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Add Tiktoken support for TRTLLM (#10306)
* Add tiktoken tokenizer * Add special token * Remove unused import * Apply isort and black reformatting Signed-off-by: meatybobby <[email protected]> * Remove unused import * Fix after merge * Change qnemo loading * Apply isort and black reformatting Signed-off-by: meatybobby <[email protected]> * Clean up --------- Signed-off-by: meatybobby <[email protected]> Co-authored-by: meatybobby <[email protected]> Co-authored-by: Matvei Novikov <[email protected]>
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| # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. | ||
| # | ||
| # 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. | ||
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| import base64 | ||
| import json | ||
| from pathlib import Path | ||
| from typing import Dict, Optional | ||
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| import numpy as np | ||
| import tiktoken | ||
| import torch | ||
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| PATTERN_TIKTOKEN = "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+" | ||
| DEFAULT_TIKTOKEN_MAX_VOCAB = 2**17 # 131072 | ||
| SPECIAL_TOKENS = ["<unk>", "<s>", "</s>"] | ||
| SPECIAL_TOKEN_TEMPLATE = "<SPECIAL_{id}>" | ||
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| def reload_mergeable_ranks( | ||
| path: str, | ||
| max_vocab: Optional[int] = None, | ||
| ) -> Dict[bytes, int]: | ||
| """ | ||
| Reload the tokenizer JSON file and convert it to Tiktoken format. | ||
| """ | ||
| assert path.endswith(".json") | ||
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| # reload vocab | ||
| with open(path, "r", encoding='utf-8') as f: | ||
| vocab = json.load(f) | ||
| assert isinstance(vocab, list) | ||
| print(f"Vocab size: {len(vocab)}") | ||
| if max_vocab is not None: | ||
| vocab = vocab[:max_vocab] | ||
| print(f"Cutting vocab to first {len(vocab)} tokens.") | ||
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| # build ranks | ||
| ranks: Dict[bytes, int] = {} | ||
| for i, x in enumerate(vocab): | ||
| assert x.keys() == {"rank", "token_bytes", "token_str"} | ||
| assert x["rank"] == i | ||
| merge = base64.b64decode(x["token_bytes"]) | ||
| assert i >= 256 or merge == bytes([i]) | ||
| ranks[merge] = x["rank"] | ||
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| # sanity check | ||
| assert len(ranks) == len(vocab) | ||
| assert set(ranks.values()) == set(range(len(ranks))) | ||
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| return ranks | ||
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| class TiktokenTokenizer: | ||
| def __init__(self, vocab_file: str): | ||
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| self.num_special_tokens = 1000 | ||
| vocab_size = DEFAULT_TIKTOKEN_MAX_VOCAB | ||
| pattern = PATTERN_TIKTOKEN | ||
| special_tokens = SPECIAL_TOKENS.copy() | ||
| inner_vocab_size = vocab_size - self.num_special_tokens | ||
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| token2id = reload_mergeable_ranks(vocab_file, max_vocab=inner_vocab_size) | ||
| self.tokenizer = tiktoken.Encoding( | ||
| name=Path(vocab_file).parent.name, | ||
| pat_str=pattern, | ||
| mergeable_ranks=token2id, | ||
| special_tokens={}, # special tokens are handled manually | ||
| ) | ||
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| # BOS / EOS / Pad token IDs | ||
| self._bos_id = special_tokens.index("<s>") | ||
| self._eos_id = special_tokens.index("</s>") | ||
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| def encode(self, text): | ||
| tokens = self.tokenizer.encode(text) | ||
| tokens = [t + self.num_special_tokens for t in tokens] | ||
| return tokens | ||
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| def decode(self, tokens): | ||
| # Filter out special tokens and adjust the remaining tokens | ||
| adjusted_tokens = [ | ||
| t - self.num_special_tokens | ||
| for t in tokens | ||
| if t not in {self._bos_id, self._eos_id} and t >= self.num_special_tokens | ||
| ] | ||
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| # Decode only if there are tokens left after filtering | ||
| if adjusted_tokens: | ||
| return self.tokenizer.decode(adjusted_tokens) | ||
| else: | ||
| return "" # Return an empty string if all tokens were filtered out | ||
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| def batch_decode(self, ids): | ||
| if isinstance(ids, np.ndarray) or torch.is_tensor(ids): | ||
| ids = ids.tolist() | ||
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| if isinstance(ids[0], list): | ||
| ids = ids[0] | ||
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| return self.decode(ids) | ||
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| @property | ||
| def pad_id(self): | ||
| return self._eos_id | ||
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| @property | ||
| def bos_token_id(self): | ||
| return self._bos_id | ||
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| @property | ||
| def eos_token_id(self): | ||
| return self._eos_id |
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