mirror of
https://github.com/open-thought/reasoning-gym.git
synced 2025-10-09 13:40:09 +03:00
c69bc5d4e6
* feat: Add optional curriculum support to dataset registration and creation * docs: Add docstrings to create_curriculum() and register_dataset() * feat: Add curriculum configuration classes for CurriculumExperiment * feat: Add weight parameter to CurriculumAttributeConfig and use in DatasetSpec * refactor: Simplify CurriculumAttributeConfig with "*" attribute level support * test: Add unit tests for CurriculumExperiment class * feat: Add from_yaml() method to CurriculumExperimentConfig with unit test
286 lines
9.7 KiB
Python
286 lines
9.7 KiB
Python
# This example is an adapted version of Bytedance's code:
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# https://github.com/volcengine/verl/blob/a65c9157bc0b85b64cd753de19f94e80a11bd871/verl/trainer/main_ppo.py
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from typing import Optional
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import hydra
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import ray
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import torch
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import verl.utils.torch_functional as verl_F
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from omegaconf import OmegaConf, open_dict
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from torch.utils.data import DataLoader, Dataset
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from transformers import PreTrainedTokenizer
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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 verl.utils.model import compute_position_id_with_mask
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import reasoning_gym
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import reasoning_gym.utils
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from reasoning_gym.utils import extract_answer
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class ReasoningGymDataset(Dataset):
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def __init__(
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self,
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tokenizer: PreTrainedTokenizer,
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dataset_name: str,
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seed: int,
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size: int,
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developer_prompt: Optional[str] = None,
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developer_role: str = "system",
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max_prompt_length: int = 2048,
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truncation: str = "error", ## ['left', 'right', 'error']
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return_raw_chat: bool = False,
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):
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self.tokenizer = tokenizer
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self.dataset_name = dataset_name
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self.data = reasoning_gym.create_dataset(dataset_name, seed=seed, size=size)
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self.developer_prompt = developer_prompt
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self.developer_role = developer_role
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self.max_prompt_length = max_prompt_length
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self.truncation = truncation
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self.return_raw_chat = return_raw_chat
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def __len__(self) -> int:
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return len(self.data)
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def __getitem__(self, index):
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row_dict = self.data[index].copy()
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q = row_dict["question"]
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chat = []
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if self.developer_prompt is not None:
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chat.append({"role": self.developer_role, "content": self.developer_prompt})
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chat.append({"role": "user", "content": q})
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prompt = self.tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
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input_ids, attention_mask = verl_F.tokenize_and_postprocess_data(
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prompt=prompt,
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tokenizer=self.tokenizer,
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max_length=self.max_prompt_length,
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pad_token_id=self.tokenizer.pad_token_id,
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left_pad=True,
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truncation=self.truncation,
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)
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position_ids = compute_position_id_with_mask(attention_mask)
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row_dict["data_source"] = "reasoning_gym/" + self.dataset_name
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row_dict["input_ids"] = input_ids[0]
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row_dict["attention_mask"] = attention_mask[0]
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row_dict["position_ids"] = position_ids[0]
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# encode prompts without chat template
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if self.return_raw_chat:
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row_dict["raw_prompt"] = chat.tolist()
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# add index for each prompt
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# index = row_dict.get("extra_info", {}).get("index", 0)
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row_dict["index"] = index
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return row_dict
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class RayPPOTrainerCustom(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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role_worker_mapping: dict,
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resource_pool_manager,
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ray_worker_group_cls,
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dataset_name: str = "chain_sum",
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dataset_size: int = 10000,
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):
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self.dataset_name = dataset_name
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self.dataset_size = dataset_size
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developer_prompt = reasoning_gym.utils.SYSTEM_PROMPTS["DeepSeekZero"]
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self.train_dataset = ReasoningGymDataset(
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tokenizer=tokenizer,
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dataset_name=self.dataset_name,
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seed=1,
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size=self.dataset_size,
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developer_prompt=developer_prompt,
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)
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self.val_dataset = ReasoningGymDataset(
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tokenizer=tokenizer,
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dataset_name=self.dataset_name,
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seed=2,
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size=self.dataset_size,
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developer_prompt=developer_prompt,
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)
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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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sequences = torch.cat((valid_prompt_ids, valid_response_ids))
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sequences_str = self.tokenizer.decode(sequences)
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index = data_item.non_tensor_batch["index"]
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score = self._compute_score(
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solution_str=sequences_str,
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index=index,
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)
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reward_tensor[i, valid_response_length - 1] = score
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if num_printed < num_examine:
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print(f"reward={score}, seq={sequences_str}")
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num_printed += 1
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return reward_tensor
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def _compute_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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entry = self.train_dataset.data[index]
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reward = self.train_dataset.data.score_answer(found_answer, entry=entry)
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# print(f"found answer={found_answer}; reward: {reward};")
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return reward
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def _create_dataloader(self):
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self.train_dataloader = DataLoader(
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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 = DataLoader(
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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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@ray.remote
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def main_task(config):
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# print initial config
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from pprint import pprint
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from verl.utils import hf_tokenizer
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from verl.utils.fs import copy_local_path_from_hdfs
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pprint(OmegaConf.to_container(config, resolve=True)) # resolve=True will eval symbol values
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OmegaConf.resolve(config)
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# download the checkpoint from hdfs
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local_path = copy_local_path_from_hdfs(config.actor_rollout_ref.model.path)
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# instantiate tokenizer
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tokenizer = hf_tokenizer(local_path)
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# define worker classes
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if config.actor_rollout_ref.actor.strategy == "fsdp":
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assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
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from verl.single_controller.ray import RayWorkerGroup
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from verl.workers.fsdp_workers import ActorRolloutRefWorker, CriticWorker
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ray_worker_group_cls = RayWorkerGroup
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elif config.actor_rollout_ref.actor.strategy == "megatron":
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assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
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from verl.single_controller.ray.megatron import NVMegatronRayWorkerGroup
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from verl.workers.megatron_workers import ActorRolloutRefWorker, CriticWorker
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ray_worker_group_cls = NVMegatronRayWorkerGroup
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else:
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raise NotImplementedError
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from verl.trainer.ppo.ray_trainer import ResourcePoolManager, Role
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role_worker_mapping = {
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Role.ActorRollout: ray.remote(ActorRolloutRefWorker),
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Role.Critic: ray.remote(CriticWorker),
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Role.RefPolicy: ray.remote(ActorRolloutRefWorker),
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}
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global_pool_id = "global_pool"
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resource_pool_spec = {
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global_pool_id: [config.trainer.n_gpus_per_node] * config.trainer.nnodes,
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}
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mapping = {
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Role.ActorRollout: global_pool_id,
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Role.Critic: global_pool_id,
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Role.RefPolicy: global_pool_id,
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}
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resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping)
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trainer = RayPPOTrainerCustom(
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config=config,
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tokenizer=tokenizer,
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role_worker_mapping=role_worker_mapping,
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resource_pool_manager=resource_pool_manager,
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ray_worker_group_cls=ray_worker_group_cls,
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)
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trainer.init_workers()
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trainer.fit()
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@hydra.main(config_path="config", config_name="ppo_trainer", version_base=None)
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def main(config):
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if not ray.is_initialized():
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# this is for local ray cluster
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ray.init(runtime_env={"env_vars": {"TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN"}})
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ray.get(main_task.remote(config))
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if __name__ == "__main__":
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main()
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