From 31faf9b0578b4c7d7c2d7b068aaa80e518ac0509 Mon Sep 17 00:00:00 2001
From: liudan <403644786@qq.com>
Date: Mon, 21 Oct 2024 22:35:09 +0800
Subject: [PATCH] delete the error file

---
 .../minicpm3_4b_full_custom_pretrain_e1.py    | 199 ------------------
 1 file changed, 199 deletions(-)
 delete 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
deleted file mode 100644
index ce9d0bcd9..000000000
--- a/xtuner/configs/custom_dataset/pretrain/minicpm/minicpm3_4b_full_custom_pretrain_e1.py
+++ /dev/null
@@ -1,199 +0,0 @@
-# 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)