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rl_training.py
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rl_training.py
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import os, json
import argparse
from typing import Optional, List
import torch
import torch.nn as nn
from accelerate import Accelerator
from datasets import load_dataset, Dataset
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
AutoConfig,
pipeline,
set_seed,
)
import trlx
from trlx.data.configs import (
ModelConfig,
OptimizerConfig,
SchedulerConfig,
TokenizerConfig,
TrainConfig,
TRLConfig,
)
from trlx.models.modeling_ppo import PPOConfig
from utils import Prompter
prompter = Prompter()
def get_model_trl_config(args):
print("Setup config...")
return TRLConfig(
train=TrainConfig(
seq_length=args.max_seq_length,
epochs=args.num_epochs,
total_steps=10000,
batch_size=args.ppo_batch_size,
eval_interval=args.eval_freq,
minibatch_size=1,
checkpoint_interval=10000,
pipeline="PromptPipeline",
trainer="AcceleratePPOTrainer",
save_best=False,
checkpoint_dir=args.output_dir,
),
model=ModelConfig(
model_path=args.sft_model_path,
num_layers_unfrozen=args.num_layers_unfrozen,
),
tokenizer=TokenizerConfig(
tokenizer_path=args.tokenizer_path,
truncation_side='left',
padding_side='left'
),
optimizer=OptimizerConfig(
name="adamw",
kwargs=dict(lr=args.learning_rate, betas=[0.9, 0.95], eps=1.0e-8, weight_decay=args.weight_decay)
),
scheduler=SchedulerConfig(name="cosine_annealing", kwargs=dict(T_max=10000, eta_min=1.0e-6)),
method=PPOConfig(
name="PPOConfig",
num_rollouts=args.num_rollouts,
chunk_size=args.chunk_size,
ppo_epochs=args.ppo_epochs,
init_kl_coef=args.kl_coef, # should choose either 0.1 or 0.05
target=6,
horizon=10000,
gamma=1,
lam=0.95,
cliprange=0.2,
cliprange_value=0.2,
vf_coef=1.0,
scale_reward=None,
ref_mean=None,
ref_std=None,
cliprange_reward=10,
gen_kwargs={
"max_new_tokens": args.max_new_tokens,
"do_sample": True,
"top_k": 0,
"top_p": 1,
"temperature": 1,
})
)
def create_reward_fn(args):
if accelerator.is_main_process:
print("Setup reward model")
# if os.environ.get("RANK", "0") == "0":
reward_tokenizer = AutoTokenizer.from_pretrained(args.reward_tokenizer_path)
config = AutoConfig.from_pretrained(args.reward_model_path)
if "Llama" in config.architectures[0]:
print("Setting EOS, BOS, UNK, and PAD tokens for LLama tokenizer")
reward_tokenizer.add_special_tokens(
{
"eos_token": "</s>",
"bos_token": "<s>",
"unk_token": "<unk>",
}
)
reward_tokenizer.pad_token_id = (
0
)
reward_tokenizer.truncation_side = "left"
reward_tokenizer.padding_side = "left"
reward_model = AutoModelForSequenceClassification.from_pretrained(
args.reward_model_path,
torch_dtype=torch.bfloat16,
)
reward_model.requires_grad_(False)
reward_model.config.pad_token_id = reward_tokenizer.pad_token_id
reward_model.config.use_cache = True
rm_device = torch.cuda.device_count() - 1
print("Rewad device:", rm_device)
reward_model.eval().to(rm_device)
sigmoid_fn = nn.Sigmoid()
def get_reward(samples: List[str]):
all_scores = []
for i in range(0, len(samples), args.rw_batch_size):
batch = reward_tokenizer(
samples[i : i + args.rw_batch_size],
padding=True,
truncation=True,
max_length=768,
return_tensors="pt").to(rm_device)
with torch.no_grad():
scores = reward_model(
batch['input_ids'],
attention_mask=batch['attention_mask']
)[0].squeeze(-1).cpu()
all_scores.append(scores)
scores = torch.hstack(all_scores)
return scores
def reward_fn(samples: List[str], original_output: List[str], **kwargs) -> torch.Tensor:
rewards = get_reward(samples)
return rewards
return reward_fn
else:
return True
def create_datasets(args, save_test_set=False):
print("Start create_datasets")
def create_prompt(data_point):
prompt = prompter.generate_prompt(
data_point["instruction"],
data_point["input"],
# data_point["output"],
)
original_output = prompter.generate_prompt(
data_point["instruction"],
data_point["input"],
data_point["output"],
)
return {'prompt': prompt, 'original_output': original_output}
prompter = Prompter()
try:
dataset = load_dataset('json', split=args.split, data_files=args.data_path)
except:
with open(args.data_path, 'r', encoding='utf-8') as f:
dataset = json.loads(f.read())
for entry in dataset:
for k, v in entry.items():
if not isinstance(v, str):
entry[k] = str(v)
dataset = Dataset.from_list(dataset)
dataset = dataset.train_test_split(test_size=args.size_valid_set, seed=args.seed)
train_prompts = dataset["train"].shuffle().map(create_prompt)
valid_prompts = dataset["test"].map(create_prompt)
train_prompts = [{'prompt': instance['prompt'], 'original_output': instance['original_output']} for instance in train_prompts]
valid_prompts = [{'prompt': instance['prompt'], 'original_output': instance['original_output']} for instance in valid_prompts]
print(f"Size of the train set: {len(train_prompts)}. Size of the validation set: {len(valid_prompts)}")
if save_test_set:
dataset["test"].to_json('dataset/val_data_hlhf.json', force_ascii=False)
return train_prompts, valid_prompts
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--sft_model_path", type=str, default="bloom-560m",
help="LLaMa/Bloom weight that has trained by supervised finetuning")
parser.add_argument("--tokenizer_path", type=str, default="bloom-560m",
help="LLaMa/Bloom tokenizer path")
parser.add_argument("--data_path", type=str, default="dataset/data_rlhf.json")
parser.add_argument("--reward_model_path", type=str, default="checkpoints/reward_model/checkpoint-7000")
parser.add_argument("--reward_tokenizer_path", type=str, default="bloom-560m")
parser.add_argument("--rw_batch_size", type=int, default=16)
parser.add_argument("--split", type=str, default="train")
parser.add_argument("--size_valid_set", type=float, default=0.05)
parser.add_argument("--max_seq_length", type=int, default=512)
parser.add_argument("--num_epochs", type=int, default=5)
parser.add_argument("--ppo_epochs", type=int, default=4)
parser.add_argument("--num_layers_unfrozen", type=int, default=5)
parser.add_argument("--ppo_batch_size", type=int, default=2)
parser.add_argument("--num_rollouts", type=int, default=128)
parser.add_argument("--chunk_size", type=int, default=16)
parser.add_argument("--kl_coef", type=int, default=0.1)
parser.add_argument("--learning_rate", type=float, default=1e-6)
parser.add_argument("--weight_decay", type=float, default=1e-6)
parser.add_argument("--max_new_tokens", type=int, default=256)
parser.add_argument("--eval_freq", default=500, type=int)
parser.add_argument("--save_freq", default=2000, type=int)
parser.add_argument("--output_dir", type=str, default="./checkpoints/rl_output_dir")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--hub_model_id", default='rlhf.debug', type=str)
args = parser.parse_args()
set_seed(args.seed)
accelerator = Accelerator()
reward_fn = create_reward_fn(args)
config = get_model_trl_config(args)
train_prompts, val_prompts = create_datasets(args, save_test_set=False)
accelerator.wait_for_everyone()
trainer = trlx.train(
reward_fn=reward_fn,
prompts=train_prompts,
eval_prompts=val_prompts,
config=config,
)
# push to huggingface
model = trainer.accelerator.unwrap_model(trainer.model)
if accelerator.is_main_process:
model.save_pretrained(args.output_dir)