refactor autoencoder_kl_cogvideox tests

tests/models/autoencoders/test_models_autoencoder_kl_cogvideox.py

@@ -13,30 +13,38 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
+import pytest
 import torch
 
 from diffusers import AutoencoderKLCogVideoX
+from diffusers.utils.torch_utils import randn_tensor
 
-from ...testing_utils import (
-    enable_full_determinism,
-    floats_tensor,
-    torch_device,
-)
-from ..test_modeling_common import ModelTesterMixin
-from .testing_utils import AutoencoderTesterMixin
+from ...testing_utils import enable_full_determinism, torch_device
+from ..testing_utils import BaseModelTesterConfig, MemoryTesterMixin, ModelTesterMixin, TrainingTesterMixin
+from .testing_utils import NewAutoencoderTesterMixin
 
 
 enable_full_determinism()
 
 
-class AutoencoderKLCogVideoXTests(ModelTesterMixin, AutoencoderTesterMixin, unittest.TestCase):
-    model_class = AutoencoderKLCogVideoX
-    main_input_name = "sample"
-    base_precision = 1e-2
+class AutoencoderKLCogVideoXTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return AutoencoderKLCogVideoX
 
-    def get_autoencoder_kl_cogvideox_config(self):
+    @property
+    def main_input_name(self) -> str:
+        return "sample"
+
+    @property
+    def output_shape(self) -> tuple:
+        return (3, 8, 16, 16)
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self) -> dict:
         return {
             "in_channels": 3,
             "out_channels": 3,
@@ -59,29 +67,27 @@ def get_autoencoder_kl_cogvideox_config(self):
             "temporal_compression_ratio": 4,
         }
 
-    @property
-    def dummy_input(self):
+    def get_dummy_inputs(self) -> dict:
         batch_size = 4
         num_frames = 8
         num_channels = 3
         sizes = (16, 16)
+        image = randn_tensor(
+            (batch_size, num_channels, num_frames, *sizes), generator=self.generator, device=torch_device
+        )
+        return {"sample": image}
 
-        image = floats_tensor((batch_size, num_channels, num_frames) + sizes).to(torch_device)
 
-        return {"sample": image}
+class TestAutoencoderKLCogVideoX(AutoencoderKLCogVideoXTesterConfig, ModelTesterMixin):
+    base_precision = 1e-2
 
-    @property
-    def input_shape(self):
-        return (3, 8, 16, 16)
+    @pytest.mark.skip("Unsupported test.")
+    def test_outputs_equivalence(self):
+        super().test_outputs_equivalence()
 
-    @property
-    def output_shape(self):
-        return (3, 8, 16, 16)
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = self.get_autoencoder_kl_cogvideox_config()
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
+class TestAutoencoderKLCogVideoXTraining(AutoencoderKLCogVideoXTesterConfig, TrainingTesterMixin):
+    """Training tests for AutoencoderKLCogVideoX."""
 
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {
@@ -93,8 +99,18 @@ def test_gradient_checkpointing_is_applied(self):
         }
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
+
+class TestAutoencoderKLCogVideoXMemory(AutoencoderKLCogVideoXTesterConfig, MemoryTesterMixin):
+    """Memory optimization tests for AutoencoderKLCogVideoX."""
+
+
+class TestAutoencoderKLCogVideoXSlicingTiling(AutoencoderKLCogVideoXTesterConfig, NewAutoencoderTesterMixin):
+    """Slicing and tiling tests for AutoencoderKLCogVideoX."""
+
+    # Overwritten because the base test's block_out_channels doesn't account for the length of down_block_types.
     def test_forward_with_norm_groups(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        init_dict = self.get_init_dict()
+        inputs_dict = self.get_dummy_inputs()
 
         init_dict["norm_num_groups"] = 16
         init_dict["block_out_channels"] = (16, 32, 32, 32)
@@ -109,10 +125,6 @@ def test_forward_with_norm_groups(self):
             if isinstance(output, dict):
                 output = output.to_tuple()[0]
 
-        self.assertIsNotNone(output)
+        assert output is not None
         expected_shape = inputs_dict["sample"].shape
-        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
-
-    @unittest.skip("Unsupported test.")
-    def test_outputs_equivalence(self):
-        pass
+        assert output.shape == expected_shape, "Input and output shapes do not match"