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https://github.com/NVIDIA/TensorRT-LLM.git
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728cc0044b
* Update TensorRT-LLM --------- Co-authored-by: Morgan Funtowicz <funtowiczmo@gmail.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
125 lines
4.8 KiB
Python
125 lines
4.8 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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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import tensorrt_llm
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# TODO(enweiz): Update for refactored models
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DEFAULT_HF_MODEL_DIRS = {
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'BaichuanForCausalLM': 'baichuan-inc/Baichuan-13B-Chat',
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'BloomForCausalLM': 'bigscience/bloom-560m',
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'ChatGLMForCausalLM': 'THUDM/chatglm3-6b',
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'FalconForCausalLM': 'tiiuae/falcon-rw-1b',
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'gpt': 'gpt2-medium',
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'GPTJForCausalLM': 'EleutherAI/gpt-j-6b',
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'GPTNeoXForCausalLM': 'EleutherAI/gpt-neox-20b',
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'InternLMForCausalLM': 'internlm/internlm-chat-7b',
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'LlamaForCausalLM': 'meta-llama/Llama-2-7b-hf',
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'MPTForCausalLM': 'mosaicml/mpt-7b',
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'PhiForCausalLM': 'microsoft/phi-2',
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'OPTForCausalLM': '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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'InternLMForCausalLM':
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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(engine_dir: str):
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engine_version = tensorrt_llm.runtime.engine.get_engine_version(engine_dir)
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with open(Path(engine_dir) / "config.json", 'r') as f:
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config = json.load(f)
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if engine_version is None:
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return config['builder_config']['name'], None
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model_arch = config['pretrained_config']['architecture']
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model_version = None
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if model_arch == 'ChatGLMForCausalLM':
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model_version = config['pretrained_config']['chatglm_version']
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return model_arch, model_version
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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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model_version: Optional[str] = None,
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tokenizer_type: Optional[str] = None):
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if vocab_file is None:
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use_fast = True
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if tokenizer_type is not None and tokenizer_type == "llama":
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use_fast = False
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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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tokenizer_type=tokenizer_type,
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use_fast=use_fast)
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elif model_name == 'GemmaForCausalLM':
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from transformers import GemmaTokenizer
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# Initialize tokenizer from vocab file.
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tokenizer = GemmaTokenizer(vocab_file=vocab_file,
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padding_side='left',
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truncation_side='left',
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legacy=False)
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else:
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# For gpt-next, directly load from tokenizer.model
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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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legacy=False)
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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' or chat_format == 'chatml':
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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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else:
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raise Exception(f"unknown chat format: {chat_format}")
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elif model_name == 'ChatGLMForCausalLM' and model_version == 'glm':
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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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