GRPO script

README.md

@@ -57,7 +57,17 @@ sudo apt-get install git-lfs
 
 ## Training models
 
+### GRPO
 
+```
+accelerate launch scripts/training/grpo.py \
+    --output_dir Qwen2.5-0.5B-GRPO \
+    --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
+    --dataset_name AI-MO/NuminaMath-TIR \
+    --per_device_train_batch_size 2 \
+    --gradient_accumulation_steps 8 \
+    --logging_steps 10
+```
 
 ## Evaluating models
 

src/open_r1/grpo.py

@@ -0,0 +1,133 @@
+# 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 re
+from dataclasses import dataclass, field
+
+from datasets import load_dataset
+
+from trl import GRPOConfig, GRPOTrainer, ModelConfig, ScriptArguments, TrlParser, get_peft_config
+
+
+@dataclass
+class GRPOScriptArguments(ScriptArguments):
+    """
+    Script arguments for the GRPO training script.
+
+    Args:
+        reward_funcs (`list[str]`):
+            List of reward functions. Possible values: 'accuracy', 'format'.
+    """
+
+    reward_funcs: list[str] = field(
+        default_factory=lambda: ["accuracy", "format"],
+        metadata={"help": "List of reward functions. Possible values: 'accuracy', 'format'"},
+    )
+
+
+def extract_boxed_content(text):
+    start = text.find("boxed{")  # Find the starting index of "\boxed{"
+    if start == -1:
+        return ""  # No match found
+
+    # Start reading from the first '{' after "boxed{"
+    start += len("boxed{")
+    brace_count = 1
+    content = []
+
+    for i in range(start, len(text)):
+        char = text[i]
+        if char == "{":
+            brace_count += 1
+        elif char == "}":
+            brace_count -= 1
+
+        # Add the character to the content
+        if brace_count > 0:
+            content.append(char)
+        else:
+            # We've matched all opening braces
+            break
+
+    # If the braces didn't balance, it's malformed
+    if brace_count != 0:
+        return ""
+
+    return "".join(content)
+
+
+def accuracy_reward(completions, ground_truth, **kwargs):
+    """Reward function that checks if the completion is the same as the ground truth."""
+    # Regular expression to capture content inside \boxed{}
+    contents = [completion[0]["content"] for completion in completions]
+    answers = [extract_boxed_content(content) for content in contents]
+    # Reward 1 if the content is the same as the ground truth, 0 otherwise
+    return [1.0 if answer == gt else 0.0 for answer, gt in zip(answers, ground_truth)]
+
+
+def format_reward_func(completions, **kwargs):
+    """Reward function that checks if the completion has a specific format."""
+    pattern = r"^<think>.*?</think><answer>.*?</answer>$"
+    completion_contents = [completion[0]["content"] for completion in completions]
+    matches = [re.match(pattern, content) for content in completion_contents]
+    return [1.0 if match else 0.0 for match in matches]
+
+
+reward_funcs_registry = {
+    "accuracy": accuracy_reward,
+    "format": format_reward_func,
+}
+
+
+def main(script_args, training_args, model_args):
+    # Get reward functions
+    reward_funcs = [reward_funcs_registry[func] for func in script_args.reward_funcs]
+
+    # Load the dataset
+    dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
+
+    # Format into conversation
+    def make_conversation(example):
+        ground_truth = extract_boxed_content(example["solution"])
+        return {
+            "prompt": [{"role": "user", "content": example["problem"]}],
+            "ground_truth": ground_truth,
+        }
+
+    dataset = dataset.map(make_conversation)
+    dataset = dataset.remove_columns("messages")
+
+    # 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),
+    )
+
+    # Train and push the model to the Hub
+    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)
+
+
+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)