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https://github.com/NVIDIA/TensorRT-LLM.git
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105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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 json
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from pathlib import Path
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from typing import Optional
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from transformers import AutoTokenizer, T5Tokenizer
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DEFAULT_HF_MODEL_DIRS = {
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'baichuan': 'baichuan-inc/Baichuan-13B-Chat',
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'bloom': 'bigscience/bloom-560m',
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'chatglm_6b': 'THUDM/chatglm-6b',
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'chatglm2_6b': 'THUDM/chatglm2-6b',
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'chatglm2_6b_32k': 'THUDM/chatglm2-6b-32k',
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'chatglm3_6b': 'THUDM/chatglm3-6b',
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'chatglm3_6b_base': 'THUDM/chatglm3-6b-base',
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'chatglm3_6b_32k': 'THUDM/chatglm3-6b-32k',
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'falcon': 'tiiuae/falcon-rw-1b',
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'glm_10b': 'THUDM/glm-10b',
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'gpt': 'gpt2-medium',
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'gptj': 'EleutherAI/gpt-j-6b',
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'gptneox': 'EleutherAI/gpt-neox-20b',
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'internlm': 'internlm/internlm-chat-7b',
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'llama': 'meta-llama/Llama-2-7b-hf',
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'mpt': 'mosaicml/mpt-7b',
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'opt': 'facebook/opt-350m',
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'qwen': 'Qwen/Qwen-7B',
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}
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DEFAULT_PROMPT_TEMPLATES = {
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'internlm':
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"<|User|>:{input_text}<eoh>\n<|Bot|>:",
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'qwen':
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"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{input_text}<|im_end|>\n<|im_start|>assistant\n",
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}
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def read_model_name_from_config(config_path: Path):
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with open(config_path, 'r') as f:
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config = json.load(f)
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return config['builder_config']['name']
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def throttle_generator(generator, stream_interval):
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for i, out in enumerate(generator):
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if not i % stream_interval:
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yield out
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if i % stream_interval:
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yield out
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def load_tokenizer(tokenizer_dir: Optional[str] = None,
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vocab_file: Optional[str] = None,
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model_name: str = 'gpt'):
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if vocab_file is None:
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# Should set both padding_side and truncation_side to be 'left'
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir,
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legacy=False,
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padding_side='left',
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truncation_side='left',
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trust_remote_code=True)
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else:
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# For gpt-next, directly load from tokenizer.model
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assert model_name == 'gpt'
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tokenizer = T5Tokenizer(vocab_file=vocab_file,
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padding_side='left',
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truncation_side='left')
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if model_name == 'qwen':
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with open(Path(tokenizer_dir) / "generation_config.json") as f:
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gen_config = json.load(f)
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chat_format = gen_config['chat_format']
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if chat_format == 'raw':
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pad_id = gen_config['pad_token_id']
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end_id = gen_config['eos_token_id']
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elif chat_format == 'chatml':
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pad_id = tokenizer.im_end_id
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end_id = tokenizer.im_end_id
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else:
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raise Exception(f"unknown chat format: {chat_format}")
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elif model_name == 'glm_10b':
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pad_id = tokenizer.pad_token_id
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end_id = tokenizer.eop_token_id
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else:
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if tokenizer.pad_token_id is None:
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tokenizer.pad_token_id = tokenizer.eos_token_id
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pad_id = tokenizer.pad_token_id
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end_id = tokenizer.eos_token_id
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return tokenizer, pad_id, end_id
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