diff --git a/SwissArmyTransformer/tokenization/__init__.py b/SwissArmyTransformer/tokenization/__init__.py
index 64f9e3610a2f912b560372ed100d2f3afd3951f8..b54c6072c82cab8e45fff74e0cc1303fedc48427 100644
--- a/SwissArmyTransformer/tokenization/__init__.py
+++ b/SwissArmyTransformer/tokenization/__init__.py
@@ -15,6 +15,7 @@ import torch
from SwissArmyTransformer.training.utils import print_rank_0
+
def _export_vocab_size_to_args(args, original_num_tokens):
tokenizer = get_tokenizer(args)
num_tokens = original_num_tokens
@@ -32,6 +33,7 @@ def _export_vocab_size_to_args(args, original_num_tokens):
print_rank_0("prepare tokenizer done")
return tokenizer
+
def get_tokenizer(args=None, outer_tokenizer=None):
'''
If you're using outer_tokenizer, call `get_tokenizer(args, outer_tokenizer)`
@@ -53,7 +55,7 @@ def get_tokenizer(args=None, outer_tokenizer=None):
)
elif args.tokenizer_type.startswith('glm_'):
kwargs = {"add_block_symbols": True, "add_task_mask": args.task_mask,
- "add_decoder_mask": False}
+ "add_decoder_mask": False}
if args.tokenizer_type == "glm_GPT2BPETokenizer":
from .glm import GPT2BPETokenizer
get_tokenizer.tokenizer = GPT2BPETokenizer(args.tokenizer_model_type, **kwargs)
diff --git a/SwissArmyTransformer/tokenization/glm/sp_tokenizer.py b/SwissArmyTransformer/tokenization/glm/sp_tokenizer.py
index b044d917d4e579b995e70be398a3389ea0fd93bd..a76903497aeb1cd2eba6bb5eca1124ae58f462c4 100644
--- a/SwissArmyTransformer/tokenization/glm/sp_tokenizer.py
+++ b/SwissArmyTransformer/tokenization/glm/sp_tokenizer.py
@@ -3,6 +3,11 @@ from https://github.com/openai/gpt-2/, changed for chinese
"""
import json
import os
+import csv
+import nltk
+import random
+
+from nltk import tokenize as nltk_tokenize
import sentencepiece as spm
"""
@@ -22,129 +27,72 @@ python setup.py install
PRETRAINED_MODEL_FILE = os.path.join(os.path.dirname(os.path.dirname(__file__)),
'embed_assets', 'chinese_sentencepiece/cog-pretrain.model')
-def get_pairs(word):
- pairs = set()
- prev_char = word[0]
- for char in word[1:]:
- pairs.add((prev_char, char))
- prev_char = char
- return pairs
-
-
-class Encoder:
- def __init__(self, encoder, bpe_merges):
- self.encoder = encoder
- self.decoder = {v: k for k, v in self.encoder.items()}
- self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
- self.cache = {}
- self.max_len = 0
-
- def bpe(self, token):
- if token in self.cache:
- return self.cache[token]
- word = tuple(token)
- pairs = get_pairs(word)
- if not pairs:
- return token
-
- while True:
- bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
- if bigram not in self.bpe_ranks:
- break
- first, second = bigram
- new_word = []
- i = 0
- while i < len(word):
- try:
- j = word.index(first, i)
- new_word.extend(word[i:j])
- i = j
- except:
- new_word.extend(word[i:])
- break
-
- if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
- new_word.append(first + second)
- i += 2
- else:
- new_word.append(word[i])
- i += 1
- new_word = tuple(new_word)
- word = new_word
- if len(word) == 1:
- break
- else:
- pairs = get_pairs(word)
- word = ' '.join(word)
- self.cache[token] = word
- return word
-
- def encode(self, text):
- return [self.encoder.get(token, 1) for token in self.tokenize(text)]
-
- def decode(self, tokens):
- text = ''.join([self.decoder[token] for token in tokens])
- return text
-
- def tokenize(self, text):
- bpe_tokens = []
- bpe_tokens.extend(bpe_token for bpe_token in self.bpe(text).split(' '))
- return bpe_tokens
-
- def convert_tokens_to_ids(self, tokens):
- return [self.encoder.get(token, 1) for token in tokens]
-
-
-class Encoder_SP:
- def __init__(self, model_path):
+class SentencePieceTokenizer:
+ """Trains and uses sentencepiece for text tokenization"""
+
+ def __init__(self, model_path=None, **kwargs):
+ self.spm_model = model_path
+ self._tokens = []
+ self._vocab = {}
+ self.sp, self.vocab_size = None, 0
+ self.load_spm_model()
+
+ @classmethod
+ def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
+ if pretrained_model_name_or_path in ['glm-large', 'glm-10b']:
+ return cls(model_path=PRETRAINED_MODEL_FILE)
+ else:
+ return cls(model_path=pretrained_model_name_or_path)
+
+ def __len__(self):
+ return self.num_text_tokens
+
+ def load_spm_model(self):
+ """load sentencepiece model and parse vocab"""
+ if not os.path.exists(self.spm_model) and not self.spm_model.endswith('.model'):
+ self.spm_model = self.spm_model + '.model'
self.sp = spm.SentencePieceProcessor()
- self.sp.Load(model_path)
+ self.sp.Load(self.spm_model)
+ self.vocab_size = self.num_text_tokens = len(self.sp)
+ self._tokens = [self.IdToToken(t) for t in range(self.vocab_size)]
+ self._vocab = {t: i for i, t in enumerate(self._tokens)}
+
+ @property
+ def tokens(self):
+ return self._tokens
+
+ @property
+ def vocab(self):
+ return self._vocab
+
+ @staticmethod
+ def exists(model_path):
+ if model_path is None:
+ return False
+ # check if path exists
+ dne = not os.path.exists(model_path)
+ # check if path.model exists
+ if dne and not model_path.endswith('.model'):
+ dne = not os.path.exists(model_path + '.model')
+ return not dne
def encode(self, text):
- """
- text="...."
- """
- return self.sp.EncodeAsIds(text)
-
- def decode(self, tokens):
- """
- tokens=[x1,x2,...]
- """
- text = [int(token) for token in tokens]
- # print(text)
- return self.sp.DecodeIds(text)
-
- def tokenize(self, text):
- return self.sp.EncodeAsPieces(text)
-
- def convert_tokens_to_ids(self, tokens):
- return [self.sp.PieceToId(token) for token in tokens]
-
- def convert_token_to_id(self, token):
- return self.sp.PieceToId(token)
+ """convert text to sentencepiece Ids"""
+ tokens = self.sp.EncodeAsIds(text)
+ return tokens
- def convert_id_to_token(self, idx):
- return self.sp.IdToPiece(idx)
+ def IdToToken(self, Id):
+ """convert Id to sentencpiece token"""
+ return self.sp.IdToPiece(Id)
+ def TokenToId(self, token):
+ """convert sentencpiece token to Id"""
+ return self.sp.PieceToId(token)
-def get_encoder(encoder_file, bpe_file):
- # 以下是为了同一个函数入兼容sentencepiece
- filepath, filename = os.path.split(encoder_file)
- shotname, extension = os.path.splitext(filename)
-
- if (".model" == extension) and (bpe_file == ""):
- return Encoder_SP(encoder_file)
- else:
- with open(encoder_file, 'r', encoding="utf-8") as f:
- encoder = json.load(f)
- with open(bpe_file, 'r', encoding="utf-8") as f:
- bpe_data = f.read()
- bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]]
- return Encoder(
- encoder=encoder,
- bpe_merges=bpe_merges,
- )
+ def decode(self, Ids):
+ """converts ids to a text string"""
+ return self.sp.DecodeIds(Ids)
def from_pretrained():
- return get_encoder(PRETRAINED_MODEL_FILE, "")
+ return SentencePieceTokenizer(model_path=PRETRAINED_MODEL_FILE)
\ No newline at end of file
diff --git a/SwissArmyTransformer/tokenization/glm/tokenization.py b/SwissArmyTransformer/tokenization/glm/tokenization.py
index 1ff0170e089f372cf8c22aa2ee2818902c30bf70..674815ae00180827f8ff64fbe76e344fa669f6bb 100644
--- a/SwissArmyTransformer/tokenization/glm/tokenization.py
+++ b/SwissArmyTransformer/tokenization/glm/tokenization.py
@@ -20,15 +20,9 @@ import csv
import torch
import itertools
-import nltk
-from nltk import tokenize as nltk_tokenize
-import sentencepiece as spm
from .tokenization_gpt2 import GPT2Tokenizer
-from . import sp_tokenizer
-import regex as re
-
-
+from .sp_tokenizer import SentencePieceTokenizer
class Tokenization(object):
"""
@@ -36,7 +30,7 @@ class Tokenization(object):
text. Can hold tokenization as Ids or tokens.
It also holds command tokens (pad, unk, etc.) for the tokenization.
- This allows functions to pad/operate on tokenizations without having
+ This allows functions to pad/operate on tokenization without having
access to the full tokenizer, just the tokenization.
Several standard array operations are implemented (insert, append, extend).
@@ -73,8 +67,11 @@ class Tokenization(object):
def __len__(self):
return len(self.tokenization)
+ def __str__(self):
+ return f"Tokenization = {self.tokenization}, Text = {self.text}"
+
def insert(self, idx, other):
- if isinstance(other, (CommandToken, TypeToken)):
+ if isinstance(other, CommandToken):
self.tokenization.insert(idx, other.Id)
if idx == 0:
self.text = other.token + self.text
@@ -88,7 +85,7 @@ class Tokenization(object):
self.tokenization = self.tokenization[:idx] + other.tokenization + self.tokenization[idx:]
def append(self, other):
- if isinstance(other, (CommandToken, TypeToken)):
+ if isinstance(other, CommandToken):
self.tokenization.append(other.Id)
self.text += other.token
self.original_text += other.token
@@ -101,11 +98,11 @@ class Tokenization(object):
return self
def extend(self, other):
- if isinstance(other, (CommandToken, TypeToken)):
+ if isinstance(other, CommandToken):
self.tokenization.append(other.Id)
self.text += other.token
self.original_text += other.token
- elif isinstance(other, list) and isinstance(other[0], (CommandToken, TypeToken)):
+ elif isinstance(other, list) and isinstance(other[0], CommandToken):
self.tokenization.extend([o.Id for o in other])
self.text += [o.token for o in other]
self.original_text += [o.token for o in other]
@@ -136,57 +133,10 @@ class CommandToken(object):
self.lstrip = lstrip
self.rstrip = rstrip
- def __str__(self):
+ def __repr__(self):
return str(COMMAND_TUPLE(self.name, self.token, self.Id))
-DEFAULT_COMMAND_TOKENS = [
- ('pad', 0),
- ('eos', 1),
- ('bos', 2),
- ('unk', 3),
- ('sep', 4),
- ('L2R', 5),
- ('ENC', 6),
- ('MASK', 7),
-]
-DEFAULT_COMMAND_TOKENS = prep_command_tokens(DEFAULT_COMMAND_TOKENS)
-
-"""define some default type tokens for bert training"""
-
-TYPE_TUPLE = namedtuple('TypeToken', ('name', 'token', 'Id'))
-
-
-def prep_type_tokens(tokenlist, token_format=token_format):
- return [TypeToken(tok[0], token_format.format(tok[0]), tok[1]) for tok in tokenlist]
-
-
-class TypeToken(object):
- def __init__(self, name, token, Id):
- self.name = name
- self.token = token
- self.Id = Id
-
- def __str__(self):
- return str(TYPE_TUPLE(self.name, self.token, self.Id))
-
-
-DEFAULT_TYPE_TOKENS = [
- ('function', 0),
- ('command', 1),
- ('str0', 2),
- ('str1', 3),
- ('str2', 4),
- ('embedding0', 5),
- ('embedding1', 6),
- ('embedding2', 7),
- ('arg0', 8),
- ('arg1', 9),
- ('arg2', 10),
-]
-DEFAULT_TYPE_TOKENS = prep_type_tokens(DEFAULT_TYPE_TOKENS)
-
-
class Tokenizer(object):
"""
Tokenizer object that handles text tokenization, command tokens, and type tokens.
@@ -198,47 +148,42 @@ class Tokenizer(object):
Token types are stored in a separate mapping of size `len(type_tokens)`.
"""
- def __init__(self, text_tokenizer, command_tokens=None, type_tokens=None):
+ def __init__(self, text_tokenizer, command_tokens=None):
# set text tokenizer
self.text_tokenizer = text_tokenizer
if not hasattr(self, 'num_text_tokens'):
self.num_text_tokens = len(self.text_tokenizer)
-
- # set command tokens
- if command_tokens is None:
- command_tokens = DEFAULT_COMMAND_TOKENS
+ print(command_tokens)
self._command_tokens = command_tokens
- self.command_name_map = {tok.name: tok for tok in self._command_tokens}
- self.command_token_map = {tok.token: tok for tok in self._command_tokens}
- self.command_id_map = {tok.Id: tok for tok in self._command_tokens}
- if not hasattr(self, 'num_command_tokens'):
- self.num_command_tokens = len(self._command_tokens)
- if not hasattr(self, 'num_tokens'):
- self.num_tokens = self.num_command_tokens + self.num_text_tokens
-
- # set type tokens
- if type_tokens is None:
- type_tokens = DEFAULT_TYPE_TOKENS
- self.type_tokens = type_tokens
- self.type_name_map = {tok.name: tok for tok in self.type_tokens}
- self.type_token_map = {tok.token: tok for tok in self.type_tokens}
- self.type_id_map = {tok.Id: tok for tok in self.type_tokens}
- if not hasattr(self, 'num_type_tokens'):
- self.num_type_tokens = len(self.type_tokens)
+ self.command_name_map = {tok.name: tok for tok in self.command_tokens}
+ self.command_token_map = {tok.token: tok for tok in self.command_tokens}
+ self.command_id_map = {tok.Id: tok for tok in self.command_tokens}
# parse tokens and vocabs from tokenizer
- self._tokens = list(self.command_token_map.keys()) + list(self.text_tokenizer.tokens)
- self._vocab = {t: Id for Id, t in self.command_id_map.items()}
- self._vocab.update({t: Id + self.num_command_tokens for t, Id in self.text_tokenizer.vocab.items()})
+ max_token_id = max(len(self.text_tokenizer.tokens) - 1, max(self.command_id_map.keys()))
+ self._tokens = [self.text_tokenizer.tokens[i] if i < len(self.text_tokenizer.tokens) else f'[UNUSED{i}]' for i
+ in range(max_token_id + 1)]
+ for idx, token in self.command_id_map.items():
+ self._tokens[idx] = token.token
+ self._vocab = {t.token: Id for Id, t in self.command_id_map.items()}
+ self._vocab.update(self.text_tokenizer.vocab)
+
+ if not hasattr(self, 'num_command_tokens'):
+ self.num_command_tokens = len(self.command_tokens)
+ if not hasattr(self, 'num_tokens'):
+ self.num_tokens = len(self.tokens)
self._text_tokens = list(self.text_tokenizer.tokens)
- self._text_token_vocab = {t: Id + self.num_command_tokens for t, Id in self.text_tokenizer.vocab.items()}
+ self._text_token_vocab = {t: Id for t, Id in self.text_tokenizer.vocab.items()}
self._command_token_tokens = list(self.command_token_map.keys())
self._command_token_vocab = {t: Id for Id, t in self.command_id_map.items()}
- self._token_types = list(self.type_token_map.keys())
- self._token_type_vocab = {t: Id for Id, t in self.type_id_map.items()}
+ self.spaces_between_special_tokens = True
+
+ @property
+ def command_tokens(self):
+ return self._command_tokens
def __call__(self, text, process_fn=None):
"""run preprocessing and encode text as Ids"""
@@ -252,10 +197,6 @@ class Tokenizer(object):
"""get command token corresponding to `name`"""
return self.command_name_map[name]
- def get_type(self, name):
- """get type token corresponding to `name`"""
- return self.type_name_map[name]
-
@property
def tokens(self):
"""list (or iterable) of all tokens for tokenizer"""
@@ -266,21 +207,6 @@ class Tokenizer(object):
"""dictionary mapping tokens to ids for tokenizer"""
return self._vocab
- @property
- def token_types(self):
- """list (or iterable) of all token types for tokenizer"""
- return self._token_types
-
- @property
- def token_type_vocab(self):
- """dictionary mapping token types to ids for tokenizer"""
- return self._token_type_vocab
-
- @property
- def command_tokens(self):
- """list (or iterable) of all command tokens for tokenizer"""
- return self._command_token_tokens
-
@property
def command_token_vocab(self):
"""dictionary mapping command tokens to ids for tokenizer"""
@@ -370,725 +296,179 @@ class Tokenizer(object):
def _encode(self, text):
raise NotImplementedError
+ def _decode(self, ids):
+ raise NotImplementedError
+
+ @staticmethod
+ def clean_up_tokenization(out_string: str) -> str:
+ return out_string
+
def EncodeAsTokens(self, text, process_fn=None):
"""
encode text as tokens using text tokenizer
"""
- tokenization = self.text_tokenizer.EncodeAsTokens(text, process_fn=process_fn)
- tokenization.set_command_tokens(self._command_tokens)
+ tokenization = self.EncodeAsIds(text, process_fn=process_fn)
+ tokenization.tokenization = [self.IdToToken(idx) for idx in tokenization.tokenization]
return tokenization
- def IdToToken(self, Id, type_token=False):
- """convert Id to token accounting for command and type tokens"""
- if isinstance(Id, (TypeToken, CommandToken)):
+ def IdToToken(self, Id):
+ """convert Id to token accounting for command tokens"""
+ if isinstance(Id, CommandToken):
return Id.token
- if type_token:
- return self.type_id_map[Id].token
- if Id < self.num_command_tokens:
- return self.command_id_map[Id].token
- return self.text_tokenizer.IdToToken(Id - self.num_command_tokens)
-
- def TokenToId(self, token, type_token=False):
- """convert token to Id accounting for command and type tokens"""
- if isinstance(token, (TypeToken, CommandToken)):
+ return self.tokens[Id]
+
+ def TokenToId(self, token):
+ """convert token to Id accounting for command tokens"""
+ if isinstance(token, CommandToken):
return token.Id
- if type_token:
- return self.type_token_map[token].Id
- if token in self.command_token_map:
- return self.command_token_map[token].Id
- return self.text_tokenizer.TokenToId(token) + self.num_command_tokens
+ return self.vocab[token]
- def DecodeIds(self, Ids, type_token=False):
+ def DecodeIds(self, Ids):
"""
- convert Ids to tokens accounting for command and type tokens, tokens
+ convert Ids to tokens accounting for command tokens, tokens
are joined and returned as a string.
"""
- if type_token:
- return ' '.join(Id.token if isinstance(Id, TypeToken) else self.type_id_map[Id].token for Id in Ids)
rtn_strs = []
current_str = []
if isinstance(Ids, Tokenization):
Ids = Ids.tokenization
for Id in Ids:
if isinstance(Id, CommandToken):
- rtn_strs.append(self.text_tokenizer.DecodeIds(current_str))
+ rtn_strs.append(self._decode(current_str))
current_str = []
rtn_strs.append(Id.token)
- elif Id < self.num_command_tokens:
- rtn_strs.append(self.text_tokenizer.DecodeIds(current_str))
+ elif Id in self.command_id_map:
+ rtn_strs.append(self._decode(current_str))
current_str = []
rtn_strs.append(self.command_id_map[Id].token)
else:
- current_str.append(Id - self.num_command_tokens)
- if current_str != []:
- rtn_strs.append(self.text_tokenizer.DecodeIds(current_str))
- return ' '.join(rtn_strs)
+ current_str.append(Id)
+ if current_str:
+ rtn_strs.append(self._decode(current_str))
+ if self.spaces_between_special_tokens:
+ output = ' '.join(rtn_strs)
+ else:
+ output = "".join(rtn_strs)
+ output = self.clean_up_tokenization(output)
+ return output
- def DecodeTokens(self, Tokens, type_token=False):
+ def DecodeTokens(self, Tokens):
"""
convert tokens to a string accounting for command and type tokens.
"""
- if type_token:
- return ' '.join(t.token if isinstance(t, TypeToken) else t for t in Tokens)
- rtn_strs = []
- current_str = []
- if isinstance(Tokens, Tokenization):
- Tokens = Tokens.tokenization
- for t in Tokens:
- if isinstance(t, CommandToken):
- rtn_strs.append(self.text_tokenizer.DecodeTokens(current_str))
- current_str = []
- rtn_strs.append(t.token)
- elif t in self.command_token_map:
- rtn_strs.append(self.text_tokenizer.DecodeTokens(current_str))
- current_str = []
- rtn_strs.append(t)
- else:
- current_str.append(t)
- if current_str != []:
- rtn_strs.append(self.text_tokenizer.DecodeTokens(current_str))
- return ' '.join(rtn_strs)
-
-
-class TextTokenizer(object):
- """
- Interface for text tokenizer
- """
-
- def __init__(self):
- if not hasattr(self, 'num_text_tokens'):
- self.num_text_tokens = 0
- if not hasattr(self, 'num_tokens'):
- self.num_tokens = self.num_text_tokens
-
- def __call__(self, text, process_fn=None):
- return self.EncodeAsIds(text, process_fn)
-
- def __len__(self):
- return self.num_text_tokens
-
- @property
- def tokens(self):
- """list (or iterable) of text tokens for text tokenizer"""
- raise NotImplementedError('TextTokenizer tokens property not implemented')
-
- @property
- def vocab(self):
- """dictionary mapping tokens to ids"""
- raise NotImplementedError('TextTokenizer vocab property not implemented')
-
- @staticmethod
- def exists(model_path):
- """check if the filepath for a text tokenizer exists"""
- raise NotImplementedError('TextTokenizer exists method not implemented')
-
- def Train(self, corpus):
- """train a tokenizer on a data corpus and save model for future use"""
- raise NotImplementedError('TextTokenizer Train not implemented')
-
- def EncodeAsIds(self, text, process_fn=None):
- """
- Preprocess text and encode as ids. Return a tokenization object with
- original text, processed text, and id tokenization.
- """
- raise NotImplementedError('TextTokenizer EncodeAsIds not implemented')
-
- def EncodeAsTokens(self, text, process_fn=None):
- """
- Preprocess text and encode as tokens. Return a tokenization object with
- original text, processed text, and token tokenization.
- """
- raise NotImplementedError('TextTokenizer EncodeAsTokens not implemented')
-
- def IdToToken(self, Id):
- """Convert an Id to Token. Reverse lookup of self.vocab"""
- raise NotImplementedError('TextTokenizer IdToToken not implemented')
-
- def TokenToId(self, token):
- """Convert a Token to Id. Lookup of self.vocab"""
- raise NotImplementedError('TextTokenizer TokenToId not implemented')
-
- def DecodeIds(self, Ids):
- """Convert a list or tokenization object of Ids to a text string"""
- raise NotImplementedError('TextTokenizer DecodeIds not implemented')
-
- def DecodeTokens(self, Tokens):
- """Convert a list or tokenization object of tokens to a text string"""
- raise NotImplementedError('TextTokenizer DecodeTokens not implemented')
-
-
-class CharacterLevelTokenizer(TextTokenizer):
- """
- Text tokenizer for ASCII-256 Character Level Tokenization.
- """
-
- def __init__(self, **kwargs):
- self.num_text_tokens = 256
- super(CharacterLevelTokenizer, self).__init__()
- self._tokens = [self.IdToToken(Id) for Id in range(self.num_text_tokens)]
- self._vocab = {t: i for i, t in enumerate(self._tokens)}
-
- def __len__(self):
- return 256
-
- @staticmethod
- def exists(model_path):
- return True
-
- def Train(self, corpus):
- pass
-
- @property
- def tokens(self):
- return self._tokens
-
- @property
- def vocab(self):
- return self._vocab
-
- def EncodeAsIds(self, text, process_fn=None):
- """convert text to ascii 256 Ids"""
- processed_text = text
- if process_fn is not None:
- processed_text = process_fn(processed_text)
- processed_text = str(processed_text)
- tokens = [self.TokenToId(c) for c in processed_text]
- return Tokenization(tokens, processed_text, text)
-
- def EncodeAsTokens(self, text, process_fn=None):
- """convert text to ascii 256 characters"""
- processed_text = text
- if process_fn is not None:
- processed_text = process_fn(processed_text)
- processed_text = str(processed_text)
- tokens = [c for c in processed_text]
- return Tokenization(tokens, processed_text, text, asIds=False)
-
- def IdToToken(self, Id):
- """ascii index to character"""
- return chr(Id)
-
- def TokenToId(self, token):
- """ascii character to index"""
- return ord(token)
-
- def DecodeIds(self, Ids):
- """converts ascii ids to tokens before joining them into text"""
- if isinstance(Ids, Tokenization):
- Ids = Ids.tokenization
- return ''.join([self.IdToToken(tok) for tok in Ids])
-
- def DecodeTokens(self, Tokens):
- """just concatenates ascii tokens into text"""
- if isinstance(Tokens, Tokenization):
- Tokens = Tokens.tokenization
- return ''.join(Tokens)
-
-
-MAX_SENTENCEPIECE_SENTENCES = 100000000
-
-
-def get_corpus_freq(dataset, filepath, filetype='tsv'):
- """
- Take corpus, split it into sentences, and extract word frequencies.
- Write frequencies to `filepath` as a tsv. Only write the first
- MAX_SENTENCEPIECE_SENTENCES most common words to the file.
- """
- nltk.download('punkt', download_dir="./nltk")
- if filetype == 'tsv':
- delimiter = '\t'
- else:
- delimiter = ','
-
- print("compute corpus frequency\n", flush=True)
-
- total_sentence_count = 0
- maxlen = 0
- freqs = {}
- for entry in dataset:
- if isinstance(entry, dict):
- entry = entry['text']
- lines = entry.strip().split('\n')
- for line in lines:
- sentences = nltk_tokenize.sent_tokenize(line)
- total_sentence_count += len(sentences)
- for sentence in sentences:
- maxlen = max(len(line), maxlen)
- for word in sentence.split():
- if word not in freqs:
- freqs[word] = 0
- freqs[word] += 1
-
- print("length of freqs before truncating " + str(len(freqs)), flush=True)
- print("file path for freq " + str(filepath), flush=True)
-
- freqs_sorted = {}
- counter = 0
- for word, count in sorted(freqs.items(), key=lambda x: x[1], reverse=True):
- if counter >= MAX_SENTENCEPIECE_SENTENCES:
- break
- counter += 1
- freqs_sorted[word] = count
-
- print("length of freqs after trancating " + str(len(freqs_sorted)), flush=True)
-
- with open(filepath, 'w') as f:
- writer = csv.writer(f, delimiter=delimiter)
- for k, v in freqs_sorted.items():
- writer.writerow([str(k), str(v)])
-
- return total_sentence_count, maxlen
-
-
-class SentencePieceTokenizer(TextTokenizer):
- """Trains and uses sentencepiece for text tokenization"""
-
- def __init__(self, model_type='bpe', vocab_size=None, corpus=None, model_path=None, character_coverage=1.0,
- **kwargs):
- self.character_coverage = character_coverage
- self.model_type = model_type.lower()
- self.spm_model = model_path
- self.num_text_tokens = vocab_size
- make_train = not SentencePieceTokenizer.exists(self.spm_model)
- if make_train:
- assert corpus is not None and self.num_text_tokens is not None
- self.Train(corpus, self.num_text_tokens)
- self._tokens = []
- self._vocab = {}
- self.load_spm_model()
- super(SentencePieceTokenizer, self).__init__()
-
- def __len__(self):
- return self.num_text_tokens
-
- @property
- def tokens(self):
- return self._tokens
-
- @property
- def vocab(self):
- return self._vocab
-
- @staticmethod
- def exists(model_path):
- if model_path is None:
- return False
- # check if path exists
- dne = not os.path.exists(model_path)
- # check if path.model exists
- if dne and not model_path.endswith('.model'):
- dne = not os.path.exists(model_path + '.model')
- return not dne
-
- def load_spm_model(self):
- """load sentencepiece model and parse vocab"""
- if not os.path.exists(self.spm_model) and not self.spm_model.endswith('.model'):
- self.spm_model = self.spm_model + '.model'
- self.sp = spm.SentencePieceProcessor()
- self.sp.Load(self.spm_model)
- self.vocab_size = self.num_text_tokens = len(self.sp)
- self._tokens = [self.IdToToken(t) for t in range(self.vocab_size)]
- self._vocab = {t: i for i, t in enumerate(self._tokens)}
-
- def Train(self, corpus, num_text_tokens):
- """train sentencepiece model on corpus using word frequencies"""
- self.num_text_tokens = num_text_tokens
- use_model_path = self.spm_model
- random_hash = str(random.randint(0, 2147483647))
- if use_model_path is None:
- use_model_path = random_hash
- if use_model_path.endswith('.model'):
- use_model_path = use_model_path[:use_model_path.rfind('.model')]
- input_path = use_model_path + '.tsv.' + random_hash
- line_count, maxlenline = get_corpus_freq(corpus, input_path)
- line_count = min(line_count, MAX_SENTENCEPIECE_SENTENCES)
- print('line count used as input_sentence_size ', line_count, flush=True)
- print('training sentencepiece model', flush=True)
- train_string = '--input={file_path} --model_prefix={model_prefix} --vocab_size={vocab_size}' \
- + ' --model_type={model_type} --character_coverage={character_coverage} ' \
- + '--input_sentence_size={input_sentence_size} ' \
- + '--input_format=tsv'
- train_string = train_string.format(file_path=input_path, model_prefix=use_model_path,
- vocab_size=num_text_tokens,
- model_type=self.model_type, character_coverage=self.character_coverage,
- input_sentence_size=int(line_count)) # , #)#,
- print("calling spm.SentencePieceTrainer.Train(%s)" % (train_string), flush=True)
- spm.SentencePieceTrainer.Train(train_string)
- os.remove(input_path)
- self.spm_model = use_model_path + '.model'
- print('sentencepiece model written to ' + self.spm_model, flush=True)
-
- def EncodeAsIds(self, text, process_fn=None):
- """convert text to sentencepiece Ids"""
- processed_text = text
- if process_fn is not None:
- processed_text = process_fn(processed_text)
- tokens = self.sp.EncodeAsIds(processed_text)
- return Tokenization(tokens, processed_text, text)
-
- def EncodeAsTokens(self, text, process_fn=None):
- """convert text to sentencepiece tokens"""
- processed_text = text
- if process_fn is not None:
- processed_text = process_fn(processed_text)
- tokens = self.sp.EncodeAsTokens(processed_text)
- return Tokenization(tokens, processed_text, text, asIds=False)
-
- def IdToToken(self, Id):
- """convert Id to sentencpiece token"""
- return self.sp.IdToPiece(Id)
-
- def TokenToId(self, token):
- """convert sentencpiece token to Id"""
- return self.sp.PieceToId(token)
-
- def DecodeIds(self, Ids):
- """converts ids to a text string"""
- if isinstance(Ids, Tokenization):
- Ids = Ids.tokenization
- return self.sp.DecodeIds(Ids)
-
- def DecodeTokens(self, Tokens):
- """converts sentencepiece tokens to a text string"""
- if isinstance(Tokens, Tokenization):
- Tokens = Tokens.tokenization
- return self.sp.DecodeTokens(Tokens)
+ Ids = [self.TokenToId(token) for token in Tokens]
+ return self.DecodeIds(Ids)
class GPT2BPETokenizer(Tokenizer):
def __init__(self, model_type_or_path, cache_dir=None, add_block_symbols=False, add_task_mask=False,
add_decoder_mask=False, **kwargs):
- self.text_tokenizer = GPT2Tokenizer.from_pretrained(model_type_or_path,
- cache_dir=cache_dir)
+ text_tokenizer = GPT2Tokenizer.from_pretrained(model_type_or_path,
+ cache_dir=cache_dir)
# disable max len warnings by increasing max len
- self.text_tokenizer.max_len = int(1e12)
- self.num_tokens = len(self.text_tokenizer.encoder)
- self.num_type_tokens = 2
+ text_tokenizer.max_len = int(1e12)
+ num_tokens = len(text_tokenizer.encoder)
if model_type_or_path.startswith('roberta'):
- self.num_command_tokens = 6
- self.num_text_tokens = self.num_tokens - 3
- self._command_tokens = [
- CommandToken('pad', '<|endoftext|>', self.text_tokenizer.encoder['</s>']),
- CommandToken('eos', '<|endoftext|>', self.text_tokenizer.encoder['</s>']),
- CommandToken('sep', '[SEP]', self.text_tokenizer.encoder['<pad>']),
- CommandToken('ENC', '[CLS]', self.text_tokenizer.encoder['<s>']),
- CommandToken('MASK', '[MASK]', self.text_tokenizer.encoder['<mask>'], lstrip=True),
- CommandToken('unk', '[UNK]', self.text_tokenizer.encoder['<unk>'])
+ command_tokens = [
+ CommandToken('pad', '<|endoftext|>', text_tokenizer.encoder['</s>']),
+ CommandToken('eos', '<|endoftext|>', text_tokenizer.encoder['</s>']),
+ CommandToken('sep', '[SEP]', text_tokenizer.encoder['<pad>']),
+ CommandToken('ENC', '[CLS]', text_tokenizer.encoder['<s>']),
+ CommandToken('MASK', '[MASK]', text_tokenizer.encoder['<mask>'], lstrip=True),
+ CommandToken('unk', '[UNK]', text_tokenizer.encoder['<unk>'])
]
if add_block_symbols:
- self._command_tokens.extend([
- CommandToken('sop', '<|startofpiece|>', self.num_tokens),
- CommandToken('eop', '<|endofpiece|>', self.num_tokens + 1)
+ command_tokens.extend([
+ CommandToken('sop', '<|startofpiece|>', num_tokens),
+ CommandToken('eop', '<|endofpiece|>', num_tokens + 1)
])
- self.num_tokens += 2
- self.num_command_tokens += 2
+ num_tokens += 2
else:
- self.num_command_tokens = 2
- self.num_text_tokens = self.num_tokens - 1
- self._command_tokens = [
- CommandToken('pad', '<|endoftext|>', self.text_tokenizer.encoder['<|endoftext|>']),
- CommandToken('eos', '<|endoftext|>', self.text_tokenizer.encoder['<|endoftext|>'])
+ command_tokens = [
+ CommandToken('pad', '<|endoftext|>', text_tokenizer.encoder['<|endoftext|>']),
+ CommandToken('eos', '<|endoftext|>', text_tokenizer.encoder['<|endoftext|>'])
]
if add_block_symbols:
- self._command_tokens.extend([
- CommandToken('sop', '<|startofpiece|>', self.num_tokens),
- CommandToken('eop', '<|endofpiece|>', self.num_tokens + 1),
- CommandToken('ENC', '[CLS]', self.num_tokens + 2),
- CommandToken('MASK', '[MASK]', self.num_tokens + 3, lstrip=True),
- CommandToken('sep', '[SEP]', self.num_tokens + 4),
- CommandToken('unk', '[UNK]', self.num_tokens + 5)
+ command_tokens.extend([
+ CommandToken('sop', '<|startofpiece|>', num_tokens),
+ CommandToken('eop', '<|endofpiece|>', num_tokens + 1),
+ CommandToken('ENC', '[CLS]', num_tokens + 2),
+ CommandToken('MASK', '[MASK]', num_tokens + 3, lstrip=True),
+ CommandToken('sep', '[SEP]', num_tokens + 4),
+ CommandToken('unk', '[UNK]', num_tokens + 5)
])
- self.num_tokens += 6
- self.num_command_tokens += 6
+ num_tokens += 6
if add_block_symbols:
if add_task_mask:
- self._command_tokens.extend([
- CommandToken('gMASK', '[gMASK]', self.num_tokens, lstrip=True),
- CommandToken('sMASK', '[sMASK]', self.num_tokens + 1, lstrip=True)
+ command_tokens.extend([
+ CommandToken('gMASK', '[gMASK]', num_tokens, lstrip=True),
+ CommandToken('sMASK', '[sMASK]', num_tokens + 1, lstrip=True)
])
- self.num_tokens += 2
- self.num_command_tokens += 2
+ num_tokens += 2
if add_decoder_mask:
- self._command_tokens.extend([
- CommandToken('dBLOCK', '[dBLOCK]', self.num_tokens)
+ command_tokens.extend([
+ CommandToken('dBLOCK', '[dBLOCK]', num_tokens)
])
- self.num_tokens += 1
- self.num_command_tokens += 1
- self.command_name_map = {tok.name: tok for tok in self._command_tokens}
- self.command_token_map = {tok.token: tok for tok in self._command_tokens}
- self.command_id_map = {tok.Id: tok for tok in self._command_tokens}
-
- self.type_tokens = [
- TypeToken('str0', '<str0>', 0),
- TypeToken('str1', '<str1>', 1),
- ]
- self.type_name_map = {tok.name: tok for tok in self.type_tokens}
- self.type_token_map = {tok.token: tok for tok in self.type_tokens}
- self.type_id_map = {tok.Id: tok for tok in self.type_tokens}
-
- self._tokens = list(self.text_tokenizer.encoder.keys())
- self._vocab = {k: v for k, v in self.text_tokenizer.encoder.items()}
-
- self._text_tokens = list(self._tokens)
- self._text_token_vocab = {k: v for k, v in self.text_tokenizer.encoder.items()}
-
- self._command_token_tokens = list(self.command_token_map.keys())
- self._command_token_vocab = {t: Id for Id, t in self.command_id_map.items()}
-
- self._token_types = list(self.type_token_map.keys())
- self._token_type_vocab = {t: Id for Id, t in self.type_id_map.items()}
-
- for idx, tok in self.command_id_map.items():
- self.text_tokenizer.decoder[idx] = tok.token
-
- def EncodeAsIds(self, text, process_fn=None):
- processed_text = text
- if process_fn is not None:
- processed_text = process_fn(processed_text)
-
- def split_on_token(tok_extended: CommandToken, text):
- result = []
- tok = tok_extended.token
- split_text = text.split(tok)
- for i, sub_text in enumerate(split_text):
- # CommandToken can control whitespace stripping around them.
- # We use them for GPT2 and Roberta to have different behavior depending on the special token
- # Cf. https://github.com/huggingface/transformers/pull/2778
- # and https://github.com/huggingface/transformers/issues/3788
- # Strip white spaces on the right
- if tok_extended.rstrip and i > 0:
- # A bit counter-intuitive but we strip the left of the string
- # since tok_extended.rstrip means the special token is eating all white spaces on its right
- sub_text = sub_text.lstrip()
- # Strip white spaces on the left
- if tok_extended.lstrip and i < len(split_text) - 1:
- sub_text = sub_text.rstrip() # Opposite here
-
- if i == 0 and not sub_text:
- result.append(tok)
- elif i == len(split_text) - 1:
- if sub_text:
- result.append(sub_text)
- else:
- pass
- else:
- if sub_text:
- result.append(sub_text)
- result.append(tok)
- return result
-
- def split_on_tokens(tok_list, text):
- if not text.strip():
- return []
- if not tok_list:
- return self.text_tokenizer.encode(text)
-
- tokenized_text = []
- text_list = [text]
- for tok in tok_list:
- tokenized_text = []
- for sub_text in text_list:
- if sub_text not in self._command_token_tokens:
- tokenized_text.extend(split_on_token(tok, sub_text))
- else:
- tokenized_text.append(sub_text)
- text_list = tokenized_text
-
- return list(
- itertools.chain.from_iterable(
- (
- self.text_tokenizer.encode(token) if token not in self._command_token_tokens else [
- self.command_token_map[token].Id] for token in tokenized_text
- )
- )
- )
-
- no_split_tokens = self._command_tokens
- Ids = split_on_tokens(no_split_tokens, processed_text)
- tokenization = Tokenization(Ids, processed_text, text)
- tokenization.set_command_tokens(self._command_tokens)
- return tokenization
+ num_tokens += 1
+ super().__init__(text_tokenizer, command_tokens=command_tokens)
def _encode(self, text):
return self.text_tokenizer.encode(text)
- def EncodeAsTokens(self, text, process_fn=None):
- processed_text = text
- if process_fn is not None:
- processed_text = process_fn(processed_text)
- tokens = []
- for token in re.findall(self.text_tokenizer.pat, processed_text):
- token = ''.join(self.text_tokenizer.bye_encoder[b] for b in token.encode('utf-8'))
- tokens.extend(bpe_token for bpe_token in self.text_tokenizer.bpe(token).split(' '))
- tokenization = Tokenization(tokens, processed_text, text, asIds=False)
- tokenization.set_command_tokens(self._command_tokens)
- return tokenization
-
- def DecodeAsTokens(self, Ids):
- return [self.IdToToken(x) for x in Ids]
-
- def IdToToken(self, Id, type_token=False):
- if isinstance(Id, (TypeToken, CommandToken)):
- return Id.token
- if type_token:
- return self.type_id_map[Id].token
- if Id in self.command_id_map:
- return self.command_id_map[Id].token
- return self.text_tokenizer.decoder[Id]
-
- def TokenToId(self, token, type_token=False):
- if isinstance(token, (TypeToken, CommandToken)):
- return token.Id
- if type_token:
- return self.type_token_map[token].Id
- return self.text_tokenizer.encoder[token]
-
- def DecodeIds(self, Ids, type_token=False):
- if type_token:
- return ' '.join(Id.token if isinstance(Id, TypeToken) else self.type_id_map[Id].token for Id in Ids)
- if isinstance(Ids, Tokenization):
- Ids = Ids.tokenization
- return self.text_tokenizer.decode(Ids)
-
- def DecodeTokens(self, Tokens, type_token=False):
- if type_token:
- return ' '.join(t.token if isinstance(t, TypeToken) else t for t in Tokens)
- if isinstance(Tokens, Tokenization):
- Tokens = Tokens.tokenization
- return self.text_tokenizer.decode([self.TokenToId(tok) for tok in Tokens])
+ def _decode(self, ids):
+ return self.text_tokenizer.decode(ids)
class ChineseSPTokenizer(Tokenizer):
def __init__(self, model_type_or_path, add_block_symbols=False, add_task_mask=False, add_decoder_mask=False,
**kwargs):
- self.text_tokenizer = sp_tokenizer.from_pretrained()
-
- self.num_command_tokens = 0
- self.num_text_tokens = self.text_tokenizer.sp.vocab_size()
- self.num_tokens = self.num_text_tokens
- self.num_type_tokens = 2
-
- self._command_tokens = [
- CommandToken('pad', '<|endoftext|>', self.num_text_tokens),
- CommandToken('eos', '<|endoftext|>', self.num_text_tokens),
- CommandToken('sep', '[SEP]', self.num_text_tokens + 1),
- CommandToken('ENC', '[CLS]', self.num_text_tokens + 2),
- CommandToken('MASK', '[MASK]', self.num_text_tokens + 3, lstrip=True),
- CommandToken('unk', '[UNK]', self.num_text_tokens + 4)
+ text_tokenizer = SentencePieceTokenizer.from_pretrained(model_type_or_path)
+ num_tokens = len(text_tokenizer.tokens)
+
+ command_tokens = [
+ CommandToken('pad', '<|endoftext|>', num_tokens),
+ CommandToken('eos', '<|endoftext|>', num_tokens),
+ CommandToken('sep', '[SEP]', num_tokens + 1),
+ CommandToken('ENC', '[CLS]', num_tokens + 2),
+ CommandToken('MASK', '[MASK]', num_tokens + 3, lstrip=True),
+ CommandToken('unk', '[UNK]', num_tokens + 4)
]
- self.num_tokens += 5
- self.num_command_tokens += 6
+ num_tokens += 5
if add_block_symbols:
- self._command_tokens.extend([
- CommandToken('sop', '<|startofpiece|>', self.num_tokens + 1),
- CommandToken('eop', '<|endofpiece|>', self.num_tokens + 2)
+ command_tokens.extend([
+ CommandToken('sop', '<|startofpiece|>', num_tokens + 1),
+ CommandToken('eop', '<|endofpiece|>', num_tokens + 2)
])
if model_type_or_path == 'glm-large':
- self.num_tokens += 3
+ num_tokens += 3
else:
- self.num_tokens += 2
- self.num_command_tokens += 2
+ num_tokens += 2
if add_task_mask:
if model_type_or_path == 'glm-large':
- self._command_tokens.extend([
- CommandToken('sMASK', '[sMASK]', self.num_tokens, lstrip=True),
- CommandToken('gMASK', '[gMASK]', self.num_tokens + 1, lstrip=True)
+ command_tokens.extend([
+ CommandToken('sMASK', '[sMASK]', num_tokens, lstrip=True),
+ CommandToken('gMASK', '[gMASK]', num_tokens + 1, lstrip=True)
])
else:
- self._command_tokens.extend([
- CommandToken('gMASK', '[gMASK]', self.num_tokens, lstrip=True),
- CommandToken('sMASK', '[sMASK]', self.num_tokens + 1, lstrip=True)
+ command_tokens.extend([
+ CommandToken('gMASK', '[gMASK]', num_tokens, lstrip=True),
+ CommandToken('sMASK', '[sMASK]', num_tokens + 1, lstrip=True)
])
- self.num_tokens += 2
- self.num_command_tokens += 2
+ num_tokens += 2
if add_decoder_mask:
- self._command_tokens.extend([
- CommandToken('dBLOCK', '[dBLOCK]', self.num_tokens)
+ command_tokens.extend([
+ CommandToken('dBLOCK', '[dBLOCK]', num_tokens)
])
- self.num_tokens += 1
- self.num_command_tokens += 1
- self.command_name_map = {tok.name: tok for tok in self._command_tokens}
- self.command_token_map = {tok.token: tok for tok in self._command_tokens}
- self.command_id_map = {tok.Id: tok for tok in self._command_tokens}
- self.type_tokens = [
- TypeToken('str0', '<str0>', 0),
- TypeToken('str1', '<str1>', 1),
- ]
- self.type_name_map = {tok.name: tok for tok in self.type_tokens}
- self.type_token_map = {tok.token: tok for tok in self.type_tokens}
- self.type_id_map = {tok.Id: tok for tok in self.type_tokens}
-
- # self._tokens = list(self.text_tokenizer.encoder.keys())
- # self._vocab = {k:v for k,v in self.text_tokenizer.encoder.items()}
- #
- # self._text_tokens = list(self._tokens)
- # self._text_token_vocab = {k:v for k,v in self.text_tokenizer.encoder.items()}
-
- self._command_token_tokens = list(self.command_token_map.keys())
- self._command_token_vocab = {t: Id for Id, t in self.command_id_map.items()}
-
- self._token_types = list(self.type_token_map.keys())
- self._token_type_vocab = {t: Id for Id, t in self.type_id_map.items()}
+ num_tokens += 1
+ super().__init__(text_tokenizer, command_tokens=command_tokens)
+ if model_type_or_path in ['glm-large', 'glm-10b']:
+ self.spaces_between_special_tokens = False
def _encode(self, text):
ids = self.text_tokenizer.encode(text)
return ids
- def EncodeAsTokens(self, text, process_fn=None):
- processed_text = text
- if process_fn is not None:
- processed_text = process_fn(processed_text)
- tokens = self.text_tokenizer.tokenize(processed_text)
- tokenization = Tokenization(tokens, processed_text, text, asIds=False)
- tokenization.set_command_tokens(self._command_tokens)
- return tokenization
- # return Tokenization(tokens, processed_text, text, asIds=False)
-
- def IdToToken(self, Id, type_token=False):
- if isinstance(Id, (TypeToken, CommandToken)):
- return Id.token
- if type_token:
- return self.type_id_map[Id].token
- if Id in self.command_id_map:
- return self.command_id_map[Id].token
- elif Id in self.type_id_map:
- return self.type_id_map[Id].token
- else:
- return self.text_tokenizer.convert_id_to_token(int(Id))
-
- def TokenToId(self, token, type_token=False):
- if isinstance(token, (TypeToken, CommandToken)):
- return token.Id
- if type_token:
- return self.type_token_map[token].Id
- return self.text_tokenizer.convert_token_to_id(token)
-
- def DecodeIds(self, Ids, type_token=False):
- if type_token:
- return ' '.join(Id.token if isinstance(Id, TypeToken) else self.type_id_map[Id].token for Id in Ids)
- if isinstance(Ids, Tokenization):
- Ids = Ids.tokenization
- Ids = list(map(int, Ids))
- pieces = []
- last = 0
- for i, token_id in enumerate(Ids):
- if token_id in self.command_id_map:
- pieces.append(Ids[last: i])
- pieces.append(token_id)
- last = i + 1
- pieces.append(Ids[last:])
- text = ""
- for piece in pieces:
- if isinstance(piece, int):
- text += self.command_id_map[piece].token
- elif piece:
- text += self.text_tokenizer.decode(piece)
- return text
-
- def DecodeTokens(self, Tokens, type_token=False):
- if type_token:
- return ' '.join(t.token if isinstance(t, TypeToken) else t for t in Tokens)
- if isinstance(Tokens, Tokenization):
- Tokens = Tokens.tokenization
- return self.text_tokenizer.decode([self.TokenToId(tok) for tok in Tokens])
+ def _decode(self, ids):
+ text = self.text_tokenizer.decode(ids)
+ return text
\ No newline at end of file
diff --git a/SwissArmyTransformer/tokenization/glm/tokenization_gpt2.py b/SwissArmyTransformer/tokenization/glm/tokenization_gpt2.py
index 46b2e6ca607a682f00e2095644d67c871efd4794..c263729a8dd719d2ee84d60a6ccd73027dc74d25 100644
--- a/SwissArmyTransformer/tokenization/glm/tokenization_gpt2.py
+++ b/SwissArmyTransformer/tokenization/glm/tokenization_gpt2.py
@@ -168,6 +168,14 @@ class GPT2Tokenizer(object):
self.special_tokens_decoder = {}
self.set_special_tokens(special_tokens)
+ @property
+ def tokens(self):
+ return self.decoder
+
+ @property
+ def vocab(self):
+ return self.encoder
+
def __len__(self):
return len(self.encoder) + len(self.special_tokens)
@@ -309,4 +317,4 @@ class GPT2Tokenizer(object):
writer.write(token + u'\n')
index += 1
- return vocab_file, merge_file, special_tokens_file
+ return vocab_file, merge_file, special_tokens_file
\ No newline at end of file
diff --git a/SwissArmyTransformer/training/deepspeed_training.py b/SwissArmyTransformer/training/deepspeed_training.py
index bc8ea682180fd6c8ef134d89377ceb6c8efb3d39..b3dfc225b9fba4dd5a86263275d9bbaade29dc98 100644
--- a/SwissArmyTransformer/training/deepspeed_training.py
+++ b/SwissArmyTransformer/training/deepspeed_training.py
@@ -204,9 +204,9 @@ def get_params_for_weight_decay_optimization(module):
if p is not None and n == 'bias' and p.requires_grad])
if len(weight_decay_params['params']) == 0:
- return tuple(no_weight_decay_params)
+ return (no_weight_decay_params,)
elif len(no_weight_decay_params['params']) == 0:
- return tuple(weight_decay_params)
+ return (weight_decay_params,)
return weight_decay_params, no_weight_decay_params