ColossalAI/applications/ColossalChat/examples/training_scripts/train_sft.py

import argparse
import inspect
import json
import math
import os
import resource
import sys
from contextlib import nullcontext

import torch
from coati.dataset import DataCollatorForSupervisedDataset, load_tokenized_dataset, setup_distributed_dataloader
from coati.models import convert_to_lora_module
from coati.trainer import SFTTrainer
from coati.utils import load_checkpoint
from transformers import AutoModelForCausalLM, AutoTokenizer

import colossalai
from colossalai.booster import Booster
from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin, LowLevelZeroPlugin, TorchDDPPlugin
from colossalai.cluster import DistCoordinator
from colossalai.logging import get_dist_logger
from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
from colossalai.nn.optimizer import HybridAdam

logger = get_dist_logger()


def train(args):
    print(colossalai.__version__, inspect.getfile(colossalai))
    print(sys.executable)
    # check lora compatibility
    if "gemini" in args.plugin and args.lora_rank > 0:
        raise ValueError("LoRA is not supported in GeminiPlugin. Please use other plugin")
    if args.plugin == "gemini_auto" and args.accumulation_steps > 1:
        raise ValueError("Gradient accumulation is not supported in GeminiPlugin. Please use other plugin")
    # ==============================
    # Initialize Distributed Training
    # ==============================
    colossalai.launch_from_torch()
    coordinator = DistCoordinator()

    # ==============================
    # Initialize Booster
    # ==============================
    init_ctx = nullcontext()
    with init_ctx:
        model = AutoModelForCausalLM.from_pretrained(
            args.pretrain,
            torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16,
            trust_remote_code=True,
        )
    # check if the hybrid parallel plugin is compatible with the model
    try:
        from colossalai.shardformer.policies.auto_policy import get_autopolicy

        policy = get_autopolicy(model)
        if policy is not None:
            if args.plugin in ["zero2", "zero2_cpu"]:
                # if compatible, set the plugin to hybrid, which use colo-attention
                args.plugin = "3d"
                args.zero_stage = 2
                if args.plugin == "zero2_cpu":
                    args.zero_cpu_offload = True
                else:
                    args.zero_cpu_offload = False
                logger.info(
                    f"Model is compatible with hybrid parallel plugin, set plugin to {args.plugin} with zero_stage {args.zero_stage} and zero_cpu_offload {args.zero_cpu_offload}"
                )
    except NotImplementedError:
        logger.warning(f"Unable to find a policy for the model, use {args.plugin} plugin instead")
        if args.use_flash_attn:
            del model
            model = AutoModelForCausalLM.from_pretrained(
                args.pretrain,
                torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16,
                attn_implementation="flash_attention_2",
                trust_remote_code=True,
            )
    if args.lora_rank > 0:
        model = convert_to_lora_module(model, args.lora_rank, lora_train_bias=args.lora_train_bias)

    if args.plugin == "ddp":
        """
        Default torch ddp plugin without any acceleration, for
        debugging purpose acceleration, for debugging purpose
        """
        plugin = TorchDDPPlugin(find_unused_parameters=True)
    elif args.plugin == "gemini":
        plugin = GeminiPlugin(
            precision=args.mixed_precision,
            placement_policy="static",
            initial_scale=2**16,
            max_norm=args.grad_clip,
            enable_gradient_accumulation=True if args.accumulation_steps > 1 else False,
            enable_flash_attention=args.use_flash_attn,
        )
    elif args.plugin == "gemini_auto":
        plugin = GeminiPlugin(
            precision=args.mixed_precision,
            placement_policy="auto",
            initial_scale=2**16,
            max_norm=args.grad_clip,
            enable_flash_attention=args.use_flash_attn,
        )
    elif args.plugin == "zero2":
        plugin = LowLevelZeroPlugin(
            stage=2,
            precision=args.mixed_precision,
            initial_scale=2**16,
            max_norm=args.grad_clip,
        )
    elif args.plugin == "zero2_cpu":
        plugin = LowLevelZeroPlugin(
            stage=2,
            precision=args.mixed_precision,
            initial_scale=2**16,
            cpu_offload=True,
            max_norm=args.grad_clip,
        )
    elif args.plugin == "3d":
        plugin = HybridParallelPlugin(
            tp_size=args.tp,
            pp_size=args.pp,
            sp_size=args.sp,
            sequence_parallelism_mode=args.sp_mode,
            zero_stage=args.zero_stage,
            enable_flash_attention=args.use_flash_attn,
            enable_sequence_parallelism=True if args.sp > 1 else False,
            cpu_offload=True if args.zero_stage >= 1 and args.zero_cpu_offload else False,
            parallel_output=False,
            max_norm=args.grad_clip,
            precision=args.mixed_precision,
        )
    else:
        raise ValueError(f"Unknown plugin {args.plugin}")

    booster = Booster(plugin=plugin)

    # ======================================================
    # Initialize Model, Objective, Optimizer and LR Scheduler
    # ======================================================
    # Temp Fix: Disable lazy init due to version conflict
    # init_ctx = (
    #     LazyInitContext(default_device=get_current_device()) if isinstance(plugin, (GeminiPlugin,)) else nullcontext()
    # )

    if args.grad_checkpoint and args.lora_rank == 0:
        # lora layers are not supported by gradient checkpointing
        model.gradient_checkpointing_enable()
        coordinator.print_on_master(msg="Gradient checkpointing enabled successfully")
    elif args.lora_rank > 0:
        coordinator.print_on_master(msg="Gradient checkpointing will be disabled when LoRA is enabled")

    # configure tokenizer
    tokenizer = AutoTokenizer.from_pretrained(
        args.tokenizer_dir or args.pretrain, use_fast=False, trust_remote_code=True
    )
    if hasattr(tokenizer, "pad_token") and hasattr(tokenizer, "eos_token") and tokenizer.eos_token is not None:
        try:
            # Some tokenizers doesn't allow to set pad_token mannually e.g., Qwen
            tokenizer.pad_token = tokenizer.eos_token
        except AttributeError as e:
            logger.warning(f"Unable to set pad token to eos token, {str(e)}")
    if not hasattr(tokenizer, "pad_token") or tokenizer.pad_token is None:
        logger.warning(
            "The tokenizer does not have a pad token which is required. May lead to unintended behavior in training, Please consider manually set them."
        )

    tokenizer.add_bos_token = False
    tokenizer.add_eos_token = False
    tokenizer.padding_side = "right"

    coordinator.print_on_master(f"Configuration file will be saved at: {args.config_file}")
    coordinator.print_on_master(f"Model checkpoint will be saved at: {args.save_path}")

    # configure optimizer
    optim = HybridAdam(
        model_params=model.parameters(),
        lr=args.lr,
        betas=(0.9, 0.95),
        weight_decay=args.weight_decay,
        adamw_mode=True,
    )

    # configure dataset
    coordinator.print_on_master(
        f"Max CUDA memory before data loader: {torch.cuda.max_memory_allocated() / 1024 ** 2:.2f} MB"
    )
    dataset = load_tokenized_dataset(dataset_paths=args.dataset, mode="train")
    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer, max_length=args.max_len)
    train_dataloader = setup_distributed_dataloader(
        dataset=dataset,
        batch_size=args.batch_size,
        shuffle=True,
        drop_last=True,
        collate_fn=data_collator,
        tp_size=args.tp,
    )
    # print(len(train_dataloader))
    # for batch in train_dataloader:
    #     print(dist.get_rank(), tokenizer.batch_decode(batch["input_ids"]))
    #     break

    coordinator.print_on_master(
        f"Max CUDA memory after data loader: {torch.cuda.max_memory_allocated() / 1024 ** 2:.2f} MB"
    )

    num_update_steps_per_epoch = len(train_dataloader) // args.accumulation_steps
    math.ceil(args.max_epochs * num_update_steps_per_epoch)

    if args.warmup_steps is None:
        args.warmup_steps = int(args.max_epochs * 0.025 * (len(train_dataloader) // args.accumulation_steps))
        coordinator.print_on_master(f"Warmup steps is set to {args.warmup_steps}")

    lr_scheduler = CosineAnnealingWarmupLR(
        optimizer=optim,
        total_steps=args.max_epochs * num_update_steps_per_epoch,
        warmup_steps=args.warmup_steps,
        eta_min=0.1 * args.lr,
    )

    # Flash attention will be disabled because it does NOT support fp32.
    default_dtype = torch.float16 if args.mixed_precision == "fp16" else torch.bfloat16
    torch.set_default_dtype(default_dtype)
    model, optim, _, train_dataloader, lr_scheduler = booster.boost(
        model=model,
        optimizer=optim,
        lr_scheduler=lr_scheduler,
        dataloader=train_dataloader,
    )
    torch.set_default_dtype(torch.float)

    coordinator.print_on_master(f"Booster init max CUDA memory: {torch.cuda.max_memory_allocated() / 1024 ** 2:.2f} MB")
    coordinator.print_on_master(
        f"Booster init max CPU memory: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024:.2f} MB"
    )

    start_epoch = 0
    sampler_start_idx = 0
    start_step = 0
    if args.checkpoint_path is not None:
        if "modeling" in args.checkpoint_path:
            coordinator.print_on_master(f"Continued pretrain from checkpoint {args.checkpoint_path}")
            booster.load_model(model, args.checkpoint_path)
        else:
            coordinator.print_on_master(f"Load model checkpoint from {args.checkpoint_path}")
            start_epoch, start_step, sampler_start_idx = load_checkpoint(
                load_dir=args.checkpoint_path,
                booster=booster,
                model=model,
                optimizer=optim,
                lr_scheduler=lr_scheduler,
            )
            train_dataloader.sampler.set_start_index(start_index=sampler_start_idx)

            coordinator.print_on_master(
                f"Loaded checkpoint {args.checkpoint_path} at epoch {start_epoch} step {start_step}"
            )
            coordinator.print_on_master(f"Loaded sample at index {sampler_start_idx}")

        coordinator.print_on_master(
            f"Checkpoint loaded max CUDA memory: {torch.cuda.max_memory_allocated() / 1024 ** 2:.2f} MB"
        )
        coordinator.print_on_master(
            f"Checkpoint loaded CUDA memory: {torch.cuda.memory_allocated() / 1024 ** 2:.2f} MB"
        )
        coordinator.print_on_master(
            f"Checkpoint loaded max CPU memory: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024:.2f} MB"
        )

    trainer = SFTTrainer(
        model=model,
        booster=booster,
        optim=optim,
        lr_scheduler=lr_scheduler,
        max_epochs=args.max_epochs,
        accumulation_steps=args.accumulation_steps,
        start_epoch=start_epoch,
        save_interval=args.save_interval,
        save_dir=args.save_path,
        coordinator=coordinator,
    )

    trainer.fit(
        train_dataloader=train_dataloader,
        eval_dataloader=None,
        log_dir=args.log_dir,
        use_wandb=args.use_wandb,
    )

    if args.lora_rank > 0 and args.merge_lora_weights:
        from coati.models.lora import LORA_MANAGER

        # NOTE: set model to eval to merge LoRA weights
        LORA_MANAGER.merge_weights = True
        model.eval()
    # save model checkpoint after fitting on only rank0
    coordinator.print_on_master("Start saving final model checkpoint")

    # booster.save_model(model, os.path.join(args.save_path, "modeling"), shard=True)
    coordinator.print_on_master(f"Saved final model checkpoint at epoch {args.max_epochs} at folder {args.save_path}")

    coordinator.print_on_master(f"Max CUDA memory usage: {torch.cuda.max_memory_allocated()/1024**2:.2f} MB")


if __name__ == "__main__":
    # ==============================
    # Parse Arguments
    # ==============================
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--plugin",
        type=str,
        default="gemini",
        choices=["gemini", "gemini_auto", "3d", "ddp", "zero2_cpu", "zero2"],
        help="Choose which plugin to use",
    )
    parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping value")
    parser.add_argument("--weight_decay", type=float, default=0.1, help="Weight decay")
    parser.add_argument("--warmup_steps", type=int, default=None, help="Warmup steps")
    parser.add_argument("--tp", type=int, default=1)
    parser.add_argument("--pp", type=int, default=1)
    parser.add_argument("--sp", type=int, default=1)
    parser.add_argument("--zero_stage", type=int, default=0, help="Zero stage", choices=[0, 1, 2])
    parser.add_argument("--zero_cpu_offload", default=False, action="store_true")
    parser.add_argument("--sp_mode", type=str, default="split_gather", choices=["split_gather", "ring", "all_to_all"])
    parser.add_argument("--pretrain", type=str, default=None)
    parser.add_argument("--tokenizer_dir", type=str, default=None)
    parser.add_argument("--dataset", nargs="+", default=[])
    parser.add_argument(
        "--checkpoint_path", type=str, default=None, help="Checkpoint path if need to resume training form a checkpoint"
    )
    parser.add_argument("--save_path", type=str, default="output")
    parser.add_argument("--max_epochs", type=int, default=3)
    parser.add_argument("--batch_size", type=int, default=4)
    parser.add_argument("--max_len", type=int, default=512)
    parser.add_argument("--mixed_precision", type=str, default="bf16", choices=["fp16", "bf16"], help="Mixed precision")
    parser.add_argument("--lora_rank", type=int, default=0, help="low-rank adaptation matrices rank")
    parser.add_argument(
        "--lora_train_bias",
        type=str,
        default="none",
        help="'none' means it doesn't train biases. 'all' means it trains all biases. 'lora_only' means it only trains biases of LoRA layers",
    )
    parser.add_argument("--save_interval", type=int, default=1000, help="number of step between two checkpoints")
    parser.add_argument("--merge_lora_weights", type=bool, default=True)
    parser.add_argument("--lr", type=float, default=5e-6)
    parser.add_argument("--config_file", type=str, default="config_file", help="Config file")
    parser.add_argument("--accumulation_steps", type=int, default=8)
    parser.add_argument("--log_dir", default="logs", type=str)
    parser.add_argument("--use_wandb", default=False, action="store_true")
    parser.add_argument("--grad_checkpoint", default=False, action="store_true")
    parser.add_argument("--use_flash_attn", default=False, action="store_true")
    args = parser.parse_args()
    os.makedirs(os.path.dirname(args.config_file), exist_ok=True)
    with open(args.config_file, "w") as f:
        json.dump(args.__dict__, f, indent=4)
    train(args)