >>
# write_to_file_fn('too_many_answers.jsonl')))
max_tokens = num_blocks_per_example * block_length
max_num_annotations = num_blocks_per_example * max_num_annotations_per_block
max_lengths = dict(token_ids=max_tokens,
is_continuation=max_tokens,
block_ids=num_blocks_per_example,
answer_annotation_begins=max_num_annotations,
answer_annotation_ends=max_num_annotations,
answer_annotation_labels=max_num_annotations,
entity_annotation_begins=max_num_annotations,
entity_annotation_ends=max_num_annotations,
entity_annotation_labels=max_num_annotations,
prefix_length=num_blocks_per_example)
if generate_summaries:
max_lengths['summary_token_ids'] = max_tokens
example_packer = beam_utils.PriorityExamplePacker(
priority_feature='token_ids',
max_lengths=max_lengths,
breakpoint_features=dict(),
cumulative_features=[],
min_packing_fraction=1.0,
max_cache_len=num_blocks_per_example)
_ = (outputs[MakeExampleOutput.SUCCESS]
| 'ShuffleBeforePacking' >> beam.Reshuffle()
| 'PackExamples' >> beam_utils.PackExamples(example_packer)
| 'ShuffleAfterPacking' >> beam.Reshuffle()
| 'WriteTfExamples' >> beam.io.WriteToTFRecord(
os.path.join(output_prefix + '.tfrecord'),
coder=beam.coders.ProtoCoder(tf.train.Example),
num_shards=output_num_shards))
def test_priority_example_packer_for_read_it_twice_model(self):
"""Test for PriorityExamplePacker with read-it-twice model's data format.
For brevity reasons, we omit `is_continuation` feature.
Note that in all examples below the `block_length` is 5
"""
# Test case 1: Simple combination of blocks
packer = beam_utils.PriorityExamplePacker(
priority_feature='token_ids',
max_lengths=dict(token_ids=20, block_ids=4),
breakpoint_features={},
cumulative_features=[],
min_packing_fraction=1.0,
max_cache_len=2)
self.assertEqual([],
packer.add_example(
text_format.Parse(
"""
features {
feature {
key: "token_ids"
value {
int64_list {
value: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
}
}
}
feature {
key: "block_ids"
value {
int64_list {
value: [1, 1]
}
}
}
}
""", tf.train.Example())))
self.assertEqual([],
packer.add_example(
text_format.Parse(
"""
features {
feature {
key: "token_ids"
value {
int64_list {
value: [1, 0, 0, 0, 0]
}
}
}
feature {
key: "block_ids"
value {
int64_list {
value: [2]
}
}
}
}
""", tf.train.Example())))
self.assertEqual([
text_format.Parse(
"""
features {
feature {
key: "token_ids"
value {
int64_list {
value: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
1, 0, 0, 0, 0, 1, 0, 0, 0, 0]
}
}
}
feature {
key: "block_ids"
value {
int64_list {
value: [1, 1, 2, 5]
}
}
}
feature {
key: "packing_status"
value {
bytes_list {
value: ['packed']
}
}
}
}
""", tf.train.Example())
],
packer.add_example(
text_format.Parse(
"""
features {
feature {
key: "token_ids"
value {
int64_list {
value: [1, 0, 0, 0, 0]
}
}
}
feature {
key: "block_ids"
value {
int64_list {
value: [5]
}
}
}
}
""", tf.train.Example())))
# Test case 2: The block is expected to be filled in `flush_examples`
packer = beam_utils.PriorityExamplePacker(
priority_feature='token_ids',
max_lengths=dict(token_ids=20, block_ids=4),
breakpoint_features={},
cumulative_features=[],
min_packing_fraction=1.0,
max_cache_len=2,
padding_token_ids=dict(token_ids=-1, block_ids=0))
self.assertEqual([],
packer.add_example(
text_format.Parse(
"""
features {
feature {
key: "token_ids"
value {
int64_list {
value: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
}
}
}
feature {
key: "block_ids"
value {
int64_list {
value: [1, 1]
}
}
}
}
""", tf.train.Example())))
self.assertEqual([],
packer.add_example(
text_format.Parse(
"""
features {
feature {
key: "token_ids"
value {
int64_list {
value: [1, 0, 0, 0, 0]
}
}
}
feature {
key: "block_ids"
value {
int64_list {
value: [2]
}
}
}
}
""", tf.train.Example())))
self.assertEqual([
text_format.Parse(
"""
features {
feature {
key: "token_ids"
value {
int64_list {
value: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
1, 0, 0, 0, 0, -1, -1, -1, -1, -1]
}
}
}
feature {
key: "block_ids"
value {
int64_list {
value: [1, 1, 2, 0]
}
}
}
feature {
key: "packing_status"
value {
bytes_list {
value: ['flushed']
}
}
}
}
""", tf.train.Example())
], packer.flush_examples())
def test_priority_example_packer(self):
def make_test_example(
num_values,
token_pad_num = None,
global_token_pad_num = None,
packing_status = None):
"""Makes a tf.Example for testing packing logic.
The result will have the following features:
1. An int64 `token_ids` feature containing `num_values` values counting
from 1 upwards. If `token_pad_num` is supplied, additional `0` values
will be appended to the right until the number of values reaches
`token_pad_num`.
2. An int64 `global_token_ids` feature containing a single `1` value.
If `global_token_pad_num` is supplied, additional `0` values
will be appended to the right until the number of values reaches
`global_token_pad_num`.
3. If `packing_status` is supplied, a feature by that name will be added
with the corresponding bytes value.
Args:
num_values: Positive integer number of `token_ids` in the example.
token_pad_num: Optional length to pad `token_ids` to. Must not be less
than `num_values`.
global_token_pad_num: Optional length to pad `global_token_ids` to. Must
be positive.
packing_status: Optional bytes value to record in a `packing_status`
feature.
Returns:
The example.
"""
values = [x + 1 for x in range(num_values)]
if token_pad_num is not None:
while len(values) < token_pad_num:
values.append(0)
result = tf.train.Example()
result.features.feature['token_ids'].int64_list.value.extend(values)
global_values = [1]
if global_token_pad_num is not None:
remaining_len = global_token_pad_num - len(global_values)
global_values.extend([0] * remaining_len)
result.features.feature['global_token_ids'].int64_list.value.extend(
global_values)
if packing_status is not None:
result.features.feature['packing_status'].bytes_list.value.append(
packing_status)
return result
# Some example calls to `make_test_example()` for illustration:
self.assertEqual(
text_format.Parse(
"""
features {
feature {
key: "token_ids"
value {
int64_list {
value: [1, 2, 3, 4, 5]
}
}
}
feature {
key: "global_token_ids"
value {
int64_list {
value: [1]
}
}
}
}
""", tf.train.Example()), make_test_example(5))
self.assertEqual(
text_format.Parse(
"""
features {
feature {
key: "token_ids"
value {
int64_list {
value: [1, 2, 3, 4, 5, 6, 0, 0, 0, 0]
}
}
}
feature {
key: "global_token_ids"
value {
int64_list {
value: [1, 0, 0, 0]
}
}
}
feature {
key: "packing_status"
value {
bytes_list {
value: ['untouched']
}
}
}
}
""", tf.train.Example()),
make_test_example(
6,
token_pad_num=10,
global_token_pad_num=4,
packing_status=b'untouched'))
# Proceed to test `PriorityExamplePacker`.
# `breakpoint_features` and `cumulative_features` are tested in
# `test_pack_examples()` above, so we omit them here for brevity.
packer = beam_utils.PriorityExamplePacker(
priority_feature='token_ids',
max_lengths=dict(token_ids=10, global_token_ids=4),
breakpoint_features={},
cumulative_features=[],
min_packing_fraction=0.85,
max_cache_len=2)
self.assertEqual([], packer.add_example(make_test_example(8)))
self.assertEqual([], packer.add_example(make_test_example(4)))
self.assertEqual([
make_test_example(
8,
token_pad_num=10,
global_token_pad_num=4,
packing_status=b'evicted')
], packer.add_example(make_test_example(7)))
self.assertEqual([
text_format.Parse(
"""
features {
feature {
key: "token_ids"
value {
int64_list {
value: [1, 2, 3, 4, 5, 6, 7, 1, 2, 0]
}
}
}
feature {
key: "global_token_ids"
value {
int64_list {
value: [1, 1, 0, 0]
}
}
}
feature {
key: "packing_status"
value {
bytes_list {
value: ['packed']
}
}
}
}
""", tf.train.Example())
], packer.add_example(make_test_example(2)))
self.assertEqual([], packer.add_example(make_test_example(1)))
self.assertEqual([], packer.add_example(make_test_example(1)))
self.assertEqual([], packer.add_example(make_test_example(5)))
# We satisfy minimum `global_token_ids` instead of `token_ids` here.
self.assertEqual([
text_format.Parse(
"""
features {
feature {
key: "token_ids"
value {
int64_list {
value: [1, 2, 3, 4, 1, 1, 1, 0, 0, 0]
}
}
}
feature {
key: "global_token_ids"
value {
int64_list {
value: [1, 1, 1, 1]
}
}
}
feature {
key: "packing_status"
value {
bytes_list {
value: ['packed']
}
}
}
}
""", tf.train.Example())
], packer.add_example(make_test_example(1)))
self.assertEqual([
make_test_example(
9,
token_pad_num=10,
global_token_pad_num=4,
packing_status=b'untouched')
], packer.add_example(make_test_example(9)))
with self.assertRaises(ValueError):
packer.add_example(make_test_example(11))
with self.assertRaises(ValueError):
packer.add_example(make_test_example(3, global_token_pad_num=5))
self.assertEqual([], packer.add_example(make_test_example(8)))
self.assertEqual([
make_test_example(
8,
token_pad_num=10,
global_token_pad_num=4,
packing_status=b'flushed'),
make_test_example(
5,
token_pad_num=10,
global_token_pad_num=4,
packing_status=b'flushed')
], packer.flush_examples())
self.assertEqual([], packer.flush_examples())
def pipeline(root):
question_answers = read_question_answer_json(input_file)
question_answers = (
root | 'CreateQuestionAnswers' >> beam.Create(question_answers))
outputs = (
question_answers
| 'ReadEvidence' >> beam.ParDo(
ReadEvidence(wikipedia_dir=wikipedia_dir, web_dir=web_dir))
| 'ShuffleBeforeMakeExamples' >> beam.Reshuffle()
| 'MakeExamples' >> beam.ParDo(
MakeExamples(
spm_model_path=spm_model_path,
num_blocks_per_example=num_blocks_per_example,
block_overlap_length=block_overlap_length,
block_length=block_length,
max_num_annotations_per_block=max_num_annotations_per_block,
padding_token_id=padding_token_id,
cls_token_id=cls_token_id,
sep_token_id=sep_token_id,
generate_answers=generate_answers,
nltk_data_path=nltk_data_path)).with_outputs())
if generate_answers:
# Write failure cases, when no answer was found
_ = (outputs[MakeExampleOutput.NO_ANSWER]
| 'WriteNoAnswer' >> write_to_file_fn(output_prefix,
'no_answer.jsonl'))
_ = (outputs[MakeExampleOutput.NO_ANSWER_TOKENIZED]
| 'WriteNoAnswerTokenized' >> write_to_file_fn(
output_prefix, 'no_answer_tokenized.jsonl'))
# Write annotations that have been filtered out after tokenization
_ = (outputs[MakeExampleOutput.SUCCESS_FILTERED_ANNOTATIONS]
| 'FlattenSuccessFilteredAnnotations' >>
beam.FlatMap(lambda x: x)
| 'WriteSuccessFilteredAnnotations' >> write_to_file_fn(
output_prefix, 'success.filtered_annotations.txt'))
_ = (outputs[
MakeExampleOutput.NO_ANSWER_TOKENIZED_FILTERED_ANNOTATIONS]
| 'FlattenNoAnswerTokenizedFilteredAnnotations' >>
beam.FlatMap(lambda x: x)
| 'WriteNoAnswerTokenizedFilteredAnnotations' >>
write_to_file_fn(
output_prefix,
'no_answer_tokenized.filtered_annotations.txt'))
# Write cases where the too many answer spans were found
_ = (outputs[MakeExampleOutput.TOO_MANY_ANSWERS]
| 'WriteTooManyAnswers' >> write_to_file_fn(
output_prefix, 'too_many_answers.jsonl'))
max_tokens = num_blocks_per_example * block_length
max_num_annotations = num_blocks_per_example * max_num_annotations_per_block
example_packer = beam_utils.PriorityExamplePacker(
priority_feature='token_ids',
max_lengths=dict(token_ids=max_tokens,
is_continuation=max_tokens,
block_ids=num_blocks_per_example,
answer_annotation_begins=max_num_annotations,
answer_annotation_ends=max_num_annotations,
answer_annotation_labels=max_num_annotations,
entity_annotation_begins=max_num_annotations,
entity_annotation_ends=max_num_annotations,
entity_annotation_labels=max_num_annotations,
prefix_length=num_blocks_per_example),
breakpoint_features=dict(),
cumulative_features=[],
min_packing_fraction=1.0,
max_cache_len=num_blocks_per_example)
_ = (outputs[MakeExampleOutput.SUCCESS]
| 'ShuffleBeforePacking' >> beam.Reshuffle()
| 'PackExamples' >> beam_utils.PackExamples(example_packer)
| 'ShuffleAfterPacking' >> beam.Reshuffle()
| 'WriteTfExamples' >> beam.io.WriteToTFRecord(
os.path.join(output_prefix + '.tfrecord'),
coder=beam.coders.ProtoCoder(tf.train.Example),
num_shards=output_num_shards))
