def _set_up_test_pipeline_pb(self):
"""Read expected pipeline pb from a text proto file."""
test_pb_filepath = os.path.join(
os.path.dirname(__file__), "testdata", "iris_pipeline_ir.pbtxt")
with open(test_pb_filepath) as text_pb_file:
self._pipeline_pb = text_format.ParseLines(text_pb_file,
pipeline_pb2.Pipeline())
def __init__(self, config_file):
"""
Construct a DeepLab2 model.
Parameters
----------
config_file: str
Path to a text protobuf config file, as used by deeplab.
"""
if isinstance(config_file, str):
with tf.io.gfile.GFile(config_file, 'r') as proto_file:
self._config_text = proto_file.readlines()
else:
self._config_text = config_file['config_file']
self._config = text_format.ParseLines(self._config_text, config_pb2.ExperimentOptions())
# not used except ignore_label
ds = dataset.DatasetDescriptor(
dataset_name=None,
splits_to_sizes=None,
num_classes=None,
ignore_label=None,
panoptic_label_divisor=None,
class_has_instances_list=None,
is_video_dataset=None,
colormap=None,
is_depth_dataset=None,
ignore_depth=None,
)
super().__init__(self._config, ds)
def _get_test_pipeline_pb(self, file_name: str) -> pipeline_pb2.Pipeline:
"""Reads expected pipeline pb from a text proto file."""
test_pb_filepath = os.path.join(os.path.dirname(__file__), "testdata",
file_name)
with open(test_pb_filepath) as text_pb_file:
return text_format.ParseLines(text_pb_file,
pipeline_pb2.Pipeline())
def testProtoOperatorDescriptor(self):
test_pb_filepath = os.path.join(os.path.dirname(__file__), 'testdata',
'proto_placeholder_operator.pbtxt')
with open(test_pb_filepath) as text_pb_file:
expected_pb = text_format.ParseLines(
text_pb_file, placeholder_pb2.PlaceholderExpression())
placeholder = ph.exec_property('splits_config').analyze[0]
component_spec = standard_component_specs.TransformSpec
self.assertProtoEquals(placeholder.encode(component_spec), expected_pb)
def testParseLinesGolden(self):
opened = self.ReadGolden('text_format_unittest_data.txt')
parsed_message = unittest_pb2.TestAllTypes()
r = text_format.ParseLines(opened, parsed_message)
self.assertIs(r, parsed_message)
message = unittest_pb2.TestAllTypes()
test_util.SetAllFields(message)
self.assertEquals(message, parsed_message)
def testProtoFutureValueOperator(self):
test_pb_filepath = os.path.join(
os.path.dirname(__file__), 'testdata',
'proto_placeholder_future_value_operator.pbtxt')
with open(test_pb_filepath) as text_pb_file:
expected_pb = text_format.ParseLines(
text_pb_file, placeholder_pb2.PlaceholderExpression())
output_channel = Channel(type=standard_artifacts.Integer)
placeholder = output_channel.future()[0].value
placeholder._key = '_component.num'
self.assertProtoEquals(placeholder.encode(), expected_pb)
def test_candidates(self, mock_read):
with tf.gfile.Open(os.path.join(self._test_dir,
'interaction_03.pbtxt')) as input_file:
interaction = text_format.ParseLines(input_file,
interaction_pb2.Interaction())
_set_mock_read(mock_read, [interaction])
max_seq_length = 15
tf_example_utils._MAX_NUM_ROWS = 4
tf_example_utils._MAX_NUM_CANDIDATES = 10
pipeline = create_data.build_classifier_pipeline(
input_files=['input.tfrecord'],
output_files=[self._output_path],
config=_ClassifierConfig(
vocab_file=os.path.join(self._test_dir, 'vocab.txt'),
max_seq_length=max_seq_length,
max_column_id=4,
max_row_id=4,
strip_column_names=False,
add_aggregation_candidates=True,
),
)
result = beam.runners.direct.direct_runner.DirectRunner().run(pipeline)
result.wait_until_finish()
output = _read_examples(self._output_path)
with tf.gfile.Open(os.path.join(self._test_dir,
'tf_example_03.pbtxt')) as input_file:
expected_example = text_format.ParseLines(input_file,
tf.train.Example())
actual_example = output[0]
logging.info('%s', actual_example)
# assertEqual struggles with NaNs inside protos
del actual_example.features.feature['numeric_values']
self.assertEqual(actual_example, expected_example)
def test_process_doc(self, version):
interactions = list(
sem_tab_fact_utils._process_doc(
os.path.join(
self.test_data_dir,
'sem_tab_fact_20502.xml',
),
version,
))
if version == sem_tab_fact_utils.Version.V1:
name = 'sem_tab_fact_20502_interaction.txtpb'
elif version == sem_tab_fact_utils.Version.V2:
name = 'sem_tab_fact_20502_interaction_v2.txtpb'
else:
raise ValueError(f'Unsupported version: {version.name}')
interaction_file = os.path.join(self.test_data_dir, name)
with open(interaction_file) as input_file:
interaction = text_format.ParseLines(input_file,
interaction_pb2.Interaction())
self.assertLen(interactions, 4)
logging.info(interactions[0])
self.assertEqual(interactions[0], interaction)
questions = [
( # pylint: disable=g-complex-comprehension
i.questions[0].id,
i.questions[0].original_text,
i.questions[0].answer.class_index,
) for i in interactions
]
self.assertEqual(questions, [
(
'sem_tab_fact_20502_Table_2_2_0',
'At the same time, these networks often occur in tandem at the firm level.',
1,
),
(
'sem_tab_fact_20502_Table_2_3_0',
'For each network interaction, there is considerable variation both across and within countries.',
1,
),
(
'sem_tab_fact_20502_Table_2_5_0',
'The n value is same for Hong Kong and Malaysia.',
0,
),
(
'sem_tab_fact_20502_Table_2_8_0',
'There are 9 different types country in the given table.',
1,
),
])
def test_end_to_end(self, input_format, impl, mock_read):
self._create_vocab(list(_RESERVED_SYMBOLS))
with tf.gfile.Open(
os.path.join(self._test_dir,
'retrieval_interaction.pbtxt')) as input_file:
interaction = text_format.ParseLines(input_file,
interaction_pb2.Interaction())
if input_format == _InputFormat.INTERACTION:
samples = [interaction]
elif input_format == _InputFormat.TABLE:
samples = [interaction.table]
else:
raise ValueError(f'Unknown format: {input_format}')
_set_mock_read(mock_read, samples)
pipeline = create_data.build_retrieval_pipeline(
input_files=['input.tfrecord'],
input_format=input_format,
output_files=[self._output_path],
config=retrieval_utils.RetrievalConversionConfig(
vocab_file=self._vocab_path,
max_seq_length=15,
max_column_id=5,
max_row_id=5,
strip_column_names=False),
converter_impl=impl,
)
result = beam.runners.direct.direct_runner.DirectRunner().run(pipeline)
result.wait_until_finish()
counters = {
metric_result.key.metric.name: metric_result.committed
for metric_result in result.metrics().query()['counters']
}
if input_format == _InputFormat.INTERACTION:
self.assertEqual(counters, {
'Input question': 1,
'Conversion success': 1,
})
else:
self.assertEqual(
counters, {
'Input question': 1,
'Conversion success': 1,
'Fake Questions added for table only example': 1,
})
output = _read_examples(self._output_path)
self.assertLen(output, 1)
def create_selector(
table_pruning_config_file,
vocab_size,
hidden_size,
initializer_range,
max_num_columns,
max_num_rows,
type_vocab_size,
disabled_features,
disable_position_embeddings,
max_position_embeddings,
):
"""Activates the scoring model according to table pruning config."""
if not table_pruning_config_file:
return NoTablePruning()
config = table_pruning_pb2.TablePruningModel()
with tf.gfile.Open(table_pruning_config_file) as input_file:
# ParseLines
config = text_format.ParseLines(input_file,
table_pruning_pb2.TablePruningModel())
model = config.WhichOneof("table_pruning_model")
max_num_tokens = config.max_num_tokens
if model == "avg_cos_similarity":
return AverageCosineSimilaritySelector(
vocab_size=vocab_size,
hidden_size=hidden_size,
initializer_range=initializer_range,
max_num_columns=max_num_columns,
type_vocab_size=type_vocab_size,
disabled_features=disabled_features,
disable_position_embeddings=disable_position_embeddings,
max_position_embeddings=max_position_embeddings,
config=config.avg_cos_similarity,
max_num_tokens=max_num_tokens)
elif model == "tapas":
return TapasPruningSelector(config=config.tapas,
max_num_columns=max_num_columns,
max_num_tokens=max_num_tokens,
max_num_rows=max_num_rows)
elif model == "first_tokens":
return OnesTablePruning(config=config.first_tokens,
max_num_columns=max_num_columns,
max_num_tokens=max_num_tokens)
else:
raise NotImplementedError(f"TablePruningModel not implemented {model}")
def testMapOrderSemantics(self):
golden_lines = self.ReadGolden('map_test_data.txt')
# The C++ implementation emits defaulted-value fields, while the Python
# implementation does not. Adjusting for this is awkward, but it is
# valuable to test against a common golden file.
line_blacklist = (' key: 0\n', ' value: 0\n', ' key: false\n',
' value: false\n')
golden_lines = [
line for line in golden_lines if line not in line_blacklist
]
message = map_unittest_pb2.TestMap()
text_format.ParseLines(golden_lines, message)
candidate = text_format.MessageToString(message)
# The Python implementation emits "1.0" for the double value that the C++
# implementation emits as "1".
candidate = candidate.replace('1.0', '1', 2)
self.assertMultiLineEqual(candidate, ''.join(golden_lines))
def test_get_empty_example(self):
max_seq_length = 15
input_path = os.path.join(self.test_data_dir,
'retrieval_interaction.pbtxt')
with open(input_path) as input_file:
interaction = text_format.ParseLines(input_file,
interaction_pb2.Interaction())
number_annotation_utils.add_numeric_values(interaction)
with tempfile.TemporaryDirectory() as input_dir:
vocab_file = os.path.join(input_dir, 'vocab.txt')
_create_vocab(vocab_file, [
'by', 'created', 'do', 'dragon', 'go', 'hannibal', 'harris',
'in', 'lecter', 'movies', 'novels', 'order', 'original', 'red',
'the', 'thomas', 'what', 'work'
])
converter = tf_example_utils.ToRetrievalTensorflowExample(
config=tf_example_utils.RetrievalConversionConfig(
vocab_file=vocab_file,
max_seq_length=max_seq_length,
max_column_id=max_seq_length,
max_row_id=max_seq_length,
strip_column_names=False,
))
example = converter.convert(interaction,
index=0,
negative_example=None)
logging.info(example)
# Check the question.
self.assertEqual(
_get_int_feature(example, 'question_input_ids'),
[2, 22, 17, 8, 20, 11, 14, 15, 10, 13, 3, 0, 0, 0, 0])
self.assertEqual(_get_int_feature(example, 'question_input_mask'),
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0])
# Check document title + table.
self.assertEqual(
_get_int_feature(example, 'input_ids'),
[2, 11, 14, 3, 7, 6, 18, 23, 16, 21, 12, 19, 9, 19, 9])
self.assertEqual(_get_int_feature(example, 'input_mask'),
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
self.assertEqual(_get_int_feature(example, 'segment_ids'),
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
def test_tfrecord_io(self, mock_read):
"""Reads from TFRecord and writes to TFRecord."""
with tf.gfile.Open(os.path.join(self._test_dir,
'interaction_03.pbtxt')) as input_file:
interaction = text_format.ParseLines(input_file,
interaction_pb2.Interaction())
def dummy_read(file_pattern, coder, validate):
del file_pattern, coder, validate # Unused.
return beam.Create([interaction])
mock_read.side_effect = dummy_read
max_seq_length = 15
pipeline = create_data.build_classifier_pipeline(
input_files=['input.tfrecord'],
output_files=[self._output_path],
config=_ClassifierConfig(
vocab_file=os.path.join(self._test_dir, 'vocab.txt'),
max_seq_length=max_seq_length,
max_column_id=4,
max_row_id=4,
strip_column_names=False,
add_aggregation_candidates=False,
))
result = beam.runners.direct.direct_runner.DirectRunner().run(pipeline)
result.wait_until_finish()
output = []
for value in tf.python_io.tf_record_iterator(self._output_path):
example = tf.train.Example()
example.ParseFromString(value)
output.append(example)
self.assertLen(output, 1)
sid = output[0].features.feature['segment_ids']
self.assertLen(sid.int64_list.value, max_seq_length)
def main(argv):
if len(argv) > 3:
raise app.UsageError('Too many command-line arguments.')
# Read in the results file to see what configs to test.
results = lc_pb2.LecTiming()
if FLAGS.results_path and gfile.exists(FLAGS.results_path):
with gfile.open(FLAGS.results_path, 'r') as fd:
results = text_format.ParseLines(fd, lc_pb2.LecTiming())
with gfile.open(FLAGS.cell_library_textproto_path, 'r') as fd:
cell_library_textproto = fd.read()
lc = lc_mod.LecCharacterizer(FLAGS.synthesis_server_address)
for width in FLAGS.widths:
bits_type = xls_type_pb2.TypeProto(
type_enum=xls_type_pb2.TypeProto.BITS, bit_count=int(width))
function_type = xls_type_pb2.FunctionTypeProto()
function_type.parameters.add().CopyFrom(bits_type)
function_type.parameters.add().CopyFrom(bits_type)
function_type.return_type.CopyFrom(bits_type)
test_case = None
for result_case in results.test_cases:
# Find or create a matching test case for this function type.
if result_case.function_type == function_type:
test_case = result_case
if test_case is None:
test_case = results.test_cases.add()
test_case.function_type.CopyFrom(function_type)
runs_left = FLAGS.runs_per_type - len(test_case.exec_times_us)
if runs_left > 0:
lc.run(results, op_pb2.OpProto.Value(FLAGS.op), function_type,
int(runs_left), cell_library_textproto, z3_lec.run,
_save_results)
def test_numeric_relations(self, mock_read):
input_file = 'interaction_00.pbtxt'
expected_counters = {
'Conversion success': 1,
'Example emitted': 1,
'Input question': 1,
'Relation Set Index: 2': 5,
'Relation Set Index: 4': 13,
'Found answers: <= 4': 1,
}
with tf.gfile.Open(os.path.join(self._test_dir,
input_file)) as input_file:
interaction = text_format.ParseLines(input_file,
interaction_pb2.Interaction())
_set_mock_read(mock_read, [interaction])
max_seq_length = 512
pipeline = create_data.build_classifier_pipeline(
input_files=['input.tfrecord'],
output_files=[self._output_path],
config=_ClassifierConfig(
vocab_file=os.path.join(self._test_dir, 'vocab.txt'),
max_seq_length=max_seq_length,
max_column_id=512,
max_row_id=512,
strip_column_names=False,
add_aggregation_candidates=False,
))
result = beam.runners.direct.direct_runner.DirectRunner().run(pipeline)
result.wait_until_finish()
self.assertEqual(
{
metric_result.key.metric.name: metric_result.committed
for metric_result in result.metrics().query()['counters']
}, expected_counters)
output = _read_examples(self._output_path)
self.assertLen(output, 1)
actual_example = output[0]
self.assertIn('numeric_relations',
actual_example.features.feature.keys())
relations = actual_example.features.feature[
'numeric_relations'].int64_list.value
with tf.gfile.Open(os.path.join(self._test_dir,
'vocab.txt')) as vocab_file:
vocab = [line.strip() for line in vocab_file]
inputs = actual_example.features.feature['input_ids'].int64_list.value
pairs = [(vocab[input_id], relation)
for (input_id, relation) in zip(inputs, relations)
if input_id > 0]
logging.info('pairs: %s', pairs)
self.assertSequenceEqual(pairs, [('[CLS]', 0), ('which', 0),
('cities', 0),
('had', 0), ('less', 0), ('than', 0),
('2', 0), (',', 0), ('000', 0),
('pass', 0), ('##en', 0), ('##ge', 0),
('##rs', 0), ('?', 0), ('[SEP]', 0),
('ran', 0), ('##k', 0), ('city', 0),
('pass', 0), ('##en', 0), ('##ge', 0),
('##rs', 0), ('ran', 0), ('##ki', 0),
('##ng', 0), ('air', 0), ('##li', 0),
('##ne', 0), ('1', 4), ('united', 0),
('states', 0), (',', 0), ('los', 0),
('angeles', 0), ('14', 2), (',', 2),
('7', 2), ('##4', 2), ('##9', 2),
('[EMPTY]', 0),
('al', 0), ('##as', 0), ('##ka', 0),
('air', 0), ('##li', 0), ('##ne', 0),
('##s', 0), ('2', 4), ('united', 0),
('states', 0), (',', 0), ('h', 0),
('##ous', 0), ('##ton', 0), ('5', 2),
(',', 2), ('4', 2), ('##6', 2),
('##5', 2), ('[EMPTY]', 0),
('united', 0), ('e', 0), ('##x', 0),
('##p', 0), ('##re', 0), ('##s', 0),
('##s', 0), ('3', 4), ('canada', 0),
(',', 0), ('c', 0), ('##al', 0),
('##ga', 0), ('##ry', 0), ('3', 2),
(',', 2), ('7', 2), ('##6', 2),
('##1', 2), ('[EMPTY]', 0),
('air', 0), ('t', 0), ('##ra', 0),
('##ns', 0), ('##a', 0), ('##t', 0),
(',', 0), ('west', 0), ('##j', 0),
('##et', 0), ('4', 4), ('canada', 0),
(',', 0), ('s', 0), ('##as', 0),
('##ka', 0), ('##to', 0), ('##on', 0),
('2', 2), (',', 2), ('28', 2),
('##2', 2), ('4', 0), ('[EMPTY]', 0),
('5', 4), ('canada', 0), (',', 0),
('van', 0), ('##co', 0), ('##u', 0),
('##ve', 0), ('##r', 0), ('2', 2),
(',', 2), ('10', 2), ('##3', 2),
('[EMPTY]', 0), ('air', 0), ('t', 0),
('##ra', 0), ('##ns', 0), ('##a', 0),
('##t', 0), ('6', 4), ('united', 0),
('states', 0), (',', 0), ('p', 0),
('##h', 0), ('##o', 0), ('##en', 0),
('##i', 0), ('##x', 0), ('1', 4),
(',', 4), ('8', 4), ('##2', 4),
('##9', 4), ('1', 0), ('us', 0),
('air', 0), ('##w', 0), ('##a', 0),
('##y', 0), ('##s', 0), ('7', 4),
('canada', 0), (',', 0), ('to', 0),
('##ro', 0), ('##nt', 0), ('##o', 0),
('1', 4), (',', 4), ('20', 4),
('##2', 4), ('1', 0), ('air', 0),
('t', 0), ('##ra', 0), ('##ns', 0),
('##a', 0), ('##t', 0), (',', 0),
('can', 0), ('##j', 0), ('##et', 0),
('8', 4), ('canada', 0), (',', 0),
('ed', 0), ('##m', 0), ('##on', 0),
('##ton', 0), ('11', 4), ('##0', 4),
('[EMPTY]', 0), ('[EMPTY]', 0),
('9', 4), ('united', 0),
('states', 0), (',', 0), ('o', 0),
('##a', 0), ('##k', 0), ('##land', 0),
('10', 4), ('##7', 4), ('[EMPTY]', 0),
('[EMPTY]', 0)])
def test_gracefully_handle_big_examples(self, max_seq_length,
max_column_id, max_row_id,
expected_counters, mock_read):
with tf.gfile.Open(os.path.join(self._test_dir,
'interaction_02.pbtxt')) as input_file:
interaction = text_format.ParseLines(input_file,
interaction_pb2.Interaction())
_set_mock_read(mock_read, [interaction])
pipeline = create_data.build_classifier_pipeline(
input_files=['input.tfrecord'],
output_files=[self._output_path],
config=_ClassifierConfig(
vocab_file=self._vocab_path,
max_seq_length=60
if max_seq_length is None else max_seq_length,
max_column_id=5 if max_column_id is None else max_column_id,
max_row_id=10 if max_row_id is None else max_row_id,
strip_column_names=False,
add_aggregation_candidates=False,
))
result = beam.runners.direct.direct_runner.DirectRunner().run(pipeline)
result.wait_until_finish()
self.assertEqual(
{
metric_result.key.metric.name: metric_result.committed
for metric_result in result.metrics().query()['counters']
}, expected_counters)
if max_seq_length is None and max_column_id is None and max_row_id is None:
output = _read_examples(self._output_path)
with tf.gfile.Open(
os.path.join(self._test_dir,
'tf_example_02.pbtxt')) as input_file:
expected_example = text_format.ParseLines(
input_file, tf.train.Example())
with tf.gfile.Open(
os.path.join(self._test_dir,
'tf_example_02_conv.pbtxt')) as input_file:
expected_conversational_example = text_format.ParseLines(
input_file, tf.train.Example())
self.assertLen(output, 2)
actual_example = output[0]
del actual_example.features.feature['column_ranks']
del actual_example.features.feature['inv_column_ranks']
del actual_example.features.feature['numeric_relations']
del actual_example.features.feature['numeric_values']
del actual_example.features.feature['numeric_values_scale']
del actual_example.features.feature['question_id_ints']
# assertEqual struggles with NaNs inside protos
del actual_example.features.feature['answer']
self.assertEqual(actual_example, expected_example)
actual_example = output[1]
del actual_example.features.feature['column_ranks']
del actual_example.features.feature['inv_column_ranks']
del actual_example.features.feature['numeric_relations']
del actual_example.features.feature['numeric_values']
del actual_example.features.feature['numeric_values_scale']
del actual_example.features.feature['question_id_ints']
# assertEqual struggles with NaNs inside protos
del actual_example.features.feature['answer']
self.assertEqual(actual_example, expected_conversational_example)
def test_end_to_end_multiple_interactions(self, mock_read):
with tf.gfile.Open(os.path.join(self._test_dir,
'interaction_01.pbtxt')) as input_file:
interaction = text_format.ParseLines(input_file,
interaction_pb2.Interaction())
interactions = []
for trial in range(100):
table_id = f'table_id_{trial}'
new_interaction = interaction_pb2.Interaction()
new_interaction.CopyFrom(interaction)
new_interaction.table.table_id = table_id
new_interaction.id = table_id
interactions.append(new_interaction)
_set_mock_read(mock_read, interactions)
self._create_vocab(
list(_RESERVED_SYMBOLS) + list(string.ascii_lowercase) +
['##' + letter for letter in string.ascii_lowercase])
pipeline = create_data.build_pretraining_pipeline(
input_file='input.tfrecord',
output_suffix='.tfrecord',
output_dir=self._output_path,
config=_PretrainConfig(
vocab_file=self._vocab_path,
max_seq_length=40,
max_predictions_per_seq=10,
random_seed=5,
masked_lm_prob=0.5,
max_column_id=5,
max_row_id=5,
min_question_length=5,
max_question_length=10,
always_continue_cells=True,
strip_column_names=False,
),
dupe_factor=1,
min_num_columns=0,
min_num_rows=0,
num_random_table_bins=10,
num_corpus_bins=
100000, # High number sends all examples to train set.
add_random_table=True,
)
result = beam.runners.direct.direct_runner.DirectRunner().run(pipeline)
result.wait_until_finish()
counters = {
metric_result.key.metric.name: metric_result.committed
for metric_result in result.metrics().query()['counters']
}
self.assertEqual(
counters, {
'Examples': 100,
'Examples with tables': 100,
'Interactions': 100,
'Interactions without random interaction': 11,
'Question Length: < inf': 31,
'Question Length: <= 10': 53,
'Question Length: <= 7': 16,
'Real Table Size: <= 8': 100,
'Trimmed Table Size: <= 8': 100,
'Column Sizes: <= 8': 100,
'Row Sizes: <= 8': 100,
'Table Token Sizes: <= 8': 100,
'Inputs': 100,
})
output = _read_examples(
os.path.join(self._output_path, 'train.tfrecord'))
self.assertLen(output, 100)
def read_textproto(path: str) -> citylex_pb2.Lexicon:
"""Parses textproto."""
lexicon = citylex_pb2.Lexicon()
with open(path, "r") as source:
text_format.ParseLines(source, lexicon)
return lexicon
