Make Alfworld Rollout Parallel

trinity/common/workflows/envs/alfworld/alfworld_workflow.py

@@ -98,6 +98,7 @@ class AlfworldWorkflow(MultiTurnWorkflow):
     """A workflow for alfworld task."""
 
     is_async: bool = True
+    can_repeat: bool = False
 
     def __init__(
         self,
@@ -120,39 +121,32 @@ async def get_model_response(self, messages):
     async def get_model_response_text(self, messages):
         return (await self.get_model_response(messages))[0].response_text
 
-    async def generate_env_inference_samples(self, env, rollout_num) -> List[Experience]:
-        # TODO: Make this parallel
-        print("Generating env inference samples...")
-        experience_list = []
-        for i in range(rollout_num):
-            observation, info = env.reset()
-            final_reward = -0.1
-            memory = []
-            memory.append({"role": "system", "content": AlfWORLD_SYSTEM_PROMPT})
-            for r in range(self.max_env_steps):
-                format_obs = format_observation(observation)
-                memory = memory + [{"role": "user", "content": format_obs}]
-                response_text = await self.get_model_response_text(memory)
-                memory.append({"role": "assistant", "content": response_text})
-                action = parse_action(response_text)
-                observation, reward, done, info = env.step(action)
-                if done:
-                    final_reward = reward
-                    break
-            experience = self.process_messages_to_experience(
-                memory, final_reward, {"env_rounds": r, "env_done": 1 if done else 0}
-            )
-            experience_list.append(experience)
+    async def generate_env_inference_samples(self, env) -> List[Experience]:
+        observation, info = env.reset()
+        final_reward = -0.1
+        memory = []
+        memory.append({"role": "system", "content": AlfWORLD_SYSTEM_PROMPT})
+        for r in range(self.max_env_steps):
+            format_obs = format_observation(observation)
+            memory = memory + [{"role": "user", "content": format_obs}]
+            response_text = await self.get_model_response_text(memory)
+            memory.append({"role": "assistant", "content": response_text})
+            action = parse_action(response_text)
+            observation, reward, done, info = env.step(action)
+            if done:
+                final_reward = reward
+                break
+        experience = self.process_messages_to_experience(
+            memory, final_reward, {"env_rounds": r, "env_done": 1 if done else 0}
+        )
         # Close the env to save cpu memory
         env.close()
-        return experience_list
+        return [experience]
 
     async def run_async(self) -> List[Experience]:
         # assume the task_description is the game_file_path generated.
         # see Trinity-RFT/examples/grpo_alfworld/get_alfworld_data.py
         game_file_path = self.task_desc
-        rollout_n = self.repeat_times
-        # TODO: Make parallel envs
         try:
             import textworld
             import textworld.gym
@@ -179,7 +173,7 @@ def create_environment(game_file):
             error_message = f"Error importing AlfworldTWEnv {str(e)}. Please make sure you have installed the alfworld package successfully, following the instructions in https://github.com/alfworld/alfworld"
             raise ImportError(error_message)
         env = create_environment(game_file_path)
-        return await self.generate_env_inference_samples(env, rollout_n)
+        return await self.generate_env_inference_samples(env)
 
 
 @WORKFLOWS.register_module("step_wise_alfworld_workflow")