Basic curriculum (#198)

* 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

examples/veRL/basic_curriculum/ppo_curriculum.py

@@ -0,0 +1,298 @@
+# This example is an adapted version of Bytedance's code:
+# https://github.com/volcengine/verl/blob/a65c9157bc0b85b64cd753de19f94e80a11bd871/verl/trainer/main_ppo.py
+from io import StringIO
+from typing import Optional
+
+import hydra
+import ray
+import torch
+import verl.utils.torch_functional as verl_F
+from omegaconf import OmegaConf, open_dict
+from torch.utils.data import DataLoader, Dataset
+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
+from verl.utils.model import compute_position_id_with_mask
+
+import reasoning_gym
+import reasoning_gym.utils
+from reasoning_gym.coaching.curriculum_config import CurriculumExperimentConfig
+from reasoning_gym.coaching.experiment import CurriculumExperiment
+from reasoning_gym.utils import extract_answer
+
+curriculum_config_yaml = """
+ curricula:
+   leg_counting:
+     attribute_levels:
+       num_animals: 2
+   products:
+     attribute_levels:
+       num_terms: 4
+       num_digits: 4
+   chain_sum:
+     attribute_levels:
+       num_terms: 4
+       num_digits: 4
+     weight: 1.0
+ """
+
+
+class ReasoningGymDataset(Dataset):
+    def __init__(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        experiment_name: str,
+        seed: int,
+        size: int,
+        developer_prompt: Optional[str] = None,
+        developer_role: str = "system",
+        max_prompt_length: int = 2048,
+        truncation: str = "error",  ##  ['left', 'right', 'error']
+        return_raw_chat: bool = False,
+    ):
+        self.tokenizer = tokenizer
+        curriculum_config = CurriculumExperimentConfig.from_yaml_stream(StringIO(curriculum_config_yaml))
+        self.experiment = CurriculumExperiment(experiment_name, curriculum_config, size=size, seed=seed)
+        self.developer_prompt = developer_prompt
+        self.developer_role = developer_role
+        self.max_prompt_length = max_prompt_length
+        self.truncation = truncation
+        self.return_raw_chat = return_raw_chat
+
+    def __len__(self) -> int:
+        return len(self.experiment.composite)
+
+    def __getitem__(self, index: int):
+        row_dict = self.experiment.get_dataset_entry(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)
+
+        row_dict["data_source"] = "reasoning_gym/" + self.dataset_name
+        row_dict["input_ids"] = input_ids[0]
+        row_dict["attention_mask"] = attention_mask[0]
+        row_dict["position_ids"] = position_ids[0]
+
+        # encode prompts without chat template
+        if self.return_raw_chat:
+            row_dict["raw_prompt"] = chat.tolist()
+
+        return row_dict
+
+
+class RayPPOTrainerCustom(RayPPOTrainer):
+    def __init__(
+        self,
+        config,
+        tokenizer,
+        role_worker_mapping: dict,
+        resource_pool_manager,
+        ray_worker_group_cls,
+        experiment_name: str = "basic_curriculum",
+        dataset_size: int = 10000,
+    ):
+        self.dataset_size = dataset_size
+
+        developer_prompt = reasoning_gym.utils.SYSTEM_PROMPTS["DeepSeekZero"]
+        self.train_dataset = ReasoningGymDataset(
+            tokenizer=tokenizer,
+            experiment_name=experiment_name,
+            seed=1,
+            size=self.dataset_size,
+            developer_prompt=developer_prompt,
+        )
+
+        self.val_dataset = ReasoningGymDataset(
+            tokenizer=tokenizer,
+            experiment_name=experiment_name,
+            seed=2,
+            size=self.dataset_size,
+            developer_prompt=developer_prompt,
+        )
+
+        train_reward_fn = lambda data: self._score_output(data, num_examine=0)
+        val_reward_fn = lambda data: self._score_output(data, num_examine=1)
+
+        super().__init__(
+            config,
+            tokenizer,
+            role_worker_mapping,
+            resource_pool_manager,
+            ray_worker_group_cls,
+            train_reward_fn,
+            val_reward_fn,
+        )
+
+    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
+            sequences = torch.cat((valid_prompt_ids, valid_response_ids))
+            sequences_str = self.tokenizer.decode(sequences)
+
+            entry_id = data_item.non_tensor_batch["metadata"]["entry_id"]
+
+            score = self._compute_score(
+                solution_str=sequences_str,
+                entry_id=entry_id,
+            )
+            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, entry_id: str) -> float:
+        found_answer = extract_answer(solution_str, tag_name="answer")
+        reward = self.train_dataset.experiment.score_answer_with_id(found_answer, entry_id=entry_id)
+        print(f"entry_id: {entry_id}; found answer={found_answer}; reward: {reward};")
+        return reward
+
+    def _create_dataloader(self):
+        self.train_dataloader = DataLoader(
+            dataset=self.train_dataset,
+            batch_size=self.config.data.train_batch_size,
+            shuffle=True,
+            drop_last=True,
+            collate_fn=collate_fn,
+        )
+
+        self.val_dataloader = DataLoader(
+            dataset=self.val_dataset,
+            batch_size=len(self.val_dataset),
+            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)
+
+    # 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,
+    )
+    trainer.init_workers()
+    trainer.fit()
+
+
+@hydra.main(config_path="config", config_name="ppo_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()