update doc stringh

Author: pan-x-c

examples/ppo_countdown/countdown.yaml

@@ -7,7 +7,7 @@ algorithm:
 model:
   model_path: '/PATH/TO/MODEL/CHECKPOINT/'
   max_prompt_tokens: 256
-  max_repsonse_tokens: 1024
+  max_response_tokens: 1024
 cluster:
   node_num: 1
   gpu_per_node: 8

tests/common/config_test.py

@@ -4,17 +4,25 @@
 import unittest
 
 from tests.tools import get_template_config
-from trinity.common.config import load_config
+from trinity.common.config import InferenceModelConfig, load_config
 
 
 class TestConfig(unittest.TestCase):
     def test_load_default_config(self):
         config = get_template_config()
+        config.buffer.batch_size = 8
         config.algorithm.repeat_times = 10
         config.model.model_path = "Qwen/Qwen3-1.7B"
+        config.cluster.gpu_per_node = 8
+        config.cluster.node_num = 2
+        config.explorer.rollout_model.engine_num = 2
+        config.explorer.rollout_model.tensor_parallel_size = 2
+        config.explorer.auxiliary_models.append(
+            InferenceModelConfig(model_path="Qwen/Qwen3-32B", tensor_parallel_size=4, engine_num=1),
+        )
         config.check_and_update()
         self.assertIsNotNone(config.trainer.trainer_config)
-        self.assertEqual(config.trainer.trainer_config.trainer.n_gpus_per_node, 2)
+        self.assertEqual(config.trainer.trainer_config.trainer.n_gpus_per_node, 8)
         self.assertEqual(config.trainer.trainer_config.trainer.nnodes, 1)
         self.assertEqual(config.trainer.trainer_config.trainer.project_name, config.project)
         self.assertEqual(config.trainer.trainer_config.trainer.experiment_name, config.name)

trinity/cli/launcher.py

@@ -3,6 +3,7 @@
 import os
 import sys
 from pathlib import Path
+from pprint import pprint
 
 import ray
 
@@ -157,6 +158,7 @@ def activate_data_module(data_workflow_url: str, config_path: str):
 def run(config_path: str, dlc: bool = False):
     config = load_config(config_path)
     config.check_and_update()
+    pprint(config)
     # try to activate data module
     data_processor_config = config.data_processor
     if data_processor_config.data_workflow_url and (

trinity/common/config.py

@@ -53,12 +53,15 @@ class FormatConfig:
 
 @dataclass
 class GenerationConfig:
-    # repeat each task for `n` times (for GPRO-like algorithms)
-    n: int = 1
     temperature: float = 1.0
     top_p: float = 1.0
     top_k: int = -1
     logprobs: int = 0  # vLLM return `logprobs + 1` elements
+    # repeat each task for `n` times (for GPRO-like algorithms)
+    # this field will be automatically set to `algorithm.repeat_times` in
+    # `buffer.explorer_input.taskset.rollout_args`
+    # ! DO NOT SET in `buffer.explorer_input.taskset.rollout_args`
+    n: int = 1
 
 
 @dataclass
@@ -67,7 +70,6 @@ class StorageConfig:
 
     name: str = ""
     storage_type: StorageType = StorageType.FILE
-    algorithm_type: Optional[AlgorithmType] = None  # automatically set
     path: Optional[str] = None
 
     # used for StorageType.FILE
@@ -76,13 +78,20 @@ class StorageConfig:
     format: FormatConfig = field(default_factory=FormatConfig)
     index: int = 0
 
-    # used for algorithm_type is None
-    task_type: TaskType = TaskType.EXPLORE  # automatically set
+    # used for rollout tasks
     default_workflow_type: Optional[str] = None
     default_reward_fn_type: Optional[str] = None
-    total_epochs: int = 1  # automatically set
     rollout_args: GenerationConfig = field(default_factory=GenerationConfig)
 
+    # ! DO NOT SET, automatically set from algorithm.algorithm_type
+    algorithm_type: Optional[AlgorithmType] = None
+
+    # ! DO NOT SET, automatically set from buffer.total_epochs
+    total_epochs: int = 1  # automatically set
+
+    # ! DO NOT SET,  automatically set corresponding to train/eval
+    task_type: TaskType = TaskType.EXPLORE
+
 
 @dataclass
 class DataProcessorConfig:
@@ -124,8 +133,10 @@ class ModelConfig:
 
 @dataclass
 class InferenceModelConfig:
-    # For Rollout Model: automatically set from config.model.model_path
+    # ! DO NOT SET in explorer.rollout_model, automatically set from config.model.model_path
     model_path: str = ""
+
+    # support `vllm` or `vllm_async`,
     engine_type: str = "vllm_async"
     engine_num: int = 1
     tensor_parallel_size: int = 1
@@ -136,15 +147,22 @@ class InferenceModelConfig:
     gpu_memory_utilization: float = 0.9
     dtype: str = "bfloat16"
     seed: int = 42
+
+    # if not set, use `model.max_prompt_tokens`
     max_prompt_tokens: Optional[int] = None
+    # if not set, use `model.max_response_tokens`
     max_response_tokens: Optional[int] = None
+
     # override chat template in model
     chat_template: Optional[str] = None
+
     # For Qwen3
     enable_thinking: bool = False
+
     # For OpenAI API
     enable_openai_api: bool = False
-    # DO NOT SET this field
+
+    # ! DO NOT SET
     bundle_indices: str = ""
 
 
@@ -209,7 +227,7 @@ class BufferConfig:
     max_retry_times: int = 3
     max_retry_interval: int = 1
 
-    # for experience construct, DO NOT SET
+    # ! DO NOT SET FOLLOWING FIELDS
     read_batch_size: int = 1  # automatically set
     tokenizer_path: Optional[str] = None  # automatically set
     pad_token_id: Optional[int] = None  # automatically set
@@ -260,8 +278,7 @@ class TrainerConfig:
 class MonitorConfig:
     # TODO: support multiple monitors (List[MonitorType])
     monitor_type: MonitorType = MonitorType.WANDB
-    # ! DO NOT SET
-    # the root directory for monitor cache and meta files, automatically generated
+    # ! DO NOT SET, automatically generated as checkpoint_job_dir/monitor
     cache_dir: str = ""
 
 
@@ -288,12 +305,12 @@ class Config:
     mode: str = "both"  # `explore`, `train`, `both` or `bench`
     project: str = "Trinity-RFT"
     name: str = "rft"
-    algorithm: AlgorithmConfig = field(default_factory=AlgorithmConfig)
     # the root dir for checkpoints
     checkpoint_root_dir: str = ""
-    # DO NOT SET, automatically generated as `checkpoint_root_dir/project/name`
+    # ! DO NOT SET, automatically generated as `checkpoint_root_dir/project/name`
     checkpoint_job_dir: str = ""
 
+    algorithm: AlgorithmConfig = field(default_factory=AlgorithmConfig)
     data_processor: DataProcessorConfig = field(default_factory=DataProcessorConfig)
     model: ModelConfig = field(default_factory=ModelConfig)
     cluster: ClusterConfig = field(default_factory=ClusterConfig)

trinity/trainer/verl_trainer.py

@@ -73,7 +73,6 @@ def __init__(
         global_config: Config,
     ):
         train_config = global_config.trainer
-        pprint(train_config.trainer_config)
         config = OmegaConf.structured(train_config.trainer_config)
         # download the checkpoint from hdfs
         local_path = copy_local_path_from_hdfs(config.actor_rollout_ref.model.path)