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# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
from accelerate import PartialState
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoModelForSequenceClassification,
AutoTokenizer,
HfArgumentParser,
)
from trl import ModelConfig, RLOOConfig, RLOOTrainer, ScriptArguments
from trl.trainer.utils import SIMPLE_CHAT_TEMPLATE
"""
python examples/scripts/rloo/rloo_tldr.py \
--dataset_name trl-internal-testing/tldr-preference-sft-trl-style \
--dataset_test_split validation \
--learning_rate 3e-6 \
--output_dir models/minimal/ppo \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 64 \
--total_episodes 30000 \
--model_name_or_path EleutherAI/pythia-1b-deduped \
--sft_model_path cleanrl/EleutherAI_pythia-1b-deduped__sft__tldr \
--reward_model_path cleanrl/EleutherAI_pythia-1b-deduped__reward__tldr \
--missing_eos_penalty 1.0 \
--stop_token eos \
--response_length 53
accelerate launch --config_file examples/accelerate_configs/deepspeed_zero2.yaml \
examples/scripts/rloo/rloo_tldr.py \
--dataset_name trl-internal-testing/tldr-preference-sft-trl-style \
--dataset_test_split validation \
--output_dir models/minimal/rloo_tldr \
--num_ppo_epochs 1 \
--num_mini_batches 1 \
--learning_rate 3e-6 \
--per_device_train_batch_size 16 \
--gradient_accumulation_steps 4 \
--total_episodes 1000000 \
--model_name_or_path EleutherAI/pythia-1b-deduped \
--sft_model_path cleanrl/EleutherAI_pythia-1b-deduped__sft__tldr \
--reward_model_path cleanrl/EleutherAI_pythia-1b-deduped__reward__tldr \
--local_rollout_forward_batch_size 16 \
--missing_eos_penalty 1.0 \
--stop_token eos
"""
if __name__ == "__main__":
parser = HfArgumentParser((ScriptArguments, RLOOConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_into_dataclasses()
# remove output_dir if exists
shutil.rmtree(training_args.output_dir, ignore_errors=True)
################
# Model & Tokenizer
################
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, padding_side="left", trust_remote_code=model_args.trust_remote_code
)
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
if tokenizer.chat_template is None:
tokenizer.chat_template = SIMPLE_CHAT_TEMPLATE
reward_model = AutoModelForSequenceClassification.from_pretrained(
training_args.reward_model_path, trust_remote_code=model_args.trust_remote_code, num_labels=1
)
ref_policy = AutoModelForCausalLM.from_pretrained(
training_args.sft_model_path, trust_remote_code=model_args.trust_remote_code
)
policy = AutoModelForCausalLM.from_pretrained(
training_args.sft_model_path, trust_remote_code=model_args.trust_remote_code
)
################
# Dataset
################
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
train_dataset = dataset[script_args.dataset_train_split]
eval_dataset = dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None
def prepare_dataset(dataset, tokenizer):
"""pre-tokenize the dataset before training; only collate during training"""
def tokenize(element):
input_ids = tokenizer.apply_chat_template(
element["messages"][:1],
padding=False,
add_generation_prompt=True,
)
return {"input_ids": input_ids, "lengths": len(input_ids)}
return dataset.map(
tokenize,
remove_columns=dataset.column_names,
num_proc=training_args.dataset_num_proc,
)
# Compute that only on the main process for faster data processing.
# see: https://github.com/huggingface/trl/pull/1255
with PartialState().local_main_process_first():
train_dataset = prepare_dataset(train_dataset, tokenizer)
eval_dataset = prepare_dataset(eval_dataset, tokenizer)
# filtering
train_dataset = train_dataset.filter(lambda x: x["lengths"] <= 512, num_proc=training_args.dataset_num_proc)
eval_dataset = eval_dataset.filter(lambda x: x["lengths"] <= 512, num_proc=training_args.dataset_num_proc)
assert train_dataset[0]["input_ids"][-1] != tokenizer.eos_token_id, "The last token should not be an EOS token"
################
# Training
################
trainer = RLOOTrainer(
config=training_args,
processing_class=tokenizer,
policy=policy,
ref_policy=ref_policy,
reward_model=reward_model,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
)
trainer.train()
# Save and push to hub
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)
trainer.generate_completions()
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