def __init__(
self,
cfg: dict,
name: str,
indexed_dataset: MMapIndexedDataset,
data_prefix: str,
num_epochs: Optional[int],
max_num_samples: int,
masked_lm_prob: float,
max_seq_length: int,
short_seq_prob: float,
seed: int,
binary_head: bool,
tokenizer: Any,
):
# Params to store.
self.name = name
self.seed = seed
self.masked_lm_prob = masked_lm_prob
self.max_seq_length = max_seq_length
self.binary_head = binary_head
# Dataset.
self.indexed_dataset = indexed_dataset
# save index mappings to a configurable dir
self.index_mapping_dir = cfg.data.get('index_mapping_dir', None)
# create index_mapping_dir on rank 0
if torch.distributed.is_available(
) and torch.distributed.is_initialized():
if torch.distributed.get_rank() == 0:
if self.index_mapping_dir is not None and not os.path.isdir(
self.index_mapping_dir):
os.makedirs(self.index_mapping_dir)
torch.distributed.barrier()
# Build the samples mapping.
self.samples_mapping = get_samples_mapping(
self.indexed_dataset,
data_prefix,
num_epochs,
max_num_samples,
self.max_seq_length - 3, # account for added tokens
short_seq_prob,
self.seed,
self.name,
self.binary_head,
index_mapping_dir=self.index_mapping_dir,
)
# Vocab stuff.
self.vocab_id_list = list(tokenizer.ids_to_tokens.keys())
self.vocab_id_to_token_dict = tokenizer.ids_to_tokens
self.cls_id = tokenizer.cls_token_id
self.sep_id = tokenizer.sep_token_id
self.mask_id = tokenizer.mask_token_id
self.pad_id = tokenizer.pad_token_id
def __init__(
self,
name,
indexed_dataset,
data_prefix,
num_epochs,
max_num_samples,
masked_lm_prob,
max_seq_length,
max_seq_length_dec,
short_seq_prob,
seed,
):
# Params to store.
self.name = name
self.seed = seed
self.masked_lm_prob = masked_lm_prob
self.max_seq_length = max_seq_length
self.max_seq_length_dec = max_seq_length_dec
# Dataset.
self.indexed_dataset = indexed_dataset
# Build the samples mapping.
self.samples_mapping = get_samples_mapping(
self.indexed_dataset,
data_prefix,
num_epochs,
max_num_samples,
self.max_seq_length - 2, # account for added tokens
short_seq_prob,
self.seed,
self.name,
False,
)
# Vocab stuff.
tokenizer = get_tokenizer()
self.vocab_id_list = list(tokenizer.inv_vocab.keys())
self.vocab_id_to_token_dict = tokenizer.inv_vocab
self.cls_id = tokenizer.cls
self.sep_id = tokenizer.sep
self.mask_id = tokenizer.mask
self.pad_id = tokenizer.pad
self.bos_id = tokenizer.bos_token_id
self.eos_id = tokenizer.eos_token_id
self.sentinel_tokens = tokenizer.additional_special_tokens_ids
assert len(
self.sentinel_tokens
) > 0, "Provide the argument --vocab-extra-ids 100 to the script"
def __init__(
self,
name,
indexed_dataset,
data_prefix,
num_epochs,
max_num_samples,
masked_lm_prob,
max_seq_length,
short_seq_prob,
seed,
binary_head,
tokenizer,
):
# Params to store.
self.name = name
self.seed = seed
self.masked_lm_prob = masked_lm_prob
self.max_seq_length = max_seq_length
self.binary_head = binary_head
# Dataset.
self.indexed_dataset = indexed_dataset
# Build the samples mapping.
self.samples_mapping = get_samples_mapping(
self.indexed_dataset,
data_prefix,
num_epochs,
max_num_samples,
self.max_seq_length - 3, # account for added tokens
short_seq_prob,
self.seed,
self.name,
self.binary_head,
)
# Vocab stuff.
self.vocab_id_list = list(tokenizer.inv_vocab.keys())
self.vocab_id_to_token_dict = tokenizer.inv_vocab
self.cls_id = tokenizer.cls_id
self.sep_id = tokenizer.sep_id
self.mask_id = tokenizer.mask_id
self.pad_id = tokenizer.pad_id
def __init__(
self,
name: str,
indexed_dataset: MMapIndexedDataset,
data_prefix: str,
num_epochs: Optional[int],
max_num_samples: int,
masked_lm_prob: float,
max_seq_length: int,
short_seq_prob: float,
seed: int,
binary_head: bool,
tokenizer: Any,
):
# Params to store.
self.name = name
self.seed = seed
self.masked_lm_prob = masked_lm_prob
self.max_seq_length = max_seq_length
self.binary_head = binary_head
# Dataset.
self.indexed_dataset = indexed_dataset
# Build the samples mapping.
self.samples_mapping = get_samples_mapping(
self.indexed_dataset,
data_prefix,
num_epochs,
max_num_samples,
self.max_seq_length - 3, # account for added tokens
short_seq_prob,
self.seed,
self.name,
self.binary_head,
)
# Vocab stuff.
self.vocab_id_list = list(tokenizer.ids_to_tokens.keys())
self.vocab_id_to_token_dict = tokenizer.ids_to_tokens
self.cls_id = tokenizer.cls_token_id
self.sep_id = tokenizer.sep_token_id
self.mask_id = tokenizer.mask_token_id
self.pad_id = tokenizer.pad_token_id
def __init__(
self,
cfg,
trainer,
tokenizer,
name,
indexed_dataset,
data_prefix,
num_epochs,
max_num_samples,
max_seq_length,
max_seq_length_dec,
seed,
masked_lm_prob=0.15,
short_seq_prob=0.1,
max_ngram_size=10,
mean_ngram_size=None,
geometric_dist=True,
permutation=False,
whole_word_masking=True,
favor_long_ngrams=False,
):
super().__init__(cfg, trainer=trainer)
# Params to store.
self.name = name
self.seed = seed
self.masked_lm_prob = masked_lm_prob
self.max_seq_length = max_seq_length
self.max_seq_length_dec = max_seq_length_dec
self.short_seq_prob = short_seq_prob
self.max_ngram_size = max_ngram_size
self.mean_ngram_size = mean_ngram_size
self.geometric_dist = geometric_dist
self.permutation = permutation
self.whole_word_masking = whole_word_masking
self.favor_long_ngrams = favor_long_ngrams
# Dataset.
self.indexed_dataset = indexed_dataset
# save index mappings to a configurable dir
self.index_mapping_dir = cfg.data.get('index_mapping_dir', None)
# create index_mapping_dir on rank 0
if torch.distributed.is_available(
) and torch.distributed.is_initialized():
if torch.distributed.get_rank() == 0:
if self.index_mapping_dir is not None and not os.path.isdir(
self.index_mapping_dir):
os.makedirs(self.index_mapping_dir)
torch.distributed.barrier()
# Build the samples mapping.
self.samples_mapping = get_samples_mapping(
indexed_dataset=self.indexed_dataset,
data_prefix=data_prefix,
num_epochs=num_epochs,
max_num_samples=max_num_samples,
max_seq_length=self.max_seq_length - 2, # account for added tokens
short_seq_prob=self.short_seq_prob,
seed=self.seed,
name=self.name,
binary_head=False,
index_mapping_dir=self.index_mapping_dir,
)
self.tokenizer = tokenizer
self.tokenizer_type = 'wordpiece' # TODO: better checks for tokenizer types. How do we do this for HF tokenizers that are not BERT?
if isinstance(self.tokenizer, YouTokenToMeTokenizer):
raise ValueError(
f"YTTM does not support special tokens and cannot be used with T5 datasets."
)
if isinstance(self.tokenizer, SentencePieceTokenizer):
if not self.tokenizer.legacy:
raise ValueError(
"Sentencepiece Tokenizer must have legacy = False to add special tokens."
)
self.tokenizer_type = 'sentencepiece'
if whole_word_masking:
raise ValueError(
"Whole word masking is not supported with sentencepiece tokenizers and only with wordpiece tokenizers. Please set it to False."
)
self.cls_id = tokenizer.cls_id
self.sep_id = tokenizer.sep_id
self.mask_id = tokenizer.mask_id
self.pad_id = tokenizer.pad_id
self.bos_id = tokenizer.bos_id
self.eos_id = tokenizer.eos_id
self.vocab_id_list = self.tokenizer.vocab
self.vocab_id_to_token_dict = {
idx: token
for idx, token in enumerate(self.vocab_id_list)
}
self._build()
def __init__(
self,
cfg,
trainer,
tokenizer,
name,
indexed_dataset,
data_prefix,
num_epochs,
max_num_samples,
masked_lm_prob,
max_seq_length,
max_seq_length_dec,
short_seq_prob,
seed,
):
super().__init__(cfg, trainer=trainer)
# Params to store.
self.name = name
self.seed = seed
self.masked_lm_prob = masked_lm_prob
self.max_seq_length = max_seq_length
self.max_seq_length_dec = max_seq_length_dec
# Dataset.
self.indexed_dataset = indexed_dataset
# Build the samples mapping.
self.samples_mapping = get_samples_mapping(
self.indexed_dataset,
data_prefix,
num_epochs,
max_num_samples,
self.max_seq_length - 2, # account for added tokens
short_seq_prob,
self.seed,
self.name,
False,
)
self.tokenizer = tokenizer
if isinstance(self.tokenizer, YouTokenToMeTokenizer):
raise ValueError(
f"YTTM does not support special tokens and cannot be used with T5 datasets."
)
if isinstance(self.tokenizer, SentencePieceTokenizer):
if not self.tokenizer.legacy:
raise ValueError(
"Sentencepiece Tokenizer must have legacy = False to add special tokens."
)
self.cls_id = tokenizer.cls_id
self.sep_id = tokenizer.sep_id
self.mask_id = tokenizer.mask_id
self.pad_id = tokenizer.pad_id
self.bos_id = tokenizer.bos_id
self.eos_id = tokenizer.eos_id
self.vocab_id_list = self.tokenizer.vocab
self.vocab_id_to_token_dict = {
idx: token
for idx, token in enumerate(self.vocab_id_list)
}
self.sentinel_tokens = tokenizer.additional_special_tokens_ids
assert len(self.sentinel_tokens) > 0
