[Bug Fix] OnlineDPOTrainer with vLLM Server Mode

tests/test_online_dpo_trainer.py

@@ -274,6 +274,14 @@ def test_training_with_judge(self, config_name):
     @require_vllm
     @pytest.mark.slow
     def test_training_with_vllm(self, config_name):
+        def cleanup_vllm_communicator(trainer):
+            """Clean up vLLM communicator to avoid conflicts between test runs"""
+            try:
+                if hasattr(trainer, "vllm_client") and trainer.vllm_client is not None:
+                    trainer.vllm_client.close_communicator()
+            except Exception:
+                pass  # Continue if cleanup fails
+
         model_id = "trl-internal-testing/small-Qwen2ForCausalLM-2.5"  # We need a bigger model
         model = AutoModelForCausalLM.from_pretrained(model_id)
         tokenizer = AutoTokenizer.from_pretrained(model_id)
@@ -295,10 +303,14 @@ def test_training_with_vllm(self, config_name):
             processing_class=tokenizer,
             reward_processing_classes=self.reward_tokenizer,
         )
-        trainer.train()
 
-        # Check if training loss is available
-        assert "train_loss" in trainer.state.log_history[-1]
+        # Ensure cleanup of vLLM communicator after the test
+        try:
+            trainer.train()
+            # Check if training loss is available
+            assert "train_loss" in trainer.state.log_history[-1]
+        finally:
+            cleanup_vllm_communicator(trainer)
 
     @require_vllm
     def test_training_with_vllm_colocate(self):

trl/extras/vllm_client.py

@@ -495,6 +495,9 @@ def close_communicator(self):
             if response.status_code != 200:
                 raise Exception(f"Request failed: {response.status_code}, {response.text}")
 
+        if self.communicator is not None:
+            self.communicator = None
+
 
 # Example usage
 if __name__ == "__main__":

trl/trainer/online_dpo_trainer.py

@@ -463,7 +463,11 @@ def __init__(
                     else:
                         base_url = f"http://{args.vllm_server_host}:{args.vllm_server_port}"
                     self.vllm_client = VLLMClient(base_url=base_url, connection_timeout=args.vllm_server_timeout)
-                    self.vllm_client.init_communicator(device=torch.cuda.current_device())
+
+                    # Determine device type (supports cuda, xpu, etc.)
+                    accelerator_type = torch.accelerator.current_accelerator().type
+                    current_device = getattr(torch, accelerator_type).current_device()
+                    self.vllm_client.init_communicator(device=current_device)
                 else:
                     self.vllm_client = None
             elif self.vllm_mode == "colocate":
@@ -755,7 +759,7 @@ def _generate_vllm_server(self, prompts, images=None):
                 max_tokens=self.generation_config.max_tokens,
                 guided_decoding_regex=self.guided_decoding_regex if hasattr(self, "guided_decoding_regex") else None,
                 generation_kwargs=self.args.generation_kwargs,
-            )
+            )["completion_ids"]
             # Flatten: each prompt generates 2 completions
             completion_ids = [[comp_id] for prompt_completions in completion_ids for comp_id in prompt_completions]
         else: