# Copyright (c) OpenMMLab. All rights reserved. import torch from datasets import load_dataset from mmengine.dataset import DefaultSampler from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook, LoggerHook, ParamSchedulerHook) from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR from peft import LoraConfig from torch.optim import AdamW from transformers import (AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig) from xtuner.dataset import process_hf_dataset from xtuner.dataset.collate_fns import default_collate_fn from xtuner.dataset.map_fns import template_map_fn_factory from xtuner.engine import DatasetInfoHook, EvaluateChatHook from xtuner.model import SupervisedFinetune from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE ####################################################################### # PART 1 Settings # ####################################################################### # Model pretrained_model_name_or_path = '/root/share/model_repos/internlm2-chat-7b' # Data data_path = 'merge.json' prompt_template = PROMPT_TEMPLATE.internlm2_chat max_length = 2048 pack_to_max_length = True # Scheduler & Optimizer batch_size = 8 # per_device accumulative_counts = 2 dataloader_num_workers = 0 max_epochs = 3 optim_type = AdamW lr = 2e-4 betas = (0.9, 0.999) weight_decay = 0 max_norm = 1 # grad clip warmup_ratio = 0.03 # Evaluate the generation performance during the training evaluation_freq = 500 SYSTEM = "现在你是一个心理专家，我有一些心理问题，请你用专业的知识帮我解决。" evaluation_inputs = [ '我最近总是感到很焦虑，尤其是在学业上。我有个特别崇拜的同学，他好像在各方面都比我优秀，我总觉得自己怎么努力也追不上他，这让我压力特别大。', '我知道应该理性看待，但就是忍不住会去比较。我甚至晚上会因为这个睡不着觉，总想着怎样才能像他那样出色。' ] ####################################################################### # 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 # ####################################################################### alpaca_en = dict( type=process_hf_dataset, dataset=dict(type=load_dataset, path='json', data_files=dict(train=data_path)), tokenizer=tokenizer, max_length=max_length, dataset_map_fn=None, 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=alpaca_en, 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=LinearLR, start_factor=1e-5, by_epoch=True, begin=0, end=warmup_ratio * max_epochs, convert_to_iter_based=True), dict( type=CosineAnnealingLR, eta_min=0.0, by_epoch=True, begin=warmup_ratio * max_epochs, 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)