reduce veRL example size

examples/veRL/main_ppo_custom_reward.py

@@ -1,24 +1,5 @@
-# This example is a modified version of:
+# This example is an adapted version of Bytedance's code:
 # https://github.com/volcengine/verl/blob/a65c9157bc0b85b64cd753de19f94e80a11bd871/verl/trainer/main_ppo.py
-
-
-# Copyright 2024 Bytedance Ltd. and/or its affiliates
-#
-# 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.
-"""
-Note that we don't combine the main with ray_trainer as ray_trainer is used by other main.
-"""
-
 from typing import Optional
 
 import hydra
@@ -38,79 +19,11 @@ import reasoning_gym.utils
 from reasoning_gym.utils import extract_answer
 
 
-class RewardManager:
-    """The reward manager."""
-
-    def __init__(self, tokenizer, num_examine, compute_score) -> None:
-        self.tokenizer = tokenizer
-        self.num_examine = num_examine  # the number of batches of decoded responses to print to the console
-        self.compute_score = compute_score
-
-    def __call__(self, data: DataProto):
-        """We will expand this function gradually based on the available datasets"""
-
-        # If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn
-        if "rm_scores" in data.batch.keys():
-            return data.batch["rm_scores"]
-
-        reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32)
-
-        already_print_data_sources = {}
-
-        for i in range(len(data)):
-            data_item = data[i]  # DataProtoItem
-
-            prompt_ids = data_item.batch["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)
-
-            data_source = data_item.non_tensor_batch["data_source"]
-            ground_truth = data_item.non_tensor_batch["answer"]
-            index = data_item.non_tensor_batch["index"]
-
-            score = self.compute_score(
-                data_source=data_source,
-                solution_str=sequences_str,
-                ground_truth=ground_truth,
-                index=index,
-            )
-            reward_tensor[i, valid_response_length - 1] = score
-
-            if data_source not in already_print_data_sources:
-                already_print_data_sources[data_source] = 0
-
-            if already_print_data_sources[data_source] < self.num_examine:
-                already_print_data_sources[data_source] += 1
-                print(sequences_str)
-
-        return reward_tensor
-
-
-@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))
-
-
 class ReasoningGymDataset(Dataset):
     def __init__(
         self,
-        dataset_name: str,
         tokenizer: PreTrainedTokenizer,
+        dataset_name: str,
         seed: int,
         size: int,
         developer_prompt: Optional[str] = None,
@@ -177,8 +90,6 @@ class RayPPOTrainerCustom(RayPPOTrainer):
         role_worker_mapping: dict,
         resource_pool_manager,
         ray_worker_group_cls,
-        reward_fn=None,
-        val_reward_fn=None,
         dataset_name: str = "chain_sum",
         dataset_size: int = 10000,
     ):
@@ -187,22 +98,23 @@ class RayPPOTrainerCustom(RayPPOTrainer):
 
         developer_prompt = reasoning_gym.utils.SYSTEM_PROMPTS["DeepSeekZero"]
         self.train_dataset = ReasoningGymDataset(
-            dataset_name=self.dataset_name,
             tokenizer=tokenizer,
+            dataset_name=self.dataset_name,
             seed=1,
             size=self.dataset_size,
             developer_prompt=developer_prompt,
         )
 
         self.val_dataset = ReasoningGymDataset(
-            dataset_name=self.dataset_name,
             tokenizer=tokenizer,
+            dataset_name=self.dataset_name,
             seed=2,
             size=self.dataset_size,
             developer_prompt=developer_prompt,
         )
 
-        reward_fn = RewardManager(tokenizer=tokenizer, num_examine=0, compute_score=self._compute_score)
+        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,
@@ -210,15 +122,51 @@ class RayPPOTrainerCustom(RayPPOTrainer):
             role_worker_mapping,
             resource_pool_manager,
             ray_worker_group_cls,
-            reward_fn,
+            train_reward_fn,
             val_reward_fn,
         )
 
-    def _compute_score(self, data_source, solution_str, ground_truth, index) -> float:
+    def _score_output(self, data: DataProto, num_examine: int = 0) -> torch.Tensor:
-        print("Solution:", solution_str, ground_truth, index, data_source)
+        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)
+
+            index = data_item.non_tensor_batch["index"]
+
+            score = self._compute_score(
+                solution_str=sequences_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]
-        return self.train_dataset.data.score_answer(found_answer, entry=entry)
+        reward = self.train_dataset.data.score_answer(found_answer, entry=entry)
+        # print(f"found answer={found_answer}; reward: {reward};")
+        return reward
 
     def _create_dataloader(self):
         self.train_dataloader = DataLoader(
@@ -259,12 +207,11 @@ class RayPPOTrainerCustom(RayPPOTrainer):
 
 
 @ray.remote
-def main_task(config, compute_score=None):
+def main_task(config):
     # print initial config
     from pprint import pprint
 
-    from omegaconf import OmegaConf
+    from verl.utils import hf_tokenizer
-    from transformers import AutoTokenizer
     from verl.utils.fs import copy_local_path_from_hdfs
 
     pprint(OmegaConf.to_container(config, resolve=True))  # resolve=True will eval symbol values
@@ -274,8 +221,6 @@ def main_task(config, compute_score=None):
     local_path = copy_local_path_from_hdfs(config.actor_rollout_ref.model.path)
 
     # instantiate tokenizer
-    from verl.utils import hf_tokenizer
-
     tokenizer = hf_tokenizer(local_path)
 
     # define worker classes
@@ -314,25 +259,6 @@ def main_task(config, compute_score=None):
         Role.RefPolicy: global_pool_id,
     }
 
-    # we should adopt a multi-source reward function here
-    # - for rule-based rm, we directly call a reward score
-    # - for model-based rm, we call a model
-    # - for code related prompt, we send to a sandbox if there are test cases
-    # - finally, we combine all the rewards together
-    # - The reward type depends on the tag of the data
-    if config.reward_model.enable:
-        if config.reward_model.strategy == "fsdp":
-            from verl.workers.fsdp_workers import RewardModelWorker
-        elif config.reward_model.strategy == "megatron":
-            from verl.workers.megatron_workers import RewardModelWorker
-        else:
-            raise NotImplementedError
-        role_worker_mapping[Role.RewardModel] = ray.remote(RewardModelWorker)
-        mapping[Role.RewardModel] = global_pool_id
-
-    # Note that we always use function-based RM for validation
-    val_reward_fn = RewardManager(tokenizer=tokenizer, num_examine=1, compute_score=compute_score)
-
     resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping)
 
     trainer = RayPPOTrainerCustom(
@@ -341,11 +267,19 @@ def main_task(config, compute_score=None):
         role_worker_mapping=role_worker_mapping,
         resource_pool_manager=resource_pool_manager,
         ray_worker_group_cls=ray_worker_group_cls,
-        val_reward_fn=val_reward_fn,
     )
     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()