Add trainer_strategy and save_hf_checkpoint

docs/sphinx_doc/source/tutorial/trinity_configs.md

@@ -436,10 +436,12 @@ Specifies the backend and behavior of the trainer.
 ```yaml
 trainer:
   name: trainer
-  trainer_type: 'verl'
-  save_interval: 100
+  trainer_type: "verl"
+  trainer_strategy: "fsdp"
   total_steps: 1000
+  save_interval: 100
   save_strategy: "unrestricted"
+  save_hf_checkpoint: "last"
   grad_clip: 1.0
   use_dynamic_bsz: true
   max_token_len_per_gpu: 16384
@@ -449,13 +451,21 @@ trainer:
 
 - `name`: Name of the trainer. This name will be used as the Ray actor's name, so it must be unique.
 - `trainer_type`: Trainer backend implementation. Currently only supports `verl`.
-- `save_interval`: Frequency (in steps) at which to save model checkpoints.
+- `trainer_strategy`: Strategy for VeRL trainer. Default is `fsdp`. Options include:
+  - `fsdp`: Use PyTorch FSDP.
+  - `fsdp2`: Use PyTorch FSDP2.
+  - `megatron`: Use Megatron-LM.
 - `total_steps`: Total number of training steps.
+- `save_interval`: Frequency (in steps) at which to save model checkpoints.
 - `save_strategy`: The parallel strategy used when saving the model. Defaults to `unrestricted`. The available options are as follows:
   - `single_thread`: Only one thread across the entire system is allowed to save the model; saving tasks from different threads are executed sequentially.
   - `single_process`: Only one process across the entire system is allowed to perform saving; multiple threads within that process can handle saving tasks in parallel, while saving operations across different processes are executed sequentially.
   - `single_node`: Only one compute node across the entire system is allowed to perform saving; processes and threads within that node can work in parallel, while saving operations across different nodes are executed sequentially.
   - `unrestricted`: No restrictions on saving operations; multiple nodes, processes, or threads are allowed to save the model simultaneously.
+- `save_hf_checkpoint`: Whether to save the model in HuggingFace format. Default is `last`. Note that saving in HuggingFace format consumes additional time, storage space, and GPU memory, which may impact training performance or lead to out-of-memory errors. Options include:
+  - `last`: Save only the last checkpoint in HuggingFace format.
+  - `always`: Save all checkpoints in HuggingFace format.
+  - `never`: Do not save in HuggingFace format.
 - `grad_clip`: Gradient clipping for updates.
 - `use_dynamic_bsz`: Whether to use dynamic batch size.
 - `max_token_len_per_gpu`:  The maximum number of tokens to be processed in forward and backward when updating the policy. Effective when `use_dynamic_bsz=true`.

docs/sphinx_doc/source_zh/tutorial/trinity_configs.md

@@ -227,7 +227,7 @@ buffer:
 - `total_epochs`: 总训练轮数。
 - `total_steps`: 总训练步数（可选）。若指定，则 `total_epochs` 不生效。
 
-### Explorer 输入
+### Explorer 输入配置
 
 定义 explorer 用于训练和评估的数据集。
 
@@ -289,7 +289,7 @@ buffer:
 - `default_reward_fn_type`: 探索过程中使用的奖励函数。若未指定，则使用 `buffer.default_reward_fn_type`。
 - `workflow_args`: 用于补充数据集级别参数的字典。
 
-### Trainer 输入
+### Trainer 输入配置
 
 定义 trainer 使用的 experience buffer 和可选的辅助数据集。
 
@@ -433,10 +433,12 @@ synchronizer:
 ```yaml
 trainer:
   name: trainer
-  trainer_type: 'verl'
-  save_interval: 100
+  trainer_type: "verl"
+  trainer_strategy: "fsdp"
   total_steps: 1000
+  save_interval: 100
   save_strategy: "unrestricted"
+  save_hf_checkpoint: "last"
   grad_clip: 1.0
   use_dynamic_bsz: true
   max_token_len_per_gpu: 16384
@@ -446,13 +448,21 @@ trainer:
 
 - `name`: trainer 的名称。该名称将用作 Ray actor 的名称，因此必须唯一。
 - `trainer_type`: trainer 后端实现。目前仅支持 `verl`。
+- `trainer_strategy`: VeRL 的训练策略。默认值为 `fsdp`。可选值如下：
+  - `fsdp`: 使用 PyTorch FSDP。
+  - `fsdp2`: 使用 PyTorch FSDP2。
+  - `megatron`: 使用 Megatron-LM。
 - `save_interval`: 保存模型检查点的频率（步）。
 - `total_steps`: 总训练步数。
 - `save_strategy`: 模型保存时的并行策略。默认值为`unrestricted`。可选值如下：
   - `single_thread`：整个系统中，仅允许一个线程进行模型保存，不同保存线程之间串行执行。
   - `single_process`：整个系统中，仅允许一个进程执行保存，该进程内的多个线程可以并行处理保存任务，不同进程之间串行执行。
   - `single_node`：整个系统中，仅允许一个计算节点执行保存，该节点内的进程和线程可并行工作，不同节点的保存串行执行。
   - `unrestricted`：不限制保存操作，允许多个节点、进程或线程同时保存模型。
+- `save_hf_checkpoint`: 指定保存 HuggingFace 格式检查点的时机，默认为 "last"。注意在保存为 HuggingFace 格式会消耗额外的时间、存储空间和显存，可能影响训练性能或导致显存不足错误。可选值：
+  - `last`: 仅训练产生的最后一个检查点保存为 HuggingFace 格式。
+  - `always`: 所有检查点均保存为 HuggingFace 格式。
+  - `never`: 不保存 HuggingFace 格式检查点。
 - `grad_clip`: 梯度裁剪阈值。
 - `use_dynamic_bsz`: 是否使用动态批量大小。
 - `max_token_len_per_gpu`: 训练过程中，每个 GPU 最大 token 长度; 当 `use_dynamic_bsz=true` 时生效。

tests/template/config.yaml

@@ -45,6 +45,7 @@ explorer:
 trainer:
   trainer_type: verl
   save_interval: 100
+  save_hf_checkpoint: never
   grad_clip: 1.0
   use_dynamic_bsz: true
   max_token_len_per_gpu: 16384

tests/trainer/trainer_test.py

@@ -90,6 +90,7 @@ def test_trainer(self):
         eval_tasksets[0].repeat_times = 4
         eval_tasksets[1].repeat_times = 4
         self.config.trainer.save_interval = 4
+        self.config.trainer.save_hf_checkpoint = "always"
         self.config.check_and_update()
         _trainer_config = self.config.trainer.trainer_config
         if self.strategy == "megatron":
@@ -134,6 +135,12 @@ def test_trainer(self):
         )
         self.assertTrue(len(os.listdir(os.path.join(checkpoint_step_4, "actor"))) > 0)
         self.assertTrue(len(os.listdir(os.path.join(checkpoint_step_8, "actor"))) > 0)
+        self.assertTrue(
+            len(os.listdir(os.path.join(checkpoint_step_4, "actor", "huggingface"))) > 0
+        )
+        self.assertTrue(
+            len(os.listdir(os.path.join(checkpoint_step_8, "actor", "huggingface"))) > 0
+        )
         self.assertEqual(step_num, 8)
         ray.init(ignore_reinit_error=True, namespace=self.config.ray_namespace)
         # test bench mode
@@ -234,10 +241,10 @@ def test_trainer(self):
         # self.config.buffer.batch_size = 96  # TODO: used for real testing
         self.config.buffer.total_epochs = 1
         self.config.buffer.explorer_input.taskset = get_unittest_dataset_config("gsm8k")
+        self.config.trainer.trainer_strategy = self.fsdp_strategy
         self.config.check_and_update()
         self.config.trainer.trainer_config.trainer.max_actor_ckpt_to_keep = 2
         actor_rollout_ref = self.config.trainer.trainer_config.actor_rollout_ref
-        actor_rollout_ref.actor.strategy = self.fsdp_strategy
         actor_rollout_ref.actor.optim.lr = 1e-5
         if self.fsdp_strategy == "fsdp":
             actor_rollout_ref.actor.fsdp_config.param_offload = self.offloading
@@ -679,16 +686,16 @@ def setUp(self):
         self.config.explorer.eval_interval = 4
         self.config.buffer.explorer_input.taskset = get_unittest_dataset_config("countdown")
         self.config.trainer.save_interval = 4
+        self.config.trainer.save_hf_checkpoint = "last"
+        self.config.trainer.trainer_strategy = self.strategy
         self.config.check_and_update()
 
     def test_trainer(self):
         """Test the checkpoint saving."""
         _trainer_config = self.config.trainer.trainer_config
         if self.strategy == "megatron":
-            _trainer_config.actor_rollout_ref.actor.strategy = "megatron"
             _trainer_config.actor_rollout_ref.actor.megatron.tensor_model_parallel_size = 2
             _trainer_config.actor_rollout_ref.ref.megatron.tensor_model_parallel_size = 2
-            _trainer_config.critic.strategy = "megatron"
             _trainer_config.critic.megatron.tensor_model_parallel_size = 2
         _trainer_config.trainer.max_actor_ckpt_to_keep = 2
         _trainer_config.trainer.max_critic_ckpt_to_keep = 2

trinity/common/config.py

@@ -683,12 +683,18 @@ class ExplorerConfig:
 class TrainerConfig:
     name: str = TRAINER_NAME
     trainer_type: str = "verl"
+    trainer_strategy: str = "fsdp"
     save_interval: int = 0
     enable_preview: bool = True  # enable rollout preview in wandb
     total_steps: Optional[
         int
     ] = None  # total training steps, training stops when reaching this step, None means no limit
 
+    save_hf_checkpoint: str = "last"  # whether to save checkpoint in HuggingFace format
+    # "always": save all checkpoints in HF format
+    # "never": never save checkpoint in HF format
+    # "last": only save the last checkpoint in HF format
+
     # trainer configs
     grad_clip: float = 1.0
     use_dynamic_bsz: bool = True
@@ -1217,6 +1223,8 @@ def __iter__(self):
                     setattr(new_config, field_name, stage_value)
             if stage.stage_name:
                 new_config.name = f"{self.name}/{stage.stage_name}"
+            # set trainer.save_hf_checkpoint to "last" to make sure next stage can load from HF checkpoint
+            new_config.trainer.save_hf_checkpoint = "last"
             new_config.stages = []
             yield new_config
 
@@ -1398,6 +1406,11 @@ def check_and_update(self) -> Config:  # noqa: C901
                     self.trainer.max_token_len_per_gpu = math.ceil(
                         2 * self.model.max_model_len / self.trainer.ulysses_sequence_parallel_size  # type: ignore [operator]
                     )
+                if self.trainer.save_hf_checkpoint not in {"last", "always", "never"}:
+                    raise ValueError(
+                        f"Invalid trainer.save_hf_checkpoint: {self.trainer.save_hf_checkpoint}, "
+                        "must be one of 'last', 'always', or 'never'."
+                    )
             else:
                 raise ValueError(f"Invalid trainer type: {self.trainer_type}")
             self.trainer.trainer_config.synchronize_config(self)

trinity/common/verl_config.py

@@ -137,7 +137,7 @@ class ProfileConfig:
 
 @dataclass
 class Actor:
-    strategy: str = "fsdp"
+    strategy: Optional[str] = None
     ppo_mini_batch_size: int = 256
     ppo_micro_batch_size: Optional[int] = None
     ppo_micro_batch_size_per_gpu: int = 1
@@ -232,7 +232,7 @@ class CriticModel:
 
 @dataclass
 class Critic:
-    strategy: str = "fsdp"
+    strategy: Optional[str] = None
     optim: Optim = field(default_factory=Optim)
     model: CriticModel = field(default_factory=CriticModel)
     ppo_mini_batch_size: int = 0
@@ -270,7 +270,7 @@ class _RewardModel:
 @dataclass
 class RewardModel:
     enable: bool = False
-    strategy: str = "fsdp"
+    strategy: Optional[str] = None
     model: _RewardModel = field(default_factory=_RewardModel)
     micro_batch_size_per_gpu: int = 1
     max_length: Optional[int] = None
@@ -416,6 +416,8 @@ def synchronize_config(self, config: Config) -> None:  # noqa: C901
         self.critic.ray_namespace = config.synchronizer.ray_namespace
 
         # Actor / Rollout Config
+        if self.actor_rollout_ref.actor.strategy is None:
+            self.actor_rollout_ref.actor.strategy = config.trainer.trainer_strategy
         self.actor_rollout_ref.model.path = config.model.model_path
         self.actor_rollout_ref.model.custom_chat_template = config.model.custom_chat_template
         self.actor_rollout_ref.model.rope_scaling = config.model.rope_scaling
@@ -488,7 +490,8 @@ def synchronize_config(self, config: Config) -> None:  # noqa: C901
             )
 
         # Critic config
-        self.critic.strategy = self.actor_rollout_ref.actor.strategy
+        if self.critic.strategy is None:
+            self.critic.strategy = config.trainer.trainer_strategy
         self.critic.model.path = config.model.critic_model_path
         self.critic.model.tokenizer_path = config.model.critic_model_path
         self.critic.ppo_mini_batch_size = config.buffer.train_batch_size

trinity/trainer/trainer.py

@@ -63,6 +63,7 @@ def __init__(self, config: Config) -> None:
         self.last_sync_step = None
         self.last_sync_time = None
         self.total_steps = config.trainer.total_steps or float("inf")
+        self.save_hf_checkpoint = config.trainer.save_hf_checkpoint
 
     async def prepare(self) -> None:
         """Prepare the trainer."""
@@ -90,7 +91,9 @@ async def train(self) -> str:
                 if await self.need_sync():
                     metrics.update(await self.sync_weight())
                 if self.need_save():
-                    metrics.update(self.save_checkpoint())
+                    metrics.update(
+                        self.save_checkpoint(save_as_hf=self.save_hf_checkpoint == "always")
+                    )
                 if self.config.trainer.enable_preview:
                     self._log_experiences(repr_samples)
                 self.monitor.log(metrics, self.train_step_num)
@@ -101,7 +104,7 @@ async def train(self) -> str:
                 self.logger.error(f"Error in Trainer:\n{traceback.format_exc()}")
                 break
 
-        self.save_checkpoint(block_until_saved=True, save_as_hf=True)
+        self.save_checkpoint(block_until_saved=True, save_as_hf=self.save_hf_checkpoint != "never")
         await self.synchronizer.set_trainer_status.remote(RunningStatus.STOPPED)
         self.logger.info("--------------------\n> Trainer finished.\n--------------------")
         return self.config.trainer.name