ColossalAI/applications/Chat/examples/ray/mmmt_prompt.py

import argparse
import os
import socket
from functools import partial

import pandas as pd
import ray
import torch
from coati.quant import llama_load_quant, low_resource_init
from coati.ray.detached_trainer_ppo import DetachedPPOTrainer
from coati.ray.experience_maker_holder import ExperienceMakerHolder
from coati.ray.utils import (
    get_actor_from_args,
    get_critic_from_args,
    get_receivers_per_sender,
    get_reward_model_from_args,
    get_strategy_from_args,
)
from torch.utils.data import DataLoader
from transformers import AutoConfig, AutoTokenizer
from transformers.modeling_utils import no_init_weights


def get_free_port():
    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        s.bind(('', 0))
        return s.getsockname()[1]


def get_local_ip():
    with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
        s.connect(('8.8.8.8', 80))
        return s.getsockname()[0]


def main(args):
    master_addr = str(get_local_ip())
    # trainer_env_info
    trainer_port = str(get_free_port())
    env_info_trainers = [{
        'local_rank': '0',
        'rank': str(rank),
        'world_size': str(args.num_trainers),
        'master_port': trainer_port,
        'master_addr': master_addr
    } for rank in range(args.num_trainers)]

    # maker_env_info
    maker_port = str(get_free_port())
    env_info_makers = [{
        'local_rank': '0',
        'rank': str(rank),
        'world_size': str(args.num_makers),
        'master_port': maker_port,
        'master_addr': master_addr
    } for rank in range(args.num_makers)]

    # configure tokenizer
    tokenizer = AutoTokenizer.from_pretrained(args.pretrain)
    tokenizer.pad_token = tokenizer.eos_token

    def model_fn():
        actor = get_actor_from_args(args.model, args.pretrain).requires_grad_(False).half().cuda()
        critic = get_critic_from_args(args.model, args.critic_pretrain).requires_grad_(False).half().cuda()
        reward_model = get_reward_model_from_args(args.model, args.critic_pretrain).requires_grad_(False).half().cuda()
        if args.initial_model_quant_ckpt is not None and args.model == 'llama':
            # quantize initial model
            actor_cfg = AutoConfig.from_pretrained(args.pretrain)
            with low_resource_init(), no_init_weights():
                initial_model = get_actor_from_args(args.model, config=actor_cfg)
            initial_model.model = llama_load_quant(initial_model.model, args.initial_model_quant_ckpt, args.quant_bits,
                                                   args.quant_group_size).cuda().requires_grad_(False)
        else:
            initial_model = get_actor_from_args(args.model, args.pretrain).requires_grad_(False).half().cuda()
        return actor, critic, reward_model, initial_model

    # configure Experience Maker
    experience_holder_refs = [
        ExperienceMakerHolder.options(name=f"maker{i}", num_gpus=1, max_concurrency=2).remote(
            detached_trainer_name_list=[
                f'trainer{x}'
                for x in get_receivers_per_sender(i, args.num_makers, args.num_trainers, allow_idle_sender=False)
            ],
            strategy_fn=partial(get_strategy_from_args, args.maker_strategy),
            model_fn=model_fn,
            env_info=env_info_maker,
            kl_coef=0.1,
            debug=args.debug,
            update_lora_weights=not (args.lora_rank == 0),
            # sync_models_from_trainers=True,
            # generation kwargs:
            max_length=512,
            do_sample=True,
            temperature=1.0,
            top_k=50,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id,
            eval_performance=True,
            use_cache=True,
        )
        for i, env_info_maker in enumerate(env_info_makers)
    ]

    def trainer_model_fn():
        actor = get_actor_from_args(args.model, args.pretrain, lora_rank=args.lora_rank).half().cuda()
        critic = get_critic_from_args(args.model, args.critic_pretrain, lora_rank=args.lora_rank).half().cuda()
        return actor, critic

    # configure Trainer
    trainer_refs = [
        DetachedPPOTrainer.options(name=f"trainer{i}", num_gpus=1, max_concurrency=2).remote(
            experience_maker_holder_name_list=[
                f"maker{x}"
                for x in get_receivers_per_sender(i, args.num_trainers, args.num_makers, allow_idle_sender=True)
            ],
            strategy_fn=partial(get_strategy_from_args, args.trainer_strategy),
            model_fn=trainer_model_fn,
            env_info=env_info_trainer,
            train_batch_size=args.train_batch_size,
            buffer_limit=16,
            eval_performance=True,
            debug=args.debug,
            update_lora_weights=not (args.lora_rank == 0),
        )
        for i, env_info_trainer in enumerate(env_info_trainers)
    ]

    dataset_size = args.experience_batch_size * 4

    def build_dataloader():

        def tokenize_fn(texts):
            batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True)
            return {k: v.cuda() for k, v in batch.items()}

        dataset = pd.read_csv(args.prompt_path)['prompt']
        dataloader = DataLoader(dataset=dataset, batch_size=dataset_size, shuffle=True, collate_fn=tokenize_fn)
        return dataloader

    # uncomment this function if sync_models_from_trainers is True
    # ray.get([
    #     trainer_ref.sync_models_to_remote_makers.remote()
    #     for trainer_ref in trainer_refs
    # ])

    wait_tasks = []

    for experience_holder_ref in experience_holder_refs:
        wait_tasks.append(experience_holder_ref.workingloop.remote(build_dataloader, num_steps=args.experience_steps))

    total_steps = args.experience_batch_size * args.experience_steps * \
        args.num_makers // (args.num_trainers * args.train_batch_size)
    for trainer_ref in trainer_refs:
        wait_tasks.append(trainer_ref.fit.remote(total_steps, args.update_steps, args.train_epochs))

    ray.get(wait_tasks)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--prompt_path', type=str, default=None)
    parser.add_argument('--num_makers', type=int, default=1)
    parser.add_argument('--num_trainers', type=int, default=1)
    parser.add_argument('--trainer_strategy',
                        choices=[
                            'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu',
                            'colossalai_zero2_cpu'
                        ],
                        default='ddp')
    parser.add_argument('--maker_strategy', choices=['naive'], default='naive')
    parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama'])
    parser.add_argument('--critic_model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama'])
    parser.add_argument('--pretrain', type=str, default=None)
    parser.add_argument('--critic_pretrain', type=str, default=None)
    parser.add_argument('--experience_steps', type=int, default=4)
    parser.add_argument('--experience_batch_size', type=int, default=8)
    parser.add_argument('--train_epochs', type=int, default=1)
    parser.add_argument('--update_steps', type=int, default=2)
    parser.add_argument('--train_batch_size', type=int, default=8)
    parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank")

    parser.add_argument('--initial_model_quant_ckpt', type=str, default=None)
    parser.add_argument('--quant_bits', type=int, default=4)
    parser.add_argument('--quant_group_size', type=int, default=128)
    parser.add_argument('--debug', action='store_true')
    args = parser.parse_args()

    ray.init(namespace=os.environ["RAY_NAMESPACE"], runtime_env={"env_vars": dict(os.environ)})
    main(args)