Make test_averaging_cancel more robust

tests/test_averaging.py

@@ -521,31 +521,45 @@ def test_averaging_trigger():
 
 
 @pytest.mark.forked
-def test_averaging_cancel():
+@pytest.mark.parametrize("target_group_size", [None, 2])
+def test_averaging_cancel(target_group_size):
+    dht_instances = launch_dht_instances(4)
     averagers = tuple(
         DecentralizedAverager(
             averaged_tensors=[torch.randn(3)],
             dht=dht,
             min_matchmaking_time=0.5,
             request_timeout=0.3,
             client_mode=(i % 2 == 0),
+            target_group_size=target_group_size,
             prefix="mygroup",
             start=True,
         )
-        for i, dht in enumerate(launch_dht_instances(4))
+        for i, dht in enumerate(dht_instances)
     )
 
-    step_controls = [averager.step(wait=False, scheduled_time=hivemind.get_dht_time() + 1) for averager in averagers]
+    step_controls = [averager.step(wait=False, require_trigger=True) for averager in averagers]
+
+    peer_inds_to_cancel = (0, 1)
+
+    for peer_index in peer_inds_to_cancel:
+        step_controls[peer_index].cancel()
 
     time.sleep(0.05)
-    step_controls[0].cancel()
-    step_controls[1].cancel()
 
     for i, control in enumerate(step_controls):
-        if i in (0, 1):
+        if i not in peer_inds_to_cancel:
+            control.allow_allreduce()
+
+    for i, control in enumerate(step_controls):
+        if i in peer_inds_to_cancel:
             assert control.cancelled()
         else:
-            assert control.result() is not None and len(control.result()) == 2
+            result = control.result()
+            assert result is not None
+            # Don't check group size when target_group_size=None, as it could change
+            if target_group_size is not None:
+                assert len(result) == target_group_size
 
     for averager in averagers:
         averager.shutdown()