cleaned up examples

examples/veRL/grpo_train.py

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+# This example is an adapted version of Bytedance's code:
+# https://github.com/volcengine/verl/blob/a65c9157bc0b85b64cd753de19f94e80a11bd871/verl/trainer/main_ppo.py
+from typing import Optional
+
+import hydra
+import numpy as np
+import ray
+import torch
+import verl.utils.torch_functional as verl_F
+from omegaconf import OmegaConf, open_dict
+from torch.utils.data import Dataset
+from torchdata.stateful_dataloader import StatefulDataLoader
+from transformers import PreTrainedTokenizer
+from verl import DataProto
+from verl.trainer.ppo.ray_trainer import RayPPOTrainer
+from verl.utils.dataset.rl_dataset import collate_fn as verl_collate_fn
+from verl.utils.model import compute_position_id_with_mask
+
+import reasoning_gym
+import reasoning_gym.utils
+from reasoning_gym.coaching.experiment import Experiment
+from reasoning_gym.composite import CompositeDataset, DatasetSpec
+from reasoning_gym.dataset import ProceduralDataset
+from reasoning_gym.utils import extract_answer
+
+
+class ReasoningGymDataset(Dataset):
+    def __init__(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        procedural_dataset: Optional[ProceduralDataset] = None,
+        experiment: Optional[Experiment] = None,
+        developer_prompt: Optional[str] = None,
+        developer_role: str = "system",
+        max_prompt_length: int = 2048,
+        truncation: str = "error",  ##  ['left', 'right', 'error']
+    ):
+        assert procedural_dataset or experiment, "One of `procedural_dataset` or `experiment` must be provided"
+        assert (
+            procedural_dataset is None or experiment is None
+        ), "Only one of `procedural_dataset` or `experiment` may be provided"
+
+        self.tokenizer = tokenizer
+        self.data = procedural_dataset or experiment.composite
+        self.experiment = experiment
+        self.developer_prompt = developer_prompt
+        self.developer_role = developer_role
+        self.max_prompt_length = max_prompt_length
+        self.truncation = truncation
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def __getitem__(self, index):
+        row_dict = self.data[index].copy()
+        q = row_dict["question"]
+
+        chat = []
+        if self.developer_prompt is not None:
+            chat.append({"role": self.developer_role, "content": self.developer_prompt})
+        chat.append({"role": "user", "content": q})
+
+        prompt = self.tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+
+        input_ids, attention_mask = verl_F.tokenize_and_postprocess_data(
+            prompt=prompt,
+            tokenizer=self.tokenizer,
+            max_length=self.max_prompt_length,
+            pad_token_id=self.tokenizer.pad_token_id,
+            left_pad=True,
+            truncation=self.truncation,
+        )
+
+        position_ids = compute_position_id_with_mask(attention_mask)
+
+        item = {}
+        item["index"] = index
+
+        item["input_ids"] = input_ids[0]
+        item["attention_mask"] = attention_mask[0]
+        item["position_ids"] = position_ids[0]
+
+        item["raw_prompt_ids"] = item["input_ids"].tolist()
+
+        return item
+
+
+def make_dataset(
+    tokenizer,
+    data_source: Experiment | ProceduralDataset,
+    developer_prompt: str,
+    max_prompt_length: int = 2048,
+) -> ReasoningGymDataset:
+    """
+    Create ReasoningGymDataset object using either a ProceduralDataset or Experiment as the underlying data source.
+    """
+    if isinstance(data_source, Experiment):
+        return ReasoningGymDataset(
+            tokenizer=tokenizer,
+            experiment=data_source,
+            developer_prompt=developer_prompt,
+            developer_role="system",
+            max_prompt_length=max_prompt_length,
+            truncation="error",
+        )
+    else:
+        return ReasoningGymDataset(
+            tokenizer=tokenizer,
+            procedural_dataset=data_source,
+            developer_prompt=developer_prompt,
+            developer_role="system",
+            max_prompt_length=max_prompt_length,
+            truncation="error",
+        )
+
+
+def prepare_datasets(config, tokenizer) -> tuple[ReasoningGymDataset, ReasoningGymDataset]:
+    """Prepare training and validation datasets."""
+    dataset_size = config.reasoning_gym.dataset_size
+    developer_prompt_setting = config.reasoning_gym.developer_prompt
+    developer_prompt = reasoning_gym.utils.SYSTEM_PROMPTS[developer_prompt_setting]
+    dataset_specs = [
+        DatasetSpec(
+            name=name,
+            weight=ds.weight,
+            config=OmegaConf.to_container(ds.config, resolve=True) if "config" in ds else {},
+        )
+        for name, ds in config.reasoning_gym.datasets.items()
+    ]
+    train_data_source = reasoning_gym.create_dataset("composite", seed=1, size=dataset_size, datasets=dataset_specs)
+    val_data_source = reasoning_gym.create_dataset("composite", seed=2, size=dataset_size, datasets=dataset_specs)
+    train_dataset = make_dataset(
+        tokenizer, train_data_source, developer_prompt, max_prompt_length=config.data.max_prompt_length
+    )
+    val_dataset = make_dataset(
+        tokenizer, val_data_source, developer_prompt, max_prompt_length=config.data.max_prompt_length
+    )
+    return train_dataset, val_dataset
+
+
+class RayPPOTrainerCustom(RayPPOTrainer):
+    def __init__(
+        self,
+        config,
+        tokenizer,
+        role_worker_mapping: dict,
+        resource_pool_manager,
+        ray_worker_group_cls,
+        train_dataset: ReasoningGymDataset,
+        val_dataset: ReasoningGymDataset,
+        dataset_name: str = "chain_sum",
+        dataset_size: int = 10000,
+    ):
+        self.dataset_name = dataset_name
+        self.dataset_size = dataset_size
+
+        developer_prompt = reasoning_gym.utils.SYSTEM_PROMPTS["DeepSeekZero"]
+
+        self.train_dataset = train_dataset
+        self.val_dataset = val_dataset
+
+        def make_reward_fn(num_examine: int):
+            def reward_fn(data: DataProto, return_dict: bool = False, **unused_kwargs):
+                tensor = self._score_output(data, num_examine=num_examine)
+                if return_dict:
+                    # wrap it so trainer can pull out extras
+                    return {"reward_tensor": tensor, "reward_extra_info": {}}
+                return tensor
+
+            return reward_fn
+
+        train_reward_fn = make_reward_fn(num_examine=0)
+        val_reward_fn = make_reward_fn(num_examine=1)
+
+        super().__init__(
+            config,
+            tokenizer,
+            role_worker_mapping,
+            resource_pool_manager,
+            ray_worker_group_cls,
+            train_reward_fn,
+            val_reward_fn,
+            train_dataset=train_dataset,
+            val_dataset=val_dataset,
+            train_sampler=None,
+        )
+
+    def _score_output(self, data: DataProto, num_examine: int = 0) -> torch.Tensor:
+        reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32)
+
+        num_printed = 0
+        for i in range(len(data)):
+            data_item = data[i]  # DataProtoItem
+
+            prompt_ids = data_item.batch["prompts"]  # tokenized prompts
+            prompt_length = prompt_ids.shape[-1]
+
+            valid_prompt_length = data_item.batch["attention_mask"][:prompt_length].sum()
+            valid_prompt_ids = prompt_ids[-valid_prompt_length:]
+
+            response_ids = data_item.batch["responses"]
+            valid_response_length = data_item.batch["attention_mask"][prompt_length:].sum()
+            valid_response_ids = response_ids[:valid_response_length]
+
+            # decode
+            prompt_str = self.tokenizer.decode(valid_prompt_ids)
+            response_str = self.tokenizer.decode(valid_response_ids)
+            sequences_str = prompt_str + response_str
+
+            index = data_item.non_tensor_batch["index"]
+            score = self._compute_score(
+                solution_str=response_str,
+                index=index,
+            )
+
+            reward_tensor[i, valid_response_length - 1] = score
+
+            if num_printed < num_examine:
+                print(f"reward={score}, seq={sequences_str}")
+                num_printed += 1
+
+        return reward_tensor
+
+    def _compute_score(self, solution_str: str, index: int) -> float:
+        found_answer = extract_answer(solution_str, tag_name="answer")
+        entry = self.train_dataset.data[index]
+        reward = self.train_dataset.data.score_answer(found_answer, entry=entry)
+        return reward
+
+    def _create_dataloader(self, train_dataset, val_dataset, collate_fn=None, sampler=None):
+
+        if collate_fn is None:
+            collate_fn = verl_collate_fn
+
+        self.train_dataloader = StatefulDataLoader(
+            dataset=train_dataset,
+            batch_size=self.config.data.train_batch_size,
+            shuffle=True,
+            drop_last=True,
+            collate_fn=collate_fn,
+        )
+
+        self.val_dataloader = StatefulDataLoader(
+            dataset=val_dataset,
+            batch_size=self.config.data.val_batch_size,
+            shuffle=True,
+            drop_last=True,
+            collate_fn=collate_fn,
+        )
+
+        assert len(self.train_dataloader) >= 1
+        assert len(self.val_dataloader) >= 1
+
+        print(f"Size of train dataloader: {len(self.train_dataloader)}")
+        print(f"Size of val dataloader: {len(self.val_dataloader)}")
+
+        # inject total_training_steps to actor/critic optim_config. This is hacky.
+        total_training_steps = len(self.train_dataloader) * self.config.trainer.total_epochs
+
+        if self.config.trainer.total_training_steps is not None:
+            total_training_steps = self.config.trainer.total_training_steps
+
+        self.total_training_steps = total_training_steps
+        print(f"Total training steps: {self.total_training_steps}")
+
+        OmegaConf.set_struct(self.config, True)
+        with open_dict(self.config):
+            self.config.actor_rollout_ref.actor.optim.total_training_steps = total_training_steps
+            self.config.critic.optim.total_training_steps = total_training_steps
+
+
+@ray.remote
+def main_task(config):
+    # print initial config
+    from pprint import pprint
+
+    from verl.utils import hf_tokenizer
+    from verl.utils.fs import copy_local_path_from_hdfs
+
+    pprint(OmegaConf.to_container(config, resolve=True))  # resolve=True will eval symbol values
+    OmegaConf.resolve(config)
+
+    # download the checkpoint from hdfs
+    local_path = copy_local_path_from_hdfs(config.actor_rollout_ref.model.path)
+
+    # instantiate tokenizer
+    tokenizer = hf_tokenizer(local_path)
+    train_dataset, val_dataset = prepare_datasets(config, tokenizer)
+
+    # define worker classes
+    if config.actor_rollout_ref.actor.strategy == "fsdp":
+        assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
+        from verl.single_controller.ray import RayWorkerGroup
+        from verl.workers.fsdp_workers import ActorRolloutRefWorker, CriticWorker
+
+        ray_worker_group_cls = RayWorkerGroup
+
+    elif config.actor_rollout_ref.actor.strategy == "megatron":
+        assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
+        from verl.single_controller.ray.megatron import NVMegatronRayWorkerGroup
+        from verl.workers.megatron_workers import ActorRolloutRefWorker, CriticWorker
+
+        ray_worker_group_cls = NVMegatronRayWorkerGroup
+
+    else:
+        raise NotImplementedError
+
+    from verl.trainer.ppo.ray_trainer import ResourcePoolManager, Role
+
+    role_worker_mapping = {
+        Role.ActorRollout: ray.remote(ActorRolloutRefWorker),
+        Role.Critic: ray.remote(CriticWorker),
+        Role.RefPolicy: ray.remote(ActorRolloutRefWorker),
+    }
+
+    global_pool_id = "global_pool"
+    resource_pool_spec = {
+        global_pool_id: [config.trainer.n_gpus_per_node] * config.trainer.nnodes,
+    }
+    mapping = {
+        Role.ActorRollout: global_pool_id,
+        Role.Critic: global_pool_id,
+        Role.RefPolicy: global_pool_id,
+    }
+
+    resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping)
+
+    trainer = RayPPOTrainerCustom(
+        config=config,
+        tokenizer=tokenizer,
+        role_worker_mapping=role_worker_mapping,
+        resource_pool_manager=resource_pool_manager,
+        ray_worker_group_cls=ray_worker_group_cls,
+        train_dataset=train_dataset,
+        val_dataset=val_dataset,
+    )
+    trainer.init_workers()
+    trainer.fit()
+
+
+@hydra.main(config_path="config", config_name="grpo_trainer", version_base=None)
+def main(config):
+    if not ray.is_initialized():
+        # this is for local ray cluster
+        ray.init(runtime_env={"env_vars": {"TOKENIZERS_PARALLELISM": "true", "NCCL_DEBUG": "WARN"}})
+
+    ray.get(main_task.remote(config))
+
+
+if __name__ == "__main__":
+    main()