Split args.offload to train and rollout

slime/backends/fsdp_utils/actor.py

@@ -129,7 +129,7 @@ def init(self, args: Namespace, role: str, wandb_run_id: str, with_ref: bool = F
         # Initialize data packing parameters
         self.max_tokens_per_gpu = args.max_tokens_per_gpu  # From main arguments
 
-        if self.args.offload:
+        if self.args.offload_train:
             self.sleep(("model"))
 
         Timer().start("train_wait")
@@ -318,7 +318,7 @@ def train(self, rollout_id: int, rollout_data_ref: Box) -> None:
         """
         Timer().end("train_wait")
 
-        if self.args.offload:
+        if self.args.offload_train:
             self.wake_up(("model"))
 
         world_size = dist.get_world_size()
@@ -559,7 +559,7 @@ def update_weights(self) -> None:  # type: ignore[override]
             self.weight_updater.connect_rollout_engines(rollout_engines, rollout_engine_lock)
             dist.barrier(group=get_gloo_group())
 
-        with torch_memory_saver.disable() if self.args.offload and not torch.version.hip else nullcontext():
+        with torch_memory_saver.disable() if self.args.offload_train and not torch.version.hip else nullcontext():
             self.weight_updater.update_weights()
 
     @torch.no_grad()

slime/backends/megatron_utils/actor.py

@@ -73,7 +73,7 @@ def init(
         )
 
         if role == "critic":
-            if self.args.offload:
+            if self.args.offload_train:
                 self.sleep(("model"))
             Timer().start("train_wait")
             return
@@ -106,7 +106,7 @@ def init(
         # empty cache after initialization
         clear_memory()
 
-        if self.args.offload:
+        if self.args.offload_train:
             # recover to actor in the end.
             self.update_gpu_params_dict(self.weights["actor"])
             self.sleep(("model"))
@@ -156,7 +156,7 @@ def update_gpu_params_dict(self, params_dict: Dict[str, torch.Tensor]) -> None:
 
     @timer
     def sleep(self, tags: Union[str, Tuple[str, ...]]) -> None:
-        assert self.args.offload
+        assert self.args.offload_train
         assert "model" in tags
         if isinstance(tags, str):
             tags = (tags,)
@@ -171,7 +171,7 @@ def sleep(self, tags: Union[str, Tuple[str, ...]]) -> None:
 
     @timer
     def wake_up(self, tags: Union[str, Tuple[str, ...]]) -> None:
-        assert self.args.offload
+        assert self.args.offload_train
 
         # there are weird times when sglang is not offloaded immediately, so we wait here.
         mem_fraction_static = self.args.sglang_mem_fraction_static or 0.8
@@ -243,7 +243,7 @@ def compute_log_prob(
     def train(self, rollout_id: int, rollout_data_ref: Box) -> None:
         Timer().end("train_wait")
 
-        if self.args.offload:
+        if self.args.offload_train:
             self.wake_up(("model"))
 
         with timer("data_preprocess"):
@@ -408,7 +408,7 @@ def update_weights(self) -> None:
         if self.args.debug_train_only or self.args.debug_rollout_only:
             return
 
-        if self.args.offload:
+        if self.args.offload_train:
             reload_process_groups()
 
         rollout_engines, rollout_engine_lock, num_new_engines = ray.get(
@@ -418,7 +418,7 @@ def update_weights(self) -> None:
             self.weight_updater.connect_rollout_engines(rollout_engines, rollout_engine_lock)
             dist.barrier(group=get_gloo_group())
 
-        with torch_memory_saver.disable() if self.args.offload else nullcontext():
+        with torch_memory_saver.disable() if self.args.offload_train else nullcontext():
             print_memory("before update_weights")
             self.weight_updater.update_weights()
             print_memory("after update_weights")
@@ -435,7 +435,7 @@ def update_weights(self) -> None:
                 else:
                     self.update_cpu_params_dict(self.weights["old_actor"])
 
-        if self.args.offload:
+        if self.args.offload_train:
             destroy_process_groups()
 
     def load_other_checkpoint(self, model_tag: str, path: str) -> None:

slime/backends/sglang_utils/sglang_engine.py

@@ -334,7 +334,7 @@ def _compute_server_args(args, rank, dist_init_addr, nccl_port, host, port):
         "trust_remote_code": True,
         "random_seed": args.seed + rank,
         # memory
-        "enable_memory_saver": args.offload,
+        "enable_memory_saver": args.offload_rollout,
         # distributed
         "host": host,
         "port": port,

slime/ray/actor_group.py

@@ -61,7 +61,7 @@ def _allocate_gpus_for_actor(self, pg, num_gpus_per_actor, wandb_run_id: Optiona
             **{name: "1" for name in NOSET_VISIBLE_DEVICES_ENV_VARS_LIST},
         }
 
-        if self.args.offload:
+        if self.args.offload_train:
             import torch_memory_saver
 
             dynlib_path = os.path.join(

slime/ray/placement_group.py

@@ -172,7 +172,7 @@ def create_rollout_manager(args, pg, wandb_run_id):
         args.num_rollout = num_rollout_per_epoch * args.num_epoch
         assert args.num_rollout > 0
 
-    if args.offload:
+    if args.offload_rollout:
         ray.get(rollout_manager.offload.remote())
 
     return rollout_manager, num_rollout_per_epoch

slime/utils/arguments.py

@@ -79,8 +79,23 @@ def add_cluster_arguments(parser):
                 "--offload",
                 action="store_true",
                 default=False,
+                help=("Equivalent to --offload-train + --offload-rollout. "),
+            )
+            parser.add_argument(
+                "--offload-train",
+                action="store_true",
+                default=False,
+                help=(
+                    "Whether to offload the training actor to CPU during training. "
+                    "This will always be true when --colocate is set."
+                ),
+            )
+            parser.add_argument(
+                "--offload-rollout",
+                action="store_true",
+                default=False,
                 help=(
-                    "Whether to offload the rollout generator and training actor to CPU during training. "
+                    "Whether to offload the rollout generator to CPU during training. "
                     "This will always be true when --colocate is set."
                 ),
             )
@@ -1190,22 +1205,27 @@ def slime_validate_args(args):
     if args.critic_lr is None:
         args.critic_lr = args.lr
 
+    if args.offload:
+        args.offload_train = True
+        args.offload_rollout = True
+    del args.offload
+
     if args.debug_rollout_only:
         if args.colocate and args.rollout_num_gpus is None:
             args.rollout_num_gpus = args.actor_num_gpus_per_node * args.actor_num_nodes
         else:
             args.actor_num_gpus_per_node = min(8, args.rollout_num_gpus)
             args.actor_num_nodes = args.rollout_num_gpus // args.actor_num_gpus_per_node
         args.colocate = False
-        args.offload = False
+        args.offload_train = args.offload_rollout = False
 
     assert not (args.debug_rollout_only and args.debug_train_only), (
         "debug_rollout_only and debug_train_only cannot be set at the same time, " "please set only one of them."
     )
 
     # always true on offload for colocate at the moment.
     if args.colocate:
-        args.offload = True
+        args.offload_train = args.offload_rollout = True
         if args.rollout_num_gpus != args.actor_num_gpus_per_node * args.actor_num_nodes:
             print(
                 f"rollout_num_gpus {args.rollout_num_gpus} != actor_num_gpus_per_node {args.actor_num_gpus_per_node} "

train.py

@@ -23,13 +23,13 @@ def train(args):
     # create the actor and critic models
     actor_model, critic_model = create_training_models(args, pgs, rollout_manager, wandb_run_id=wandb_run_id)
 
-    if args.offload:
+    if args.offload_rollout:
         ray.get(rollout_manager.onload.remote(tags=[GPU_MEMORY_TYPE_WEIGHTS]))
 
     # always update weight first so that sglang has the loaded weights from training.
     actor_model.update_weights()
 
-    if args.offload:
+    if args.offload_rollout:
         if GPU_MEMORY_TYPE_CUDA_GRAPH is not None:
             ray.get(rollout_manager.onload.remote(tags=[GPU_MEMORY_TYPE_CUDA_GRAPH]))
         ray.get(rollout_manager.onload.remote(tags=[GPU_MEMORY_TYPE_KV_CACHE]))
@@ -47,7 +47,7 @@ def train(args):
 
         rollout_data_ref = ray.get(rollout_manager.generate.remote(rollout_id))
 
-        if args.offload:
+        if args.offload_rollout:
             ray.get(rollout_manager.offload.remote())
 
         if args.use_critic:
@@ -69,7 +69,7 @@ def train(args):
             if args.rollout_global_dataset:
                 ray.get(rollout_manager.save.remote(rollout_id))
 
-        if args.offload:
+        if args.offload_train:
             if args.use_critic:
                 critic_model.offload()
                 if rollout_id >= args.num_critic_only_steps:
@@ -81,7 +81,7 @@ def train(args):
 
         actor_model.update_weights()
 
-        if args.offload:
+        if args.offload_rollout:
             if GPU_MEMORY_TYPE_CUDA_GRAPH is not None:
                 ray.get(rollout_manager.onload.remote(tags=[GPU_MEMORY_TYPE_CUDA_GRAPH]))
             ray.get(rollout_manager.onload.remote(tags=[GPU_MEMORY_TYPE_KV_CACHE]))