mirror of
https://github.com/open-thought/reasoning-gym.git
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695aad4dbc
* prepend think token in format reward * pre commit + fix some default vals * add checkpoint config
192 lines
7.6 KiB
Python
192 lines
7.6 KiB
Python
# Adapted version of Bytedance code:
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# https://github.com/volcengine/verl/blob/a65c9157bc0b85b64cd753de19f94e80a11bd871/verl/trainer/main_ppo.py
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import re
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import torch
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from omegaconf import OmegaConf, open_dict
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from torchdata.stateful_dataloader import StatefulDataLoader
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from utils import ReasoningGymDataset
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from verl import DataProto
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from verl.trainer.ppo.ray_trainer import RayPPOTrainer
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from verl.utils.dataset.rl_dataset import collate_fn
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from reasoning_gym.utils import extract_answer
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class RayGRPOTrainer(RayPPOTrainer):
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def __init__(
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self,
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config,
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tokenizer,
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train_dataset: ReasoningGymDataset,
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val_dataset: ReasoningGymDataset,
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role_worker_mapping: dict,
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resource_pool_manager,
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ray_worker_group_cls,
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max_output_length: int = 1024,
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):
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self.train_dataset = train_dataset
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self.val_dataset = val_dataset
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self.max_output_length = max_output_length
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self.format_reward_scaling_factor = config.reward.format_reward.scaling_factor
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self.format_reward_prepend_think_token = config.reward.format_reward.prepend_think_token
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self.length_reward_scaling_factor = config.reward.length_reward.scaling_factor
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train_reward_fn = lambda data: self._score_output(data, num_examine=0)
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val_reward_fn = lambda data: self._score_output(data, num_examine=1)
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super().__init__(
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config,
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tokenizer,
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role_worker_mapping,
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resource_pool_manager,
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ray_worker_group_cls,
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train_reward_fn,
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val_reward_fn,
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)
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def _score_output(self, data: DataProto, num_examine: int = 0) -> torch.Tensor:
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reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32)
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num_printed = 0
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for i in range(len(data)):
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data_item = data[i] # DataProtoItem
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prompt_ids = data_item.batch["prompts"] # tokenized prompts
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prompt_length = prompt_ids.shape[-1]
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valid_prompt_length = data_item.batch["attention_mask"][:prompt_length].sum()
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valid_prompt_ids = prompt_ids[-valid_prompt_length:]
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response_ids = data_item.batch["responses"]
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valid_response_length = data_item.batch["attention_mask"][prompt_length:].sum()
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valid_response_ids = response_ids[:valid_response_length]
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# decode
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prompt_str = self.tokenizer.decode(valid_prompt_ids)
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response_str = self.tokenizer.decode(valid_response_ids)
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sequences_str = prompt_str + response_str
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index = data_item.non_tensor_batch["index"]
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reward = score = self._compute_correctness_score(
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solution_str=response_str,
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index=index,
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)
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if self.config.reward.format_reward.enable:
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format_reward = self._compute_format_reward(response_str)
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reward += format_reward
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else:
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format_reward = 0.0
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if self.config.reward.length_reward.enable:
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length_reward = self._compute_length_reward(response_str, score)
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reward += length_reward
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else:
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length_reward = 0.0
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reward_tensor[i, valid_response_length - 1] = reward
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if num_printed < num_examine:
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print(
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f"reward={reward} (score={score}, format={format_reward}, length={length_reward}), seq={sequences_str}"
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)
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num_printed += 1
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return reward_tensor
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def _compute_format_reward(self, solution_str: str) -> float:
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"""Reward use of exactly one correctly structured <think> and <answer> block."""
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if self.format_reward_prepend_think_token:
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solution_str = "<think>" + solution_str
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scaling_factor = self.format_reward_scaling_factor
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# check <think> and <answer> blocks are present
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pattern = r"\s*<think>.*?</think>\s*<answer>.*?</answer>"
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if not re.match(pattern, solution_str, re.DOTALL):
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return 0.0
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# check exactly one properly structured <think> block and one <answer> block
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think_matches = list(re.finditer(r"<think>(.*?)</think>", solution_str, re.DOTALL))
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answer_matches = list(re.finditer(r"<answer>(.*?)</answer>", solution_str, re.DOTALL))
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if len(think_matches) != 1 or len(answer_matches) != 1:
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return 0.0
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# check for <think> or <answer> inside <think>
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think_content = think_matches[0].group(1)
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if "<think>" in think_content or "<answer>" in think_content:
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return 0.0
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# check for nested <think> or <answer> inside <answer>
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answer_content = answer_matches[0].group(1)
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if "<answer>" in answer_content or "<think>" in answer_content:
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return 0.0
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return 1.0 * scaling_factor
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def _compute_length_reward(
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self,
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solution_str: str,
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correctness_score: float,
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max_score: float = 1.0,
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) -> float:
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"""
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Reward shorter solutions for perfect answers, longer solutions for imperfect answers.
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The scaling factor for this should be set far below 1.0, to avoid dominating the reward signal over correctness.
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"""
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epsilon = 1e-6
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scaling_factor = self.length_reward_scaling_factor
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generation_len = len(solution_str)
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progress = min(generation_len / self.max_output_length, 1.0)
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if correctness_score < max_score - epsilon:
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# for imperfect answers, incentivise longer ones
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length_reward = (max_score - correctness_score) * progress
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else:
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# for perfect answers, penalise longer ones
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length_reward = -progress
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return length_reward * scaling_factor
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def _compute_correctness_score(self, solution_str: str, index: int) -> float:
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found_answer = extract_answer(solution_str, tag_name="answer")
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data = self.train_dataset.data
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entry = data[index]
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if self.train_dataset.experiment:
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experiment = self.train_dataset.experiment
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return experiment.score_answer_with_id(found_answer, entry["metadata"]["entry_id"])
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else:
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return data.score_answer(found_answer, entry=entry)
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def _create_dataloader(self):
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self.train_dataloader = StatefulDataLoader(
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dataset=self.train_dataset,
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batch_size=self.config.data.train_batch_size,
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shuffle=True,
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drop_last=True,
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collate_fn=collate_fn,
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)
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self.val_dataloader = StatefulDataLoader(
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dataset=self.val_dataset,
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batch_size=len(self.val_dataset),
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shuffle=True,
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drop_last=True,
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collate_fn=collate_fn,
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)
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assert len(self.train_dataloader) >= 1
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assert len(self.val_dataloader) >= 1
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print(f"Size of train dataloader: {len(self.train_dataloader)}")
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print(f"Size of val dataloader: {len(self.val_dataloader)}")
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# inject total_training_steps to actor/critic optim_config. This is hacky.
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total_training_steps = len(self.train_dataloader) * self.config.trainer.total_epochs
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if self.config.trainer.total_training_steps is not None:
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total_training_steps = self.config.trainer.total_training_steps
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self.total_training_steps = total_training_steps
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print(f"Total training steps: {self.total_training_steps}")
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OmegaConf.set_struct(self.config, True)
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with open_dict(self.config):
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self.config.actor_rollout_ref.actor.optim.total_training_steps = total_training_steps
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self.config.critic.optim.total_training_steps = total_training_steps
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