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Merge branch 'main' into other-hf
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starmountain1997 authored Oct 23, 2024
2 parents 9e1ac88 + 697bc77 commit d692e99
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# Copyright (c) OpenMMLab. All rights reserved.
"""Data format:
[
{
"text": "xxx"
},
{
"text": "xxx"
},
...
]
""" # noqa: E501

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 torch.optim import AdamW
from transformers import AutoModelForCausalLM, AutoTokenizer

from xtuner.dataset import process_hf_dataset
from xtuner.dataset.collate_fns import default_collate_fn
from xtuner.dataset.map_fns import pretrain_map_fn
from xtuner.engine.hooks import (
DatasetInfoHook,
EvaluateChatHook,
VarlenAttnArgsToMessageHubHook,
)
from xtuner.engine.runner import TrainLoop
from xtuner.model import SupervisedFinetune

#######################################################################
# PART 1 Settings #
#######################################################################
# Model
pretrained_model_name_or_path = "openbmb/MiniCPM3-4B"
use_varlen_attn = False

# Data
data_files = ["/path/to/your.json"]
max_length = 1024
pack_to_max_length = True

# Scheduler & Optimizer
batch_size = 1 # per_device
accumulative_counts = 1 # bs = 1 GPU * 1 batch_size_per_device * 16 acc
dataloader_num_workers = 0
max_steps = 10000
optim_type = AdamW
lr = 2e-5
betas = (0.9, 0.999)
weight_decay = 0
max_norm = 1 # grad clip
warmup_ratio = 0.03

# Save
save_steps = 500
save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited)

# Evaluate the generation performance during the training
evaluation_freq = 500
SYSTEM = ""
evaluation_inputs = ["上海是", "Shanghai is"]

#######################################################################
# 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",
eos_token="<|im_end|>",
)

model = dict(
type=SupervisedFinetune,
use_varlen_attn=use_varlen_attn,
llm=dict(
type=AutoModelForCausalLM.from_pretrained,
pretrained_model_name_or_path=pretrained_model_name_or_path,
trust_remote_code=True,
),
)

#######################################################################
# PART 3 Dataset & Dataloader #
#######################################################################
train_dataset = dict(
type=process_hf_dataset,
dataset=dict(type=load_dataset, path="json", data_files=data_files),
tokenizer=tokenizer,
max_length=max_length,
dataset_map_fn=pretrain_map_fn,
template_map_fn=None,
remove_unused_columns=True,
shuffle_before_pack=False,
pack_to_max_length=pack_to_max_length,
use_varlen_attn=use_varlen_attn,
)

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, use_varlen_attn=use_varlen_attn),
)

#######################################################################
# 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
param_scheduler = [
dict(
type=LinearLR,
start_factor=1e-5,
by_epoch=True,
begin=0,
end=max_steps * warmup_ratio,
convert_to_iter_based=True,
),
dict(
type=CosineAnnealingLR,
eta_min=0.0,
by_epoch=True,
begin=max_steps * warmup_ratio,
end=max_steps,
convert_to_iter_based=True,
),
]

# train, val, test setting
train_cfg = dict(type=TrainLoop, max_iters=max_steps)

#######################################################################
# 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,
),
]

if use_varlen_attn:
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, log_metric_by_epoch=False, interval=10),
# enable the parameter scheduler.
param_scheduler=dict(type=ParamSchedulerHook),
# save checkpoint per `save_steps`.
checkpoint=dict(
type=CheckpointHook,
by_epoch=False,
interval=save_steps,
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)
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