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[legacy] clean up legacy code (#4743)

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* [legacy] remove outdated codes of pipeline (#4692)

* [legacy] remove cli of benchmark and update optim (#4690)

* [legacy] remove cli of benchmark and update optim

* [doc] fix cli doc test

* [legacy] fix engine clip grad norm

* [legacy] remove outdated colo tensor (#4694)

* [legacy] remove outdated colo tensor

* [test] fix test import

* [legacy] move outdated zero to legacy (#4696)

* [legacy] clean up utils (#4700)

* [legacy] clean up utils

* [example] update examples

* [legacy] clean up amp

* [legacy] fix amp module

* [legacy] clean up gpc (#4742)

* [legacy] clean up context

* [legacy] clean core, constants and global vars

* [legacy] refactor initialize

* [example] fix examples ci

* [example] fix examples ci

* [legacy] fix tests

* [example] fix gpt example

* [example] fix examples ci

* [devops] fix ci installation

* [example] fix examples ci
This commit is contained in:
Hongxin Liu
2023-09-18 16:31:06 +08:00
committed by GitHub
parent 32e7f99416
commit b5f9e37c70
342 changed files with 2919 additions and 4182 deletions
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30
examples/tutorial/sequence_parallel/data/tokenizer/tokenizer.py
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@@ -12,13 +12,12 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Megatron tokenizers."""
from abc import ABC
from abc import abstractmethod
from colossalai.core import global_context as gpc
from colossalai.context import ParallelMode
from abc import ABC, abstractmethod
from colossalai.legacy.context import ParallelMode
from colossalai.legacy.core import global_context as gpc
from .bert_tokenization import FullTokenizer as FullBertTokenizer
@@ -26,18 +25,13 @@ from .bert_tokenization import FullTokenizer as FullBertTokenizer
def build_tokenizer(vocab_file, tokenizer_type, vocab_extra_ids=0):
"""Initialize tokenizer."""
if not gpc.is_initialized(ParallelMode.GLOBAL) or gpc.get_global_rank() == 0:
print('> building {} tokenizer ...'.format(tokenizer_type),
flush=True)
print('> building {} tokenizer ...'.format(tokenizer_type), flush=True)
# Select and instantiate the tokenizer.
if tokenizer_type == 'BertWordPieceLowerCase':
tokenizer = _BertWordPieceTokenizer(vocab_file=vocab_file,
lower_case=True,
vocab_extra_ids=vocab_extra_ids)
tokenizer = _BertWordPieceTokenizer(vocab_file=vocab_file, lower_case=True, vocab_extra_ids=vocab_extra_ids)
elif tokenizer_type == 'BertWordPieceCase':
tokenizer = _BertWordPieceTokenizer(vocab_file=vocab_file,
lower_case=False,
vocab_extra_ids=vocab_extra_ids)
tokenizer = _BertWordPieceTokenizer(vocab_file=vocab_file, lower_case=False, vocab_extra_ids=vocab_extra_ids)
else:
raise NotImplementedError('{} tokenizer is not '
'implemented.'.format(tokenizer_type))
@@ -62,8 +56,8 @@ def _vocab_size_with_padding(orig_vocab_size, make_vocab_size_divisible_by=128):
after += 1
if not gpc.is_initialized(ParallelMode.GLOBAL) or gpc.get_global_rank() == 0:
print(' > padded vocab (size: {}) with {} dummy tokens '
'(new size: {})'.format(
orig_vocab_size, after - orig_vocab_size, after), flush=True)
'(new size: {})'.format(orig_vocab_size, after - orig_vocab_size, after),
flush=True)
return after
@@ -142,8 +136,7 @@ class _BertWordPieceTokenizer(AbstractTokenizer):
self._additional_special_tokens = []
# (dsachan) Add BOS and EOS tokens
SPECIAL_TOKENS = {'eos_token': '[EOS]',
'bos_token': '[BOS]'}
SPECIAL_TOKENS = {'eos_token': '[EOS]', 'bos_token': '[BOS]'}
self._bos_token = '[BOS]'
self.add_token(self._bos_token)
self._bos_token_id = self.vocab.get(self._bos_token)
@@ -155,8 +148,7 @@ class _BertWordPieceTokenizer(AbstractTokenizer):
# (dsachan) Add additional special tokens
# These can be used as sentinel tokens in T5 model inputs
additional_special_tokens = []
additional_special_tokens.extend(
["<extra_id_{}>".format(i) for i in range(vocab_extra_ids)])
additional_special_tokens.extend(["<extra_id_{}>".format(i) for i in range(vocab_extra_ids)])
self.add_additional_special_tokens(additional_special_tokens)
def add_token(self, token):