Update T5 tokenizer (adding additional tokens to tokenizer config) (#…

…10972)

* initial commit

* restore t5_pretraining

* Apply isort and black reformatting

Signed-off-by: huvunvidia <huvunvidia@users.noreply.github.com>

---------

Signed-off-by: huvunvidia <huvunvidia@users.noreply.github.com>
Co-authored-by: Huy Vu2 <huvu@login-eos02.eos.clusters.nvidia.com>
Co-authored-by: huvunvidia <huvunvidia@users.noreply.github.com>

nemo/collections/common/tokenizers/huggingface/auto_tokenizer.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from collections import OrderedDict
-from typing import Optional
+from typing import List, Optional
 
 from transformers import AutoTokenizer as AUTOTOKENIZER
 
@@ -43,6 +43,7 @@ def __init__(
         sep_token: Optional[str] = None,
         cls_token: Optional[str] = None,
         unk_token: Optional[str] = None,
+        additional_special_tokens: Optional[List] = [],
         use_fast: Optional[bool] = False,
         trust_remote_code: Optional[bool] = False,
     ):
@@ -60,6 +61,7 @@ def __init__(
             sep_token: token used for separating sequences
             cls_token: class token. Usually equal to bos_token
             unk_token: token to use for unknown tokens
+            additional_special_tokens: list of other tokens beside standard special tokens (bos, eos, pad, etc.). For example, sentinel tokens for T5 (<extra_id_0>, <extra_id_1>, etc.)
             use_fast: whether to use fast HuggingFace tokenizer
         """
         try:
@@ -124,10 +126,17 @@ def __init__(
         elif self.tokenizer.cls_token is None and self.tokenizer.bos_token:
             special_tokens_dict["cls_token"] = self.tokenizer.bos_token
 
+        # add additional special tokens (not standard special tokens such as bos, eod, sep)
+        if additional_special_tokens is not None:
+            special_tokens_dict["additional_special_tokens"] = additional_special_tokens
+
         new_tokens_in_vocab = []
         for token in [mask_token, bos_token, eos_token, pad_token, sep_token, cls_token, unk_token]:
             if token is not None and token not in self.tokenizer.get_vocab():
                 new_tokens_in_vocab.append(token)
+        for token in additional_special_tokens:
+            if token is not None and token not in self.tokenizer.get_vocab():
+                new_tokens_in_vocab.append(token)
 
         if len(new_tokens_in_vocab) > 0:
             """

nemo/collections/llm/t5/data/fine_tuning.py

@@ -61,8 +61,6 @@ def __init__(
         from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
 
         self.tokenizer = tokenizer or get_nmt_tokenizer("megatron", "BertWordPieceCase")
-        additional_tokens = {'additional_special_tokens': [f'<extra_id_{i}>' for i in range(100)]}
-        self.tokenizer.add_special_tokens(additional_tokens)
 
         self.memmap_workers = memmap_workers
         self.num_workers = num_workers

nemo/collections/llm/t5/data/pre_training.py

@@ -130,10 +130,6 @@ def __init__(
         # add additional tokens for T5 tokenizer
         from nemo.collections.nlp.modules.common.tokenizer_utils import get_nmt_tokenizer
 
-        self.tokenizer = tokenizer or get_nmt_tokenizer("megatron", "BertWordPieceCase")
-        additional_tokens = {'additional_special_tokens': [f'<extra_id_{i}>' for i in range(100)]}
-        self.tokenizer.add_special_tokens(additional_tokens)
-
         self.data_sampler = MegatronDataSampler(
             seq_len=self.seq_length,
             micro_batch_size=micro_batch_size,

nemo/collections/nlp/modules/common/tokenizer_utils.py

@@ -69,7 +69,8 @@ def get_tokenizer(
             To see the list of all HuggingFace pretrained models, use:
             nemo_nlp.modules.common.get_huggingface_pretrained_lm_models_list()
         tokenizer_model: tokenizer model file of sentencepiece
-        special_tokens: dict of special tokens
+        special_tokens: dict of special tokens.
+            For additional special tokens besides standard special tokens (bos, eos, pad, etc.), such as sentinel tokens for T5 (<extra_id_0>, <extra_id_1>, etc.), use key 'additional_special_tokens'
         vocab_file: path to vocab file
         use_fast: (only for HuggingFace AutoTokenizer) set to True to use fast HuggingFace tokenizer
         bpe_dropout: (experimental) BPE dropout tries to corrupt the standard segmentation
@@ -224,7 +225,11 @@ def get_nmt_tokenizer(
             f'Getting Megatron tokenizer for pretrained model name: {model_name}, custom vocab file: {vocab_file}, and merges file: {merges_file}'
         )
         return get_tokenizer(
-            tokenizer_name=model_name, vocab_file=vocab_file, merges_file=merges_file, chat_template=chat_template
+            tokenizer_name=model_name,
+            vocab_file=vocab_file,
+            merges_file=merges_file,
+            special_tokens=special_tokens_dict,
+            chat_template=chat_template,
         )
     elif library == 'tabular':
         from nemo.collections.common.tokenizers.tabular_tokenizer import TabularTokenizer

tests/collections/llm/megatron_t5_finetuning.py

@@ -35,9 +35,12 @@ def get_args():
 
     args = get_args()
 
+    special_tokens = {}
+    special_tokens['additional_special_tokens'] = [f'<extra_id_{i}>' for i in range(100)]
     tokenizer = get_nmt_tokenizer(
         "megatron",
         "BertWordPieceCase",
+        special_tokens=special_tokens,
     )
 
     data = SquadDataModule(

tests/collections/llm/megatron_t5_pretraining.py

@@ -50,10 +50,13 @@ def get_args():
 
     args = get_args()
 
+    special_tokens = {}
+    special_tokens['additional_special_tokens'] = [f'<extra_id_{i}>' for i in range(100)]
     tokenizer = get_nmt_tokenizer(
         "megatron",
         "BertWordPieceCase",
         vocab_file=args.vocab_path,
+        special_tokens=special_tokens,
     )
     data = PreTrainingDataModule(
         paths=args.data_path,