TextRL

Text generation with reinforcement learning using huggingface's transformer.
RLHF (Reinforcement Learning with Human Feedback) Implementation of ChatGPT for human interaction to improve generation model with reinforcement learning.

Introduction

This project is trying to use reinforcement learning to adjust text generation results. It is based on any text-generation model on huggingaface's transformer with PFRL and OpenAI GYM.

Example 1

Run on 7B multi-lingual bloom: bigscience/bloomz-7b1-mt

import pfrl
from textrl import TextRLEnv, TextRLActor
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "bigscience/bloomz-7b1-mt"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto")

model = model.cuda()

class MyRLEnv(TextRLEnv):
    def get_reward(self, input_item, predicted_list, finish):  # predicted will be the list of predicted token
        reward = 0
        if finish:
            reward = len(predicted_list)
        return reward

observaton_list = [["explain how attention work in seq2seq model"]]
env = MyRLEnv(model, tokenizer, observation_input=observaton_list)
actor = TextRLActor(env, model, tokenizer,act_deterministically=False)
agent = actor.agent_ppo(update_interval=2, minibatch_size=2, epochs=10)

print(actor.predict(observaton_list[0]))

pfrl.experiments.train_agent_with_evaluation(
    agent,
    env,
    steps=100,
    eval_n_steps=None,
    eval_n_episodes=1,       
    eval_interval=2,
    outdir='bloom—test', 
)

print(actor.predict(observaton_list[0]))

Example 2

Training on 176B BLOOM model using petals.

Strongly recommend joining swarm to increase petals capacity

https://github.com/bigscience-workshop/petals

install pip install petals -U first

import pfrl
from textrl import TextRLEnv, TextRLActor
from transformers import BloomTokenizerFast 
from petals import DistributedBloomForCausalLM

MODEL_NAME = "bigscience/bloom-petals"
tokenizer = BloomTokenizerFast.from_pretrained(MODEL_NAME)
model = DistributedBloomForCausalLM.from_pretrained(MODEL_NAME)
model = model.cuda()

class MyRLEnv(TextRLEnv):
    def get_reward(self, input_item, predicted_list, finish):  # predicted will be the list of predicted token
        reward = 0
        if finish:
            reward = len(predicted_list)
        return reward

observaton_list = [["explain how attention work in seq2seq model"]]
env = MyRLEnv(model, tokenizer, observation_input=observaton_list)
actor = TextRLActor(env, model, tokenizer,act_deterministically=False)
agent = actor.agent_ppo(update_interval=2, minibatch_size=2, epochs=10)

print(actor.predict(observaton_list[0]))

pfrl.experiments.train_agent_with_evaluation(
    agent,
    env,
    steps=100,
    eval_n_steps=None,
    eval_n_episodes=1,       
    eval_interval=2,
    outdir='bloom—test', 
)

print(actor.predict(observaton_list[0]))

Example 3

Controllable generation via RL to let Elon Musk speak ill of DOGE

before: i think dogecoin is a great idea.
after: i think dogecoin is a great idea, but I think it is a little overused.

Installation

pip install

pip install pfrl@git+https://github.com/voidful/pfrl.git
pip install textrl

Build from source

git clone and cd into this project.

pip install -e .

Usage

init agent and environment

import torch
from textrl import TextRLEnv, TextRLActor
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "bigscience/bloomz-7b1-mt"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto")

model = model.cuda()

setup reward function for environment

• predicted(list[str]): will be the list of predicted token
• finish(bool): it met the end of sentence or not

class MyRLEnv(TextRLEnv):
    def get_reward(self, input_item, predicted_list, finish):  # predicted will be the list of predicted token
        if "[UNK]" in predicted_list:
            reward = -1
        else:
            reward = 1
        return reward

prepare for training

• observaton_list should be a list of all possible input string for model training

  eg: observaton_list = [['testing sent 1'],['testing sent 2']]

env = MyRLEnv(model, tokenizer, observation_input=observaton_list)
actor = TextRLActor(env, model, tokenizer)
agent = actor.agent_ppo(update_interval=10, minibatch_size=2000, epochs=20)

Train

n_episodes = 1000
max_episode_len = 200  # max sentence length

for i in range(1, n_episodes + 1):
    obs = env.reset()
    R = 0
    t = 0
    while True:
        action = agent.act(obs)
        obs, reward, done, pred = env.step(action)
        R += reward
        t += 1
        reset = t == max_episode_len
        agent.observe(obs, reward, done, reset)
        if done or reset:
            break
    if i % 10 == 0:
        print('episode:', i, 'R:', R)
    if i % 50 == 0:
        print('statistics:', agent.get_statistics())
print('Finished.')

another way to train

import logging
import sys

logging.basicConfig(level=logging.INFO, stream=sys.stdout, format='')

pfrl.experiments.train_agent_with_evaluation(
    agent,
    env,
    steps=1000,
    eval_n_steps=None,
    eval_n_episodes=1500,
    train_max_episode_len=50,
    eval_interval=10000,
    outdir='somewhere',
)

prediction

agent.load("somewhere/best")  # loading the best model
actor.predict("input text")

dump trained model to huggingface's model

textrl-dump --model ./model_path_before_rl --rl ./rl_path --dump ./output_dir