From cd695dc828fd0e83ce018f658739f045403d9b4e Mon Sep 17 00:00:00 2001 From: liudan <403644786@qq.com> Date: Tue, 22 Oct 2024 19:29:36 +0800 Subject: [PATCH] fix lint --- .../minicpm3_4b_full_custom_pretrain_e1.py | 216 ++++++++++++++++++ 1 file changed, 216 insertions(+) create mode 100644 xtuner/configs/custom_dataset/pretrain/minicpm/minicpm3_4b_full_custom_pretrain_e1.py diff --git a/xtuner/configs/custom_dataset/pretrain/minicpm/minicpm3_4b_full_custom_pretrain_e1.py b/xtuner/configs/custom_dataset/pretrain/minicpm/minicpm3_4b_full_custom_pretrain_e1.py new file mode 100644 index 000000000..936b48f4a --- /dev/null +++ b/xtuner/configs/custom_dataset/pretrain/minicpm/minicpm3_4b_full_custom_pretrain_e1.py @@ -0,0 +1,216 @@ +# 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)