KaleiNeely commited on
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config.json CHANGED
@@ -1,6 +1,6 @@
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  {
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  "architectures": [
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- "RwkvForCausalLM"
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  ],
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  "auto_map": {
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  "AutoConfig": "configuration_rwkv5.Rwkv5Config",
@@ -8,17 +8,17 @@
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  },
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  "attention_hidden_size": 2560,
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  "bos_token_id": 0,
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- "context_length": 4096,
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  "eos_token_id": 0,
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  "head_size": 64,
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  "hidden_size": 2560,
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  "intermediate_size": null,
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  "layer_norm_epsilon": 1e-05,
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  "model_type": "rwkv5",
 
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  "num_hidden_layers": 32,
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  "rescale_every": 6,
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  "tie_word_embeddings": false,
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  "transformers_version": "4.34.0",
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  "use_cache": true,
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  "vocab_size": 65536
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- }
 
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  {
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  "architectures": [
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+ "Rwkv5ForCausalLM"
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  ],
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  "auto_map": {
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  "AutoConfig": "configuration_rwkv5.Rwkv5Config",
 
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  },
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  "attention_hidden_size": 2560,
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  "bos_token_id": 0,
 
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  "eos_token_id": 0,
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  "head_size": 64,
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  "hidden_size": 2560,
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  "intermediate_size": null,
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  "layer_norm_epsilon": 1e-05,
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  "model_type": "rwkv5",
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+ "num_attention_heads": 64,
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  "num_hidden_layers": 32,
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  "rescale_every": 6,
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  "tie_word_embeddings": false,
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  "transformers_version": "4.34.0",
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  "use_cache": true,
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  "vocab_size": 65536
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+ }
special_tokens_map.json CHANGED
@@ -1 +1,6 @@
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- {}
 
 
 
 
 
 
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+ {
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+ "bos_token": "<s>",
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+ "eos_token": "<s>",
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+ "pad_token": "<s>",
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+ "unk_token": "<s>"
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+ }
tokenization_rwkv5.py ADDED
@@ -0,0 +1,230 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # coding=utf-8
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+ # Copyright 2024 The HuggingFace Inc. team.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """Tokenization classes for RWKV5."""
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+
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+ import os
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+ from typing import TYPE_CHECKING, List, Optional, Tuple
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+ import re
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+
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+ from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
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+ from transformers.utils import logging
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+
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+
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+ if TYPE_CHECKING:
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+ pass
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+
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+ logger = logging.get_logger(__name__)
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+
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+ VOCAB_FILES_NAMES = {
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+ "vocab_file": "vocab.txt",
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+ }
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+ PRETRAINED_VOCAB_FILES_MAP = {
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+ "vocab_file": {
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+ "ArthurZ/rwkv-5-utf": "https://huggingface.co/ArthurZ/rwkv-5-utf/blob/main/vocab.txt",
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+ },
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+ }
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+
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+
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+
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+ def whitespace_tokenize(text):
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+ """Runs basic whitespace cleaning and splitting on a piece of text.
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+ The separators are kept
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+ """
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+ text = text.strip()
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+ if not text:
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+ return []
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+ tokens = re.split(b"(?= )", text)
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+ return tokens
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+
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+
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+ class WordpieceTokenizer(object):
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+ """Runs WordPiece tokenization."""
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+
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+ def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
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+ self.vocab = vocab
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+ self.unk_token = unk_token
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+ self.max_input_chars_per_word = max_input_chars_per_word
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+
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+ def tokenize(self, text):
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+ """
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+ Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
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+ tokenization using the given vocabulary.
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+
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+ For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.
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+
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+ Args:
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+ text: A single token or whitespace separated tokens. This should have
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+ already been passed through *BasicTokenizer*.
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+
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+ Returns:
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+ A list of wordpiece tokens.
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+ """
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+
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+ output_tokens = []
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+ for token in whitespace_tokenize(text):
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+ chars = list(token)
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+ if len(chars) > self.max_input_chars_per_word:
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+ output_tokens.append(self.unk_token)
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+ continue
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+
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+ is_bad = False
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+ start = 0
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+ sub_tokens = []
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+ while start < len(chars):
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+ end = len(chars)
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+ cur_substr = None
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+ while start < end:
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+ substr = bytes(chars[start:end])
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+ if substr in self.vocab:
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+ cur_substr = substr
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+ break
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+ end -= 1
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+ if cur_substr is None:
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+ is_bad = True
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+ break
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+ sub_tokens.append(cur_substr.decode())
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+ start = end
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+
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+ if is_bad:
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+ output_tokens.append(self.unk_token)
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+ else:
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+ output_tokens.extend(sub_tokens)
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+ return output_tokens
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+
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+
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+ class Rwkv5Tokenizer(PreTrainedTokenizer):
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+ vocab_files_names = VOCAB_FILES_NAMES
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+ pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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+ max_model_input_sizes = {"ArthurZ/rwkv-5-utf": 2048}
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+
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+ model_input_names = ["input_ids", "attention_mask"]
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+
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+ def __init__(self, vocab_file, bos_token="<s>", eos_token="<s>", unk_token="<s>", pad_token="<s>",**kwargs):
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+ if not os.path.isfile(vocab_file):
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+ raise ValueError(
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+ f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
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+ " model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
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+ )
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+
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+ with open(vocab_file, "r") as reader:
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+ tokens = reader.readlines()
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+ vocab = {}
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+ for index, token in enumerate(tokens):
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+ token = eval(token.rstrip("\n"))
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+ vocab[token] = index
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+
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+ self.add_bos_token = True
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+ self.encoder = vocab
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+ self.decoder = {v: k for k, v in vocab.items()}
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+ self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.encoder, unk_token=str(unk_token))
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+ self._added_tokens_decoder = {0: AddedToken(str(bos_token))}
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+ super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, **kwargs)
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+
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+ @property
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+ def vocab_size(self):
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+ return len(self.encoder)
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+
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+ def get_vocab(self):
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+ vocab = {str(self.convert_ids_to_tokens(i)): i for i in range(self.vocab_size)}
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+ vocab.update(self.added_tokens_encoder)
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+ return vocab
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+
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+ def _tokenize(self, text, split_special_tokens=False):
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+ return self.wordpiece_tokenizer.tokenize(text.encode("utf-8"))
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+
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+ def _convert_token_to_id(self, token):
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+ """Converts a token (byte) to an id using the vocab."""
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+ if not isinstance(token, bytes):
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+ token = token.encode("utf-8", errors="replace")
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+ return self.encoder.get(token, self.unk_token_id)
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+
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+ def _convert_id_to_token(self, index):
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+ """Converts an index (integer) in a token (byte) using the vocab."""
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+ token = self.decoder.get(index, self.unk_token)
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+ if isinstance(token, (bytes)):
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+ token = token.decode("utf-8", errors="replace")
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+ return token
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+
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+ def convert_tokens_to_string(self, tokens):
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+ """Converts a sequence of tokens (bytes) in a single string. Additional tokens are encoded to bytes"""
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+ out_string = b"".join([k.encode(errors="replace") if isinstance(k, str) else k for k in tokens]).decode(
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+ "utf-8"
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+ )
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+ return out_string
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+
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+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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+ index = 0
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+ if os.path.isdir(save_directory):
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+ vocab_file = os.path.join(
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+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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+ )
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+ else:
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+ vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
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+ with open(vocab_file, "w") as writer:
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+ for token, token_index in sorted(self.encoder.items(), key=lambda kv: kv[1]):
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+ if index != token_index:
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+ logger.warning(
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+ f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
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+ " Please check that the vocabulary is not corrupted!"
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+ )
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+ index = token_index
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+ writer.write(str(token) + "\n")
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+ index += 1
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+ return (vocab_file,)
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+
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+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
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+ if self.add_bos_token:
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+ bos_token_ids = [self.bos_token_id]
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+ else:
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+ bos_token_ids = []
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+
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+ output = bos_token_ids + token_ids_0
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+
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+ if token_ids_1 is None:
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+ return output
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+
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+ return output + bos_token_ids + token_ids_1
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+
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+ def get_special_tokens_mask(
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+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
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+ ) -> List[int]:
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+ """
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+ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
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+ special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
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+
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+ Args:
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+ token_ids_0 (`List[int]`):
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+ List of IDs.
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+ token_ids_1 (`List[int]`, *optional*):
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+ Optional second list of IDs for sequence pairs.
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+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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+ Whether or not the token list is already formatted with special tokens for the model.
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+
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+ Returns:
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+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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+ """
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+ if already_has_special_tokens:
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+ return super().get_special_tokens_mask(
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+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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+ )
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+
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+ if not self.add_bos_token:
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+ return super().get_special_tokens_mask(
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+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=False
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+ )
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+
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+ if token_ids_1 is None:
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+ return [1] + ([0] * len(token_ids_0))
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+ return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
tokenizer_config.json CHANGED
@@ -1,12 +1,23 @@
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  {
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- "name_or_path": "rwkv-world",
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- "add_prefix_space": false,
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- "tokenizer_class": "RWKVWorldTokenizer",
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- "use_fast": false,
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  "auto_map": {
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  "AutoTokenizer": [
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- "tokenization_rwkv_world.RWKVWorldTokenizer",
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  null
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  ]
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  }
 
1
  {
 
 
 
 
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  "auto_map": {
3
  "AutoTokenizer": [
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+ "tokenization_rwkv5.Rwkv5Tokenizer",
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  null
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  ]
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+ },
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+ "added_tokens_decoder": {
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+ "0": {
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+ "content": "<s>",
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+ "lstrip": false,
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+ "normalized": true,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": false
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+ }
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+ },
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+ "bos_token": "<s>",
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+ "clean_up_tokenization_spaces": true,
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+ "eos_token": "<s>",
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+ "model_max_length": 1000000000000000019884624838656,
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+ "unk_token": "<s>"
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  }
vocab.txt ADDED
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