parametrize test

src/test/distributed/checkpoint_test.py

@@ -822,7 +822,7 @@ def test_save_and_load_torch_fsdp_model(
     )
 
 
-def run_save_and_load_tensor_parallel_model(dir):
+def run_save_and_load_tensor_parallel_model(dir, take_step_before_checkpoint):
     tp_mesh = init_device_mesh("cuda", (dist.get_world_size(),))
 
     class FeedForward(nn.Module):
@@ -853,17 +853,22 @@ def forward(self, x):
     # Take a forward and backward pass.
     feed_forward(torch.rand((2, feed_forward.dim), device="cuda")).sum().backward()
 
-    # Take an optimizer step.
+    if take_step_before_checkpoint:
+        # Take an optimizer step.
+        optim.step()
 
     # Save checkpoint.
     save_model_and_optim_state(dir, feed_forward, optim)
 
 
 @requires_multi_gpu
-def test_save_and_load_tensor_parallel_model(tmp_path):
+@pytest.mark.parametrize(
+    "take_step_before_checkpoint", [pytest.param(True, id="after-step"), pytest.param(False, id="pre-step")]
+)
+def test_save_and_load_tensor_parallel_model(tmp_path, take_step_before_checkpoint):
     run_distributed_test(
         run_save_and_load_tensor_parallel_model,
         backend="nccl",
         start_method="spawn",
-        func_args=(tmp_path,),
+        func_args=(tmp_path, take_step_before_checkpoint),
     )