266 lines
9.6 KiB
Python
266 lines
9.6 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import os
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import sys
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from dataclasses import dataclass, field
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import datasets
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import torch
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import transformers
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from datasets import load_dataset
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from transformers import set_seed
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from transformers.trainer_utils import get_last_checkpoint
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from open_r1.configs import GRPOConfig
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from open_r1.rewards import (
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accuracy_reward,
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format_reward,
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get_cosine_scaled_reward,
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get_repetition_penalty_reward,
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len_reward,
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reasoning_steps_reward,
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)
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from open_r1.utils.callbacks import get_callbacks
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from open_r1.utils.wandb_logging import init_wandb_training
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from trl import GRPOTrainer, ModelConfig, ScriptArguments, TrlParser, get_peft_config
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logger = logging.getLogger(__name__)
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@dataclass
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class GRPOScriptArguments(ScriptArguments):
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"""
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Script arguments for the GRPO training script.
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Args:
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reward_funcs (`list[str]`):
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List of reward functions. Possible values: 'accuracy', 'format', 'reasoning_steps', 'cosine', 'repetition_penalty', 'length'.
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cosine_min_value_wrong (`float`):
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Minimum reward for cosine scaling for wrong answers.
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cosine_max_value_wrong (`float`):
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Maximum reward for cosine scaling for wrong answers.
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cosine_min_value_correct (`float`):
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Minimum reward for cosine scaling for correct answers.
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cosine_max_value_correct (`float`):
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Maximum reward for cosine scaling for correct answers.
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cosine_max_len (`int`):
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Maximum length for cosine scaling.
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"""
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reward_funcs: list[str] = field(
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default_factory=lambda: ["accuracy", "format"],
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metadata={
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"help": "List of reward functions. Possible values: 'accuracy', 'format', 'reasoning_steps', 'cosine', 'repetition_penalty', 'length'"
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},
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)
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cosine_min_value_wrong: float = field(
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default=0.0,
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metadata={"help": "Minimum reward for wrong answers"},
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)
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cosine_max_value_wrong: float = field(
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default=-0.5,
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metadata={"help": "Maximum reward for wrong answers"},
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)
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cosine_min_value_correct: float = field(
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default=0.5,
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metadata={"help": "Minimum reward for correct answers"},
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)
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cosine_max_value_correct: float = field(
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default=1.0,
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metadata={"help": "Maximum reward for correct answers"},
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)
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cosine_max_len: int = field(
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default=1000,
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metadata={"help": "Maximum length for scaling"},
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)
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repetition_n_grams: int = field(
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default=3,
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metadata={"help": "Number of n-grams for repetition penalty reward"},
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)
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repetition_max_penalty: float = field(
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default=-1.0,
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metadata={"help": "Maximum (negative) penalty for for repetition penalty reward"},
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)
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SYSTEM_PROMPT = (
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"A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant "
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"first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning "
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"process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., "
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"<think> reasoning process here </think><answer> answer here </answer>"
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)
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def main(script_args, training_args, model_args):
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# Set seed for reproducibility
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set_seed(training_args.seed)
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###############
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# Setup logging
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###############
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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handlers=[logging.StreamHandler(sys.stdout)],
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)
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log_level = training_args.get_process_log_level()
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logger.setLevel(log_level)
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datasets.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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# Log on each process a small summary
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logger.warning(
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f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
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+ f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
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)
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logger.info(f"Model parameters {model_args}")
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logger.info(f"Script parameters {script_args}")
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logger.info(f"Training parameters {training_args}")
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# Check for last checkpoint
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last_checkpoint = None
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if os.path.isdir(training_args.output_dir):
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last_checkpoint = get_last_checkpoint(training_args.output_dir)
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if last_checkpoint is not None and training_args.resume_from_checkpoint is None:
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logger.info(f"Checkpoint detected, resuming training at {last_checkpoint=}.")
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if "wandb" in training_args.report_to:
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init_wandb_training(training_args)
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# Load the dataset
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dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
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# Get reward functions
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REWARD_FUNCS_REGISTRY = {
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"accuracy": accuracy_reward,
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"format": format_reward,
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"reasoning_steps": reasoning_steps_reward,
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"cosine": get_cosine_scaled_reward(
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min_value_wrong=script_args.cosine_min_value_wrong,
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max_value_wrong=script_args.cosine_max_value_wrong,
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min_value_correct=script_args.cosine_min_value_correct,
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max_value_correct=script_args.cosine_max_value_correct,
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max_len=script_args.cosine_max_len,
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),
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"repetition_penalty": get_repetition_penalty_reward(
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ngram_size=script_args.repetition_n_grams,
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max_penalty=script_args.repetition_max_penalty,
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),
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"length": len_reward,
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}
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reward_funcs = [REWARD_FUNCS_REGISTRY[func] for func in script_args.reward_funcs]
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# Format into conversation
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def make_conversation(example):
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return {
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"prompt": [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": example["problem"]},
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],
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}
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dataset = dataset.map(make_conversation)
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for split in dataset:
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if "messages" in dataset[split].column_names:
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dataset[split] = dataset[split].remove_columns("messages")
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logger.info("*** Initializing model kwargs ***")
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torch_dtype = (
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model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
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)
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model_kwargs = dict(
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revision=model_args.model_revision,
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trust_remote_code=model_args.trust_remote_code,
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attn_implementation=model_args.attn_implementation,
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torch_dtype=torch_dtype,
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use_cache=False if training_args.gradient_checkpointing else True,
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)
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training_args.model_init_kwargs = model_kwargs
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#############################
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# Initialize the GRPO trainer
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#############################
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trainer = GRPOTrainer(
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model=model_args.model_name_or_path,
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reward_funcs=reward_funcs,
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args=training_args,
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train_dataset=dataset[script_args.dataset_train_split],
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eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
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peft_config=get_peft_config(model_args),
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callbacks=get_callbacks(training_args, model_args),
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)
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###############
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# Training loop
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###############
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logger.info("*** Train ***")
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checkpoint = None
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if training_args.resume_from_checkpoint is not None:
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checkpoint = training_args.resume_from_checkpoint
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elif last_checkpoint is not None:
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checkpoint = last_checkpoint
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train_result = trainer.train(resume_from_checkpoint=checkpoint)
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metrics = train_result.metrics
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metrics["train_samples"] = len(dataset[script_args.dataset_train_split])
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trainer.log_metrics("train", metrics)
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trainer.save_metrics("train", metrics)
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trainer.save_state()
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##################################
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# Save model and create model card
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##################################
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logger.info("*** Save model ***")
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trainer.save_model(training_args.output_dir)
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logger.info(f"Model saved to {training_args.output_dir}")
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# Save everything else on main process
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kwargs = {
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"dataset_name": script_args.dataset_name,
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"tags": ["open-r1"],
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}
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if trainer.accelerator.is_main_process:
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trainer.create_model_card(**kwargs)
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# Restore k,v cache for fast inference
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trainer.model.config.use_cache = True
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trainer.model.config.save_pretrained(training_args.output_dir)
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##########
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# Evaluate
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##########
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if training_args.do_eval:
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logger.info("*** Evaluate ***")
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metrics = trainer.evaluate()
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metrics["eval_samples"] = len(dataset[script_args.dataset_test_split])
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trainer.log_metrics("eval", metrics)
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trainer.save_metrics("eval", metrics)
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#############
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# push to hub
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#############
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if training_args.push_to_hub:
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logger.info("Pushing to hub...")
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trainer.push_to_hub(**kwargs)
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if __name__ == "__main__":
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parser = TrlParser((GRPOScriptArguments, GRPOConfig, ModelConfig))
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script_args, training_args, model_args = parser.parse_args_and_config()
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main(script_args, training_args, model_args)
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