Fix absmethod in workflow

Author: chenyushuo

trinity/common/models/model.py

@@ -106,7 +106,7 @@ async def prepare(self) -> None:
                         if response.status_code == 200:
                             return
                 except Exception as e:
-                    self.logger.info(f"API server not ready (attempt {i+1}/{max_retries}): {e}")
+                    self.logger.info(f"API server not ready (attempt {i + 1}/{max_retries}): {e}")
                 await asyncio.sleep(interval)
             raise RuntimeError(
                 f"API server at {self.api_address} not ready after {max_retries} attempts."

trinity/common/workflows/step_wise_workflow.py

@@ -1,5 +1,3 @@
-from abc import abstractmethod
-
 import openai
 
 from trinity.common.experience import Experience
@@ -45,7 +43,6 @@ def run(self) -> list[Experience]:
 
         return experiences
 
-    @abstractmethod
     def step(self, step_num: int) -> bool:
         """Run a single step of your agent application.
 
@@ -59,17 +56,16 @@ def step(self, step_num: int) -> bool:
             You can use the openai client (`self.client`) to migrate your existing
             applications at low cost.
         """
-        pass
+        raise NotImplementedError
 
-    @abstractmethod
     def reward(self, exps: list[Experience], step_num: int) -> float:
         """Calculate the reward for the given experiences at the specified step."""
-        pass
+        raise NotImplementedError
 
     @property
-    @abstractmethod
     def max_step_num(self):
         """Return the maximum number of steps in the task."""
+        raise NotImplementedError
 
     @property
     def repeatable(self):
@@ -104,7 +100,6 @@ async def run_async(self) -> list[Experience]:
 
         return experiences
 
-    @abstractmethod
     async def step_async(self, step_num: int) -> bool:
         """Run a single step of your agent application asynchronously.
 
@@ -118,12 +113,11 @@ async def step_async(self, step_num: int) -> bool:
             You can use the openai client (`self.client`) to migrate your existing
             applications at low cost.
         """
-        pass
+        raise NotImplementedError
 
-    @abstractmethod
     async def reward_async(self, exps: list[Experience], step_num: int) -> float:
         """Calculate the reward for the given experiences at the specified step asynchronously."""
-        pass
+        raise NotImplementedError
 
 
 class RewardPropagationWorkflow(Workflow):
@@ -166,7 +160,6 @@ def run(self) -> list[Experience]:
             exp.metrics["actual_env_steps"] = step + 1  # +1 because step starts from 0
         return experiences
 
-    @abstractmethod
     def step(self, step_num: int) -> bool:
         """Run a single step of your agent application.
 
@@ -180,17 +173,16 @@ def step(self, step_num: int) -> bool:
             You can use the openai client (`self.client`) to migrate your existing
             applications at low cost.
         """
-        pass
+        raise NotImplementedError
 
-    @abstractmethod
     def reward(self, exps: list[Experience]) -> float:
         """Calculate the reward for the given experiences of the entire run."""
-        pass
+        raise NotImplementedError
 
     @property
-    @abstractmethod
     def max_step_num(self):
         """Return the maximum number of steps in the task."""
+        raise NotImplementedError
 
     @property
     def repeatable(self):
@@ -227,7 +219,6 @@ async def run_async(self) -> list[Experience]:
             exp.metrics["actual_env_steps"] = step + 1  # +1 because step starts from 0
         return experiences
 
-    @abstractmethod
     async def step_async(self, step_num: int) -> bool:
         """Run a single step of your agent application asynchronously.
 
@@ -241,9 +232,8 @@ async def step_async(self, step_num: int) -> bool:
             You can use the openai client (`self.client`) to migrate your existing
             applications at low cost.
         """
-        pass
+        raise NotImplementedError
 
-    @abstractmethod
     async def reward_async(self, exps: list[Experience]) -> float:
         """Calculate the reward for the given experiences of the entire run asynchronously."""
-        pass
+        raise NotImplementedError

trinity/common/workflows/workflow.py

@@ -3,7 +3,7 @@
 
 from __future__ import annotations
 
-from abc import ABC, abstractmethod
+from abc import ABC
 from dataclasses import asdict, dataclass, field
 from typing import Any, List, Optional, Type, Union
 
@@ -158,10 +158,6 @@ def set_repeat_times(self, repeat_times, run_id_base):
         self.repeat_times = repeat_times
         self.run_id_base = run_id_base
 
-    @abstractmethod
-    def run(self) -> List[Experience]:
-        """Run workflow and return a list of experiences."""
-
     def process_messages_to_experience(self, messages, reward, info={}) -> Experience:
         converted_experience = self.model.convert_messages_to_experience(messages)