[CI/Build] Replace mean with torch.all in test_pynccl.py (#10876)

Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com>

tests/distributed/test_pynccl.py

@@ -62,8 +62,7 @@ def worker_fn():
     with pynccl_comm.change_state(enable=True):
         tensor = pynccl_comm.all_reduce(tensor)
     torch.cuda.synchronize()
-    result = tensor.mean().cpu().item()
-    assert result == pynccl_comm.world_size
+    assert torch.all(tensor == pynccl_comm.world_size).cpu().item()
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2,
@@ -88,13 +87,11 @@ def multiple_allreduce_worker_fn():
             tensor = pynccl_comm.all_reduce(tensor)
             tensor = pynccl_comm.all_reduce(tensor)
             torch.cuda.synchronize()
-            result = tensor.mean().cpu().item()
-            assert result == 4
+            assert torch.all(tensor == 4).cpu().item()
         else:
             tensor = pynccl_comm.all_reduce(tensor)
             torch.cuda.synchronize()
-            result = tensor.mean().cpu().item()
-            assert result == 2
+            assert torch.all(tensor == 2).cpu().item()
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 4,
@@ -116,13 +113,11 @@ def multiple_allreduce_with_vllm_worker_fn():
             tensor = tensor_model_parallel_all_reduce(tensor)
             tensor = tensor_model_parallel_all_reduce(tensor)
             torch.cuda.synchronize()
-            result = tensor.mean().cpu().item()
-            assert result == 4
+            assert torch.all(tensor == 4).cpu().item()
         else:
             tensor = tensor_model_parallel_all_reduce(tensor)
             torch.cuda.synchronize()
-            result = tensor.mean().cpu().item()
-            assert result == 2
+            assert torch.all(tensor == 2).cpu().item()
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 4,
@@ -149,7 +144,7 @@ def worker_fn_with_cudagraph():
         torch.cuda.synchronize()
         graph.replay()
         torch.cuda.synchronize()
-        assert a_out.mean().cpu().item() == pynccl_comm.world_size**1
+        assert torch.all(a_out == pynccl_comm.world_size).cpu().item()
 
 
 @worker_fn_wrapper
@@ -249,8 +244,7 @@ def send_recv_worker_fn():
                              src=(pynccl_comm.rank - 1) %
                              pynccl_comm.world_size)
     torch.cuda.synchronize()
-    result = tensor.mean().cpu().item()
-    assert result == 1
+    assert torch.all(tensor == 1).cpu().item()
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2,
@@ -289,11 +283,10 @@ def multiple_send_recv_worker_fn():
                              src=(pynccl_comm.rank - 1) %
                              pynccl_comm.world_size)
     torch.cuda.synchronize()
-    result = tensor.mean().cpu().item()
     if torch.distributed.get_rank() in [0, 2]:
-        assert result == 1
+        assert torch.all(tensor == 1).cpu().item()
     else:
-        assert result == 2
+        assert torch.all(tensor == 2).cpu().item()
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 4,