refactor: test_d_adapt_no_progress

Author: kozistr

tests/test_gradients.py

@@ -133,6 +133,16 @@ def test_sam_no_gradient():
     optimizer.second_step(zero_grad=True)
 
 
+@pytest.mark.parametrize('optimizer_name', ['DAdaptAdaGrad', 'DAdaptAdam', 'DAdaptSGD', 'DAdaptAdan'])
+def test_d_adapt_no_progress(optimizer_name):
+    param = simple_parameter(True)
+    param.grad = None
+
+    optimizer = load_optimizer(optimizer_name)([param])
+    optimizer.zero_grad()
+    optimizer.step()
+
+
 @pytest.mark.parametrize('optimizer_name', ['DAdaptAdaGrad', 'DAdaptAdam', 'DAdaptSGD', 'DAdaptAdan'])
 def test_d_adapt_2nd_stage_gradient(optimizer_name):
     p1 = simple_parameter(require_grad=False)

tests/test_optimizers.py

@@ -359,16 +359,6 @@ def test_d_adapt_reset(require_gradient, sparse_gradient, optimizer_name):
     optimizer.reset()
 
 
-@pytest.mark.parametrize('optimizer_name', ['DAdaptAdaGrad', 'DAdaptAdam', 'DAdaptSGD', 'DAdaptAdan'])
-def test_d_adapt_no_progress(optimizer_name):
-    param = simple_parameter(True)
-    param.grad = None
-
-    optimizer = load_optimizer(optimizer_name)([param])
-    optimizer.zero_grad()
-    optimizer.step()
-
-
 @pytest.mark.parametrize('pre_conditioner_type', [0, 1, 2])
 def test_scalable_shampoo_pre_conditioner_with_svd(pre_conditioner_type):
     (x_data, y_data), _, loss_fn = build_environment()