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222 lines
8.8 KiB
222 lines
8.8 KiB
import argparse
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import math
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import warnings
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import torch
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import torch.distributed as dist
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from coati.dataset import SFTDataset, SupervisedDataset
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from coati.models.bloom import BLOOMActor
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from coati.models.chatglm import ChatGLMActor
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from coati.models.chatglm.chatglm_tokenizer import ChatGLMTokenizer
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from coati.models.gpt import GPTActor
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from coati.models.llama import LlamaActor
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from coati.models.opt import OPTActor
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from coati.trainer import SFTTrainer
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from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy
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from datasets import load_dataset
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from torch.optim import Adam
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from torch.utils.data import DataLoader
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from torch.utils.data.distributed import DistributedSampler
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from transformers import AutoTokenizer, BloomTokenizerFast, LlamaTokenizer
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from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
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from transformers.trainer import get_scheduler
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from colossalai.logging import get_dist_logger
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from colossalai.nn.optimizer import HybridAdam
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def train(args):
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# configure strategy
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if args.strategy == "ddp":
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strategy = DDPStrategy()
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elif args.strategy == "colossalai_gemini":
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strategy = GeminiStrategy(placement_policy="auto")
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elif args.strategy == "colossalai_zero2":
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strategy = LowLevelZeroStrategy(stage=2, placement_policy="cuda")
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elif args.strategy == "colossalai_zero2_cpu":
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strategy = LowLevelZeroStrategy(stage=2, placement_policy="cpu")
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else:
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raise ValueError(f'Unsupported strategy "{args.strategy}"')
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# configure model
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if args.lora_rank > 0:
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warnings.warn("Lora is not supported yet.")
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args.lora_rank = 0
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with strategy.model_init_context():
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if args.model == "bloom":
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model = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank, checkpoint=args.grad_checkpoint)
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elif args.model == "opt":
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model = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank, checkpoint=args.grad_checkpoint)
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elif args.model == "gpt2":
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model = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank, checkpoint=args.grad_checkpoint)
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elif args.model == "llama":
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model = LlamaActor(pretrained=args.pretrain, lora_rank=args.lora_rank, checkpoint=args.grad_checkpoint)
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elif args.model == "chatglm":
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model = ChatGLMActor(pretrained=args.pretrain)
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else:
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raise ValueError(f'Unsupported model "{args.model}"')
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model.to(torch.bfloat16).to(torch.cuda.current_device())
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# configure tokenizer
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if args.model == "gpt2":
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2" if args.tokenizer is None else args.tokenizer)
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tokenizer.pad_token = tokenizer.eos_token
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elif args.model == "bloom":
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tokenizer = BloomTokenizerFast.from_pretrained(
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"bigscience/bloom-560m" if args.tokenizer is None else args.tokenizer
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)
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tokenizer.pad_token = tokenizer.eos_token
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elif args.model == "opt":
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tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m" if args.tokenizer is None else args.tokenizer)
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tokenizer.pad_token = tokenizer.eos_token
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elif args.model == "llama":
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tokenizer = LlamaTokenizer.from_pretrained(
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"hf-internal-testing/llama-tokenizer" if args.tokenizer is None else args.tokenizer
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)
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tokenizer.eos_token = "</s>"
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tokenizer.pad_token = tokenizer.unk_token
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elif args.model == "chatglm":
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tokenizer = ChatGLMTokenizer.from_pretrained(
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"THUDM/chatglm-6b" if args.tokenizer is None else args.tokenizer, trust_remote_code=True
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)
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else:
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raise ValueError(f'Unsupported model "{args.model}"')
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# configure optimizer
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if args.strategy.startswith("colossalai"):
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optim = HybridAdam(model.parameters(), lr=args.lr, clipping_norm=1.0)
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else:
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optim = Adam(model.parameters(), lr=args.lr)
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# configure dataset
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if args.dataset == "yizhongw/self_instruct":
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train_data = load_dataset(args.dataset, "super_natural_instructions", split="train")
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eval_data = load_dataset(args.dataset, "super_natural_instructions", split="test")
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if args.max_datasets_size is not None:
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train_data = train_data.select(range(min(args.max_datasets_size, len(train_data))))
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eval_data = eval_data.select(range(min(args.max_datasets_size, len(eval_data))))
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train_dataset = SFTDataset(train_data, tokenizer, args.max_len)
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eval_dataset = SFTDataset(eval_data, tokenizer, args.max_len)
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else:
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train_dataset = SupervisedDataset(
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tokenizer=tokenizer,
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data_path=args.dataset,
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max_datasets_size=args.max_datasets_size,
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max_length=args.max_len,
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)
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eval_dataset = None
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if dist.is_initialized() and dist.get_world_size() > 1:
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train_sampler = DistributedSampler(
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train_dataset,
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shuffle=True,
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seed=42,
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drop_last=True,
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rank=dist.get_rank(),
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num_replicas=dist.get_world_size(),
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)
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if eval_dataset is not None:
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eval_sampler = DistributedSampler(
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eval_dataset,
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shuffle=False,
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seed=42,
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drop_last=False,
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rank=dist.get_rank(),
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num_replicas=dist.get_world_size(),
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)
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else:
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train_sampler = None
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eval_sampler = None
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train_dataloader = DataLoader(
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train_dataset,
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shuffle=(train_sampler is None),
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sampler=train_sampler,
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batch_size=args.batch_size,
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pin_memory=True,
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)
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if eval_dataset is not None:
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eval_dataloader = DataLoader(
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eval_dataset,
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shuffle=(eval_sampler is None),
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sampler=eval_sampler,
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batch_size=args.batch_size,
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pin_memory=True,
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)
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else:
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eval_dataloader = None
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num_update_steps_per_epoch = len(train_dataloader) // args.accumulation_steps
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max_steps = math.ceil(args.max_epochs * num_update_steps_per_epoch)
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lr_scheduler = get_scheduler(
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"cosine", optim, num_warmup_steps=math.ceil(max_steps * 0.03), num_training_steps=max_steps
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)
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strategy_dict = strategy.prepare(dict(model=model, optimizer=optim, lr_scheduler=lr_scheduler))
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model = strategy_dict["model"]
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optim = strategy_dict["optimizer"]
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lr_scheduler = strategy_dict["lr_scheduler"]
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trainer = SFTTrainer(
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model=model,
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strategy=strategy,
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optim=optim,
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lr_scheduler=lr_scheduler,
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max_epochs=args.max_epochs,
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accumulation_steps=args.accumulation_steps,
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)
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logger = get_dist_logger()
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trainer.fit(
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train_dataloader=train_dataloader,
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eval_dataloader=eval_dataloader,
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logger=logger,
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log_dir=args.log_dir,
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use_wandb=args.use_wandb,
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)
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if args.lora_rank > 0 and args.merge_lora_weights:
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from coati.models.lora import LORA_MANAGER
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# NOTE: set model to eval to merge LoRA weights
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LORA_MANAGER.merge_weights = True
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model.eval()
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# save model checkpoint after fitting on only rank0
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strategy.save_pretrained(model, path=args.save_path, tokenizer=tokenizer)
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# save optimizer checkpoint on all ranks
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if args.need_optim_ckpt:
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strategy.save_optimizer(
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trainer.optimizer, "rm_optim_checkpoint_%d.pt" % (torch.cuda.current_device()), only_rank0=False
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)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--strategy",
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choices=["ddp", "colossalai_gemini", "colossalai_zero2", "colossalai_zero2_cpu"],
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default="colossalai_zero2",
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)
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parser.add_argument("--model", choices=["gpt2", "bloom", "opt", "llama", "chatglm"], default="bloom")
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parser.add_argument("--tokenizer", type=str, default=None)
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parser.add_argument("--pretrain", type=str, default=None)
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parser.add_argument("--dataset", type=str, default=None)
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parser.add_argument("--max_datasets_size", type=int, default=None)
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parser.add_argument("--save_path", type=str, default="output")
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parser.add_argument("--need_optim_ckpt", type=bool, default=False)
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parser.add_argument("--max_epochs", type=int, default=3)
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parser.add_argument("--batch_size", type=int, default=4)
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parser.add_argument("--max_len", type=int, default=512)
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parser.add_argument("--lora_rank", type=int, default=0, help="low-rank adaptation matrices rank")
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parser.add_argument("--merge_lora_weights", type=bool, default=True)
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parser.add_argument("--lr", type=float, default=5e-6)
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parser.add_argument("--accumulation_steps", type=int, default=8)
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parser.add_argument("--log_dir", default="logs", type=str)
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parser.add_argument("--use_wandb", default=False, action="store_true")
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parser.add_argument("--grad_checkpoint", default=False, action="store_true")
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args = parser.parse_args()
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train(args)
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