internlm-7b-qlora-alpaca-enzh / xtuner_config.py

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	# 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 ConcatDataset, process_hf_dataset
	from xtuner.dataset.collate_fns import default_collate_fn
	from xtuner.dataset.map_fns import (alpaca_map_fn, alpaca_zh_map_fn,
	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 = 'internlm/internlm-7b'

	# Data
	alpaca_zh_path = 'silk-road/alpaca-data-gpt4-chinese'
	alpaca_en_path = 'tatsu-lab/alpaca'
	prompt_template = PROMPT_TEMPLATE.internlm_chat
	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
	SYSTEM = SYSTEM_TEMPLATE.alpaca
	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)

	alpaca_zh = dict(
	type=process_hf_dataset,
	dataset=dict(type=load_dataset, path=alpaca_zh_path),
	tokenizer=tokenizer,
	max_length=max_length,
	dataset_map_fn=alpaca_zh_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_dataset = dict(
	type=ConcatDataset,
	datasets_cfg=dict(alpaca_en=alpaca_en, alpaca_zh=alpaca_zh))

	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)