import os
from typing import Optional, Tuple, List, Union
from shutil import copyfile
import torch
from transformers import PreTrainedTokenizer, RobertaTokenizer, GPT2Tokenizer, BertTokenizer
from transformers.utils import logging
from transformers.tokenization_utils_base import BatchEncoding
from transformers.models.auto.tokenization_auto import get_tokenizer_config
# from transformers.utils import torch_required
from transformers.utils.generic import _is_torch_device
import sentencepiece as spm
logger = logging.get_logger(__name__)
class GLMBatchEncoding(BatchEncoding):
# @torch_required
def to(self, device: Union[str, "torch.device"]) -> "BatchEncoding":
"""
Send all values to device by calling `v.to(device)` (PyTorch only).
Args:
device (`str` or `torch.device`): The device to put the tensors on.
Returns:
[`BatchEncoding`]: The same instance after modification.
"""
# This check catches things like APEX blindly calling "to" on all inputs to a module
# Otherwise it passes the casts down and casts the LongTensor containing the token idxs
# into a HalfTensor
if isinstance(device, str) or _is_torch_device(device) or isinstance(device, int):
self.data = {k: v.to(device=device) if torch.is_tensor(v) else v for k, v in self.data.items()}
else:
logger.warning(f"Attempting to cast a BatchEncoding to type {str(device)}. This is not supported.")
return self
class GLMTokenizerMixin:
@property
def sop_token(self) -> Optional[str]:
return "<|startofpiece|>"
@property
def sop_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the start token in the vocabulary, used when training a model with autoregressive blank filling.
"""
return self.convert_tokens_to_ids(self.sop_token)
@property
def eop_token(self) -> Optional[str]:
return "<|endofpiece|>"
@property
def eop_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the end token in the vocabulary, used when training a model with autoregressive blank filling.
"""
return self.convert_tokens_to_ids(self.eop_token)
@property
def gmask_token_id(self) -> int:
return self.convert_tokens_to_ids("[gMASK]")
@property
def smask_token_id(self) -> int:
return self.convert_tokens_to_ids("[sMASK]")
@property
def mask_token_ids(self):
return [self.mask_token_id, self.smask_token_id, self.gmask_token_id]
def _build_input_for_multiple_choice(self, context, choices):
context_id = context["input_ids"]
if torch.is_tensor(context_id):
context_id = context_id.tolist()
division = len(context_id)
mask_position = context_id.index(self.mask_token_id)
token = torch.tensor(context_id, dtype=torch.long)
attention_mask = [context["attention_mask"].expand(division, -1)]
position_id = torch.arange(division, dtype=torch.long)
block_position_id = torch.zeros(division, dtype=torch.long)
choice_ids, choice_indices = [], []
for choice_str in choices:
choice = torch.tensor(self(choice_str, add_special_tokens=False, padding=False)['input_ids'],
dtype=torch.long)
choice_ids.append(choice)
choice_indices.append(torch.arange(len(token), len(token) + len(choice), dtype=torch.long))
attention_mask.append(torch.tril(torch.ones((len(choice), len(choice)), dtype=torch.long)))
token = torch.cat((token, torch.tensor([self.sop_token_id], dtype=torch.long), choice[:-1]))
position_id = torch.cat((position_id, torch.tensor([mask_position] * len(choice), dtype=torch.long)))
block_position_id = torch.cat((block_position_id, torch.arange(1, 1 + len(choice), dtype=torch.long)))
attention_mask = torch.block_diag(*attention_mask)
attention_mask[division:, :division] = context["attention_mask"].unsqueeze(0)
return {
"input_ids": token,
"position_ids": torch.stack((position_id, block_position_id)),
"attention_mask": attention_mask,
"choice_ids": choice_ids,
"choice_indices": choice_indices
}
def _pad_batch(self, tokens, position_ids, attention_mask, max_seq_length):
pad_length = max_seq_length - len(tokens)
attention_mask = torch.nn.functional.pad(
attention_mask,
(0, pad_length, 0, pad_length),
mode="constant",
value=0,
)
tokens = torch.cat((tokens, torch.zeros(pad_length, dtype=torch.long)))
position_ids = torch.cat((position_ids, position_ids[..., -1:].expand(-1, pad_length)), dim=-1)
return tokens, position_ids, attention_mask
def _collate(self, samples):
TILE = 1
length_to_pad = (max(map(lambda spl: len(spl["input_ids"]), samples)) + TILE - 1) // TILE * TILE
token_batch, position_id_batch, attention_mask_batch = [], [], []
choices_batch, choice_target_ids_batch = [], []
for sample in samples:
token, position_id, attention_mask = self._pad_batch(
sample["input_ids"], sample["position_ids"], sample["attention_mask"], length_to_pad
)
token_batch.append(token)
position_id_batch.append(position_id)
attention_mask_batch.append(attention_mask)
choices_batch.append(sample["choice_ids"])
choice_target_ids_batch.append(sample["choice_indices"])
return {
"input_ids": torch.stack(token_batch),
"position_ids": torch.stack(position_id_batch),
"attention_mask": torch.stack(attention_mask_batch).unsqueeze(1),
"choice_ids": choices_batch,
"choice_indices": choice_target_ids_batch,
}
def build_inputs_for_multiple_choice(self, model_input: BatchEncoding, choices, max_length=None):
samples = [{key: value[i] for key, value in model_input.items()} for i in range(len(model_input["input_ids"]))]
samples = [self._build_input_for_multiple_choice(sample, choice) for sample, choice in
zip(samples, choices)]
inputs = self._collate(samples)
return GLMBatchEncoding(inputs)
def build_inputs_for_generation(self, model_input: BatchEncoding, max_gen_length=512, targets=None, padding=False):
mask_ids = self.mask_token_ids
input_ids = model_input.input_ids
batch_size, seq_length = input_ids.shape[:2]
position_id, block_position_id = list(range(seq_length)), [0 for _ in range(seq_length)]
position_ids, block_position_ids = [], []
labels = None
if targets is not None:
is_batched = isinstance(targets, (list, tuple))
targets = self(targets, add_special_tokens=False, padding=False).input_ids
if not is_batched:
targets = [targets]
assert len(targets) == len(input_ids)
targets = [target[:(max_gen_length-1)] + [self.eop_token_id] for target in targets]
if not padding:
max_gen_length = max(map(len, targets))
targets = [[self.sop_token_id] + target for target in targets]
labels = [target[1:] for target in targets]
targets = [target + [self.pad_token_id] * (max_gen_length + 1 - len(target)) for target in targets]
labels = [label + [self.pad_token_id] * (max_gen_length - len(label)) for label in labels]
targets = torch.tensor(targets, dtype=input_ids.dtype, device=input_ids.device)
labels = torch.tensor(labels, dtype=input_ids.dtype, device=input_ids.device)
labels = torch.cat((input_ids.new_full((batch_size, seq_length), self.pad_token_id), labels), dim=1)
for i in range(batch_size):
mask_positions = []
for mask_id in mask_ids:
mask_positions += (input_ids[i] == mask_id).nonzero(as_tuple=True)[0].tolist()
if not mask_positions:
raise ValueError("Cannot find mask token in the input")
mask_positions.sort()
mask_pos = mask_positions[0]
position_ids.append(position_id + [mask_pos] * max_gen_length)
block_position_ids.append(block_position_id + list(range(1, max_gen_length + 1)))
position_ids = torch.tensor(position_ids, dtype=input_ids.dtype, device=input_ids.device)
block_position_ids = torch.tensor(block_position_ids, dtype=input_ids.dtype, device=input_ids.device)
position_ids = torch.stack((position_ids, block_position_ids), dim=1)
attention_mask = model_input.attention_mask
attention_mask = attention_mask.unsqueeze(1).expand(-1, seq_length + max_gen_length, -1)
generation_attention_mask = torch.cat([attention_mask.new_zeros((seq_length, max_gen_length)),
torch.tril(attention_mask.new_ones((max_gen_length, max_gen_length)))],
dim=0).unsqueeze(0).expand(batch_size, -1, -1)
attention_mask = torch.cat((attention_mask, generation_attention_mask), dim=2)
attention_mask = attention_mask.unsqueeze(1)
if targets is None:
input_ids = torch.cat((input_ids, input_ids.new_full((batch_size, 1), self.sop_token_id)), dim=-1)
else:
input_ids = torch.cat((input_ids, targets[:, :-1]), dim=1)
batch = {"input_ids": input_ids, "position_ids": position_ids}
if labels is None:
batch["generation_attention_mask"] = attention_mask
else:
batch["attention_mask"] = attention_mask
batch["labels"] = labels
return BatchEncoding(batch)
class GLMRobertaTokenizer(RobertaTokenizer, GLMTokenizerMixin):
model_input_names = ["input_ids", "position_ids", "attention_mask"]
truncation_side: str = "left"
@property
def gmask_token_id(self) -> int:
raise NotImplementedError("The model doesn't support gMASK")
@property
def smask_token_id(self) -> int:
raise NotImplementedError("The model doesn't support sMASK")
@property
def mask_token_ids(self):
return [self.mask_token_id]
class GLMChineseTokenizer(PreTrainedTokenizer, GLMTokenizerMixin):
vocab_files_names = {"vocab_file": "cog-pretrain.model"}
truncation_side: str = "left"
def __init__(self, vocab_file, **kwargs):
super().__init__(**kwargs)
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(vocab_file)
@property
def vocab_size(self):
return len(self.sp_model)
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _tokenize(self, text, **kwargs):
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.sp_model.PieceToId(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.sp_model.IdToPiece(index)
def convert_tokens_to_string(self, tokens):
return self.sp_model.decode(tokens)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
out_vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
)
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, out_vocab_file)
elif not os.path.isfile(self.vocab_file):
with open(out_vocab_file, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (out_vocab_file,)
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:
- single sequence: ``[CLS] X [SEP]``
- pair of sequences: ``[CLS] A [SEP] B [SEP]``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
#assert token_ids_1 is None
#cls = [self.cls_token_id]
#eos = [self.eos_token_id]
#return cls + token_ids_0 + eos
cls = [self.cls_token_id]
eos = [self.eos_token_id]
#eop = [self.eop_token_id]
#mask = [self.mask_token_id]
sep = [self.sep_token_id]
#token_ids_0 = cls + token_ids_0 + mask + eos
if token_ids_1 is None:
return cls + token_ids_0 + eos
else:
return cls + token_ids_0 + sep + token_ids_1 + eos
class GLMGPT2Tokenizer(GPT2Tokenizer, GLMTokenizerMixin):
model_input_names = ["input_ids", "position_ids", "attention_mask"]
truncation_side: str = "left"
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:
- single sequence: ``[CLS] X [SEP]``
- pair of sequences: ``[CLS] A [SEP] B [SEP]``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
assert token_ids_1 is None
cls = [self.cls_token_id]
eos = [self.eos_token_id]
return cls + token_ids_0 + eos
class GLMBertTokenizer(BertTokenizer, GLMTokenizerMixin):
model_input_names = ["input_ids", "position_ids", "attention_mask"]
truncation_side: str = "left"
@property
def gmask_token_id(self) -> int:
raise NotImplementedError("The model doesn't support gMASK")
@property
def smask_token_id(self) -> int:
raise NotImplementedError("The model doesn't support sMASK")
@property
def mask_token_ids(self):
return [self.mask_token_id]
class GLMTokenizer:
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
config_tokenizer_class = tokenizer_config.get("tokenizer_class")
if config_tokenizer_class == "GLMRobertaTokenizer":
tokenizer_class = GLMRobertaTokenizer
elif config_tokenizer_class == "GLMChineseTokenizer":
tokenizer_class = GLMChineseTokenizer
elif config_tokenizer_class == "GLMGPT2Tokenizer":
tokenizer_class = GLMGPT2Tokenizer
elif config_tokenizer_class == "GLMBertTokenizer":
tokenizer_class = GLMBertTokenizer
else:
raise NotImplementedError("Not implemented tokenizer type:", config_tokenizer_class)
return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)