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import os
from typing import List
import torch
from datasets import load_dataset
from reward_model.reward_model import GPTRewardModel
from tqdm import tqdm
from transformers import AutoTokenizer
import trlx
from trlx.data.configs import (
ModelConfig,
OptimizerConfig,
SchedulerConfig,
TokenizerConfig,
TrainConfig,
TRLConfig,
)
from trlx.models.modeling_ppo import PPOConfig
REWARD_CHECKPOINT_PATH = "reward_model/rm_checkpoint/pytorch_model.bin"
if not os.path.exists(REWARD_CHECKPOINT_PATH):
os.makedirs("reward_model/rm_checkpoint", exist_ok=True)
os.system(
f"wget -O {REWARD_CHECKPOINT_PATH} \
https://huggingface.co/CarperAI/openai_summarize_tldr_rm_checkpoint/resolve/main/pytorch_model.bin"
)
SFT_MODEL_PATH = "CarperAI/openai_summarize_tldr_sft"
config = TRLConfig(
train=TrainConfig(
seq_length=550,
epochs=50,
total_steps=100000,
batch_size=4,
checkpoint_interval=10000,
eval_interval=200,
pipeline="PromptPipeline",
trainer="AcceleratePPOTrainer",
),
model=ModelConfig(
model_path="CarperAI/openai_summarize_tldr_sft",
num_layers_unfrozen=8,
),
tokenizer=TokenizerConfig(
tokenizer_path="gpt2",
truncation_side="right",
),
optimizer=OptimizerConfig(
name="adamw",
kwargs={
"lr": 5.0e-6,
"betas": [0.9, 0.999],
"eps": 1.0e-8,
"weight_decay": 0.01,
},
),
scheduler=SchedulerConfig(
name="cosine_annealing",
kwargs={
"T_max": 100000,
"eta_min": 5.0e-6,
},
),
method=PPOConfig(
name="PPOConfig",
num_rollouts=128,
chunk_size=16,
ppo_epochs=4,
init_kl_coef=0.1,
target=6,
horizon=10000,
gamma=1,
lam=0.95,
cliprange=0.2,
cliprange_value=0.2,
vf_coef=0.2,
scale_reward=None,
ref_mean=None,
ref_std=None,
cliprange_reward=10,
gen_kwargs={
"max_new_tokens": 50,
},
),
)
if __name__ == "__main__":
# Load the pre-trained reward model
rw_tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
rw_tokenizer.pad_token = rw_tokenizer.eos_token
rw_model = GPTRewardModel(SFT_MODEL_PATH)
rw_model.load_state_dict(torch.load(REWARD_CHECKPOINT_PATH), strict=False)
rw_model.half()
rw_model.eval()
rw_device = torch.device("cuda:{}".format(1)) # set reward model device
rw_model.to(rw_device)
def get_scores(samples: List[str]):
scores_list = []
batch_size = 2
for i in range(0, len(samples), batch_size):
sub_samples = samples[i : i + batch_size]
sub_samples = ["<|startoftext|>" + chosen + "<|endoftext|>" for chosen in sub_samples]
encodings_dict = rw_tokenizer(
sub_samples,
truncation=True,
max_length=config.train.seq_length,
padding="max_length",
return_tensors="pt",
)
input_ids = encodings_dict["input_ids"].to(rw_device)
attn_masks = encodings_dict["attention_mask"].to(rw_device)
input_ids = input_ids.repeat(2, 1)
attn_masks = attn_masks.repeat(2, 1)
with torch.no_grad():
sub_scores = rw_model(input_ids=input_ids, attention_mask=attn_masks)
scores_list.append(sub_scores["chosen_end_scores"])
scores = torch.cat(scores_list, dim=0)
return scores
def get_prompt_dataset(prompts, max_length):
"""
Get the prompt after T5 decoding to make sure dictionary
of prompts and summaries is consistent decode prompt from trlX pipeline
"""
formatted_prompts = []
for i in tqdm(range(len(prompts))):
tmp = tokenizer.decode(
tokenizer(
prompts[i].split("TL;DR:")[0],
truncation=True,
max_length=max_length - 5, # to make sure "TL;DR" dont get truncated
add_special_tokens=False,
)["input_ids"],
skip_special_tokens=True,
).strip()
tmp = tmp + "\nTL;DR:"
tmp = tokenizer.decode(
tokenizer(tmp, truncation=True, max_length=max_length, add_special_tokens=False)["input_ids"],
skip_special_tokens=True,
).strip()
formatted_prompts.append(tmp)
return formatted_prompts
def reward_fn(samples: List[str], **kwargs):
original_samples = [text.split("TL;DR:")[0] + "TL;DR: " for text in samples]
original_samples = [text + post_summary_dict[text.strip()] for text in original_samples]
original_scores = get_scores(original_samples)
scores = get_scores(samples)
norms_scores = scores - original_scores
return norms_scores
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer.tokenizer_path)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
max_length_input = config.train.seq_length - config.method.gen_kwargs["max_new_tokens"]
dataset = load_dataset("CarperAI/openai_summarize_tldr")
# Store data into prompt and label pairs
train_set = [(sample["prompt"], sample["label"]) for sample in dataset["train"]]
val_set = [(sample["prompt"], sample["label"]) for sample in dataset["valid"]]
# Split contents into summaries and labels
train_posts, train_summaries = zip(*train_set)
val_posts, val_summaries = zip(*val_set)
# Get the OpenAI summaries
post_summary_dict = {}
train_prompts = get_prompt_dataset(train_posts, max_length_input)
for i in range(len(train_prompts)):
post_summary_dict[train_prompts[i]] = train_summaries[i]
val_prompts = get_prompt_dataset(val_posts, max_length_input)
for i in range(len(val_prompts)):
post_summary_dict[val_prompts[i]] = val_summaries[i]
trainer = trlx.train(
reward_fn=reward_fn,
prompts=train_prompts,
eval_prompts=val_prompts[0:1000], # sampling 1000 validation prompts for evaluation speed in training
config=config,
)
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