chore: import upstream snapshot with attribution

python/ray/util/collective/examples/nccl_allreduce_multigpu_example.py

@@ -0,0 +1,43 @@
+import cupy as cp
+from cupy.cuda import Device
+
+import ray
+import ray.util.collective as collective
+
+
+@ray.remote(num_gpus=2)
+class Worker:
+    def __init__(self):
+        with Device(0):
+            self.send1 = cp.ones((4,), dtype=cp.float32)
+        with Device(1):
+            self.send2 = cp.ones((4,), dtype=cp.float32) * 2
+
+        self.recv = cp.zeros((4,), dtype=cp.float32)
+
+    def setup(self, world_size, rank):
+        collective.init_collective_group(world_size, rank, "nccl", "177")
+        return True
+
+    def compute(self):
+        collective.allreduce_multigpu([self.send1, self.send2], "177")
+        return [self.send1, self.send2], self.send1.device, self.send2.device
+
+    def destroy(self):
+        collective.destroy_collective_group("177")
+
+
+if __name__ == "__main__":
+    ray.init(address="auto")
+    num_workers = 2
+    workers = []
+    init_rets = []
+    for i in range(num_workers):
+        w = Worker.remote()
+        workers.append(w)
+        init_rets.append(w.setup.remote(num_workers, i))
+    a = ray.get(init_rets)
+    results = ray.get([w.compute.remote() for w in workers])
+    print(results)
+    ray.get([w.destroy.remote() for w in workers])
+    ray.shutdown()