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reduce veRL example size
This commit is contained in:
Andreas Koepf
parent
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commit
8202f234be
1 changed files with 57 additions and 123 deletions
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@ -1,24 +1,5 @@
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# This example is a modified version of:
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# 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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# Copyright 2024 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Note that we don't combine the main with ray_trainer as ray_trainer is used by other main.
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"""
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from typing import Optional
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import hydra
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@ -38,79 +19,11 @@ import reasoning_gym.utils
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from reasoning_gym.utils import extract_answer
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class RewardManager:
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"""The reward manager."""
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def __init__(self, tokenizer, num_examine, compute_score) -> None:
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self.tokenizer = tokenizer
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self.num_examine = num_examine # the number of batches of decoded responses to print to the console
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self.compute_score = compute_score
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def __call__(self, data: DataProto):
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"""We will expand this function gradually based on the available datasets"""
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# If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn
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if "rm_scores" in data.batch.keys():
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return data.batch["rm_scores"]
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reward_tensor = torch.zeros_like(data.batch["responses"], dtype=torch.float32)
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already_print_data_sources = {}
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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"]
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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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data_source = data_item.non_tensor_batch["data_source"]
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ground_truth = data_item.non_tensor_batch["answer"]
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index = data_item.non_tensor_batch["index"]
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score = self.compute_score(
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data_source=data_source,
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solution_str=sequences_str,
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ground_truth=ground_truth,
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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 data_source not in already_print_data_sources:
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already_print_data_sources[data_source] = 0
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if already_print_data_sources[data_source] < self.num_examine:
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already_print_data_sources[data_source] += 1
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print(sequences_str)
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return reward_tensor
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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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class ReasoningGymDataset(Dataset):
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def __init__(
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self,
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dataset_name: str,
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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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@ -177,8 +90,6 @@ class RayPPOTrainerCustom(RayPPOTrainer):
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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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reward_fn=None,
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val_reward_fn=None,
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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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@ -187,22 +98,23 @@ class RayPPOTrainerCustom(RayPPOTrainer):
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developer_prompt = reasoning_gym.utils.SYSTEM_PROMPTS["DeepSeekZero"]
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self.train_dataset = ReasoningGymDataset(
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dataset_name=self.dataset_name,
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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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dataset_name=self.dataset_name,
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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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reward_fn = RewardManager(tokenizer=tokenizer, num_examine=0, compute_score=self._compute_score)
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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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@ -210,15 +122,51 @@ class RayPPOTrainerCustom(RayPPOTrainer):
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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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reward_fn,
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train_reward_fn,
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val_reward_fn,
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)
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def _compute_score(self, data_source, solution_str, ground_truth, index) -> float:
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print("Solution:", solution_str, ground_truth, index, data_source)
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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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return self.train_dataset.data.score_answer(found_answer, entry=entry)
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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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@ -259,12 +207,11 @@ class RayPPOTrainerCustom(RayPPOTrainer):
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@ray.remote
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def main_task(config, compute_score=None):
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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 omegaconf import OmegaConf
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from transformers import AutoTokenizer
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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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@ -274,8 +221,6 @@ def main_task(config, compute_score=None):
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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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from verl.utils import hf_tokenizer
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tokenizer = hf_tokenizer(local_path)
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# define worker classes
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@ -314,25 +259,6 @@ def main_task(config, compute_score=None):
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Role.RefPolicy: global_pool_id,
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}
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# we should adopt a multi-source reward function here
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# - for rule-based rm, we directly call a reward score
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# - for model-based rm, we call a model
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# - for code related prompt, we send to a sandbox if there are test cases
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# - finally, we combine all the rewards together
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# - The reward type depends on the tag of the data
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if config.reward_model.enable:
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if config.reward_model.strategy == "fsdp":
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from verl.workers.fsdp_workers import RewardModelWorker
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elif config.reward_model.strategy == "megatron":
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from verl.workers.megatron_workers import RewardModelWorker
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else:
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raise NotImplementedError
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role_worker_mapping[Role.RewardModel] = ray.remote(RewardModelWorker)
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mapping[Role.RewardModel] = global_pool_id
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# Note that we always use function-based RM for validation
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val_reward_fn = RewardManager(tokenizer=tokenizer, num_examine=1, compute_score=compute_score)
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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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@ -341,11 +267,19 @@ def main_task(config, compute_score=None):
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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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val_reward_fn=val_reward_fn,
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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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