fix multimodal envs. add view_run_multimodal

environments/multimodal_dpo/pixmo_clocks.py

@@ -1,9 +1,7 @@
 import base64
 import io
-import os
 import random
 import re
-import sys
 import traceback
 from typing import List, Optional, Tuple
 
@@ -68,13 +66,9 @@ class ClockDatasetEnv(BaseEnv):
             history: GameHistory = (user_msg, assistant_msg)
             to_score.append((history, item[1], base64_image))
 
-        return to_score, to_backlog
+        to_postprocess = await self.score(to_score)
 
-    async def postprocess_histories(
-        self, trajectories: List[GameHistory]
-    ) -> ScoredDataGroup:
-        # No custom post-processing
-        pass
+        return to_postprocess, to_backlog
 
     async def evaluate(self, *args, **kwargs):
         # No custom evaluation
@@ -87,6 +81,7 @@ class ClockDatasetEnv(BaseEnv):
         self.iter = 0
 
     async def get_next_item(self) -> Item:
+
         try:
             entry = self.train[self.iter % len(self.train)]
             self.iter += 1
@@ -128,6 +123,7 @@ class ClockDatasetEnv(BaseEnv):
         scores["scores"] = []
         scores["images"] = []
         random.shuffle(rollout_group_data)
+
         for item in rollout_group_data:
             out = tokenize_for_trainer(self.tokenizer, item[0])
             tokens = out["tokens"]
@@ -169,29 +165,26 @@ class ClockDatasetEnv(BaseEnv):
 
     @classmethod
     def config_init(cls) -> Tuple[BaseEnvConfig, List[OpenaiConfig]]:
-        if not os.environ.get("OPENAI_API_KEY"):
-            print("ERROR: OPENAI_API_KEY environment variable is not set!")
-            sys.exit(1)
 
         config = BaseEnvConfig(
-            wandb_name="clocks",
-            tokenizer_name="gpt2",
-            group_size=2,
-            use_wandb=False,
+            wandb_name="pixmo_clocks",
+            tokenizer_name="Qwen/Qwen2-VL-2B-Instruct",
+            group_size=8,
+            use_wandb=True,
             max_num_workers=2,
             rollout_server_url="http://localhost:8000",
             total_steps=1000,
-            batch_size=1,
-            steps_per_eval=10,
-            ensure_scores_are_not_same=False,
+            batch_size=12,
+            steps_per_eval=100,
+            max_token_length=2048,
         )
 
         server_configs = [
             OpenaiConfig(
-                model_name="gpt-4o",
-                base_url=None,
-                api_key=os.environ.get("OPENAI_API_KEY"),
-                num_requests_for_eval=1,
+                model_name="Qwen/Qwen2-VL-2B-Instruct",
+                base_url="http://localhost:9001/v1",
+                api_key="x",
+                num_requests_for_eval=256,
             ),
         ]