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[Feature] Support Baichuan2 models (#102)
* add baichuan2_7b_base * fix lm_head bug for Baichuan2 * Update README.md * Update README_zh-CN.md * Update README.md * remove infrequent configs * Update README.md * Update README_zh-CN.md * add baichuan2 chat template * Update README_zh-CN.md
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xtuner/configs/baichuan/baichuan2_7b_base/baichuan2_7b_base_qlora_alpaca_e3.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,180 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| import torch | ||
| from bitsandbytes.optim import PagedAdamW32bit | ||
| 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 | ||
| from peft import LoraConfig | ||
| 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 alpaca_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 = 'baichuan-inc/Baichuan2-7B-Base' | ||
|
|
||
| # Data | ||
| alpaca_en_path = 'tatsu-lab/alpaca' | ||
| prompt_template = PROMPT_TEMPLATE.alpaca | ||
| max_length = 2048 | ||
| pack_to_max_length = True | ||
|
|
||
| # Scheduler & Optimizer | ||
| batch_size = 1 # per_device | ||
| accumulative_counts = 16 | ||
| dataloader_num_workers = 0 | ||
| 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 | ||
| evaluation_inputs = [ | ||
| '请给我介绍五个上海的景点', 'Please tell me five scenic spots in Shanghai' | ||
| ] | ||
|
|
||
| ####################################################################### | ||
| # 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=alpaca_en_path), | ||
| tokenizer=tokenizer, | ||
| max_length=max_length, | ||
| dataset_map_fn=alpaca_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=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=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, | ||
| instruction=prompt_template.INSTRUCTION_START) | ||
| ] | ||
|
|
||
| # 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) |
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