Reduce WanAnimate TorchAO test input sizes to prevent OOM

Shrink dummy inputs to avoid OOM on devices without FlashAttention.
Reduce hidden_states spatial from 64x64 to 16x16 and frames from 21 to 5,
bringing self-attention sequence length from 21,504 to 320.

Author: jiqing-feng

tests/models/transformers/test_models_transformer_wan_animate.py

@@ -219,7 +219,7 @@ def get_dummy_inputs(self):
         """Override to provide inputs matching the tiny Wan Animate model dimensions."""
         return {
             "hidden_states": randn_tensor(
-                (1, 36, 21, 64, 64), generator=self.generator, device=torch_device, dtype=self.torch_dtype
+                (1, 36, 5, 16, 16), generator=self.generator, device=torch_device, dtype=self.torch_dtype
             ),
             "encoder_hidden_states": randn_tensor(
                 (1, 512, 4096), generator=self.generator, device=torch_device, dtype=self.torch_dtype
@@ -228,10 +228,10 @@ def get_dummy_inputs(self):
                 (1, 257, 1280), generator=self.generator, device=torch_device, dtype=self.torch_dtype
             ),
             "pose_hidden_states": randn_tensor(
-                (1, 16, 20, 64, 64), generator=self.generator, device=torch_device, dtype=self.torch_dtype
+                (1, 16, 4, 16, 16), generator=self.generator, device=torch_device, dtype=self.torch_dtype
             ),
             "face_pixel_values": randn_tensor(
-                (1, 3, 77, 512, 512), generator=self.generator, device=torch_device, dtype=self.torch_dtype
+                (1, 3, 13, 512, 512), generator=self.generator, device=torch_device, dtype=self.torch_dtype
             ),
             "timestep": torch.tensor([1.0]).to(torch_device, self.torch_dtype),
         }