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[chat]polish prompts training (#3300) 

* polish train_prompts

* polish readme
pull/3302/head
BlueRum 2023-03-29 08:44:16 +08:00 committed by GitHub
parent 62f7156131
commit 8257e1055d
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2 changed files with 57 additions and 41 deletions
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1
applications/Chat/examples/README.md
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@ -125,6 +125,7 @@ torchrun --standalone --nproc_per_node=4 train_prompts.py \
- --strategy: the strategy using for training, choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'], default='naive'
- --model: model type of actor, choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom'
- --pretrain: pretrain model, type=str, default=None
- --rm_model: reward model type, type=str, choices=['gpt2', 'bloom', 'opt', 'llama'], default=None
- --rm_pretrain: pretrain model for reward model, type=str, default=None
- --rm_path: the path of rm model, type=str, default=None
- --save_path: path to save the model, type=str, default='output'

97
applications/Chat/examples/train_prompts.py
View File

@ -1,20 +1,19 @@
import argparse
import pandas as pd
import torch
import torch.distributed as dist
from coati.dataset import DataCollatorForSupervisedDataset, PromptDataset, SupervisedDataset
from coati.models.bloom import BLOOMRM, BLOOMActor, BLOOMCritic
from coati.models.gpt import GPTRM, GPTActor, GPTCritic
from coati.models.llama import LlamaActor
from coati.models.opt import OPTRM, OPTActor, OPTCritic
from coati.models.bloom import BLOOMActor, BLOOMRM, BLOOMCritic
from coati.models.gpt import GPTActor, GPTRM, GPTCritic
from coati.models.opt import OPTActor, OPTRM, OPTCritic
from coati.models.llama import LlamaActor, LlamaRM, LlamaCritic
from coati.trainer import PPOTrainer
from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy
from coati.utils import prepare_llama_tokenizer_and_embedding
from torch.optim import Adam
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer
from transformers import AutoTokenizer, BloomTokenizerFast, LlamaTokenizer, GPT2Tokenizer
from coati.dataset import SupervisedDataset, DataCollatorForSupervisedDataset, PromptDataset
from coati.utils import prepare_llama_tokenizer_and_embedding
from colossalai.nn.optimizer import HybridAdam
@ -38,44 +37,66 @@ def main(args):
# configure model
if args.model == 'gpt2':
initial_model = GPTActor(pretrained=args.pretrain)
reward_model = GPTRM(pretrained=args.rm_pretrain)
elif args.model == 'bloom':
initial_model = BLOOMActor(pretrained=args.pretrain)
reward_model = BLOOMRM(pretrained=args.rm_pretrain)
elif args.model == 'opt':
initial_model = OPTActor(pretrained=args.pretrain)
reward_model = OPTRM(pretrained=args.rm_pretrain)
elif args.model == 'llama':
initial_model = LlamaActor(pretrained=args.pretrain)
reward_model = BLOOMRM(pretrained=args.rm_pretrain)
else:
raise ValueError(f'Unsupported model "{args.model}"')
raise ValueError(f'Unsupported actor model "{args.model}"')
if args.rm_model == None:
rm_model_name = args.model
else:
rm_model_name = args.rm_model
if rm_model_name == 'gpt2':
reward_model = GPTRM(pretrained=args.rm_pretrain)
elif rm_model_name == 'bloom':
reward_model = BLOOMRM(pretrained=args.rm_pretrain)
elif rm_model_name == 'opt':
reward_model = OPTRM(pretrained=args.rm_pretrain)
elif rm_model_name == 'llama':
reward_model = LlamaRM(pretrained=args.rm_pretrain)
else:
raise ValueError(f'Unsupported reward model "{rm_model_name}"')
if args.rm_path is not None:
reward_model.load_state_dict(state_dict)
if args.strategy != 'colossalai_gemini':
initial_model.to(torch.float16).to(torch.cuda.current_device())
reward_model.to(torch.float16).to(torch.cuda.current_device())
initial_model.to(torch.float16).to(torch.cuda.current_device())
reward_model.to(torch.float16).to(torch.cuda.current_device())
with strategy.model_init_context():
if args.model == 'gpt2':
actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank)
critic = GPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
elif args.model == 'bloom':
actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank)
critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
elif args.model == 'opt':
actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank)
critic = OPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
elif args.model == 'llama':
actor = LlamaActor(pretrained=args.pretrain, lora_rank=args.lora_rank)
critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
else:
raise ValueError(f'Unsupported model "{args.model}"')
raise ValueError(f'Unsupported actor model "{args.model}"')
if rm_model_name == 'gpt2':
critic = GPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
elif rm_model_name == 'bloom':
critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
elif rm_model_name == 'opt':
critic = OPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
elif rm_model_name == 'llama':
critic = LlamaCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
else:
raise ValueError(f'Unsupported reward model "{rm_model_name}"')
if args.rm_path is not None:
critic.load_state_dict(state_dict)
del state_dict
if args.strategy != 'colossalai_gemini':
critic.to(torch.float16).to(torch.cuda.current_device())
actor.to(torch.float16).to(torch.cuda.current_device())
@ -100,38 +121,32 @@ def main(args):
tokenizer.eos_token = '<\s>'
else:
raise ValueError(f'Unsupported model "{args.model}"')
if args.model == 'llama':
tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, actor)
else:
tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
prompt_dataset = PromptDataset(tokenizer=tokenizer, data_path=args.prompt_path, max_datasets_size=16384)
if dist.is_initialized() and dist.get_world_size() > 1:
prompt_sampler = DistributedSampler(prompt_dataset, shuffle=True, seed=42, drop_last=True)
prompt_dataloader = DataLoader(prompt_dataset,
shuffle=(prompt_sampler is None),
sampler=prompt_sampler,
batch_size=args.train_batch_size)
prompt_dataloader = DataLoader(prompt_dataset, shuffle=(prompt_sampler is None), sampler=prompt_sampler, batch_size=args.train_batch_size)
pretrain_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=args.pretrain_dataset, max_datasets_size=16384)
if dist.is_initialized() and dist.get_world_size() > 1:
pretrain_sampler = DistributedSampler(pretrain_dataset, shuffle=True, seed=42, drop_last=True)
pretrain_dataloader = DataLoader(pretrain_dataset,
shuffle=(pretrain_sampler is None),
sampler=pretrain_sampler,
batch_size=args.ptx_batch_size,
collate_fn=data_collator)
pretrain_dataloader = DataLoader(pretrain_dataset, shuffle=(pretrain_sampler is None), sampler=pretrain_sampler, batch_size=args.ptx_batch_size, collate_fn=data_collator)
def tokenize_fn(texts):
# MUST padding to max length to ensure inputs of all ranks have the same length
# Different length may lead to hang when using gemini, as different generation steps
batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True)
return {k: v.to(torch.cuda.current_device()) for k, v in batch.items()}
(actor, actor_optim), (critic, critic_optim) = strategy.prepare((actor, actor_optim), (critic, critic_optim))
(actor, actor_optim), (critic, critic_optim) = strategy.prepare(
(actor, actor_optim), (critic, critic_optim))
# configure trainer
trainer = PPOTrainer(
@ -177,10 +192,10 @@ if __name__ == '__main__':
parser.add_argument('--pretrain_dataset', type=str, default=None, help='path to the pretrained dataset')
parser.add_argument('--strategy',
choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'],
default='naive',
help='strategy to use')
default='naive', help='strategy to use')
parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama'])
parser.add_argument('--pretrain', type=str, default=None)
parser.add_argument('--rm_model', default=None, choices=['gpt2', 'bloom', 'opt', 'llama'])
parser.add_argument('--rm_path', type=str, default=None)
parser.add_argument('--rm_pretrain', type=str, default=None)
parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts')