def tokenize(self, tokenizer: BertTokenizer):
for column in self.header:
column.name_tokens = tokenizer.tokenize(column.name)
if column.sample_value is not None:
column.sample_value_tokens = tokenizer.tokenize(
str(column.sample_value))
tokenized_rows = [
{k: tokenizer.tokenize(str(v))
for k, v in row.items()} if isinstance(
row, dict) else [tokenizer.tokenize(str(v)) for v in row]
for row in self.data
]
self.data = tokenized_rows
setattr(self, 'tokenized', True)
return self
def __init__(self,
base_model_name: str = 'bert-base-uncased',
column_delimiter: str = '[SEP]',
context_first: bool = True,
cell_input_template: str = 'column | type | value',
column_representation: str = 'mean_pool',
max_cell_len: int = 5,
max_sequence_len: int = 512,
max_context_len: int = 256,
masked_context_prob: float = 0.15,
masked_column_prob: float = 0.2,
max_predictions_per_seq: int = 100,
context_sample_strategy: str = 'nearest',
table_mask_strategy: str = 'column',
do_lower_case: bool = True,
**kwargs):
super(TableBertConfig, self).__init__()
self.base_model_name = base_model_name
self.column_delimiter = column_delimiter
self.context_first = context_first
self.column_representation = column_representation
self.max_cell_len = max_cell_len
self.max_sequence_len = max_sequence_len
self.max_context_len = max_context_len
self.do_lower_case = do_lower_case
# tokenizer = BertTokenizer.from_pretrained(self.base_model_name)
if isinstance(cell_input_template, str):
if ' ' in cell_input_template:
cell_input_template = cell_input_template.split(' ')
else:
print(
f'WARNING: cell_input_template is outdated: {cell_input_template}',
file=sys.stderr)
cell_input_template = BertTokenizer.from_pretrained(
self.base_model_name).tokenize(cell_input_template)
self.cell_input_template = cell_input_template
self.masked_context_prob = masked_context_prob
self.masked_column_prob = masked_column_prob
self.max_predictions_per_seq = max_predictions_per_seq
self.context_sample_strategy = context_sample_strategy
self.table_mask_strategy = table_mask_strategy
if not hasattr(self, 'vocab_size_or_config_json_file'):
bert_config = BERT_CONFIGS[self.base_model_name]
for k, v in vars(bert_config).items():
setattr(self, k, v)
def __init__(self, config: TableBertConfig, **kwargs):
nn.Module.__init__(self)
bert_model: Union[BertForPreTraining,
BertModel] = kwargs.pop('bert_model', None)
if bert_model is not None:
logging.warning(
'using `bert_model` to initialize `TableBertModel` is deprecated. '
'I will still set `self._bert_model` this time.')
self._bert_model = bert_model
self.tokenizer = BertTokenizer.from_pretrained(config.base_model_name)
self.config = config
info.update({
'num_column_tokens_to_mask': num_column_tokens_to_mask,
'num_context_tokens_to_mask': num_context_tokens_to_mask,
})
return tokens, masked_indices, masked_token_labels, info
def remove_unecessary_instance_entries(self, instance: Dict):
del instance['tokens']
del instance['masked_lm_labels']
del instance['info']
if __name__ == '__main__':
config = TableBertConfig()
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
input_formatter = VanillaTableBertInputFormatter(config, tokenizer)
header = []
for i in range(1000):
header.append(
Column(
name='test',
type='text',
name_tokens=['test'] * 3,
sample_value='ha ha ha yay',
sample_value_tokens=['ha', 'ha', 'ha', 'yay']
)
)
print(
def main():
parser = ArgumentParser()
parser.add_argument('--train_corpus', type=Path, required=True)
parser.add_argument("--output_dir", type=Path, required=True)
parser.add_argument("--epochs_to_generate", type=int, default=3,
help="Number of epochs of preprocess to pregenerate")
parser.add_argument('--no_wiki_tables_from_common_crawl', action='store_true', default=False)
parser.add_argument('--global_rank', type=int, default=os.environ.get('SLURM_PROCID', 0))
parser.add_argument('--world_size', type=int, default=os.environ.get('SLURM_NTASKS', 1))
## YS
parser.add_argument('--use_acoustic_confusion', action='store_true', default=False)
parser.add_argument('--acoustic_confusion_prob', type=float, default=0.15,
help="Probability of replacing a token with a confused one")
# parser.add_argument('--acoustic_confusion_type', type=str, choices=['random', 'gpt2'], default='random')
parser.add_argument('--word_confusion_path', type=Path, default='')
TableBertConfig.add_args(parser)
args = parser.parse_args()
args.is_master = args.global_rank == 0
logger = logging.getLogger('DataGenerator')
handler = logging.StreamHandler(sys.stderr)
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
logger.info(f'Rank {args.global_rank} out of {args.world_size}')
sys.stderr.flush()
table_bert_config = TableBertConfig.from_dict(vars(args))
tokenizer = BertTokenizer.from_pretrained(table_bert_config.base_model_name)
## YS
if args.use_acoustic_confusion:
assert args.word_confusion_path != ''
acoustic_confuser = SentenceAcousticConfuser_RandomReplace(args.word_confusion_path, default_p=args.acoustic_confusion_prob)
input_formatter = VanillaTableBertInputFormatterWithConfusion(table_bert_config, tokenizer, acoustic_confuser)
else:
input_formatter = VanillaTableBertInputFormatter(table_bert_config, tokenizer)
total_tables_num = int(subprocess.check_output(f"wc -l {args.train_corpus}", shell=True).split()[0])
dev_table_num = min(int(total_tables_num * 0.1), 100000)
train_table_num = total_tables_num - dev_table_num
# seed the RNG to make sure each process follows the same spliting
rng = np.random.RandomState(seed=5783287)
corpus_table_indices = list(range(total_tables_num))
rng.shuffle(corpus_table_indices)
dev_table_indices = corpus_table_indices[:dev_table_num]
train_table_indices = corpus_table_indices[dev_table_num:]
local_dev_table_indices = dev_table_indices[args.global_rank::args.world_size]
local_train_table_indices = train_table_indices[args.global_rank::args.world_size]
local_indices = local_dev_table_indices + local_train_table_indices
logger.info(f'total tables: {total_tables_num}')
logger.debug(f'local dev table indices: {local_dev_table_indices[:1000]}')
logger.debug(f'local train table indices: {local_train_table_indices[:1000]}')
with TableDatabase.from_jsonl(args.train_corpus, backend='memory', tokenizer=tokenizer, indices=local_indices) as table_db:
local_indices = {idx for idx in local_indices if idx in table_db}
local_dev_table_indices = [idx for idx in local_dev_table_indices if idx in local_indices]
local_train_table_indices = [idx for idx in local_train_table_indices if idx in local_indices]
args.output_dir.mkdir(exist_ok=True, parents=True)
print(f'Num tables to be processed by local worker: {len(table_db)}', file=sys.stdout)
if args.is_master:
with (args.output_dir / 'config.json').open('w') as f:
json.dump(vars(args), f, indent=2, sort_keys=True, default=str)
(args.output_dir / 'train').mkdir(exist_ok=True)
(args.output_dir / 'dev').mkdir(exist_ok=True)
# generate dev data first
dev_file = args.output_dir / 'dev' / f'epoch_0.shard{args.global_rank}.h5'
generate_for_epoch(table_db, local_dev_table_indices, dev_file, input_formatter, args)
for epoch in trange(args.epochs_to_generate, desc='Epoch'):
gc.collect()
epoch_filename = args.output_dir / 'train' / f"epoch_{epoch}.shard{args.global_rank}.h5"
generate_for_epoch(table_db, local_train_table_indices, epoch_filename, input_formatter, args)
