Awesome
InstructGoose
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<!-- [![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/) -->Paper: InstructGPT - Training language models to follow instructions with human feedback
Install
Install from PipPy
pip install instruct-goose
Install directly from the source code
git clone https://github.com/xrsrke/instructGOOSE.git
cd instructGOOSE
pip install -e .
How to Train
For reward model
Use 🤗 Accelerate to launch distributed training
accelerate config
accelerate launch scripts/train_reward.py
Train the RL-based language model
from transformers import AutoTokenizer, AutoModelForCausalLM
from datasets import load_dataset
import torch
from torch.utils.data import DataLoader, random_split
from torch import optim
from instruct_goose import Agent, RewardModel, RLHFTrainer, RLHFConfig, create_reference_model
Step 1: Load dataset
dataset = load_dataset("imdb", split="train")
dataset, _ = random_split(dataset, lengths=[10, len(dataset) - 10]) # for demenstration purposes
train_dataloader = DataLoader(dataset, batch_size=4, shuffle=True)
Found cached dataset imdb (/Users/education/.cache/huggingface/datasets/imdb/plain_text/1.0.0/d613c88cf8fa3bab83b4ded3713f1f74830d1100e171db75bbddb80b3345c9c0)
Step 2: Load the pre-trained model and tokenizer
model_base = AutoModelForCausalLM.from_pretrained("gpt2") # for demonstration purposes
reward_model = RewardModel("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left")
eos_token_id = tokenizer.eos_token_id
tokenizer.pad_token = tokenizer.eos_token
Step 3: Create the RL-based language model agent and the reference model
model = Agent(model_base)
ref_model = create_reference_model(model)
Step 4: Train it
max_new_tokens = 20
generation_kwargs = {
"min_length":-1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
"max_new_tokens": max_new_tokens
}
config = RLHFConfig()
N_EPOCH = 1 # for demonstration purposes
trainer = RLHFTrainer(model, ref_model, config)
optimizer = optim.SGD(model.parameters(), lr=1e-3)
for epoch in range(N_EPOCH):
for batch in train_dataloader:
inputs = tokenizer(batch["text"], padding=True, truncation=True, return_tensors="pt")
response_ids = model.generate(
inputs["input_ids"], attention_mask=inputs["attention_mask"],
**generation_kwargs
)
# extract the generated text
response_ids = response_ids[:, -max_new_tokens:]
response_attention_mask = torch.ones_like(response_ids)
# evaluate from the reward model
with torch.no_grad():
text_input_ids = torch.stack([torch.concat([q, r]) for q, r in zip(inputs["input_ids"], response_ids)], dim=0)
rewards = reward_model(text_input_ids)
# calculate PPO loss
loss = trainer.compute_loss(
query_ids=inputs["input_ids"],
query_attention_mask=inputs["attention_mask"],
response_ids=response_ids,
response_attention_mask=response_attention_mask,
rewards=rewards
)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(f"loss={loss}")
loss=-824.6560668945312
loss=0.030958056449890137
loss=4.284017562866211
TODO
- Add support custom reward function
- Add support custom value function
- Add support non-transformer models
- Write config class
✅ Distributed training using 🤗 Accelerate
Resources
I implemented this using these resources
- Copied the
load_yaml
function from https://github.com/Dahoas/reward-modeling - How to build a dataset to train reward model: https://wandb.ai/carperai/summarize_RLHF/reports/Implementing-RLHF-Learning-to-Summarize-with-trlX–VmlldzozMzAwODM2
- How to add value head in PPO agent: https://github.com/lvwerra/trl
- How to calculate the loss of PPO agent: https://github.com/lvwerra/trl/blob/main/trl/trainer/ppo_trainer.py
- How to use PPO to train RLHF agent: https://github.com/voidful/TextRL
- How PPO works: https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo.py
- Copied the compute
advantages
andreturns
fromTLR
: https://github.com/lvwerra/trl/blob/d2e8bcf8373726fb92d2110c500f7df6d0bd566d/trl/trainer/ppo_trainer.py#L686