refactor autoencoder_hunyuan_video tests (#13835)

* refactor autoencoder_hunyuan_video tests

* remove unused base_precision and test_outputs_equivalence skip

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Author: akshan-main

tests/models/autoencoders/test_models_autoencoder_hunyuan_video.py

@@ -13,27 +13,38 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import torch
 
 from diffusers import AutoencoderKLHunyuanVideo
 from diffusers.models.autoencoders.autoencoder_kl_hunyuan_video import prepare_causal_attention_mask
+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 AutoencoderKLHunyuanVideoTests(ModelTesterMixin, AutoencoderTesterMixin, unittest.TestCase):
-    model_class = AutoencoderKLHunyuanVideo
-    main_input_name = "sample"
-    base_precision = 1e-2
+class AutoencoderKLHunyuanVideoTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return AutoencoderKLHunyuanVideo
+
+    @property
+    def main_input_name(self) -> str:
+        return "sample"
+
+    @property
+    def output_shape(self) -> tuple:
+        return (3, 9, 16, 16)
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
 
-    def get_autoencoder_kl_hunyuan_video_config(self):
+    def get_init_dict(self) -> dict:
         return {
             "in_channels": 3,
             "out_channels": 3,
@@ -60,29 +71,43 @@ def get_autoencoder_kl_hunyuan_video_config(self):
             "mid_block_add_attention": True,
         }
 
-    @property
-    def dummy_input(self):
+    def get_dummy_inputs(self) -> dict:
         batch_size = 2
         num_frames = 9
         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 TestAutoencoderKLHunyuanVideo(AutoencoderKLHunyuanVideoTesterConfig, ModelTesterMixin):
+    def test_prepare_causal_attention_mask(self):
+        def prepare_causal_attention_mask_orig(
+            num_frames: int, height_width: int, dtype: torch.dtype, device: torch.device, batch_size: int = None
+        ) -> torch.Tensor:
+            seq_len = num_frames * height_width
+            mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device)
+            for i in range(seq_len):
+                i_frame = i // height_width
+                mask[i, : (i_frame + 1) * height_width] = 0
+            if batch_size is not None:
+                mask = mask.unsqueeze(0).expand(batch_size, -1, -1)
+            return mask
 
-    @property
-    def input_shape(self):
-        return (3, 9, 16, 16)
+        # test with some odd shapes
+        original_mask = prepare_causal_attention_mask_orig(
+            num_frames=31, height_width=111, dtype=torch.float32, device=torch_device
+        )
+        new_mask = prepare_causal_attention_mask(
+            num_frames=31, height_width=111, dtype=torch.float32, device=torch_device
+        )
+        assert torch.allclose(original_mask, new_mask), "Causal attention mask should be the same"
 
-    @property
-    def output_shape(self):
-        return (3, 9, 16, 16)
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = self.get_autoencoder_kl_hunyuan_video_config()
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
+class TestAutoencoderKLHunyuanVideoTraining(AutoencoderKLHunyuanVideoTesterConfig, TrainingTesterMixin):
+    """Training tests for AutoencoderKLHunyuanVideo."""
 
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {
@@ -94,9 +119,18 @@ def test_gradient_checkpointing_is_applied(self):
         }
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
-    # We need to overwrite this test because the base test does not account length of down_block_types
+
+class TestAutoencoderKLHunyuanVideoMemory(AutoencoderKLHunyuanVideoTesterConfig, MemoryTesterMixin):
+    """Memory optimization tests for AutoencoderKLHunyuanVideo."""
+
+
+class TestAutoencoderKLHunyuanVideoSlicingTiling(AutoencoderKLHunyuanVideoTesterConfig, NewAutoencoderTesterMixin):
+    """Slicing and tiling tests for AutoencoderKLHunyuanVideo."""
+
+    # 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, 16, 16, 16)
@@ -111,35 +145,6 @@ 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
-
-    def test_prepare_causal_attention_mask(self):
-        def prepare_causal_attention_mask_orig(
-            num_frames: int, height_width: int, dtype: torch.dtype, device: torch.device, batch_size: int = None
-        ) -> torch.Tensor:
-            seq_len = num_frames * height_width
-            mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device)
-            for i in range(seq_len):
-                i_frame = i // height_width
-                mask[i, : (i_frame + 1) * height_width] = 0
-            if batch_size is not None:
-                mask = mask.unsqueeze(0).expand(batch_size, -1, -1)
-            return mask
-
-        # test with some odd shapes
-        original_mask = prepare_causal_attention_mask_orig(
-            num_frames=31, height_width=111, dtype=torch.float32, device=torch_device
-        )
-        new_mask = prepare_causal_attention_mask(
-            num_frames=31, height_width=111, dtype=torch.float32, device=torch_device
-        )
-        self.assertTrue(
-            torch.allclose(original_mask, new_mask),
-            "Causal attention mask should be the same",
-        )
+        assert output.shape == expected_shape, "Input and output shapes do not match"