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205 lines
9.6 KiB
205 lines
9.6 KiB
import argparse
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import math
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import os
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import loralib as lora
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import torch
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import torch.distributed as dist
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from coati.dataset import DataCollatorForSupervisedDataset, SFTDataset, SupervisedDataset
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from coati.models import convert_to_lora_module
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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, BloomConfig, BloomForCausalLM, BloomTokenizerFast, LlamaConfig, LlamaForCausalLM
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from transformers.models.gpt2.configuration_gpt2 import GPT2Config
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from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
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from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
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from transformers.models.opt.configuration_opt import OPTConfig
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from transformers.models.opt.modeling_opt import OPTForCausalLM
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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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from colossalai.tensor import ColoParameter
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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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raise NotImplementedError(
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'Gemini is not supported .from_pretrained() yet. We will update this after checkpoint io is ready.')
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strategy = GeminiStrategy(placement_policy='cuda')
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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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with strategy.model_init_context():
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if args.model == 'bloom':
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model = convert_to_lora_module(BloomForCausalLM.from_pretrained(args.pretrain),
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args.lora_rank).half().cuda()
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elif args.model == 'opt':
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model = convert_to_lora_module(OPTForCausalLM.from_pretrained(args.pretrain), args.lora_rank).half().cuda()
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elif args.model == 'gpt2':
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model = convert_to_lora_module(GPT2LMHeadModel.from_pretrained(args.pretrain), args.lora_rank).half().cuda()
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elif args.model == 'llama':
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model = convert_to_lora_module(LlamaForCausalLM.from_pretrained(args.pretrain),
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args.lora_rank).half().cuda()
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else:
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raise ValueError(f'Unsupported model "{args.model}"')
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if args.grad_checkpoint:
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model.gradient_checkpointing_enable()
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# configure tokenizer
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if args.model == 'gpt2':
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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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('bigscience/bloom-560m')
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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")
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tokenizer.pad_token = tokenizer.eos_token
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elif args.model == 'llama':
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tokenizer = AutoTokenizer.from_pretrained(
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args.pretrain,
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padding_side="right",
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use_fast=False,
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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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else:
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raise ValueError(f'Unsupported model "{args.model}"')
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if args.model == 'llama' and args.strategy == 'colossalai_gemini':
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# this is a hack to deal with the resized embedding
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# to make sure all parameters are ColoParameter for Colossal-AI Gemini Compatibility
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for name, param in model.named_parameters():
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if not isinstance(param, ColoParameter):
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sub_module_name = '.'.join(name.split('.')[:-1])
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weight_name = name.split('.')[-1]
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sub_module = model.get_submodule(sub_module_name)
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setattr(sub_module, weight_name, ColoParameter(param))
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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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logger = get_dist_logger()
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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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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(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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eval_dataset = None
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data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
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if dist.is_initialized() and dist.get_world_size() > 1:
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train_sampler = DistributedSampler(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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if eval_dataset is not None:
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eval_sampler = DistributedSampler(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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else:
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train_sampler = None
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eval_sampler = None
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train_dataloader = DataLoader(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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collate_fn=data_collator,
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pin_memory=True)
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if eval_dataset is not None:
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eval_dataloader = DataLoader(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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collate_fn=data_collator,
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pin_memory=True)
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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("cosine",
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optim,
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num_warmup_steps=math.ceil(max_steps * 0.03),
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num_training_steps=max_steps)
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strategy_dict = strategy.prepare(
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dict(model=model, optimizer=optim, lr_scheduler=lr_scheduler)
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)
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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(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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trainer.fit(train_dataloader=train_dataloader,
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eval_dataloader=eval_dataloader,
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logger=logger,
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use_wandb=args.use_wandb)
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# save model checkpoint after fitting on only rank0
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strategy.save_pretrained(model, path=args.save_path, only_rank0=True, 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(trainer.optimizer,
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'rm_optim_checkpoint_%d.pt' % (torch.cuda.current_device()),
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only_rank0=False)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--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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parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom')
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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('--log_interval', type=int, default=100, help="how many steps to log")
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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('--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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