# Copyright (c) OpenMMLab. All rights reserved.
import torch
from datasets import load_dataset
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 ConcatDataset, process_hf_dataset
from xtuner.dataset.collate_fns import default_collate_fn
from xtuner.engine.hooks import (DatasetInfoHook, ThroughputHook, EvaluateChatHook,
                                 VarlenAttnArgsToMessageHubHook)
from xtuner.engine.runner import TrainLoop
from xtuner.model import SupervisedFinetune
from xtuner.parallel.sequence import SequenceParallelSampler
from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE
from xtuner.dataset.map_fns import (alpaca_map_fn, alpaca_zh_map_fn,
                                    template_map_fn_factory)

#######################################################################
#                          PART 1  Settings                           #
#######################################################################
# Model
pretrained_model_name_or_path = '/mnt/petrelfs/share_data/caoweihan/Yi-34B-200K'
use_varlen_attn = False
sequence_parallel_size = 2

# Data
alpaca_zh_path = 'silk-road/alpaca-data-gpt4-chinese'
alpaca_en_path = 'tatsu-lab/alpaca'
prompt_template = PROMPT_TEMPLATE.default
max_length = 8192
pack_to_max_length = True

# Scheduler & Optimizer
batch_size = 1  # per_device
accumulative_counts = 2
dataloader_num_workers = 4
max_epochs = 3
optim_type = AdamW
lr = 2e-5
betas = (0.9, 0.999)
weight_decay = 0
max_norm = 1  # grad clip
warmup_ratio = 0.05

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

# Evaluate the generation performance during the training
evaluation_freq = 10
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,
    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,
        torch_dtype=torch.bfloat16,
        attn_implementation='flash_attention_2'))

#######################################################################
#                      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=False,
    pack_to_max_length=pack_to_max_length,
    use_varlen_attn=use_varlen_attn)

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=False,
    pack_to_max_length=pack_to_max_length,
    use_varlen_attn=use_varlen_attn)

train_dataset = dict(type=ConcatDataset, datasets=[alpaca_en, alpaca_zh])

train_dataloader = dict(
    batch_size=batch_size,
    num_workers=dataloader_num_workers,
    dataset=train_dataset,
    sampler=dict(type=SequenceParallelSampler, seed=1024),
    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')

# learning policy
# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md  # noqa: E501
param_scheduler = [
    # dict(
    #     type=LinearLR,
    #     start_factor=1 / 40,
    #     by_epoch=True,
    #     begin=0,
    #     end=warmup_ratio * max_epochs,
    #     convert_to_iter_based=True),
    dict(
        type=CosineAnnealingLR,
        eta_min=lr * 0.15,
        by_epoch=True,
        begin=0,
        end=max_epochs,
        convert_to_iter_based=True)
]

# train, val, test setting
train_cfg = dict(type=TrainLoop, max_iters=32)

#######################################################################
#                           PART 5  Runtime                           #
#######################################################################
# Log the dialogue periodically during the training process, optional
custom_hooks = [
    # dict(type=DatasetInfoHook, tokenizer=tokenizer),
    dict(type=ThroughputHook),
    # dict(
    #     type=EvaluateChatHook,
    #     tokenizer=tokenizer,
    #     every_n_iters=evaluation_freq,
    #     evaluation_inputs=evaluation_inputs,
    #     system=SYSTEM,
    #     max_new_tokens=100,
    #     prompt_template=prompt_template)
]

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=1),
    # enable the parameter scheduler.
    param_scheduler=dict(type=ParamSchedulerHook),
    # save checkpoint per `save_steps`.
    checkpoint=dict(
        type=CheckpointHook,
        save_optimizer=False,
        by_epoch=False,
        interval=-1,
        save_last=False,
        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,
    window_size=1)