Add workflow_args for fine-grained control

docs/sphinx_doc/source/tutorial/trinity_configs.md

@@ -223,6 +223,8 @@ The configuration for each task dataset is defined as follows:
   - `temperature`: The temperature for sampling.
 - `default_workflow_type`: Type of workflow logic applied to this dataset. If not specified, the `buffer.default_workflow_type` is used.
 - `default_reward_fn_type`: Reward function used during exploration. If not specified, the `buffer.default_reward_fn_type` is used.
+- `workflow_args`: A dictionary of arguments used to supplement dataset-level parameters.
+
 
 ### Trainer Input
 

docs/sphinx_doc/source/tutorial/trinity_programming_guide.md

@@ -45,6 +45,15 @@ To handle differences in `Task` contents, Trinity-RFT provides a unified `Task`
   - **`raw_task`** (`Dict`): An record of raw data in `Dict` format. For highly customized workflow, you can directly use `raw_task` to initialize your `Workflow` instance without relying on the following fields.
   - **`format_args`** ({class}`trinity.common.config.FormatConfig`): Parameters to facilitate the construction of `Workflow` instances. For example, the `prompt_key` and `response_key` can be used to get the prompt and response from `raw_task`. These settings come from the YAML configuration file and can be set in `buffer.explorer_input.task_set.format`.
   - **`rollout_args`** ({class}`trinity.common.config.GenerationConfig`): Parameters that control the rollout process, such as `temperature`. This field also comes from the YAML configuration file and can be set in `buffer.explorer_input.task_set.rollout_args`.
+  - **`workflow_args`** (`Dict`): A dictionary of parameters to facilitate the construction of `Workflow` instances. Provides more flexibility than `format_args` and `rollout_args` by using a dictionary. This field also comes from the YAML configuration file and can be set in `buffer.explorer_input.task_set.workflow_args`. Normally, you do not need to set this field.
+
+```{tip}
+`workflow`, `workflow_args` and `raw_task` provide different levels of customization.
+
+- `workflow` provides the global settings for all tasks that uses the same workflow. (Global Level)
+- `workflow_args` can be set for each task dataset, allowing different task datasets using the same workflow to behave differently. (Dataset Level)
+- `raw_task` provides the ability to customize the behavior of each task, which is most flexible. (Data Sample Level)
+```
 
 In the math problem scenario, the `Task` dataset can be a `jsonl` file, where each line contains JSON with `question` and `answer` fields representing the problem description and standard answer, respectively. For example:
 

tests/explorer/workflow_test.py

@@ -5,7 +5,7 @@
 from unittest.mock import MagicMock
 
 from tests.tools import get_unittest_dataset_config
-from trinity.common.workflows import MathWorkflow
+from trinity.common.workflows import MathWorkflow, Workflow
 from trinity.common.workflows.workflow import Task
 
 
@@ -15,6 +15,33 @@ class MockResponse:
     reward: float = 0.0
 
 
+class DummyWorkflow(Workflow):
+    def __init__(self, model, task: Task, auxiliary_models=None):
+        super().__init__(model, task, auxiliary_models)
+        self.obj = task.raw_task
+        self.output_format = task.workflow_args["output_format"]
+
+    @property
+    def resettable(self):
+        return True
+
+    def reset(self, task: Task):
+        self.obj = task.raw_task
+        self.output_format = task.workflow_args["output_format"]
+
+    def run(self):
+        if self.output_format == "json":
+            import json
+
+            return [json.dumps(self.obj)]
+        elif self.output_format == "yaml":
+            import yaml
+
+            return [yaml.safe_dump(self.obj)]
+        else:
+            raise ValueError("Invalid output format")
+
+
 class WorkflowTest(unittest.TestCase):
     def test_math_workflow(self) -> None:
         model = MagicMock()
@@ -150,3 +177,18 @@ def test_gsm8k_workflow(self) -> None:
         self.assertEqual(experiences[1].reward, -0.1)
         self.assertEqual(experiences[2].reward, -0.1)
         self.assertEqual(experiences[3].reward, 1.1)
+
+    def test_workflow_resettable(self) -> None:
+        model = MagicMock()
+        json_task = Task(
+            workflow=DummyWorkflow, raw_task={"a": 1}, workflow_args={"output_format": "json"}
+        )
+        yaml_task = Task(
+            workflow=DummyWorkflow, raw_task={"a": 1}, workflow_args={"output_format": "yaml"}
+        )
+        workflow = json_task.to_workflow(model)
+        answer = workflow.run()
+        self.assertEqual(answer[0], '{"a": 1}')
+        workflow.reset(yaml_task)
+        answer = workflow.run()
+        self.assertEqual(answer[0], "a: 1\n")

trinity/buffer/reader/file_reader.py

@@ -221,6 +221,7 @@ def read(self, strategy: Optional[ReadStrategy] = None):
             workflow=workflow_class,
             format_args=self.meta.format,
             rollout_args=self.meta.rollout_args,
+            workflow_args=self.meta.workflow_args,
             is_eval=self.meta.task_type == TaskType.EVAL,
             reward_fn=reward_fn,
             raw_task=sample,

trinity/common/config.py

@@ -81,6 +81,7 @@ class StorageConfig:
     default_workflow_type: Optional[str] = None
     default_reward_fn_type: Optional[str] = None
     rollout_args: GenerationConfig = field(default_factory=GenerationConfig)
+    workflow_args: dict = field(default_factory=dict)
 
     # ! DO NOT SET, automatically set from algorithm.algorithm_type
     algorithm_type: Optional[AlgorithmType] = None

trinity/common/workflows/__init__.py

@@ -3,10 +3,11 @@
 from .envs.alfworld.alfworld_workflow import AlfworldWorkflow
 from .envs.sciworld.sciworld_workflow import SciWorldWorkflow
 from .envs.webshop.webshop_workflow import WebShopWorkflow
-from .workflow import WORKFLOWS, MathWorkflow, SimpleWorkflow, Task
+from .workflow import WORKFLOWS, MathWorkflow, SimpleWorkflow, Task, Workflow
 
 __all__ = [
     "Task",
+    "Workflow",
     "WORKFLOWS",
     "SimpleWorkflow",
     "MathWorkflow",

trinity/common/workflows/workflow.py

@@ -28,8 +28,9 @@ class Task:
     """A Task class that defines a task and its associated reward function / workflow."""
 
     workflow: Type[Workflow]
-    format_args: FormatConfig
+    format_args: FormatConfig = field(default_factory=FormatConfig)
     rollout_args: GenerationConfig = field(default_factory=GenerationConfig)
+    workflow_args: dict = field(default_factory=dict)
     is_eval: bool = False
     reward_fn: Optional[Type[RewardFn]] = None
     raw_task: Optional[dict] = None  # The raw data sample

trinity/utils/eval_utils.py

@@ -15,12 +15,19 @@ def simple_answer_parser(response: str) -> str:
     return parse(response)
 
 
-def find_boxed_answer(string):
+def find_boxed_answer(raw_answer, timeout=10):
     """
-    Find answers from solutions where the answers are enclosed in LaTeX's `\boxed` tag
+    Find answers from solutions where the answers are enclosed in LaTeX's `\\boxed` tag
+
+    Args:
+        raw_answer (`str`): raw answer from model
+        timeout (`int`): timeout in seconds for regex
+
+    Returns:
+        `str`: answer if found, otherwise None
     """
     pattern = r"\\boxed\s*(({(?:\\.|[^{}]|(?2))*})|(.))"
-    res = re.findall(pattern, string)
+    res = re.findall(pattern, raw_answer, timeout=timeout)
     if res:
         answer = res[-1][0]  # regard the last boxed as the answer
         if answer.startswith("{"):