😴 Add vllm_enable_sleep_mode to RLOO Trainer

docs/source/rloo_trainer.md

@@ -230,6 +230,8 @@ We provide a [HF Space](https://huggingface.co/spaces/trl-lib/recommend-vllm-mem
 
 If the recommended value does not work in your environment, we suggest adding a small buffer (e.g., +0.05 or +0.1) to the recommended value to ensure stability.
 
+If you still find you are getting out-of-memory errors set `vllm_enable_sleep_mode` to True and the vllm parameters and cache will be offloaded during the optimization step. For more information, see [Reducing Memory Usage with vLLM Sleep Mode](reducing_memory_usage#vllm-sleep-mode).
+
 </Tip>
 
 <Tip>

trl/trainer/rloo_config.py

@@ -141,6 +141,9 @@ class RLOOConfig(TrainingArguments):
             Control the tensor parallel size for vLLM. This setting only applies when `vllm_mode` is set to
             `"colocate"`. If you are using `vllm_mode="server"`, this parameter must be passed separately when
             launching the vLLM server via the `--vllm_tensor_parallel_size` flag.
+        vllm_enable_sleep_mode (`bool`, *optional*, defaults to `False`):
+            Whether to enable sleep mode for vLLM. If `True`, vLLM will sleep during the optimization step and woken
+            for weight sync and generation.
 
         > Parameters that control the training
 
@@ -507,6 +510,13 @@ class RLOOConfig(TrainingArguments):
             "model implementation."
         },
     )
+    vllm_enable_sleep_mode: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to enable sleep mode for vLLM. If `True`, vLLM will sleep during the optimization step "
+            "and woken for weight sync and generation."
+        },
+    )
     vllm_guided_decoding_regex: Optional[str] = field(
         default=None,
         metadata={"help": "Regex for vLLM guided decoding. If `None` (default), guided decoding is disabled."},

trl/trainer/rloo_trainer.py

@@ -607,7 +607,10 @@ def decode(example, tokenizer):
                     # Latest vLLM v1 memory profiler is misled by the high default value (i.e., 32768) - thinking there's not enough memory
                     max_num_batched_tokens=4096,
                     model_impl=self.args.vllm_model_impl,
+                    enable_sleep_mode=self.args.vllm_enable_sleep_mode,
                 )
+                if self.args.vllm_enable_sleep_mode:
+                    self.llm.sleep(level=1)
             else:
                 raise ValueError(f"vllm_mode must be either 'server' or 'colocate', got '{self.vllm_mode}'.")
 
@@ -1130,6 +1133,11 @@ def _generate_and_score_completions(
 
         # Generate completions using either vLLM or regular generation
         if self.use_vllm:
+            if self.vllm_mode == "colocate" and self.args.vllm_enable_sleep_mode:
+                # wake up colocated vLLM instances if needed
+                torch.cuda.empty_cache()  # required to avoid OOM in some cases
+                self.llm.wake_up()
+
             # First, update the vLLM weights if needed
             if self.state.global_step != self._last_loaded_step:
                 self._move_model_to_vllm()
@@ -1240,6 +1248,9 @@ def _generate_and_score_completions(
                     tp_slice = slice(local_rank_in_group * orig_size, (local_rank_in_group + 1) * orig_size)
                     completion_ids = completion_ids[tp_slice]
 
+                if self.args.vllm_enable_sleep_mode:
+                    self.llm.sleep(level=1)
+
             # Pad the completions, and concatenate them with the prompts
             completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids]
             completion_ids = pad(completion_ids, padding_value=self.pad_token_id)