[CI] Refactor Bria Transformer Tests (#13341)

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* update

* update

* update

---------

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

Author: DN6

tests/models/transformers/test_models_transformer_bria.py

@@ -13,113 +13,45 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import torch
 
 from diffusers import BriaTransformer2DModel
-from diffusers.models.attention_processor import FluxIPAdapterJointAttnProcessor2_0
-from diffusers.models.embeddings import ImageProjection
+from diffusers.utils.torch_utils import randn_tensor
 
 from ...testing_utils import enable_full_determinism, torch_device
-from ..test_modeling_common import LoraHotSwappingForModelTesterMixin, ModelTesterMixin, TorchCompileTesterMixin
+from ..testing_utils import (
+    BaseModelTesterConfig,
+    ModelTesterMixin,
+    TrainingTesterMixin,
+)
 
 
 enable_full_determinism()
 
 
-def create_bria_ip_adapter_state_dict(model):
-    # "ip_adapter" (cross-attention weights)
-    ip_cross_attn_state_dict = {}
-    key_id = 0
-
-    for name in model.attn_processors.keys():
-        if name.startswith("single_transformer_blocks"):
-            continue
-
-        joint_attention_dim = model.config["joint_attention_dim"]
-        hidden_size = model.config["num_attention_heads"] * model.config["attention_head_dim"]
-        sd = FluxIPAdapterJointAttnProcessor2_0(
-            hidden_size=hidden_size, cross_attention_dim=joint_attention_dim, scale=1.0
-        ).state_dict()
-        ip_cross_attn_state_dict.update(
-            {
-                f"{key_id}.to_k_ip.weight": sd["to_k_ip.0.weight"],
-                f"{key_id}.to_v_ip.weight": sd["to_v_ip.0.weight"],
-                f"{key_id}.to_k_ip.bias": sd["to_k_ip.0.bias"],
-                f"{key_id}.to_v_ip.bias": sd["to_v_ip.0.bias"],
-            }
-        )
-
-        key_id += 1
-
-    # "image_proj" (ImageProjection layer weights)
-
-    image_projection = ImageProjection(
-        cross_attention_dim=model.config["joint_attention_dim"],
-        image_embed_dim=model.config["pooled_projection_dim"],
-        num_image_text_embeds=4,
-    )
-
-    ip_image_projection_state_dict = {}
-    sd = image_projection.state_dict()
-    ip_image_projection_state_dict.update(
-        {
-            "proj.weight": sd["image_embeds.weight"],
-            "proj.bias": sd["image_embeds.bias"],
-            "norm.weight": sd["norm.weight"],
-            "norm.bias": sd["norm.bias"],
-        }
-    )
-
-    del sd
-    ip_state_dict = {}
-    ip_state_dict.update({"image_proj": ip_image_projection_state_dict, "ip_adapter": ip_cross_attn_state_dict})
-    return ip_state_dict
-
-
-class BriaTransformerTests(ModelTesterMixin, unittest.TestCase):
-    model_class = BriaTransformer2DModel
-    main_input_name = "hidden_states"
-    # We override the items here because the transformer under consideration is small.
-    model_split_percents = [0.8, 0.7, 0.7]
-
-    # Skip setting testing with default: AttnProcessor
-    uses_custom_attn_processor = True
-
+class BriaTransformerTesterConfig(BaseModelTesterConfig):
     @property
-    def dummy_input(self):
-        batch_size = 1
-        num_latent_channels = 4
-        num_image_channels = 3
-        height = width = 4
-        sequence_length = 48
-        embedding_dim = 32
+    def model_class(self):
+        return BriaTransformer2DModel
 
-        hidden_states = torch.randn((batch_size, height * width, num_latent_channels)).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
-        text_ids = torch.randn((sequence_length, num_image_channels)).to(torch_device)
-        image_ids = torch.randn((height * width, num_image_channels)).to(torch_device)
-        timestep = torch.tensor([1.0]).to(torch_device).expand(batch_size)
+    @property
+    def main_input_name(self) -> str:
+        return "hidden_states"
 
-        return {
-            "hidden_states": hidden_states,
-            "encoder_hidden_states": encoder_hidden_states,
-            "img_ids": image_ids,
-            "txt_ids": text_ids,
-            "timestep": timestep,
-        }
+    @property
+    def model_split_percents(self) -> list:
+        return [0.8, 0.7, 0.7]
 
     @property
-    def input_shape(self):
+    def output_shape(self) -> tuple:
         return (16, 4)
 
     @property
-    def output_shape(self):
-        return (16, 4)
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
+    def get_init_dict(self) -> dict:
+        return {
             "patch_size": 1,
             "in_channels": 4,
             "num_layers": 1,
@@ -131,19 +63,42 @@ def prepare_init_args_and_inputs_for_common(self):
             "axes_dims_rope": [0, 4, 4],
         }
 
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
+    def get_dummy_inputs(self, batch_size: int = 1) -> dict[str, torch.Tensor]:
+        num_latent_channels = 4
+        num_image_channels = 3
+        height = width = 4
+        sequence_length = 48
+        embedding_dim = 32
+
+        return {
+            "hidden_states": randn_tensor(
+                (batch_size, height * width, num_latent_channels), generator=self.generator, device=torch_device
+            ),
+            "encoder_hidden_states": randn_tensor(
+                (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
+            ),
+            "img_ids": randn_tensor(
+                (height * width, num_image_channels), generator=self.generator, device=torch_device
+            ),
+            "txt_ids": randn_tensor(
+                (sequence_length, num_image_channels), generator=self.generator, device=torch_device
+            ),
+            "timestep": torch.tensor([1.0]).to(torch_device).expand(batch_size),
+        }
+
 
+class TestBriaTransformer(BriaTransformerTesterConfig, ModelTesterMixin):
     def test_deprecated_inputs_img_txt_ids_3d(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()
+
         model = self.model_class(**init_dict)
         model.to(torch_device)
         model.eval()
 
         with torch.no_grad():
             output_1 = model(**inputs_dict).to_tuple()[0]
 
-        # update inputs_dict with txt_ids and img_ids as 3d tensors (deprecated)
         text_ids_3d = inputs_dict["txt_ids"].unsqueeze(0)
         image_ids_3d = inputs_dict["img_ids"].unsqueeze(0)
 
@@ -156,26 +111,14 @@ def test_deprecated_inputs_img_txt_ids_3d(self):
         with torch.no_grad():
             output_2 = model(**inputs_dict).to_tuple()[0]
 
-        self.assertEqual(output_1.shape, output_2.shape)
-        self.assertTrue(
-            torch.allclose(output_1, output_2, atol=1e-5),
-            msg="output with deprecated inputs (img_ids and txt_ids as 3d torch tensors) are not equal as them as 2d inputs",
+        assert output_1.shape == output_2.shape
+        assert torch.allclose(output_1, output_2, atol=1e-5), (
+            "output with deprecated inputs (img_ids and txt_ids as 3d torch tensors) "
+            "are not equal as them as 2d inputs"
         )
 
+
+class TestBriaTransformerTraining(BriaTransformerTesterConfig, TrainingTesterMixin):
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"BriaTransformer2DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
-
-
-class BriaTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
-    model_class = BriaTransformer2DModel
-
-    def prepare_init_args_and_inputs_for_common(self):
-        return BriaTransformerTests().prepare_init_args_and_inputs_for_common()
-
-
-class BriaTransformerLoRAHotSwapTests(LoraHotSwappingForModelTesterMixin, unittest.TestCase):
-    model_class = BriaTransformer2DModel
-
-    def prepare_init_args_and_inputs_for_common(self):
-        return BriaTransformerTests().prepare_init_args_and_inputs_for_common()

tests/models/transformers/test_models_transformer_bria_fibo.py

@@ -13,62 +13,45 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import torch
 
 from diffusers import BriaFiboTransformer2DModel
+from diffusers.utils.torch_utils import randn_tensor
 
 from ...testing_utils import enable_full_determinism, torch_device
-from ..test_modeling_common import ModelTesterMixin
+from ..testing_utils import (
+    BaseModelTesterConfig,
+    ModelTesterMixin,
+    TrainingTesterMixin,
+)
 
 
 enable_full_determinism()
 
 
-class BriaFiboTransformerTests(ModelTesterMixin, unittest.TestCase):
-    model_class = BriaFiboTransformer2DModel
-    main_input_name = "hidden_states"
-    # We override the items here because the transformer under consideration is small.
-    model_split_percents = [0.8, 0.7, 0.7]
-
-    # Skip setting testing with default: AttnProcessor
-    uses_custom_attn_processor = True
-
+class BriaFiboTransformerTesterConfig(BaseModelTesterConfig):
     @property
-    def dummy_input(self):
-        batch_size = 1
-        num_latent_channels = 48
-        num_image_channels = 3
-        height = width = 16
-        sequence_length = 32
-        embedding_dim = 64
+    def model_class(self):
+        return BriaFiboTransformer2DModel
 
-        hidden_states = torch.randn((batch_size, height * width, num_latent_channels)).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
-        text_ids = torch.randn((sequence_length, num_image_channels)).to(torch_device)
-        image_ids = torch.randn((height * width, num_image_channels)).to(torch_device)
-        timestep = torch.tensor([1.0]).to(torch_device).expand(batch_size)
-
-        return {
-            "hidden_states": hidden_states,
-            "encoder_hidden_states": encoder_hidden_states,
-            "img_ids": image_ids,
-            "txt_ids": text_ids,
-            "timestep": timestep,
-            "text_encoder_layers": [encoder_hidden_states[:, :, :32], encoder_hidden_states[:, :, :32]],
-        }
+    @property
+    def main_input_name(self) -> str:
+        return "hidden_states"
 
     @property
-    def input_shape(self):
-        return (16, 16)
+    def model_split_percents(self) -> list:
+        return [0.8, 0.7, 0.7]
 
     @property
-    def output_shape(self):
+    def output_shape(self) -> tuple:
         return (256, 48)
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self) -> dict:
+        return {
             "patch_size": 1,
             "in_channels": 48,
             "num_layers": 1,
@@ -81,9 +64,37 @@ def prepare_init_args_and_inputs_for_common(self):
             "axes_dims_rope": [0, 4, 4],
         }
 
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
+    def get_dummy_inputs(self, batch_size: int = 1) -> dict[str, torch.Tensor]:
+        num_latent_channels = 48
+        num_image_channels = 3
+        height = width = 16
+        sequence_length = 32
+        embedding_dim = 64
+
+        encoder_hidden_states = randn_tensor(
+            (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
+        )
+        return {
+            "hidden_states": randn_tensor(
+                (batch_size, height * width, num_latent_channels), generator=self.generator, device=torch_device
+            ),
+            "encoder_hidden_states": encoder_hidden_states,
+            "img_ids": randn_tensor(
+                (height * width, num_image_channels), generator=self.generator, device=torch_device
+            ),
+            "txt_ids": randn_tensor(
+                (sequence_length, num_image_channels), generator=self.generator, device=torch_device
+            ),
+            "timestep": torch.tensor([1.0]).to(torch_device).expand(batch_size),
+            "text_encoder_layers": [encoder_hidden_states[:, :, :32], encoder_hidden_states[:, :, :32]],
+        }
+
+
+class TestBriaFiboTransformer(BriaFiboTransformerTesterConfig, ModelTesterMixin):
+    pass
+
 
+class TestBriaFiboTransformerTraining(BriaFiboTransformerTesterConfig, TrainingTesterMixin):
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"BriaFiboTransformer2DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)