Convert examples to ruff-format (#18400)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

examples/offline_inference/rlhf_utils.py

@@ -2,21 +2,20 @@
 import torch
 
 
-def stateless_init_process_group(master_address, master_port, rank, world_size,
-                                 device):
+def stateless_init_process_group(master_address, master_port, rank, world_size, device):
     """
     vLLM provides `StatelessProcessGroup` to create a process group
     without considering the global process group in torch.distributed.
     It is recommended to create `StatelessProcessGroup`, and then initialize
-    the data-plane communication (NCCL) between external (train processes) 
+    the data-plane communication (NCCL) between external (train processes)
     and vLLM workers.
     """
     from vllm.distributed.device_communicators.pynccl import PyNcclCommunicator
     from vllm.distributed.utils import StatelessProcessGroup
-    pg = StatelessProcessGroup.create(host=master_address,
-                                      port=master_port,
-                                      rank=rank,
-                                      world_size=world_size)
+
+    pg = StatelessProcessGroup.create(
+        host=master_address, port=master_port, rank=rank, world_size=world_size
+    )
     pynccl = PyNcclCommunicator(pg, device=device)
     return pynccl
 
@@ -31,9 +30,11 @@ class WorkerExtension:
     should pass the full qualified name as `worker_extension_cls` argument.
     """
 
-    def init_weight_update_group(self, master_address, master_port,
-                                 rank_offset, world_size):
+    def init_weight_update_group(
+        self, master_address, master_port, rank_offset, world_size
+    ):
         from vllm.distributed.parallel_state import get_world_group
+
         rank = get_world_group().rank + rank_offset
         self.model_update_group = stateless_init_process_group(
             master_address,
@@ -45,9 +46,9 @@ class WorkerExtension:
 
     def update_weight(self, name, dtype, shape):
         weight = torch.empty(shape, dtype=dtype, device="cuda")
-        self.model_update_group.broadcast(weight,
-                                          src=0,
-                                          stream=torch.cuda.current_stream())
+        self.model_update_group.broadcast(
+            weight, src=0, stream=torch.cuda.current_stream()
+        )
 
         self.model_runner.model.load_weights(weights=[(name, weight)])
 
@@ -59,8 +60,7 @@ class WorkerExtension:
         """
         weights_updated = True
         for name, p in self.model_runner.model.named_parameters():
-            weights_updated = weights_updated and torch.allclose(
-                p, torch.zeros_like(p))
+            weights_updated = weights_updated and torch.allclose(p, torch.zeros_like(p))
         return weights_updated
 
 
@@ -76,6 +76,7 @@ class ColocateWorkerExtension:
 
     def report_device_id(self) -> str:
         from vllm.platforms import current_platform
+
         self.device_uuid = current_platform.get_device_uuid(self.device.index)
         return self.device_uuid
 
@@ -100,6 +101,5 @@ class ColocateWorkerExtension:
         """
         weights_updated = True
         for name, p in self.model_runner.model.named_parameters():
-            weights_updated = weights_updated and torch.allclose(
-                p, torch.zeros_like(p))
+            weights_updated = weights_updated and torch.allclose(p, torch.zeros_like(p))
         return weights_updated