glm-2b / tokenization_glm.py
zxdu20's picture
init commit
aa9679f
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.generic import _is_torch_device
import sentencepiece as spm
logger = logging.get_logger(__name__)
class GLMBatchEncoding(BatchEncoding):
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 + [self.eop_token_id])[:max_gen_length] 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 + [-100] * (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), -100), 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
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)