Upload 10 files

config.json

@@ -10,6 +10,7 @@
   "bos_token_id": 0,
   "eos_token_id": 0,
   "head_size": 64,
+  "head_size_divisor": 8,
   "hidden_size": 2048,
   "intermediate_size": null,
   "layer_norm_epsilon": 1e-05,

configuration_rwkv6.py

@@ -53,11 +53,9 @@ class Rwkv6Config(PretrainedConfig):
         layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
             The epsilon to use in the layer normalization layers.
         bos_token_id (`int`, *optional*, defaults to 0):
-            The id of the beginning of sentence token in the vocabulary. Defaults to 0 as RWKV6 uses the same tokenizer
-            as GPTNeoX.
+            The id of the beginning of sentence token in the vocabulary. Defaults to 0.
         eos_token_id (`int`, *optional*, defaults to 0):
-            The id of the end of sentence token in the vocabulary. Defaults to 0 as RWKV6 uses the same tokenizer as
-            GPTNeoX.
+            The id of the end of sentence token in the vocabulary. Defaults to 0.
         rescale_every (`int`, *optional*, defaults to 6):
             At inference, the hidden states (and weights of the correponding output layers) are divided by 2 every
             `rescale_every` layer. If set to 0 or a negative number, no rescale is done.
@@ -90,7 +88,6 @@ class Rwkv6Config(PretrainedConfig):
         hidden_size=768,
         num_hidden_layers=24,
         attention_hidden_size=None,
-        num_attention_heads=64,
         head_size=64,
         head_size_divisor=8,
         intermediate_size=None,
@@ -106,7 +103,6 @@ class Rwkv6Config(PretrainedConfig):
         self.hidden_size = hidden_size
         self.num_hidden_layers = num_hidden_layers
         self.attention_hidden_size = attention_hidden_size if attention_hidden_size is not None else hidden_size
-        self.num_attention_heads = num_attention_heads
         self.head_size = head_size
         self.head_size_divisor = head_size_divisor
         self.intermediate_size = None

tokenization_rwkv5.py

@@ -15,8 +15,238 @@
 """Tokenization classes for RWKV5."""
 
 import os
+from typing import TYPE_CHECKING, List, Optional, Tuple
 import re
+
+from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
+from transformers.utils import logging
+
+
+if TYPE_CHECKING:
+    pass
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {
+    "vocab_file": "vocab.txt",
+}
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "ArthurZ/rwkv-5-utf": "https://huggingface.co/ArthurZ/rwkv-5-utf/blob/main/vocab.txt",
+    },
+}
+
+
+
+def whitespace_tokenize(text):
+    """Runs basic whitespace cleaning and splitting on a piece of text.
+    The separators are kept
+    """
+    text = text.strip()
+    if not text:
+        return []
+    tokens = re.split(b"(?= )", text)
+    return tokens
+
+
+class WordpieceTokenizer(object):
+    """Runs WordPiece tokenization."""
+
+    def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
+        self.vocab = vocab
+        self.unk_token = unk_token
+        self.max_input_chars_per_word = max_input_chars_per_word
+
+    def tokenize(self, text):
+        """
+        Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
+        tokenization using the given vocabulary.
+
+        For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
+
+        Args:
+            text: A single token or whitespace separated tokens. This should have
+                already been passed through *BasicTokenizer*.
+
+        Returns:
+            A list of wordpiece tokens.
+        """
+
+        output_tokens = []
+        for token in whitespace_tokenize(text):
+            chars = list(token)
+            if len(chars) > self.max_input_chars_per_word:
+                output_tokens.append(self.unk_token)
+                continue
+
+            is_bad = False
+            start = 0
+            sub_tokens = []
+            while start < len(chars):
+                end = len(chars)
+                cur_substr = None
+                while start < end:
+                    substr = bytes(chars[start:end])
+                    if substr in self.vocab:
+                        cur_substr = substr
+                        break
+                    end -= 1
+                if cur_substr is None:
+                    is_bad = True
+                    break
+                sub_tokens.append(cur_substr.decode())
+                start = end
+
+            if is_bad:
+                output_tokens.append(self.unk_token)
+            else:
+                output_tokens.extend(sub_tokens)
+        return output_tokens
+
+
+class Rwkv5Tokenizer(PreTrainedTokenizer):
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = {"ArthurZ/rwkv-5-utf": 2048}
+
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(self, vocab_file, bos_token="<s>", eos_token="<s>", unk_token="<s>", pad_token="<s>",**kwargs):
+        if not os.path.isfile(vocab_file):
+            raise ValueError(
+                f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
+                " model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+            )
+
+        with open(vocab_file, "r") as reader:
+            tokens = reader.readlines()
+        vocab = {}
+        for index, token in enumerate(tokens):
+            token = eval(token.rstrip("\n"))
+            vocab[token] = index
+
+        self.add_bos_token = True
+        self.encoder = vocab
+        self.decoder = {v: k for k, v in vocab.items()}
+        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.encoder, unk_token=str(unk_token))
+        self._added_tokens_decoder = {0: AddedToken(str(bos_token))}
+        super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, **kwargs)
+
+    @property
+    def vocab_size(self):
+        return len(self.encoder)
+
+    def get_vocab(self):
+        vocab = {str(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, split_special_tokens=False):
+        return self.wordpiece_tokenizer.tokenize(text.encode("utf-8"))
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (byte) to an id using the vocab."""
+        if not isinstance(token, bytes):
+            token = token.encode("utf-8", errors="replace")
+        return self.encoder.get(token, self.unk_token_id)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (byte) using the vocab."""
+        token = self.decoder.get(index, self.unk_token)
+        if isinstance(token, (bytes)):
+            token = token.decode("utf-8", errors="replace")
+        return token
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (bytes) in a single string. Additional tokens are encoded to bytes"""
+        out_string = b"".join([k.encode(errors="replace") if isinstance(k, str) else k for k in tokens]).decode(
+            "utf-8"
+        )
+        return out_string
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        index = 0
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+            )
+        else:
+            vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
+        with open(vocab_file, "w") as writer:
+            for token, token_index in sorted(self.encoder.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning(
+                        f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
+                        " Please check that the vocabulary is not corrupted!"
+                    )
+                    index = token_index
+                writer.write(str(token) + "\n")
+                index += 1
+        return (vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if self.add_bos_token:
+            bos_token_ids = [self.bos_token_id]
+        else:
+            bos_token_ids = []
+
+        output = bos_token_ids + token_ids_0
+
+        if token_ids_1 is None:
+            return output
+
+        return output + bos_token_ids + token_ids_1
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if not self.add_bos_token:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=False
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0))
+        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+"""Tokenization classes for RWKV5."""
+
+import os
 from typing import TYPE_CHECKING, List, Optional, Tuple
+import re
 
 from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
 from transformers.utils import logging
@@ -37,6 +267,7 @@ PRETRAINED_VOCAB_FILES_MAP = {
 }
 
 
+
 def whitespace_tokenize(text):
     """Runs basic whitespace cleaning and splitting on a piece of text.
     The separators are kept
@@ -51,9 +282,10 @@ def whitespace_tokenize(text):
 class WordpieceTokenizer(object):
     """Runs WordPiece tokenization."""
 
-    def __init__(self, vocab, unk_token):
+    def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
         self.vocab = vocab
         self.unk_token = unk_token
+        self.max_input_chars_per_word = max_input_chars_per_word
 
     def tokenize(self, text):
         """
@@ -73,6 +305,10 @@ class WordpieceTokenizer(object):
         output_tokens = []
         for token in whitespace_tokenize(text):
             chars = list(token)
+            if len(chars) > self.max_input_chars_per_word:
+                output_tokens.append(self.unk_token)
+                continue
+
             is_bad = False
             start = 0
             sub_tokens = []
@@ -88,12 +324,9 @@ class WordpieceTokenizer(object):
                 if cur_substr is None:
                     is_bad = True
                     break
-                try:
-                    cur_substr = cur_substr.decode()
-                except UnicodeDecodeError:
-                    cur_substr = str(cur_substr)
-                sub_tokens.append(cur_substr)
+                sub_tokens.append(cur_substr.decode())
                 start = end
+
             if is_bad:
                 output_tokens.append(self.unk_token)
             else:
@@ -108,7 +341,7 @@ class Rwkv5Tokenizer(PreTrainedTokenizer):
 
     model_input_names = ["input_ids", "attention_mask"]
 
-    def __init__(self, vocab_file, bos_token="<s>", eos_token="<s>", unk_token="<s>", **kwargs):
+    def __init__(self, vocab_file, bos_token="<s>", eos_token="<s>", unk_token="<s>", pad_token="<s>",**kwargs):
         if not os.path.isfile(vocab_file):
             raise ValueError(
                 f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
@@ -127,7 +360,7 @@ class Rwkv5Tokenizer(PreTrainedTokenizer):
         self.decoder = {v: k for k, v in vocab.items()}
         self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.encoder, unk_token=str(unk_token))
         self._added_tokens_decoder = {0: AddedToken(str(bos_token))}
-        super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
+        super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, **kwargs)
 
     @property
     def vocab_size(self):
@@ -143,9 +376,7 @@ class Rwkv5Tokenizer(PreTrainedTokenizer):
 
     def _convert_token_to_id(self, token):
         """Converts a token (byte) to an id using the vocab."""
-        if token.startswith("b'\\"):
-            token = eval(token)
-        elif not isinstance(token, bytes):
+        if not isinstance(token, bytes):
             token = token.encode("utf-8", errors="replace")
         return self.encoder.get(token, self.unk_token_id)
 

tokenizer_config.json

@@ -1,23 +1,12 @@
 {
+  "name_or_path": "rwkv-5-tokenizer",
+  "add_prefix_space": false,
+  "tokenizer_class": "Rwkv5Tokenizer",
+  "use_fast": false,
   "auto_map": {
     "AutoTokenizer": [
       "tokenization_rwkv5.Rwkv5Tokenizer",
       null
       ]
-  },
-  "added_tokens_decoder": {
-    "0": {
-      "content": "<s>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false,
-      "special": false
-    }
-  },
-  "bos_token": "<s>",
-  "clean_up_tokenization_spaces": true,
-  "eos_token": "<s>",
-  "model_max_length": 1000000000000000019884624838656,
-  "unk_token": "<s>"
+  }
 }