mirror of https://github.com/hpcaitech/ColossalAI
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182 lines
7.8 KiB
182 lines
7.8 KiB
import argparse
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from copy import deepcopy
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from coati.models.base import RewardModel
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from coati.models.opt import OPTActor, OPTCritic
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from coati.trainer import PPOTrainer
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from coati.trainer.callbacks import PerformanceEvaluator
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from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, Strategy
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from torch.optim import Adam
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from transformers import AutoTokenizer
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from transformers.models.opt.configuration_opt import OPTConfig
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from colossalai.nn.optimizer import HybridAdam
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def get_model_numel(model: nn.Module, strategy: Strategy) -> int:
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numel = sum(p.numel() for p in model.parameters())
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if isinstance(strategy, ColossalAIStrategy) and strategy.stage == 3 and strategy.shard_init:
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numel *= dist.get_world_size()
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return numel
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def preprocess_batch(samples) -> dict:
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input_ids = torch.stack(samples)
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attention_mask = torch.ones_like(input_ids, dtype=torch.long)
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return {'input_ids': input_ids, 'attention_mask': attention_mask}
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def print_rank_0(*args, **kwargs) -> None:
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if dist.get_rank() == 0:
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print(*args, **kwargs)
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def print_model_numel(model_dict: dict) -> None:
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B = 1024**3
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M = 1024**2
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K = 1024
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outputs = ''
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for name, numel in model_dict.items():
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outputs += f'{name}: '
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if numel >= B:
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outputs += f'{numel / B:.2f} B\n'
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elif numel >= M:
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outputs += f'{numel / M:.2f} M\n'
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elif numel >= K:
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outputs += f'{numel / K:.2f} K\n'
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else:
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outputs += f'{numel}\n'
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print_rank_0(outputs)
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def get_gpt_config(model_name: str) -> OPTConfig:
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model_map = {
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'125m': OPTConfig.from_pretrained('facebook/opt-125m'),
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'350m': OPTConfig(hidden_size=1024, ffn_dim=4096, num_hidden_layers=24, num_attention_heads=16),
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'700m': OPTConfig(hidden_size=1280, ffn_dim=5120, num_hidden_layers=36, num_attention_heads=20),
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'1.3b': OPTConfig.from_pretrained('facebook/opt-1.3b'),
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'2.7b': OPTConfig.from_pretrained('facebook/opt-2.7b'),
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'3.5b': OPTConfig(hidden_size=3072, ffn_dim=12288, num_hidden_layers=32, num_attention_heads=32),
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'5.5b': OPTConfig(hidden_size=3840, ffn_dim=15360, num_hidden_layers=32, num_attention_heads=32),
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'6.7b': OPTConfig.from_pretrained('facebook/opt-6.7b'),
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'10b': OPTConfig(hidden_size=5120, ffn_dim=20480, num_hidden_layers=32, num_attention_heads=32),
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'13b': OPTConfig.from_pretrained('facebook/opt-13b'),
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}
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try:
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return model_map[model_name]
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except KeyError:
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raise ValueError(f'Unknown model "{model_name}"')
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def main(args):
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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 = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5)
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elif args.strategy == 'colossalai_gemini_cpu':
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strategy = ColossalAIStrategy(stage=3, placement_policy='cpu', initial_scale=2**5)
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elif args.strategy == 'colossalai_zero2':
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strategy = ColossalAIStrategy(stage=2, placement_policy='cuda')
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elif args.strategy == 'colossalai_zero2_cpu':
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strategy = ColossalAIStrategy(stage=2, placement_policy='cpu')
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elif args.strategy == 'colossalai_zero1':
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strategy = ColossalAIStrategy(stage=1, placement_policy='cuda')
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elif args.strategy == 'colossalai_zero1_cpu':
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strategy = ColossalAIStrategy(stage=1, placement_policy='cpu')
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else:
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raise ValueError(f'Unsupported strategy "{args.strategy}"')
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torch.cuda.set_per_process_memory_fraction(args.cuda_mem_frac)
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model_config = get_gpt_config(args.model)
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with strategy.model_init_context():
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actor = OPTActor(config=model_config, lora_rank=args.lora_rank).cuda()
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critic = OPTCritic(config=model_config, lora_rank=args.lora_rank).cuda()
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initial_model = deepcopy(actor).cuda()
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reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda()
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actor_numel = get_model_numel(actor, strategy)
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critic_numel = get_model_numel(critic, strategy)
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initial_model_numel = get_model_numel(initial_model, strategy)
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reward_model_numel = get_model_numel(reward_model, strategy)
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print_model_numel({
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'Actor': actor_numel,
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'Critic': critic_numel,
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'Initial model': initial_model_numel,
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'Reward model': reward_model_numel
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})
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performance_evaluator = PerformanceEvaluator(actor_numel,
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critic_numel,
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initial_model_numel,
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reward_model_numel,
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enable_grad_checkpoint=False,
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ignore_episodes=1)
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if args.strategy.startswith('colossalai'):
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actor_optim = HybridAdam(actor.parameters(), lr=5e-6)
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critic_optim = HybridAdam(critic.parameters(), lr=5e-6)
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else:
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actor_optim = Adam(actor.parameters(), lr=5e-6)
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critic_optim = Adam(critic.parameters(), lr=5e-6)
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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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(actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare(
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(actor, actor_optim), (critic, critic_optim), reward_model, initial_model)
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trainer = PPOTrainer(strategy,
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actor,
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critic,
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reward_model,
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initial_model,
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actor_optim,
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critic_optim,
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max_epochs=args.max_epochs,
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train_batch_size=args.train_batch_size,
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experience_batch_size=args.experience_batch_size,
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tokenizer=preprocess_batch,
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max_length=512,
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do_sample=True,
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temperature=1.0,
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top_k=50,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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callbacks=[performance_evaluator])
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random_prompts = torch.randint(tokenizer.vocab_size, (1000, 1, 400), device=torch.cuda.current_device())
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random_attention_mask = torch.randint(1, (1000, 1, 400), device=torch.cuda.current_device()).to(torch.bool)
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random_pretrain = [{'input_ids':random_prompts[i], 'labels':random_prompts[i], 'attention_mask':random_attention_mask[i]} for i in range(1000)]
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trainer.fit(random_prompts, random_pretrain,
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num_episodes=args.num_episodes,
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max_timesteps=args.max_timesteps,
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update_timesteps=args.update_timesteps)
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print_rank_0(f'Peak CUDA mem: {torch.cuda.max_memory_allocated()/1024**3:.2f} GB')
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', default='125m')
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parser.add_argument('--strategy',
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choices=[
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'ddp', 'colossalai_gemini', 'colossalai_gemini_cpu', 'colossalai_zero2',
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'colossalai_zero2_cpu', 'colossalai_zero1', 'colossalai_zero1_cpu'
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],
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default='ddp')
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parser.add_argument('--num_episodes', type=int, default=3)
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parser.add_argument('--max_timesteps', type=int, default=8)
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parser.add_argument('--update_timesteps', type=int, default=8)
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parser.add_argument('--max_epochs', type=int, default=3)
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parser.add_argument('--train_batch_size', type=int, default=8)
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parser.add_argument('--experience_batch_size', type=int, default=8)
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parser.add_argument('--lora_rank', type=int, default=4)
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parser.add_argument('--cuda_mem_frac', type=float, default=1.0)
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args = parser.parse_args()
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main(args)
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