[RoFormer] Fix some issues (#12397)

* add RoFormerTokenizerFast into AutoTokenizer

* fix typo in roformer docs

* make onnx export happy

* update RoFormerConfig embedding_size

* use jieba not rjieba

* fix 12244 and make test_alignement passed

* update ARCHIVE_MAP

* make style & quality & fixup

* update

* make style & quality & fixup

* make style quality fixup

* update

* suggestion from LysandreJik
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* make style

* use rjieba
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

docs/source/model_doc/roformer.rst

@@ -56,7 +56,7 @@ RoFormerTokenizer
         create_token_type_ids_from_sequences, save_vocabulary
 
 
-RobertaTokenizerFast
+RoFormerTokenizerFast
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RoFormerTokenizerFast

src/transformers/file_utils.py

@@ -315,6 +315,10 @@ def is_datasets_available():
     return _datasets_available
 
 
+def is_rjieba_available():
+    return importlib.util.find_spec("rjieba") is not None
+
+
 def is_psutil_available():
     return importlib.util.find_spec("psutil") is not None
 

src/transformers/models/auto/tokenization_auto.py

@@ -198,6 +198,7 @@ if is_tokenizers_available():
     from ..reformer.tokenization_reformer_fast import ReformerTokenizerFast
     from ..retribert.tokenization_retribert_fast import RetriBertTokenizerFast
     from ..roberta.tokenization_roberta_fast import RobertaTokenizerFast
+    from ..roformer.tokenization_roformer_fast import RoFormerTokenizerFast
     from ..squeezebert.tokenization_squeezebert_fast import SqueezeBertTokenizerFast
     from ..t5.tokenization_t5_fast import T5TokenizerFast
     from ..xlm_roberta.tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
@@ -232,6 +233,7 @@ else:
     ReformerTokenizerFast = None
     RetriBertTokenizerFast = None
     RobertaTokenizerFast = None
+    RoFormerTokenizerFast = None
     SqueezeBertTokenizerFast = None
     T5TokenizerFast = None
     XLMRobertaTokenizerFast = None
@@ -245,7 +247,7 @@ logger = logging.get_logger(__name__)
 TOKENIZER_MAPPING = OrderedDict(
     [
         (RetriBertConfig, (RetriBertTokenizer, RetriBertTokenizerFast)),
-        (RoFormerConfig, (RoFormerTokenizer, None)),
+        (RoFormerConfig, (RoFormerTokenizer, RoFormerTokenizerFast)),
         (T5Config, (T5Tokenizer, T5TokenizerFast)),
         (MT5Config, (MT5Tokenizer, MT5TokenizerFast)),
         (MobileBertConfig, (MobileBertTokenizer, MobileBertTokenizerFast)),

src/transformers/models/roformer/configuration_roformer.py

@@ -22,7 +22,11 @@ logger = logging.get_logger(__name__)
 
 ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
-    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json"
+    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
+    "junnyu/roformer_chinese_char_small": "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json",
+    "junnyu/roformer_chinese_char_base": "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json",
+    "junnyu/roformer_small_discriminator": "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json",
+    "junnyu/roformer_small_generator": "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json",
     # See all RoFormer models at https://huggingface.co/models?filter=roformer
 }
 
@@ -43,8 +47,9 @@ class RoFormerConfig(PretrainedConfig):
             Vocabulary size of the RoFormer model. Defines the number of different tokens that can be represented by
             the :obj:`inputs_ids` passed when calling :class:`~transformers.RoFormerModel` or
             :class:`~transformers.TFRoFormerModel`.
-        embedding_size (:obj:`int`, `optional`, defaults to 768):
-            Dimensionality of the encoder layers and the pooler layer.
+        embedding_size (:obj:`int`, `optional`, defaults to None):
+            Dimensionality of the encoder layers and the pooler layer. Defaults to the :obj:`hidden_size` if not
+            provided.
         hidden_size (:obj:`int`, `optional`, defaults to 768):
             Dimension of the encoder layers and the pooler layer.
         num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
@@ -96,7 +101,7 @@ class RoFormerConfig(PretrainedConfig):
     def __init__(
         self,
         vocab_size=50000,
-        embedding_size=768,
+        embedding_size=None,
         hidden_size=768,
         num_hidden_layers=12,
         num_attention_heads=12,
@@ -117,7 +122,7 @@ class RoFormerConfig(PretrainedConfig):
         super().__init__(pad_token_id=pad_token_id, **kwargs)
 
         self.vocab_size = vocab_size
-        self.embedding_size = embedding_size
+        self.embedding_size = hidden_size if embedding_size is None else embedding_size
         self.hidden_size = hidden_size
         self.num_hidden_layers = num_hidden_layers
         self.num_attention_heads = num_attention_heads

src/transformers/models/roformer/modeling_roformer.py

@@ -60,7 +60,11 @@ _TOKENIZER_FOR_DOC = "RoFormerTokenizer"
 
 ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
     "junnyu/roformer_chinese_small",
-    "junnyu/roformer_chinese_base"
+    "junnyu/roformer_chinese_base",
+    "junnyu/roformer_chinese_char_small",
+    "junnyu/roformer_chinese_char_base",
+    "junnyu/roformer_small_discriminator",
+    "junnyu/roformer_small_generator"
     # See all RoFormer models at https://huggingface.co/models?filter=roformer
 ]
 
@@ -327,9 +331,9 @@ class RoFormerSelfAttention(nn.Module):
         # cos [batch_size, num_heads, sequence_length, embed_size_per_head//2]
         sin, cos = sinusoidal_pos.chunk(2, dim=-1)
         # sin [θ0,θ1,θ2......θd/2-1] -> sin_pos [θ0,θ0,θ1,θ1,θ2,θ2......θd/2-1,θd/2-1]
-        sin_pos = torch.repeat_interleave(sin, 2, dim=-1)
+        sin_pos = torch.stack([sin, sin], dim=-1).reshape_as(sinusoidal_pos)
         # cos [θ0,θ1,θ2......θd/2-1] -> cos_pos [θ0,θ0,θ1,θ1,θ2,θ2......θd/2-1,θd/2-1]
-        cos_pos = torch.repeat_interleave(cos, 2, dim=-1)
+        cos_pos = torch.stack([cos, cos], dim=-1).reshape_as(sinusoidal_pos)
         # rotate_half_query_layer [-q1,q0,-q3,q2......,-qd-1,qd-2]
         rotate_half_query_layer = torch.stack([-query_layer[..., 1::2], query_layer[..., ::2]], dim=-1).reshape_as(
             query_layer

src/transformers/models/roformer/modeling_tf_roformer.py

@@ -65,7 +65,11 @@ _TOKENIZER_FOR_DOC = "RoFormerTokenizer"
 
 TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
     "junnyu/roformer_chinese_small",
-    "junnyu/roformer_chinese_base"
+    "junnyu/roformer_chinese_base",
+    "junnyu/roformer_chinese_char_small",
+    "junnyu/roformer_chinese_char_base",
+    "junnyu/roformer_small_discriminator",
+    "junnyu/roformer_small_generator"
     # See all RoFormer models at https://huggingface.co/models?filter=roformer
 ]
 

src/transformers/models/roformer/tokenization_roformer.py

@@ -31,15 +31,30 @@ PRETRAINED_VOCAB_FILES_MAP = {
     "vocab_file": {
         "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
         "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
+        "junnyu/roformer_chinese_char_small": "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt",
+        "junnyu/roformer_chinese_char_base": "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt",
+        "junnyu/roformer_small_discriminator": "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt",
+        "junnyu/roformer_small_generator": "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt",
     }
 }
 
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"junnyu/roformer_chinese_small": 1536, "junnyu/roformer_chinese_base": 1536}
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "junnyu/roformer_chinese_small": 1536,
+    "junnyu/roformer_chinese_base": 1536,
+    "junnyu/roformer_chinese_char_small": 512,
+    "junnyu/roformer_chinese_char_base": 512,
+    "junnyu/roformer_small_discriminator": 128,
+    "junnyu/roformer_small_generator": 128,
+}
 
 
 PRETRAINED_INIT_CONFIGURATION = {
     "junnyu/roformer_chinese_small": {"do_lower_case": True},
     "junnyu/roformer_chinese_base": {"do_lower_case": True},
+    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
+    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
+    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
+    "junnyu/roformer_small_generator": {"do_lower_case": True},
 }
 
 
@@ -166,13 +181,8 @@ class RoFormerTokenizer(PreTrainedTokenizer):
 
     def __setstate__(self, d):
         self.__dict__ = d
-        try:
-            import rjieba
-        except ImportError:
-            raise ImportError(
-                "You need to install rjieba to use RoFormerTokenizer."
-                "See https://pypi.org/project/rjieba/ for installation."
-            )
+        import rjieba
+
         self.jieba = rjieba
 
     def get_vocab(self):

src/transformers/models/roformer/tokenization_roformer_fast.py

@@ -33,15 +33,30 @@ PRETRAINED_VOCAB_FILES_MAP = {
     "vocab_file": {
         "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
         "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
+        "junnyu/roformer_chinese_char_small": "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt",
+        "junnyu/roformer_chinese_char_base": "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt",
+        "junnyu/roformer_small_discriminator": "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt",
+        "junnyu/roformer_small_generator": "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt",
     }
 }
 
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"junnyu/roformer_chinese_small": 1536, "junnyu/roformer_chinese_base": 1536}
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "junnyu/roformer_chinese_small": 1536,
+    "junnyu/roformer_chinese_base": 1536,
+    "junnyu/roformer_chinese_char_small": 512,
+    "junnyu/roformer_chinese_char_base": 512,
+    "junnyu/roformer_small_discriminator": 128,
+    "junnyu/roformer_small_generator": 128,
+}
 
 
 PRETRAINED_INIT_CONFIGURATION = {
     "junnyu/roformer_chinese_small": {"do_lower_case": True},
     "junnyu/roformer_chinese_base": {"do_lower_case": True},
+    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
+    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
+    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
+    "junnyu/roformer_small_generator": {"do_lower_case": True},
 }
 
 

src/transformers/models/roformer/tokenization_utils.py

@@ -41,26 +41,26 @@ class JiebaPreTokenizer:
         splits = []
 
         # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
-        # for token, start, end in self.jieba.tokenize(str(normalized_string), hmm=False):
-        #     if token in self.vocab:
-        #         splits.append(normalized_string.slice((start, end)))
-        #     else:
-        #         token_list = self.normalizers.normalize_str(token).split()
-        #         for token in token_list:
-        #             if token:
-        #                 end = start + len(token)
-        #                 splits.append(normalized_string.slice((start, end)))
-        #                 start = end
-
-        # this code test_alignement_methods can't pass but fast (300ms)
-        for token in self.jieba.cut(str(normalized_string), False):
+        for token, start, end in self.jieba.tokenize(str(normalized_string), hmm=False):
             if token in self.vocab:
-                splits.append(NormalizedString(token))
+                splits.append(normalized_string[start:end])
             else:
                 token_list = self.normalizers.normalize_str(token).split()
                 for token in token_list:
                     if token:
-                        splits.append(NormalizedString(token))
+                        end = start + len(token)
+                        splits.append(normalized_string[start:end])
+                        start = end
+
+        # this code test_alignement_methods can't pass but fast (300ms)
+        # for token in self.jieba.cut(str(normalized_string), False):
+        #     if token in self.vocab:
+        #         splits.append(NormalizedString(token))
+        #     else:
+        #         token_list = self.normalizers.normalize_str(token).split()
+        #         for token in token_list:
+        #             if token:
+        #                 splits.append(NormalizedString(token))
 
         return splits
 

src/transformers/testing_utils.py

@@ -35,6 +35,7 @@ from .file_utils import (
     is_flax_available,
     is_onnx_available,
     is_pandas_available,
+    is_rjieba_available,
     is_scatter_available,
     is_sentencepiece_available,
     is_soundfile_availble,
@@ -223,6 +224,16 @@ def require_git_lfs(test_case):
         return test_case
 
 
+def require_rjieba(test_case):
+    """
+    Decorator marking a test that requires rjieba. These tests are skipped when rjieba isn't installed.
+    """
+    if not is_rjieba_available():
+        return unittest.skip("test requires rjieba")(test_case)
+    else:
+        return test_case
+
+
 def require_onnx(test_case):
     if not is_onnx_available():
         return unittest.skip("test requires ONNX")(test_case)

tests/test_tokenization_roformer.py

@@ -13,29 +13,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import importlib
 import unittest
 
 from transformers import RoFormerTokenizer, RoFormerTokenizerFast
-from transformers.testing_utils import require_tokenizers
+from transformers.testing_utils import require_rjieba, require_tokenizers
 
 from .test_tokenization_common import TokenizerTesterMixin
 
 
-def is_rjieba_available():
-    return importlib.util.find_spec("rjieba") is not None
-
-
-def require_rjieba(test_case):
-    """
-    Decorator marking a test that requires Jieba. These tests are skipped when Jieba isn't installed.
-    """
-    if not is_rjieba_available():
-        return unittest.skip("test requires rjieba")(test_case)
-    else:
-        return test_case
-
-
 @require_rjieba
 @require_tokenizers
 class RoFormerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
@@ -79,6 +64,10 @@ class RoFormerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         exp_tokens = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), exp_tokens)
 
-    # due to custom pre_tokenize , char_to_token may be error
-    def test_alignement_methods(self):
+    # can't train new_tokenizer via Tokenizers lib
+    def test_training_new_tokenizer(self):
+        pass
+
+    # can't train new_tokenizer via Tokenizers lib
+    def test_training_new_tokenizer_with_special_tokens_change(self):
         pass