[tests] Remove/rename unsupported quantization torchao type (#10263)

update

tests/quantization/torchao/test_torchao.py

@@ -228,8 +228,7 @@ class TorchAoTest(unittest.TestCase):
             ("int8wo", np.array([0.4648, 0.5195, 0.5547, 0.4199, 0.4414, 0.6445, 0.4316, 0.4531, 0.5625])),
             ("int8dq", np.array([0.4648, 0.5195, 0.5547, 0.4199, 0.4414, 0.6445, 0.4316, 0.4531, 0.5625])),
             ("uint4wo", np.array([0.4609, 0.5234, 0.5508, 0.4199, 0.4336, 0.6406, 0.4316, 0.4531, 0.5625])),
-            ("int_a8w8", np.array([0.4648, 0.5195, 0.5547, 0.4199, 0.4414, 0.6445, 0.4316, 0.4531, 0.5625])),
-            ("uint_a16w7", np.array([0.4648, 0.5195, 0.5547, 0.4219, 0.4414, 0.6445, 0.4316, 0.4531, 0.5625])),
+            ("uint7wo", np.array([0.4648, 0.5195, 0.5547, 0.4219, 0.4414, 0.6445, 0.4316, 0.4531, 0.5625])),
         ]
 
         if TorchAoConfig._is_cuda_capability_atleast_8_9():
@@ -253,8 +252,8 @@ class TorchAoTest(unittest.TestCase):
 
         for quantization_name, expected_slice in QUANTIZATION_TYPES_TO_TEST:
             quant_kwargs = {}
-            if quantization_name in ["uint4wo", "uint_a16w7"]:
-                # The dummy flux model that we use requires us to impose some restrictions on group_size here
+            if quantization_name in ["uint4wo", "uint7wo"]:
+                # The dummy flux model that we use has smaller dimensions. This imposes some restrictions on group_size here
                 quant_kwargs.update({"group_size": 16})
             quantization_config = TorchAoConfig(
                 quant_type=quantization_name, modules_to_not_convert=["x_embedder"], **quant_kwargs