upload llama3 qlora config files
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xtuner_config/Lamma3_qlora_e10_M_1e4_32_64.py
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214
xtuner_config/Lamma3_qlora_e10_M_1e4_32_64.py
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# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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from datasets import load_dataset
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from mmengine.dataset import DefaultSampler
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from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
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LoggerHook, ParamSchedulerHook)
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from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
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from peft import LoraConfig
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from torch.optim import AdamW
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from transformers import (AutoModelForCausalLM, AutoTokenizer,
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BitsAndBytesConfig)
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from xtuner.dataset import process_hf_dataset
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from xtuner.dataset.collate_fns import default_collate_fn
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from xtuner.dataset.map_fns import template_map_fn_factory
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from xtuner.engine import DatasetInfoHook, EvaluateChatHook
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from xtuner.model import SupervisedFinetune
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from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE
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#######################################################################
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# PART 1 Settings #
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#######################################################################
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# Model
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# pretrained_model_name_or_path = '/root/share/model_repos/internlm2-chat-7b'
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pretrained_model_name_or_path = '/root/models/LLM-Research/Meta-Llama-3-8B-Instruct'
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# Data
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# data_path = 'merge.json'
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data_path ='/root/StableCascade/emollm2/EmoLLM/datasets/processed/combined_data.json'
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# https://github.com/InternLM/xtuner/blob/main/xtuner/utils/templates.py#L24C25-L24C25
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# prompt_template = PROMPT_TEMPLATE.internlm2_chat # there is No internlm2_base
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# prompt_template = PROMPT_TEMPLATE.llama3_chatM # there is No internlm2_base
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prompt_template = PROMPT_TEMPLATE.llama3_chat # there is No internlm2_base
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max_length = 2048
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pack_to_max_length = True
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# Scheduler & Optimizer
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# batch_size = 8 # per_device
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# accumulative_counts = 2
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batch_size = 4 # per_device
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accumulative_counts = 1
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dataloader_num_workers = 0
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max_epochs = 3
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optim_type = AdamW
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lr = 1e-4
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betas = (0.9, 0.999)
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weight_decay = 0
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max_norm = 1 # grad clip
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warmup_ratio = 0.03
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# Evaluate the generation performance during the training
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evaluation_freq = 500
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# 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。"
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# SYSTEM = "你是心理健康助手EmoLLM，由EmoLLM团队打造。你旨在通过专业心理咨询，协助来访者完成心理诊断。请充分利用专业心理学知识与咨询技术，一步步帮助来访者解决心理问题。"
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SYSTEM = "你由EmoLLM团队打造的中文领域心理健康助手, 是一个研究过无数具有心理健康问题的病人与心理健康医生对话的心理专家, 在心理方面拥有广博的知识储备和丰富的研究咨询经验，接下来你将只使用中文来回答和咨询问题。"
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evaluation_inputs = [
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'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。',
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'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。',
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'我今天心情不好，感觉不开心，很烦。'
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# ['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。',
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# '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。']
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]
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#######################################################################
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# PART 2 Model & Tokenizer #
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#######################################################################
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tokenizer = dict(
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type=AutoTokenizer.from_pretrained,
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pretrained_model_name_or_path=pretrained_model_name_or_path,
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trust_remote_code=True,
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padding_side='right')
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model = dict(
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type=SupervisedFinetune,
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llm=dict(
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type=AutoModelForCausalLM.from_pretrained,
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pretrained_model_name_or_path=pretrained_model_name_or_path,
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trust_remote_code=True,
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torch_dtype=torch.float16,
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quantization_config=dict(
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type=BitsAndBytesConfig,
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load_in_4bit=True,
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load_in_8bit=False,
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llm_int8_threshold=6.0,
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llm_int8_has_fp16_weight=False,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type='nf4')),
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lora=dict(
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type=LoraConfig,
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# r=64,
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# lora_alpha=16,
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r=32,
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lora_alpha=64,
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# r=16,
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# lora_alpha=32,
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lora_dropout=0.1,
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bias='none',
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task_type='CAUSAL_LM'))
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#######################################################################
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# PART 3 Dataset & Dataloader #
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#######################################################################
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alpaca_en = dict(
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type=process_hf_dataset,
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dataset=dict(type=load_dataset, path='json', data_files=dict(train=data_path)),
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tokenizer=tokenizer,
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max_length=max_length,
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dataset_map_fn=None,
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template_map_fn=dict(
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type=template_map_fn_factory, template=prompt_template),
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remove_unused_columns=True,
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shuffle_before_pack=True,
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pack_to_max_length=pack_to_max_length)
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train_dataloader = dict(
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batch_size=batch_size,
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num_workers=dataloader_num_workers,
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dataset=alpaca_en,
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sampler=dict(type=DefaultSampler, shuffle=True),
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collate_fn=dict(type=default_collate_fn))
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#######################################################################
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# PART 4 Scheduler & Optimizer #
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#######################################################################
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# optimizer
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optim_wrapper = dict(
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type=AmpOptimWrapper,
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optimizer=dict(
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type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
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clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
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accumulative_counts=accumulative_counts,
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loss_scale='dynamic',
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dtype='float16')
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# learning policy
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# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501
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param_scheduler = [
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dict(
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type=LinearLR,
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start_factor=1e-5,
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by_epoch=True,
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begin=0,
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end=warmup_ratio * max_epochs,
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convert_to_iter_based=True),
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dict(
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type=CosineAnnealingLR,
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eta_min=0.0,
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by_epoch=True,
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begin=warmup_ratio * max_epochs,
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T_max=max_epochs,
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convert_to_iter_based=True)
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]
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# train, val, test setting
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train_cfg = dict(by_epoch=True, max_epochs=max_epochs, val_interval=1)
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#######################################################################
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# PART 5 Runtime #
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#######################################################################
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# Log the dialogue periodically during the training process, optional
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custom_hooks = [
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dict(type=DatasetInfoHook, tokenizer=tokenizer),
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dict(
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type=EvaluateChatHook,
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tokenizer=tokenizer,
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every_n_iters=evaluation_freq,
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evaluation_inputs=evaluation_inputs,
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system=SYSTEM,
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prompt_template=prompt_template)
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]
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# configure default hooks
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default_hooks = dict(
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# record the time of every iteration.
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timer=dict(type=IterTimerHook),
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# print log every 100 iterations.
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logger=dict(type=LoggerHook, interval=10),
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# enable the parameter scheduler.
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param_scheduler=dict(type=ParamSchedulerHook),
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# save checkpoint per epoch.
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checkpoint=dict(type=CheckpointHook, interval=1),
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# set sampler seed in distributed evrionment.
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sampler_seed=dict(type=DistSamplerSeedHook),
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)
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# configure environment
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env_cfg = dict(
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# whether to enable cudnn benchmark
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cudnn_benchmark=False,
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# set multi process parameters
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mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
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# set distributed parameters
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dist_cfg=dict(backend='nccl'),
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)
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# set visualizer
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visualizer = None
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# set log level
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log_level = 'INFO'
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# load from which checkpoint
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load_from = None
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# whether to resume training from the loaded checkpoint
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resume = False
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# Defaults to use random seed and disable `deterministic`
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randomness = dict(seed=None, deterministic=False)
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226
xtuner_config/llama3_8b_instruct_qlora_alpaca_e3_M.py
Normal file
226
xtuner_config/llama3_8b_instruct_qlora_alpaca_e3_M.py
Normal file
@ -0,0 +1,226 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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from datasets import load_dataset
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from mmengine.dataset import DefaultSampler
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from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
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LoggerHook, ParamSchedulerHook)
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from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
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from peft import LoraConfig
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from torch.optim import AdamW
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from transformers import (AutoModelForCausalLM, AutoTokenizer,
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BitsAndBytesConfig)
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from xtuner.dataset import process_hf_dataset
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from xtuner.dataset.collate_fns import default_collate_fn
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from xtuner.dataset.map_fns import alpaca_map_fn, template_map_fn_factory
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from xtuner.engine.hooks import (DatasetInfoHook, EvaluateChatHook,
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VarlenAttnArgsToMessageHubHook)
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from xtuner.engine.runner import TrainLoop
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from xtuner.model import SupervisedFinetune
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from xtuner.parallel.sequence import SequenceParallelSampler
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from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE
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#######################################################################
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# PART 1 Settings #
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#######################################################################
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# Model
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pretrained_model_name_or_path = '/root/models/LLM-Research/Meta-Llama-3-8B-Instruct'
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use_varlen_attn = False # new
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# Data
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data_path = '/root/StableCascade/emollm2/EmoLLM/datasets/processed/combined_data.json'
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prompt_template = PROMPT_TEMPLATE.llama3_chat
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max_length = 2048
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pack_to_max_length = True
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# parallel
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sequence_parallel_size = 1
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# Scheduler & Optimizer
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batch_size = 16 # per_device
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accumulative_counts = 1
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accumulative_counts *= sequence_parallel_size
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dataloader_num_workers = 0
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max_epochs = 3
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optim_type = AdamW
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lr = 1e-4
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betas = (0.9, 0.999)
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weight_decay = 0
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max_norm = 1 # grad clip
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warmup_ratio = 0.03
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# Save
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save_steps = 500
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save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited)
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# Evaluate the generation performance during the training
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evaluation_freq = 500
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# 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。"
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# SYSTEM = "你是心理健康助手EmoLLM，由EmoLLM团队打造。你旨在通过专业心理咨询，协助来访者完成心理诊断。请充分利用专业心理学知识与咨询技术，一步步帮助来访者解决心理问题。"
|
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SYSTEM = "你由EmoLLM团队打造的中文领域心理健康助手, 是一个研究过无数具有心理健康问题的病人与心理健康医生对话的心理专家, 在心理方面拥有广博的知识储备和丰富的研究咨询经验，接下来你将只使用中文来回答和咨询问题。"
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evaluation_inputs = [
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'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。',
|
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'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。',
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'我今天心情不好，感觉不开心，很烦。'
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]
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#######################################################################
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# PART 2 Model & Tokenizer #
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#######################################################################
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tokenizer = dict(
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type=AutoTokenizer.from_pretrained,
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pretrained_model_name_or_path=pretrained_model_name_or_path,
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trust_remote_code=True,
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padding_side='right')
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model = dict(
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type=SupervisedFinetune,
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use_varlen_attn=use_varlen_attn,
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llm=dict(
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type=AutoModelForCausalLM.from_pretrained,
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pretrained_model_name_or_path=pretrained_model_name_or_path,
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trust_remote_code=True,
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torch_dtype=torch.float16,
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quantization_config=dict(
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type=BitsAndBytesConfig,
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load_in_4bit=True,
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load_in_8bit=False,
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llm_int8_threshold=6.0,
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llm_int8_has_fp16_weight=False,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type='nf4')),
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lora=dict(
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type=LoraConfig,
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r=32,
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lora_alpha=64,
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lora_dropout=0.1,
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bias='none',
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task_type='CAUSAL_LM'))
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#######################################################################
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# PART 3 Dataset & Dataloader #
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#######################################################################
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alpaca_en = dict(
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type=process_hf_dataset,
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dataset=dict(type=load_dataset, path='json', data_files=dict(train=data_path)),
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tokenizer=tokenizer,
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max_length=max_length,
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dataset_map_fn=None,
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template_map_fn=dict(
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type=template_map_fn_factory, template=prompt_template),
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remove_unused_columns=True,
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shuffle_before_pack=True,
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pack_to_max_length=pack_to_max_length,
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use_varlen_attn=use_varlen_attn)
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sampler = SequenceParallelSampler \
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if sequence_parallel_size > 1 else DefaultSampler
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train_dataloader = dict(
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batch_size=batch_size,
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num_workers=dataloader_num_workers,
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dataset=alpaca_en,
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sampler=dict(type=DefaultSampler, shuffle=True),
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collate_fn=dict(type=default_collate_fn, use_varlen_attn=use_varlen_attn))
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#######################################################################
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# PART 4 Scheduler & Optimizer #
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#######################################################################
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# optimizer
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optim_wrapper = dict(
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type=AmpOptimWrapper,
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optimizer=dict(
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type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
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clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
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accumulative_counts=accumulative_counts,
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loss_scale='dynamic',
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dtype='float16')
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# learning policy
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# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501
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param_scheduler = [
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dict(
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type=LinearLR,
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start_factor=1e-5,
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by_epoch=True,
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begin=0,
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end=warmup_ratio * max_epochs,
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convert_to_iter_based=True),
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dict(
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type=CosineAnnealingLR,
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eta_min=0.0,
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by_epoch=True,
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begin=warmup_ratio * max_epochs,
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end=max_epochs, # T_max=max_epochs,
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convert_to_iter_based=True)
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]
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# train, val, test setting
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# train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)
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train_cfg = dict(by_epoch=True, max_epochs=max_epochs, val_interval=1)
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#######################################################################
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# PART 5 Runtime #
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#######################################################################
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# Log the dialogue periodically during the training process, optional
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custom_hooks = [
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dict(type=DatasetInfoHook, tokenizer=tokenizer),
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dict(
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type=EvaluateChatHook,
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tokenizer=tokenizer,
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every_n_iters=evaluation_freq,
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evaluation_inputs=evaluation_inputs,
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system=SYSTEM,
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prompt_template=prompt_template)
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]
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if use_varlen_attn:
|
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custom_hooks += [dict(type=VarlenAttnArgsToMessageHubHook)]
|
||||
|
||||
# configure default hooks
|
||||
default_hooks = dict(
|
||||
# record the time of every iteration.
|
||||
timer=dict(type=IterTimerHook),
|
||||
# print log every 10 iterations.
|
||||
logger=dict(type=LoggerHook, interval=10,
|
||||
log_metric_by_epoch=False),
|
||||
# enable the parameter scheduler.
|
||||
param_scheduler=dict(type=ParamSchedulerHook),
|
||||
# save checkpoint per `save_steps`.
|
||||
checkpoint=dict(
|
||||
type=CheckpointHook,
|
||||
# by_epoch=False,
|
||||
# interval=save_steps,
|
||||
interval=1,
|
||||
max_keep_ckpts=save_total_limit),
|
||||
# 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)
|
||||
|
||||
# set log processor
|
||||
log_processor = dict(by_epoch=False)
|
||||
Loading…
Reference in New Issue
Block a user