Kashif Rasul 90a6de94c7
Revert "Weighted reward functions (#213)" (#317)
This reverts commit fbea53267b9676fc89e92c9a24c83cb23e0884d0.
2025-02-13 15:00:05 +01:00

266 lines
9.6 KiB
Python

# Copyright 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 logging
import os
import sys
from dataclasses import dataclass, field
import datasets
import torch
import transformers
from datasets import load_dataset
from transformers import set_seed
from transformers.trainer_utils import get_last_checkpoint
from open_r1.configs import GRPOConfig
from open_r1.rewards import (
accuracy_reward,
format_reward,
get_cosine_scaled_reward,
get_repetition_penalty_reward,
len_reward,
reasoning_steps_reward,
)
from open_r1.utils.callbacks import get_callbacks
from open_r1.utils.wandb_logging import init_wandb_training
from trl import GRPOTrainer, ModelConfig, ScriptArguments, TrlParser, get_peft_config
logger = logging.getLogger(__name__)
@dataclass
class GRPOScriptArguments(ScriptArguments):
"""
Script arguments for the GRPO training script.
Args:
reward_funcs (`list[str]`):
List of reward functions. Possible values: 'accuracy', 'format', 'reasoning_steps', 'cosine', 'repetition_penalty', 'length'.
cosine_min_value_wrong (`float`):
Minimum reward for cosine scaling for wrong answers.
cosine_max_value_wrong (`float`):
Maximum reward for cosine scaling for wrong answers.
cosine_min_value_correct (`float`):
Minimum reward for cosine scaling for correct answers.
cosine_max_value_correct (`float`):
Maximum reward for cosine scaling for correct answers.
cosine_max_len (`int`):
Maximum length for cosine scaling.
"""
reward_funcs: list[str] = field(
default_factory=lambda: ["accuracy", "format"],
metadata={
"help": "List of reward functions. Possible values: 'accuracy', 'format', 'reasoning_steps', 'cosine', 'repetition_penalty', 'length'"
},
)
cosine_min_value_wrong: float = field(
default=0.0,
metadata={"help": "Minimum reward for wrong answers"},
)
cosine_max_value_wrong: float = field(
default=-0.5,
metadata={"help": "Maximum reward for wrong answers"},
)
cosine_min_value_correct: float = field(
default=0.5,
metadata={"help": "Minimum reward for correct answers"},
)
cosine_max_value_correct: float = field(
default=1.0,
metadata={"help": "Maximum reward for correct answers"},
)
cosine_max_len: int = field(
default=1000,
metadata={"help": "Maximum length for scaling"},
)
repetition_n_grams: int = field(
default=3,
metadata={"help": "Number of n-grams for repetition penalty reward"},
)
repetition_max_penalty: float = field(
default=-1.0,
metadata={"help": "Maximum (negative) penalty for for repetition penalty reward"},
)
SYSTEM_PROMPT = (
"A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant "
"first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning "
"process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., "
"<think> reasoning process here </think><answer> answer here </answer>"
)
def main(script_args, training_args, model_args):
# Set seed for reproducibility
set_seed(training_args.seed)
###############
# Setup logging
###############
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process a small summary
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Model parameters {model_args}")
logger.info(f"Script parameters {script_args}")
logger.info(f"Training parameters {training_args}")
# Check for last checkpoint
last_checkpoint = None
if os.path.isdir(training_args.output_dir):
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(f"Checkpoint detected, resuming training at {last_checkpoint=}.")
if "wandb" in training_args.report_to:
init_wandb_training(training_args)
# Load the dataset
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
# Get reward functions
REWARD_FUNCS_REGISTRY = {
"accuracy": accuracy_reward,
"format": format_reward,
"reasoning_steps": reasoning_steps_reward,
"cosine": get_cosine_scaled_reward(
min_value_wrong=script_args.cosine_min_value_wrong,
max_value_wrong=script_args.cosine_max_value_wrong,
min_value_correct=script_args.cosine_min_value_correct,
max_value_correct=script_args.cosine_max_value_correct,
max_len=script_args.cosine_max_len,
),
"repetition_penalty": get_repetition_penalty_reward(
ngram_size=script_args.repetition_n_grams,
max_penalty=script_args.repetition_max_penalty,
),
"length": len_reward,
}
reward_funcs = [REWARD_FUNCS_REGISTRY[func] for func in script_args.reward_funcs]
# Format into conversation
def make_conversation(example):
return {
"prompt": [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": example["problem"]},
],
}
dataset = dataset.map(make_conversation)
for split in dataset:
if "messages" in dataset[split].column_names:
dataset[split] = dataset[split].remove_columns("messages")
logger.info("*** Initializing model kwargs ***")
torch_dtype = (
model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
)
model_kwargs = dict(
revision=model_args.model_revision,
trust_remote_code=model_args.trust_remote_code,
attn_implementation=model_args.attn_implementation,
torch_dtype=torch_dtype,
use_cache=False if training_args.gradient_checkpointing else True,
)
training_args.model_init_kwargs = model_kwargs
#############################
# Initialize the GRPO trainer
#############################
trainer = GRPOTrainer(
model=model_args.model_name_or_path,
reward_funcs=reward_funcs,
args=training_args,
train_dataset=dataset[script_args.dataset_train_split],
eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
peft_config=get_peft_config(model_args),
callbacks=get_callbacks(training_args, model_args),
)
###############
# Training loop
###############
logger.info("*** Train ***")
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
metrics["train_samples"] = len(dataset[script_args.dataset_train_split])
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
##################################
# Save model and create model card
##################################
logger.info("*** Save model ***")
trainer.save_model(training_args.output_dir)
logger.info(f"Model saved to {training_args.output_dir}")
# Save everything else on main process
kwargs = {
"dataset_name": script_args.dataset_name,
"tags": ["open-r1"],
}
if trainer.accelerator.is_main_process:
trainer.create_model_card(**kwargs)
# Restore k,v cache for fast inference
trainer.model.config.use_cache = True
trainer.model.config.save_pretrained(training_args.output_dir)
##########
# Evaluate
##########
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
metrics["eval_samples"] = len(dataset[script_args.dataset_test_split])
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
#############
# push to hub
#############
if training_args.push_to_hub:
logger.info("Pushing to hub...")
trainer.push_to_hub(**kwargs)
if __name__ == "__main__":
parser = TrlParser((GRPOScriptArguments, GRPOConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_and_config()
main(script_args, training_args, model_args)