mirror of https://github.com/hpcaitech/ColossalAI
188 lines
7.6 KiB
Python
188 lines
7.6 KiB
Python
import argparse
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import os
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import socket
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from functools import partial
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import pandas as pd
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import ray
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import torch
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from coati.quant import llama_load_quant, low_resource_init
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from coati.ray.detached_trainer_ppo import DetachedPPOTrainer
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from coati.ray.experience_maker_holder import ExperienceMakerHolder
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from coati.ray.utils import (
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get_actor_from_args,
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get_critic_from_args,
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get_receivers_per_sender,
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get_reward_model_from_args,
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get_strategy_from_args,
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)
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from torch.utils.data import DataLoader
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from transformers import AutoConfig, AutoTokenizer
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from transformers.modeling_utils import no_init_weights
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def get_free_port():
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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s.bind(('', 0))
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return s.getsockname()[1]
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def get_local_ip():
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with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
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s.connect(('8.8.8.8', 80))
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return s.getsockname()[0]
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def main(args):
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master_addr = str(get_local_ip())
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# trainer_env_info
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trainer_port = str(get_free_port())
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env_info_trainers = [{
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'local_rank': '0',
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'rank': str(rank),
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'world_size': str(args.num_trainers),
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'master_port': trainer_port,
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'master_addr': master_addr
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} for rank in range(args.num_trainers)]
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# maker_env_info
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maker_port = str(get_free_port())
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env_info_makers = [{
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'local_rank': '0',
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'rank': str(rank),
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'world_size': str(args.num_makers),
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'master_port': maker_port,
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'master_addr': master_addr
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} for rank in range(args.num_makers)]
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# configure tokenizer
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tokenizer = AutoTokenizer.from_pretrained(args.pretrain)
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tokenizer.pad_token = tokenizer.eos_token
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def model_fn():
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actor = get_actor_from_args(args.model, args.pretrain).requires_grad_(False).half().cuda()
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critic = get_critic_from_args(args.model, args.critic_pretrain).requires_grad_(False).half().cuda()
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reward_model = get_reward_model_from_args(args.model, args.critic_pretrain).requires_grad_(False).half().cuda()
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if args.initial_model_quant_ckpt is not None and args.model == 'llama':
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# quantize initial model
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actor_cfg = AutoConfig.from_pretrained(args.pretrain)
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with low_resource_init(), no_init_weights():
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initial_model = get_actor_from_args(args.model, config=actor_cfg)
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initial_model.model = llama_load_quant(initial_model.model, args.initial_model_quant_ckpt, args.quant_bits,
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args.quant_group_size).cuda().requires_grad_(False)
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else:
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initial_model = get_actor_from_args(args.model, args.pretrain).requires_grad_(False).half().cuda()
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return actor, critic, reward_model, initial_model
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# configure Experience Maker
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experience_holder_refs = [
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ExperienceMakerHolder.options(name=f"maker{i}", num_gpus=1, max_concurrency=2).remote(
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detached_trainer_name_list=[
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f'trainer{x}'
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for x in get_receivers_per_sender(i, args.num_makers, args.num_trainers, allow_idle_sender=False)
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],
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strategy_fn=partial(get_strategy_from_args, args.maker_strategy),
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model_fn=model_fn,
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env_info=env_info_maker,
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kl_coef=0.1,
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debug=args.debug,
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update_lora_weights=not (args.lora_rank == 0),
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# sync_models_from_trainers=True,
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# generation kwargs:
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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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eval_performance=True,
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use_cache=True,
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)
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for i, env_info_maker in enumerate(env_info_makers)
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]
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def trainer_model_fn():
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actor = get_actor_from_args(args.model, args.pretrain, lora_rank=args.lora_rank).half().cuda()
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critic = get_critic_from_args(args.model, args.critic_pretrain, lora_rank=args.lora_rank).half().cuda()
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return actor, critic
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# configure Trainer
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trainer_refs = [
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DetachedPPOTrainer.options(name=f"trainer{i}", num_gpus=1, max_concurrency=2).remote(
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experience_maker_holder_name_list=[
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f"maker{x}"
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for x in get_receivers_per_sender(i, args.num_trainers, args.num_makers, allow_idle_sender=True)
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],
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strategy_fn=partial(get_strategy_from_args, args.trainer_strategy),
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model_fn=trainer_model_fn,
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env_info=env_info_trainer,
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train_batch_size=args.train_batch_size,
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buffer_limit=16,
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eval_performance=True,
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debug=args.debug,
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update_lora_weights=not (args.lora_rank == 0),
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)
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for i, env_info_trainer in enumerate(env_info_trainers)
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]
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dataset_size = args.experience_batch_size * 4
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def build_dataloader():
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def tokenize_fn(texts):
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batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True)
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return {k: v.cuda() for k, v in batch.items()}
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dataset = pd.read_csv(args.prompt_path)['prompt']
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dataloader = DataLoader(dataset=dataset, batch_size=dataset_size, shuffle=True, collate_fn=tokenize_fn)
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return dataloader
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# uncomment this function if sync_models_from_trainers is True
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# ray.get([
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# trainer_ref.sync_models_to_remote_makers.remote()
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# for trainer_ref in trainer_refs
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# ])
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wait_tasks = []
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for experience_holder_ref in experience_holder_refs:
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wait_tasks.append(experience_holder_ref.workingloop.remote(build_dataloader, num_steps=args.experience_steps))
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total_steps = args.experience_batch_size * args.experience_steps * \
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args.num_makers // (args.num_trainers * args.train_batch_size)
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for trainer_ref in trainer_refs:
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wait_tasks.append(trainer_ref.fit.remote(total_steps, args.update_steps, args.train_epochs))
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ray.get(wait_tasks)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--prompt_path', type=str, default=None)
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parser.add_argument('--num_makers', type=int, default=1)
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parser.add_argument('--num_trainers', type=int, default=1)
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parser.add_argument(
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'--trainer_strategy',
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choices=['ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', 'colossalai_zero2_cpu'],
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default='ddp')
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parser.add_argument('--maker_strategy', choices=['naive'], default='naive')
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parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama'])
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parser.add_argument('--critic_model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama'])
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parser.add_argument('--pretrain', type=str, default=None)
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parser.add_argument('--critic_pretrain', type=str, default=None)
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parser.add_argument('--experience_steps', type=int, default=4)
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parser.add_argument('--experience_batch_size', type=int, default=8)
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parser.add_argument('--train_epochs', type=int, default=1)
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parser.add_argument('--update_steps', type=int, default=2)
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parser.add_argument('--train_batch_size', type=int, default=8)
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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('--initial_model_quant_ckpt', type=str, default=None)
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parser.add_argument('--quant_bits', type=int, default=4)
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parser.add_argument('--quant_group_size', type=int, default=128)
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parser.add_argument('--debug', action='store_true')
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args = parser.parse_args()
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ray.init(namespace=os.environ["RAY_NAMESPACE"], runtime_env={"env_vars": dict(os.environ)})
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main(args)
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