1612948062
Signed-off-by: HonestDeng <2958906959@qq.com> Signed-off-by: iwzbi <iwzbi@zju.edu.cn> Signed-off-by: iwzbi <wzbi@zju.edu.cn> Signed-off-by: Divyansh Singhvi <divyanshsinghvi@gmail.com> Signed-off-by: gcanlin <canlinguosdu@gmail.com> Signed-off-by: yinpeiqi <yinpeiqi809@gmail.com> Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com> Signed-off-by: 汪志鹏 <wangzhipeng628@gmail.com> Co-authored-by: catcat <108673086+iwzbi@users.noreply.github.com> Co-authored-by: iwzbi <iwzbi@zju.edu.cn> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: dsinghvi <divyanshsinghvi@gmail.com> Co-authored-by: Canlin Guo <canlinguosdu@gmail.com> Co-authored-by: Peiqi Yin <60515999+yinpeiqi@users.noreply.github.com> Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com>
402 lines
14 KiB
Python
402 lines
14 KiB
Python
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: Apache-2.0
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#
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# This file has been modified by ByteDance Ltd. on 2025-09-30.
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#
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# Original file was released under Apache-2.0, with the full license text
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# available at https://www.apache.org/licenses/LICENSE-2.0
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#
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# This modified file is released under the same license.
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#
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# --- Upstream header preserved below ---
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#
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# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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import base64
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import os
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import unicodedata
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from collections.abc import Collection
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import tiktoken
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from loguru import logger
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from transformers import AddedToken, PreTrainedTokenizer
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VOCAB_FILES_NAMES = {
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"vocab_file": "mammothu.tiktoken",
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"special_tokens_file": "mammothu_vision_tokens.txt",
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}
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PAT_STR = (
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r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?"""
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r"""[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
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)
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ENDOFTEXT = "<|endoftext|>"
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IMSTART = "<|im_start|>"
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IMEND = "<|im_end|>"
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# as the default behavior is changed to allow special tokens in
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# regular texts, the surface forms of special tokens need to be
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# as different as possible to minimize the impact
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QWEN_SPECIAL_TOKENS = (
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>",
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"<tool_call>",
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"</tool_call>",
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"<|fim_prefix|>",
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"<|fim_middle|>",
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"<|fim_suffix|>",
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"<|fim_pad|>",
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"<|repo_name|>",
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"<|file_sep|>",
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)
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# align to qwen2.5 tokenizer length (151846)
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EXTRAS = [f"<|extra_{i}|>" for i in range(181)] # 205 - 19[len(QWEN_SPECIAL_TOKENS)] - 5
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# align to qwen2.5 embedding size (152064)
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EXTRAS += [f"<|extra_margin_{i}|>" for i in range(152064 - 151846)]
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# append new token in gen embedding range
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EXTRAS += ["<|endofline|>", "<|endoffile|>", "<|gen_placeholder|>", "<|useless token|>", "<|beginoftext|>"]
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EXTRAS = tuple(EXTRAS)
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# changed to use actual index to avoid misconfiguration with vocabulary expansion
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SPECIAL_START_ID = 151643
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def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> dict[bytes, int]:
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with open(tiktoken_bpe_file, "rb") as f:
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contents = f.read()
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return {
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base64.b64decode(token): int(rank) for token, rank in (line.split() for line in contents.splitlines() if line)
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}
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class MammothUTokenizer(PreTrainedTokenizer):
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"""MammothU tokenizer."""
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vocab_files_names = VOCAB_FILES_NAMES
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def __init__(
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self,
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vocab_file: str,
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special_tokens_file: str,
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errors: str = "replace",
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bos_token: str = "<|beginoftext|>",
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eos_token: str = "<|endoftext|>",
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pad_token: str = "<|endoftext|>",
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img_token: str = "<|image token|>",
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boi_token: str = "<|image start|>",
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eoi_token: str = "<|image end|>",
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eol_token: str = "<|endofline|>",
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eof_token: str = "<|endoffile|>",
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**kwargs,
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) -> None:
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super().__init__(**kwargs)
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# how to handle errors in decoding UTF-8 byte sequences
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# use ignore if you are in streaming inference
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self.errors = errors
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self.mergeable_ranks = _load_tiktoken_bpe(vocab_file)
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with open(special_tokens_file) as f:
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vision_tokens = [t.strip() for t in f.readlines() if len(t.strip()) > 0]
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SPECIAL_TOKENS = tuple(
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enumerate(
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(
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(
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ENDOFTEXT,
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IMSTART,
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IMEND,
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)
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+ QWEN_SPECIAL_TOKENS
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+ EXTRAS
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+ tuple(vision_tokens)
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),
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start=SPECIAL_START_ID,
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)
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)
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self.special_tokens = {token: index for index, token in SPECIAL_TOKENS}
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self.special_tokens_set = set(t for _, t in SPECIAL_TOKENS)
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enc = tiktoken.Encoding(
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"mammothu",
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pat_str=PAT_STR,
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mergeable_ranks=self.mergeable_ranks,
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special_tokens=self.special_tokens,
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)
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assert len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab, (
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f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
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)
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self.decoder = {v: k for k, v in self.mergeable_ranks.items()}
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self.decoder.update({v: k for k, v in self.special_tokens.items()})
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self.tokenizer = enc
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self.eod_id = self.tokenizer.eot_token
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self.bos_token = bos_token
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self.eos_token = eos_token
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self.pad_token = pad_token
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self.img_token = img_token
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self.boi_token = boi_token
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self.eoi_token = eoi_token
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self.eol_token = eol_token
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self.eof_token = eof_token
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self.image_content_token = "<|image_pad|>" # come from Qwen2.5-VL
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self.gen_image_token = "<|gen_image_pad|>"
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self.gen_image_placeholder_token = "<|gen_placeholder|>"
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self.visual_tokens = ["<|image_pad|>", "<|video_pad|>", "<|vision_start|>", "<|vision_end|>"]
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self.visual_tokens_ids = [self.get_vocab()[token] for token in self.visual_tokens]
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self.vision_range = (self.get_vocab()[self.boi_token], self.tokenizer.n_vocab - 1)
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logger.info(f"MammothUTokeniser Vision range: {self.vision_range}")
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def __getstate__(self):
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# for pickle lovers
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state = self.__dict__.copy()
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del state["tokenizer"]
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return state
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def __setstate__(self, state):
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# tokenizer is not python native; don't pass it; rebuild it
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self.__dict__.update(state)
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enc = tiktoken.Encoding(
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"mammothu",
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pat_str=PAT_STR,
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mergeable_ranks=self.mergeable_ranks,
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special_tokens=self.special_tokens,
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)
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self.tokenizer = enc
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def __len__(self) -> int:
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return self.tokenizer.n_vocab
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def get_vocab(self) -> dict[bytes | str, int]:
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vocab = self.mergeable_ranks.copy()
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vocab.update(self.special_tokens)
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return vocab
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@property
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def gen_placeholder_id(self):
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return self.get_vocab()[self.gen_image_placeholder_token]
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def convert_tokens_to_ids(self, tokens: bytes | str | list[bytes | str]) -> list[int]:
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if isinstance(tokens, (str, bytes)):
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if tokens in self.special_tokens:
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return self.special_tokens[tokens]
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else:
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return self.mergeable_ranks.get(tokens)
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ids = []
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for token in tokens:
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if token in self.special_tokens:
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ids.append(self.special_tokens[token])
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else:
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ids.append(self.mergeable_ranks.get(token))
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return ids
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def _add_tokens(
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self,
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new_tokens: list[str] | list[AddedToken],
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special_tokens: bool = False,
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) -> int:
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if not special_tokens and new_tokens:
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raise ValueError("Adding regular tokens is not supported")
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added_tokens = 0
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for token in new_tokens:
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surface_form = token.content if isinstance(token, AddedToken) else token
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if surface_form in self.special_tokens_set:
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# Token already exists in our special tokens set
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added_tokens += 1
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else:
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logger.warning(f"Token {surface_form} is not in the predefined special tokens set and cannot be added")
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return added_tokens
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def add_special_tokens(self, special_tokens_dict: dict[str, str | AddedToken]) -> int:
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"""
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Add special tokens to the tokenizer and update the special tokens mapping.
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Only adds tokens that are already in the special_tokens_set.
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Args:
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special_tokens_dict: dictionary of special tokens to add.
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The key is the token type and the value is the token to add.
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Returns:
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Number of tokens added to the vocabulary.
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"""
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added_tokens = 0
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for token_type, token in special_tokens_dict.items():
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if token_type == "additional_special_tokens" and isinstance(token, list):
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added_tokens += self._add_tokens(token, special_tokens=True)
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else:
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token_value = token.content if isinstance(token, AddedToken) else token
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if token_value in self.special_tokens_set:
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setattr(self, token_type, token_value)
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added_tokens += 1
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else:
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logger.warning(
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f"Token {token_value} is not in the predefined special tokens set and cannot be added"
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)
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return added_tokens
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def save_vocabulary(self, save_directory: str, **kwargs) -> tuple[str]:
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"""
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Save only the vocabulary of the tokenizer (vocabulary).
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Returns:
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`tuple(str)`: Paths to the files saved.
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"""
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regular_file_path = os.path.join(save_directory, self.vocab_files_names["vocab_file"])
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with open(regular_file_path, "w", encoding="utf8") as w:
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for k, v in self.mergeable_ranks.items():
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line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
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w.write(line)
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excluded_special_tokens = set(
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(
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ENDOFTEXT,
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IMSTART,
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IMEND,
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)
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+ EXTRAS
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)
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special_file_path = os.path.join(save_directory, self.vocab_files_names["special_tokens_file"])
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with open(special_file_path, "w", encoding="utf8") as w:
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for k in self.special_tokens:
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if k not in excluded_special_tokens:
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print(k, file=w)
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return (regular_file_path, special_file_path)
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def tokenize(
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self,
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text: str,
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allowed_special: set | str = "all",
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disallowed_special: Collection | str = (),
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**kwargs,
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) -> list[bytes | str]:
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"""
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Converts a string in a sequence of tokens.
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Args:
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text (`str`):
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The sequence to be encoded.
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allowed_special (`Literal["all"]` or `set`):
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The surface forms of the tokens to be encoded as special tokens in regular texts.
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Default to "all".
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disallowed_special (`Literal["all"]` or `Collection`):
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The surface forms of the tokens that should not be in regular texts and trigger errors.
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Default to an empty tuple.
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kwargs (additional keyword arguments, *optional*):
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Will be passed to the underlying model specific encode method.
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Returns:
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`list[bytes|str]`: The list of tokens.
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"""
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tokens = []
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text = unicodedata.normalize("NFC", text)
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# this implementation takes a detour: text -> token id -> token surface forms
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for t in self.tokenizer.encode(text, allowed_special=allowed_special, disallowed_special=disallowed_special):
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tokens.append(self.decoder[t])
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return tokens
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def convert_tokens_to_string(self, tokens: list[bytes | str]) -> str:
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"""
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Converts a sequence of tokens in a single string.
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"""
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text = ""
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temp = b""
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for t in tokens:
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if isinstance(t, str):
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if temp:
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text += temp.decode("utf-8", errors=self.errors)
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temp = b""
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text += t
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elif isinstance(t, bytes):
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temp += t
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else:
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raise TypeError("token should only be of type types or str")
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if temp:
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text += temp.decode("utf-8", errors=self.errors)
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return text
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@property
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def vocab_size(self) -> int:
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return self.tokenizer.n_vocab
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def _convert_id_to_token(self, index: int) -> bytes | str:
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"""Converts an id to a token, special tokens included"""
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if index in self.decoder:
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return self.decoder[index]
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raise ValueError("unknown ids")
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def _convert_token_to_id(self, token: bytes | str) -> int:
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"""Converts a token to an id using the vocab, special tokens included"""
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if token in self.special_tokens:
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return self.special_tokens[token]
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if token in self.mergeable_ranks:
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return self.mergeable_ranks[token]
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raise ValueError("unknown token")
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def _decode(
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self,
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token_ids: int | list[int],
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skip_special_tokens: bool = False,
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errors: str | None = None,
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**kwargs,
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) -> str:
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if isinstance(token_ids, int):
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token_ids = [token_ids]
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if skip_special_tokens:
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token_ids = [i for i in token_ids if i < self.eod_id]
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return self.tokenizer.decode(token_ids, errors=errors or self.errors)
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def bytes_to_str(self, byte_tokens: dict) -> str:
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"""Convert byte tokens to string representation.
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Args:
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byte_tokens: A dictionary where keys are byte objects and values are integers,
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or a single byte object.
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Returns:
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If input is a dictionary, returns a new dictionary with byte keys converted to strings.
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If input is a single byte object, returns the string representation.
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"""
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if isinstance(byte_tokens, dict):
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return {k.decode("utf-8", errors=self.errors): v for k, v in byte_tokens.items() if isinstance(k, bytes)}
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if isinstance(byte_tokens, bytes):
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return byte_tokens.decode("utf-8", errors=self.errors)
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return byte_tokens
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