Initial commit

verl/trainer/main_ppo.py

@@ -0,0 +1,202 @@
+# 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 verl import DataProto
+import torch
+from verl.utils.reward_score import qa_em
+from verl.trainer.ppo.ray_trainer import RayPPOTrainer
+import re
+import numpy as np
+
+def _select_rm_score_fn(data_source):
+    if "nq" in data_source:
+        return qa_em.compute_score_em
+    else:
+        raise NotImplementedError
+
+
+class RewardManager():
+    """The reward manager.
+    """
+
+    def __init__(self, tokenizer, num_examine, format_score=0.) -> None:
+        self.tokenizer = tokenizer
+        self.num_examine = num_examine  # the number of batches of decoded responses to print to the console
+        self.format_score = format_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)
+
+        # all_scores = []
+
+        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)
+
+            ground_truth = data_item.non_tensor_batch['reward_model']['ground_truth']
+
+            # select rm_score
+            data_source = data_item.non_tensor_batch['data_source']
+            compute_score_fn = _select_rm_score_fn(data_source)
+
+            score = compute_score_fn(solution_str=sequences_str, ground_truth=ground_truth, format_score=self.format_score)
+
+            reward_tensor[i, valid_response_length - 1] = score
+            # all_scores.append(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)
+        
+        # print(f"[DEBUG] all_scores: {all_scores}")
+        # print(f"[DEBUG] all_scores shape: {np.array(all_scores).shape}")
+        # print(f"[DEBUG] all_scores mean: {np.mean(all_scores)}")
+        # print(f"[DEBUG] all_scores max: {np.max(all_scores)}")
+        # print(f"[DEBUG] all_scores min: {np.min(all_scores)}")
+        # print(f"[DEBUG] all_scores std: {np.std(all_scores)}")
+
+        return reward_tensor
+
+
+import ray
+import hydra
+
+
+@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))
+
+
+@ray.remote
+def main_task(config):
+    from verl.utils.fs import copy_local_path_from_hdfs
+    from transformers import AutoTokenizer
+
+    # print initial config
+    from pprint import pprint
+    from omegaconf import OmegaConf
+    pprint(OmegaConf.to_container(config, resolve=True))  # resolve=True will eval symbol values
+    OmegaConf.resolve(config)
+
+    # env_class = ENV_CLASS_MAPPING[config.env.name]
+
+    # download the checkpoint from hdfs
+    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
+    if config.actor_rollout_ref.actor.strategy == 'fsdp':
+        assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
+        from verl.workers.fsdp_workers import ActorRolloutRefWorker, CriticWorker
+        from verl.single_controller.ray import RayWorkerGroup
+        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.workers.megatron_workers import ActorRolloutRefWorker, CriticWorker
+        from verl.single_controller.ray.megatron import NVMegatronRayWorkerGroup
+        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,
+    }
+
+    # 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
+
+    reward_fn = RewardManager(tokenizer=tokenizer, num_examine=0)
+
+    # Note that we always use function-based RM for validation
+    val_reward_fn = RewardManager(tokenizer=tokenizer, num_examine=1)
+
+    resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping)
+    trainer = RayPPOTrainer(config=config,
+                            tokenizer=tokenizer,
+                            role_worker_mapping=role_worker_mapping,
+                            resource_pool_manager=resource_pool_manager,
+                            ray_worker_group_cls=ray_worker_group_cls,
+                            reward_fn=reward_fn,
+                            val_reward_fn=val_reward_fn,
+                            )
+    trainer.init_workers()
+    trainer.fit()
+
+
+if __name__ == '__main__':
+    main()