mirror of
https://github.com/hpcaitech/ColossalAI.git
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* [inference] support only TP (#4998) * support only tp * enable tp * add support for bloom (#5008) * [refactor] refactor gptq and smoothquant llama (#5012) * refactor gptq and smoothquant llama * fix import error * fix linear import torch-int * fix smoothquant llama import error * fix import accelerate error * fix bug * fix import smooth cuda * fix smoothcuda * [Inference Refactor] Merge chatglm2 with pp and tp (#5023) merge chatglm with pp and tp * [Refactor] remove useless inference code (#5022) * remove useless code * fix quant model * fix test import bug * mv original inference legacy * fix chatglm2 * [Refactor] refactor policy search and quant type controlling in inference (#5035) * [Refactor] refactor policy search and quant type controling in inference * [inference] update readme (#5051) * update readme * update readme * fix architecture * fix table * fix table * [inference] udpate example (#5053) * udpate example * fix run.sh * fix rebase bug * fix some errors * update readme * add some features * update interface * update readme * update benchmark * add requirements-infer --------- Co-authored-by: Bin Jia <45593998+FoolPlayer@users.noreply.github.com> Co-authored-by: Zhongkai Zhao <kanezz620@gmail.com>
41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
"""
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Motivated by VllM (https://github.com/vllm-project/vllm), This module is trying to resolve the tokenizer issue.
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license: MIT, see LICENSE for more details.
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"""
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from transformers import AutoTokenizer
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_FAST_LLAMA_TOKENIZER = "hf-internal-testing/llama-tokenizer"
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def get_tokenizer(
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tokenizer=None,
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tokenizer_name: str = "",
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trust_remote_code: bool = False,
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use_fast: bool = True,
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):
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if tokenizer is not None:
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tokenizer = tokenizer
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else:
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if "llama" in tokenizer_name.lower() and use_fast == True:
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print(
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"For some LLaMA-based models, initializing the fast tokenizer may "
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"take a long time. To eliminate the initialization time, consider "
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f"using '{_FAST_LLAMA_TOKENIZER}' instead of the original "
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"tokenizer. This is done automatically in Colossalai."
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)
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tokenizer_name = _FAST_LLAMA_TOKENIZER
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try:
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tokenizer = AutoTokenizer.from_pretrained(
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tokenizer_name, use_fast=use_fast, trust_remote_code=trust_remote_code
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)
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except TypeError:
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use_fast = False
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tokenizer = AutoTokenizer.from_pretrained(
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tokenizer_name, use_fast=use_fast, trust_remote_code=trust_remote_code
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)
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return tokenizer
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