Enhance AgentScope Workflow Adapter

examples/agentscope_react/gsm8k.yaml

@@ -29,7 +29,7 @@ buffer:
         response_key: 'answer'
       rollout_args:
         temperature: 1.0
-      default_workflow_type: 'as_react_workflow'
+      default_workflow_type: 'agentscope_react_workflow'
     eval_tasksets: []
   trainer_input:
     experience_buffer:

trinity/common/workflows/__init__.py

@@ -20,8 +20,9 @@
         # tool_call
         "tool_call_workflow": "trinity.common.workflows.customized_toolcall_workflows.ToolCallWorkflow",
         # agentscope
-        "agentscope_react_workflow": "trinity.common.workflows.agentscope.react.react_workflow.AgentScopeReActWorkflow",
         "agentscope_workflow_adapter": "trinity.common.workflows.agentscope_workflow.AgentScopeWorkflowAdapter",
+        "agentscope_workflow_adapter_v1": "trinity.common.workflows.agentscope_workflow.AgentScopeWorkflowAdapterV1",
+        "agentscope_react_workflow": "trinity.common.workflows.agentscope.react.react_workflow.AgentScopeReActWorkflow",
         "agentscope_react_math_workflow": "trinity.common.workflows.envs.agentscope.agentscopev1_react_workflow.AgentScopeReactMathWorkflow",
         "as_react_workflow": "trinity.common.workflows.agentscope.react.react_workflow.AgentScopeReActWorkflow",
         "agentscopev0_react_math_workflow": "trinity.common.workflows.envs.agentscope.agentscopev0_react_workflow.AgentScopeV0ReactMathWorkflow",

trinity/common/workflows/agentscope_workflow.py

@@ -22,8 +22,8 @@ def __init__(
             from agentscope.model import TrinityChatModel
         except ImportError:
             raise ImportError(
-                "This workflow requires agentscope >= 0.1.6, please install "
-                "it via `pip install agentscope>=0.1.6`",
+                "This workflow requires agentscope >= 1.0.7, please install "
+                "it via `pip install agentscope>=1.0.7`",
             )
 
         super().__init__(
@@ -72,3 +72,108 @@ async def run_async(self) -> List[Experience]:
         """Run the workflow asynchronously and return experiences."""
         reward = await self.workflow_func(self.task.raw_task, self.chat_model)  # type: ignore [arg-type]
         return self.construct_experiences(reward)
+
+
+class AgentScopeWorkflowAdapterV1(Workflow):
+    """A more general adapter to wrap agentscope trainable workflow and judge functions into a Trinity Workflow."""
+
+    is_async: bool = True
+
+    def __init__(
+        self,
+        *,
+        task: Task,
+        model: ModelWrapper,
+        auxiliary_models: Optional[List[ModelWrapper]] = None,
+    ):
+        """Initialize the adapter with the task and model."""
+        try:
+            from agentscope.model import TrinityChatModel
+        except ImportError:
+            raise ImportError(
+                "This workflow requires agentscope >= 1.0.11, please install "
+                "it via `pip install agentscope>=1.0.11`",
+            )
+
+        super().__init__(
+            task=task,
+            model=model,
+            auxiliary_models=auxiliary_models,
+        )
+        self.workflow_func = task.workflow_args.get("workflow_func", None)
+        self.judge_func = task.workflow_args.get("judge_func", None)
+
+        if self.workflow_func is None:
+            raise ValueError(
+                "The 'workflow_func' is not provided.",
+            )
+
+        self.chat_model: TrinityChatModel = TrinityChatModel(
+            model.get_openai_async_client(),
+            generate_kwargs={
+                "temperature": self.task.rollout_args.temperature,
+                "top_p": self.task.rollout_args.top_p,
+                "max_tokens": self.task.rollout_args.max_tokens or 4096,
+                "logprobs": True,
+                "top_logprobs": self.task.rollout_args.logprobs,
+            },
+        )
+        self.auxiliary_chat_models = [
+            TrinityChatModel(
+                openai_async_client=aux_model,
+                # TODO: customize generate_kwargs for auxiliary models if needed
+            )
+            for aux_model in (self.auxiliary_models or [])
+        ]
+
+    def construct_experiences(
+        self,
+        reward: float,
+        metrics: Dict,
+    ) -> List[Experience]:
+        """Construct experiences from the agent's interaction history.
+
+        Args:
+            reward (float): The reward value to assign to each experience.
+            metrics (Dict): A dictionary of metrics to be attached to the last experience.
+
+        Returns:
+            List: A list of Experience objects.
+        """
+        exps = self.model.extract_experience_from_history()
+        for exp in exps:
+            exp.reward = reward
+        # only attach metrics to the last experience
+        if len(exps) > 0:
+            exps[-1].metrics = metrics
+        return exps
+
+    async def run_async(self) -> List[Experience]:
+        """Run the workflow asynchronously and return experiences."""
+        try:
+            from agentscope.tuner import JudgeOutput, WorkflowOutput
+        except ImportError:
+            raise ImportError(
+                "Fail to import agentscope tuner related types. Please ensure agentscope>=1.0.11 is installed."
+            )
+
+        metrics = {}
+        workflow_output: WorkflowOutput = await self.workflow_func(
+            self.task.raw_task, self.chat_model, self.auxiliary_chat_models
+        )  # type: ignore [arg-type]
+        metrics.update(workflow_output.metrics or {})
+        if self.judge_func is not None:
+            assert (
+                workflow_output.response is not None
+            ), "Workflow must provide response for judging."
+            judge_output: JudgeOutput = await self.judge_func(
+                self.task.raw_task, workflow_output.response, self.auxiliary_chat_models
+            )  # type: ignore [arg-type]
+            reward = judge_output.reward
+            metrics.update(judge_output.metrics or {})
+        else:
+            assert (
+                workflow_output.reward is not None
+            ), "Either workflow or judge must provide reward."
+            reward = workflow_output.reward
+        return self.construct_experiences(reward, metrics)