train.py

import json
import pdb
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, OlmoeForCausalLM, OlmoeModel
from transformers.models.auto.modeling_auto import MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES
import copy
from transformers.modeling_outputs import (
    MoeCausalLMOutputWithPast,
    MoeModelOutputWithPast,
)
import numpy as np
import math
from torch import nn
import pandas as pd
from transformers.cache_utils import Cache, DynamicCache, StaticCache
from dataclasses import dataclass
# from transformers.models.olmoe.configuration_olmoe import OlmoeConfig
# from transformers.models.olmoe.modeling_olmoe import OlmoeMLP, OlmoeAttention, OlmoeFlashAttention2, OlmoeSdpaAttention, OlmoeRMSNorm, OlmoeSparseMoeBlock, apply_rotary_pos_emb, repeat_kv, OlmoeRotaryEmbedding
import os
import sys
import torch.distributed as dist
from tqdm import tqdm
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
import transformers
import pickle

# from transformers.models.olmoe.configuration_olmoe import OlmoeConfig
from dataset import *
# from utils import flash_attn_forward, flash_attn_prepare_decoder_attention_mask, get_multiround_data
from peft import (get_peft_model, PeftModel)
import random
from config import *
from datasets import Dataset, DatasetDict, load_dataset
import wandb
import gc
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import functools
from torch.optim.lr_scheduler import StepLR
import torch.nn.functional as F
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data.distributed import DistributedSampler

from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
    checkpoint_wrapper, CheckpointImpl)

from torch.distributed.fsdp import (
    FullyShardedDataParallel as FSDP,
    MixedPrecision,
    BackwardPrefetch,
    ShardingStrategy,
    FullStateDictConfig,
    StateDictType,
)
from torch.distributed.fsdp.wrap import (
    transformer_auto_wrap_policy,
    enable_wrap,
    wrap,
)
from functools import partial
from torch.utils.data import DataLoader
from pathlib import Path
from typing import Type, List, Optional, Tuple, Union
from modelforseminat_v3 import *


################################# FSDP Config #####################################
def setup():
    # initialize the process group
    local_rank = int(os.environ['LOCAL_RANK'])                                                                                                                                                                                                                     
    torch.cuda.set_device(local_rank)                                                                                                                                                                                                                              
    dist.init_process_group(                                                                                                                                                                                                                                       
          backend='nccl',                                                                                                                                                                                                                                          
          init_method='env://',                                                                                                                                                                                                                                    
           )  


def cleanup():
    gc.collect()
    torch.cuda.empty_cache()
    dist.destroy_process_group()


def get_fsdp_device():
    # 每个进程初始化分布式环境后调用
    local_rank = int(os.environ.get("LOCAL_RANK", 0))  # torchrun 自动设置
    device = torch.device(f"cuda:{local_rank}")
    torch.cuda.set_device(device)
    return device


def setup_model(model_name):
    model = OlmoForCausalLMForSemiNAT.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    # pdb.set_trace()
    return model, tokenizer


def fsdp_main(args):
    local_rank = int(os.environ['LOCAL_RANK'])
    rank = int(os.environ['RANK'])
    world_size = int(os.environ['WORLD_SIZE'])
    if args.use_wandb and rank == 0:
        wandb.init(entity="SemiNAT", project="SemiNAT-SFT", name=args.run_name)

    model, tokenizer = setup_model(args.model_path)

    model.config.chunk_size_limit = args.chunk_size_limit

    # pdb.set_trace()

    if ".pkl" in args.data_path:
        train_dataset = pickle.load(open(args.data_path, "rb"))
    else:
        datasets = pd.read_parquet(args.data_path)
        train_dataset = eval(f"{args.data_type}")(
            tokenizer,
            datasets,  # your data preprocessing function
            args.max_length,  # your max input length
            args.data_processess_num)

    train_sampler = DistributedSampler(train_dataset,
                                       rank=rank,
                                       num_replicas=world_size,
                                       shuffle=True)
    train_dataloader = DataLoader(dataset=train_dataset,
                                  sampler=train_sampler,
                                  batch_size=args.batch_size)

    print(f"Size of train dataset: {len(train_dataset)}")

    setup()

    OlmoDecoderLayerForSemiNAT_auto_wrap_policy = functools.partial(
        transformer_auto_wrap_policy,
        transformer_layer_cls={
            OlmoDecoderLayerForSemiNAT,
            NATDecoderForSemiNAT,
        })


    sharding_strategy: ShardingStrategy = ShardingStrategy.FULL_SHARD  #for Zero2 and FULL_SHARD for Zero3
    torch.cuda.set_device(local_rank)

    bfSixteen = MixedPrecision(
        param_dtype=torch.bfloat16,
        reduce_dtype=torch.bfloat16,
        buffer_dtype=torch.bfloat16,
    )

    # if bf16_ready:
    mp_policy = bfSixteen
    # else:
    # mp_policy = None  # defaults to fp32

    # if args.use_lora:
    #     model = get_peft_model(model, lora_config)

    # model is on CPU before input to FSDP
    model = FSDP(model,
                 auto_wrap_policy=OlmoDecoderLayerForSemiNAT_auto_wrap_policy,
                 mixed_precision=mp_policy,
                 sharding_strategy=sharding_strategy,
                 device_id=torch.cuda.current_device(),
                 use_orig_params=True)

    optimizer = optim.AdamW(model.parameters(), lr=args.lr)

    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)

    loss1_list = []
    loss2_list = []
    loss_list = []

    global_step = 0
    for epoch in range(1, args.epochs + 1):
        # t0 = time.time()
        model.train()
        local_rank = int(os.environ['LOCAL_RANK'])
        # fsdp_loss = torch.zeros(2).to(local_rank)

        if train_sampler:
            train_sampler.set_epoch(epoch)
        if rank == 0:
            inner_pbar = tqdm(range(len(train_dataloader)),
                              colour="blue",
                              desc="r0 Training Epoch")
        for batch in train_dataloader:

            optimizer.zero_grad()
            loss1, loss2 = model(input_ids=batch[0],
                                 labels=batch[1],
                                 attention_mask=batch[2],
                                 slice_pos=batch[3],
                                 use_cache=False).loss
            loss = loss1 + loss2
            loss1_list.append(loss1.item())
            loss2_list.append(loss2.item())
            loss_list.append(loss.item())
            # pdb.set_trace()
            loss.backward()
            optimizer.step()

            global_step += 1

            if global_step % args.save_steps == 0:
                save_policy = FullStateDictConfig(offload_to_cpu=True,
                                                  rank0_only=True)
                with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT,
                                          save_policy):
                    cpu_state = model.state_dict()

                if rank == 0:
                    print(f"--> steps: {str(global_step)} saving model ...")
                    if not os.path.exists(args.save_path):
                        os.makedirs(args.save_path)
                    save_name = f"{args.save_name}-steps_{str(global_step)}.pt"
                    print(f"--> saving as model name {save_name}")
                    save_path = os.path.join(args.save_path, save_name)
                    torch.save(cpu_state, save_path)

            if rank == 0:
                inner_pbar.update(1)
                if args.use_wandb:
                    wandb.log({
                        "length prediction loss":
                        sum(loss1_list[-20:]) / len(loss1_list[-20:]),
                        "nat loss":
                        sum(loss2_list[-20:]) / len(loss2_list[-20:]),
                        "loss":
                        sum(loss_list[-20:]) / len(loss_list[-20:])
                    })

        dist.all_reduce(loss, op=dist.ReduceOp.SUM)

        if rank == 0:
            inner_pbar.close()

        scheduler.step()

        if rank == 0:
            print(f"--> entering save model state")

        save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
        with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT,
                                  save_policy):
            cpu_state = model.state_dict()

        if rank == 0:
            print(f"--> epoch: {str(epoch)} saving model ...")
            if not os.path.exists(args.save_path):
                os.makedirs(args.save_path)
            save_name = f"{args.save_name}-epoch_{str(epoch)}.pt"
            print(f"--> saving as model name {save_name}")
            save_path = os.path.join(args.save_path, save_name)
            torch.save(cpu_state, save_path)

    dist.barrier()
    cleanup()


################################# FSDP Config #####################################

if __name__ == "__main__":
    # Training settings
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch-size',
                        type=int,
                        default=4,
                        metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--model_path', type=str)
    parser.add_argument('--save_path', type=str)
    parser.add_argument('--save_name', type=str)
    parser.add_argument('--data_path', type=str)
    parser.add_argument('--data_type', type=str)
    parser.add_argument('--run_name', type=str)
    parser.add_argument('--max_length', type=int)
    parser.add_argument('--chunk_size_limit', type=int)
    parser.add_argument('--save_steps', type=int, default=5000)
    parser.add_argument('--data_processess_num', type=int, default=8)
    parser.add_argument('--epochs',
                        type=int,
                        default=2,
                        metavar='N',
                        help='number of epochs to train (default: 3)')
    parser.add_argument('--lr',
                        type=float,
                        default=.002,
                        metavar='LR',
                        help='learning rate (default: .002)')
    parser.add_argument('--gamma',
                        type=float,
                        default=0.7,
                        metavar='M',
                        help='Learning rate step gamma (default: 0.7)')
    parser.add_argument('--seed',
                        type=int,
                        default=1,
                        metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--use_lora', action='store_true', default=False)
    parser.add_argument("--use_wandb",
                        action="store_true",
                        help="whether to use wandb")

    args = parser.parse_args()

    torch.manual_seed(args.seed)

    fsdp_main(args)