# Copyright (c) OpenMMLab. All rights reserved. import torch from bitsandbytes.optim import PagedAdamW32bit from mmengine.dataset import DefaultSampler from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook, LoggerHook, ParamSchedulerHook) from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR from modelscope.msdatasets import MsDataset from peft import LoraConfig from transformers import (AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig) from xtuner.dataset import process_ms_dataset from xtuner.dataset.collate_fns import default_collate_fn from xtuner.dataset.map_fns import (msagent_react_map_fn, template_map_fn_factory) from xtuner.engine import DatasetInfoHook, EvaluateChatHook from xtuner.model import SupervisedFinetune from xtuner.utils import PROMPT_TEMPLATE ####################################################################### # PART 1 Settings # ####################################################################### # Model pretrained_model_name_or_path = 'internlm/internlm-20b' # Data data_path = 'damo/MSAgent-Bench' prompt_template = PROMPT_TEMPLATE.default max_length = 2048 pack_to_max_length = False # Scheduler & Optimizer batch_size = 8 # per_device accumulative_counts = 1 dataloader_num_workers = 2 max_epochs = 3 optim_type = PagedAdamW32bit lr = 2e-4 betas = (0.9, 0.999) weight_decay = 0 max_norm = 1 # grad clip # Evaluate the generation performance during the training evaluation_freq = 500 SYSTEM = ( '你是一个可以调用外部工具的助手，可以使用的工具包括：\n' "{{\'GoogleSearch\': \'一个可以从谷歌搜索结果的API。\\n" '当你需要对于一个特定问题找到简短明了的回答时，可以使用它。\\n' "输入应该是一个搜索查询。\\n\\n\'," "\'PythonInterpreter\': \"用来执行Python代码。代码必须是一个函数，\\n" "函数名必须得是 \'solution\'，代码对应你的思考过程。代码实例格式如下：\\n" '```python\\n# import 依赖包\\nimport xxx\\ndef solution():' '\\n # 初始化一些变量\\n variable_names_with_real_meaning = xxx' '\\n # 步骤一\\n mid_variable = func(variable_names_with_real_meaning)' '\\n # 步骤 x\\n mid_variable = func(mid_variable)\\n # 最后结果' '\\n final_answer = func(mid_variable)\\n return final_answer' "\\n```\\n\"}}\n" '如果使用工具请遵循以下格式回复：\n```\n' 'Thought:思考你当前步骤需要解决什么问题，是否需要使用工具\n' "Action:工具名称，你的工具必须从 [[\'GoogleSearch\', \'PythonInterpreter\']] 选择" '\nAction Input:工具输入参数\n```\n工具返回按照以下格式回复：\n' '```\nResponse:调用工具后的结果\n```' '\n如果你已经知道了答案，或者你不需要工具，请遵循以下格式回复\n```' '\nThought:给出最终答案的思考过程\nFinal Answer:最终答案\n```\n开始!\n') evaluation_inputs = ['上海明天天气怎么样？'] ####################################################################### # PART 2 Model & Tokenizer # ####################################################################### tokenizer = dict( type=AutoTokenizer.from_pretrained, pretrained_model_name_or_path=pretrained_model_name_or_path, trust_remote_code=True, padding_side='right') model = dict( type=SupervisedFinetune, llm=dict( type=AutoModelForCausalLM.from_pretrained, pretrained_model_name_or_path=pretrained_model_name_or_path, trust_remote_code=True, torch_dtype=torch.float16, quantization_config=dict( type=BitsAndBytesConfig, load_in_4bit=True, load_in_8bit=False, llm_int8_threshold=6.0, llm_int8_has_fp16_weight=False, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4')), lora=dict( type=LoraConfig, r=64, lora_alpha=16, lora_dropout=0.1, bias='none', task_type='CAUSAL_LM')) ####################################################################### # PART 3 Dataset & Dataloader # ####################################################################### train_dataset = dict( type=process_ms_dataset, dataset=dict(type=MsDataset.load, dataset_name=data_path), tokenizer=tokenizer, max_length=max_length, dataset_map_fn=msagent_react_map_fn, template_map_fn=dict( type=template_map_fn_factory, template=prompt_template), remove_unused_columns=True, shuffle_before_pack=True, pack_to_max_length=pack_to_max_length) train_dataloader = dict( batch_size=batch_size, num_workers=dataloader_num_workers, dataset=train_dataset, sampler=dict(type=DefaultSampler, shuffle=True), collate_fn=dict(type=default_collate_fn)) ####################################################################### # PART 4 Scheduler & Optimizer # ####################################################################### # optimizer optim_wrapper = dict( type=AmpOptimWrapper, optimizer=dict( type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay), clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False), accumulative_counts=accumulative_counts, loss_scale='dynamic', dtype='float16') # learning policy # More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501 param_scheduler = dict( type=CosineAnnealingLR, eta_min=lr * 0.1, by_epoch=True, T_max=max_epochs, convert_to_iter_based=True) # train, val, test setting train_cfg = dict(by_epoch=True, max_epochs=max_epochs, val_interval=1) ####################################################################### # PART 5 Runtime # ####################################################################### # Log the dialogue periodically during the training process, optional custom_hooks = [ dict(type=DatasetInfoHook, tokenizer=tokenizer), dict( type=EvaluateChatHook, tokenizer=tokenizer, every_n_iters=evaluation_freq, evaluation_inputs=evaluation_inputs, system=SYSTEM, prompt_template=prompt_template) ] # configure default hooks default_hooks = dict( # record the time of every iteration. timer=dict(type=IterTimerHook), # print log every 100 iterations. logger=dict(type=LoggerHook, interval=10), # enable the parameter scheduler. param_scheduler=dict(type=ParamSchedulerHook), # save checkpoint per epoch. checkpoint=dict(type=CheckpointHook, interval=1), # set sampler seed in distributed evrionment. sampler_seed=dict(type=DistSamplerSeedHook), ) # configure environment env_cfg = dict( # whether to enable cudnn benchmark cudnn_benchmark=False, # set multi process parameters mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), # set distributed parameters dist_cfg=dict(backend='nccl'), ) # set visualizer visualizer = None # set log level log_level = 'INFO' # load from which checkpoint load_from = None # whether to resume training from the loaded checkpoint resume = False # Defaults to use random seed and disable `deterministic` randomness = dict(seed=None, deterministic=False)