Fix all-reduce memory usage (#2151)

vllm/worker/worker.py

@@ -48,6 +48,14 @@ class Worker:
         self.gpu_cache = None
 
     def init_model(self, cupy_port: Optional[int] = None):
+        # torch.distributed.all_reduce does not free the input tensor until
+        # the synchronization point. This causes the memory usage to grow
+        # as the number of all_reduce calls increases. This env var disables
+        # this behavior.
+        # Related issue:
+        # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573
+        os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+
         # This env var set by Ray causes exceptions with graph building.
         os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)
         # Env vars will be set by Ray.