update

.github/workflows/claude_review.yml

@@ -10,6 +10,7 @@ permissions:
   contents: write
   pull-requests: write
   issues: read
+  id-token: write
 
 jobs:
   claude-review:

tests/models/transformers/test_models_transformer_cogvideox.py

@@ -13,59 +13,53 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import torch
 
 from diffusers import CogVideoXTransformer3DModel
+from diffusers.utils.torch_utils import randn_tensor
 
-from ...testing_utils import (
-    enable_full_determinism,
-    torch_device,
+from ...testing_utils import enable_full_determinism, torch_device
+from ..testing_utils import (
+    BaseModelTesterConfig,
+    ModelTesterMixin,
+    TorchCompileTesterMixin,
+    TrainingTesterMixin,
 )
-from ..test_modeling_common import ModelTesterMixin
 
 
 enable_full_determinism()
 
 
-class CogVideoXTransformerTests(ModelTesterMixin, unittest.TestCase):
-    model_class = CogVideoXTransformer3DModel
-    main_input_name = "hidden_states"
-    uses_custom_attn_processor = True
-    model_split_percents = [0.7, 0.7, 0.8]
+# ======================== CogVideoX ========================
+
+
+class CogVideoXTransformerTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return CogVideoXTransformer3DModel
 
     @property
-    def dummy_input(self):
-        batch_size = 2
-        num_channels = 4
-        num_frames = 1
-        height = 8
-        width = 8
-        embedding_dim = 8
-        sequence_length = 8
+    def main_input_name(self) -> str:
+        return "hidden_states"
 
-        hidden_states = torch.randn((batch_size, num_frames, num_channels, height, width)).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
-        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
+    @property
+    def model_split_percents(self) -> list:
+        return [0.7, 0.7, 0.8]
 
+    @property
+    def output_shape(self) -> tuple:
+        return (1, 4, 8, 8)
+
+    @property
+    def input_shape(self) -> tuple:
+        return (1, 4, 8, 8)
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self) -> dict:
         return {
-            "hidden_states": hidden_states,
-            "encoder_hidden_states": encoder_hidden_states,
-            "timestep": timestep,
-        }
-
-    @property
-    def input_shape(self):
-        return (1, 4, 8, 8)
-
-    @property
-    def output_shape(self):
-        return (1, 4, 8, 8)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            # Product of num_attention_heads * attention_head_dim must be divisible by 16 for 3D positional embeddings.
             "num_attention_heads": 2,
             "attention_head_dim": 8,
             "in_channels": 4,
@@ -81,50 +75,66 @@ class CogVideoXTransformerTests(ModelTesterMixin, unittest.TestCase):
             "temporal_compression_ratio": 4,
             "max_text_seq_length": 8,
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
 
-    def test_gradient_checkpointing_is_applied(self):
-        expected_set = {"CogVideoXTransformer3DModel"}
-        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
-
-
-class CogVideoX1_5TransformerTests(ModelTesterMixin, unittest.TestCase):
-    model_class = CogVideoXTransformer3DModel
-    main_input_name = "hidden_states"
-    uses_custom_attn_processor = True
-
-    @property
-    def dummy_input(self):
-        batch_size = 2
+    def get_dummy_inputs(self, batch_size: int = 2) -> dict[str, torch.Tensor]:
         num_channels = 4
-        num_frames = 2
+        num_frames = 1
         height = 8
         width = 8
         embedding_dim = 8
         sequence_length = 8
 
-        hidden_states = torch.randn((batch_size, num_frames, num_channels, height, width)).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
-        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
-
         return {
-            "hidden_states": hidden_states,
-            "encoder_hidden_states": encoder_hidden_states,
-            "timestep": timestep,
+            "hidden_states": randn_tensor(
+                (batch_size, num_frames, num_channels, height, width), generator=self.generator, device=torch_device
+            ),
+            "encoder_hidden_states": randn_tensor(
+                (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
+            ),
+            "timestep": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
         }
 
+
+class TestCogVideoXTransformer(CogVideoXTransformerTesterConfig, ModelTesterMixin):
+    pass
+
+
+class TestCogVideoXTransformerTraining(CogVideoXTransformerTesterConfig, TrainingTesterMixin):
+    def test_gradient_checkpointing_is_applied(self):
+        expected_set = {"CogVideoXTransformer3DModel"}
+        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
+
+
+class TestCogVideoXTransformerCompile(CogVideoXTransformerTesterConfig, TorchCompileTesterMixin):
+    pass
+
+
+# ======================== CogVideoX 1.5 ========================
+
+
+class CogVideoX15TransformerTesterConfig(BaseModelTesterConfig):
     @property
-    def input_shape(self):
+    def model_class(self):
+        return CogVideoXTransformer3DModel
+
+    @property
+    def main_input_name(self) -> str:
+        return "hidden_states"
+
+    @property
+    def output_shape(self) -> tuple:
         return (1, 4, 8, 8)
 
     @property
-    def output_shape(self):
+    def input_shape(self) -> tuple:
         return (1, 4, 8, 8)
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            # Product of num_attention_heads * attention_head_dim must be divisible by 16 for 3D positional embeddings.
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self) -> dict:
+        return {
             "num_attention_heads": 2,
             "attention_head_dim": 8,
             "in_channels": 4,
@@ -141,9 +151,29 @@ class CogVideoX1_5TransformerTests(ModelTesterMixin, unittest.TestCase):
             "max_text_seq_length": 8,
             "use_rotary_positional_embeddings": True,
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
 
-    def test_gradient_checkpointing_is_applied(self):
-        expected_set = {"CogVideoXTransformer3DModel"}
-        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
+    def get_dummy_inputs(self, batch_size: int = 2) -> dict[str, torch.Tensor]:
+        num_channels = 4
+        num_frames = 2
+        height = 8
+        width = 8
+        embedding_dim = 8
+        sequence_length = 8
+
+        return {
+            "hidden_states": randn_tensor(
+                (batch_size, num_frames, num_channels, height, width), generator=self.generator, device=torch_device
+            ),
+            "encoder_hidden_states": randn_tensor(
+                (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
+            ),
+            "timestep": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
+        }
+
+
+class TestCogVideoX15Transformer(CogVideoX15TransformerTesterConfig, ModelTesterMixin):
+    pass
+
+
+class TestCogVideoX15TransformerCompile(CogVideoX15TransformerTesterConfig, TorchCompileTesterMixin):
+    pass

tests/models/transformers/test_models_transformer_cogview3plus.py

@@ -13,63 +13,50 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import torch
 
 from diffusers import CogView3PlusTransformer2DModel
+from diffusers.utils.torch_utils import randn_tensor
 
-from ...testing_utils import (
-    enable_full_determinism,
-    torch_device,
+from ...testing_utils import enable_full_determinism, torch_device
+from ..testing_utils import (
+    BaseModelTesterConfig,
+    ModelTesterMixin,
+    TorchCompileTesterMixin,
+    TrainingTesterMixin,
 )
-from ..test_modeling_common import ModelTesterMixin
 
 
 enable_full_determinism()
 
 
-class CogView3PlusTransformerTests(ModelTesterMixin, unittest.TestCase):
-    model_class = CogView3PlusTransformer2DModel
-    main_input_name = "hidden_states"
-    uses_custom_attn_processor = True
-    model_split_percents = [0.7, 0.6, 0.6]
+class CogView3PlusTransformerTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return CogView3PlusTransformer2DModel
 
     @property
-    def dummy_input(self):
-        batch_size = 2
-        num_channels = 4
-        height = 8
-        width = 8
-        embedding_dim = 8
-        sequence_length = 8
+    def main_input_name(self) -> str:
+        return "hidden_states"
 
-        hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
-        original_size = torch.tensor([height * 8, width * 8]).unsqueeze(0).repeat(batch_size, 1).to(torch_device)
-        target_size = torch.tensor([height * 8, width * 8]).unsqueeze(0).repeat(batch_size, 1).to(torch_device)
-        crop_coords = torch.tensor([0, 0]).unsqueeze(0).repeat(batch_size, 1).to(torch_device)
-        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
+    @property
+    def model_split_percents(self) -> list:
+        return [0.7, 0.6, 0.6]
 
+    @property
+    def output_shape(self) -> tuple:
+        return (1, 4, 8, 8)
+
+    @property
+    def input_shape(self) -> tuple:
+        return (1, 4, 8, 8)
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self) -> dict:
         return {
-            "hidden_states": hidden_states,
-            "encoder_hidden_states": encoder_hidden_states,
-            "original_size": original_size,
-            "target_size": target_size,
-            "crop_coords": crop_coords,
-            "timestep": timestep,
-        }
-
-    @property
-    def input_shape(self):
-        return (1, 4, 8, 8)
-
-    @property
-    def output_shape(self):
-        return (1, 4, 8, 8)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
             "patch_size": 2,
             "in_channels": 4,
             "num_layers": 2,
@@ -82,9 +69,37 @@ class CogView3PlusTransformerTests(ModelTesterMixin, unittest.TestCase):
             "pos_embed_max_size": 8,
             "sample_size": 8,
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
 
+    def get_dummy_inputs(self, batch_size: int = 2) -> dict[str, torch.Tensor]:
+        num_channels = 4
+        height = 8
+        width = 8
+        embedding_dim = 8
+        sequence_length = 8
+
+        return {
+            "hidden_states": randn_tensor(
+                (batch_size, num_channels, height, width), generator=self.generator, device=torch_device
+            ),
+            "encoder_hidden_states": randn_tensor(
+                (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
+            ),
+            "original_size": torch.tensor([height * 8, width * 8]).unsqueeze(0).repeat(batch_size, 1).to(torch_device),
+            "target_size": torch.tensor([height * 8, width * 8]).unsqueeze(0).repeat(batch_size, 1).to(torch_device),
+            "crop_coords": torch.tensor([0, 0]).unsqueeze(0).repeat(batch_size, 1).to(torch_device),
+            "timestep": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
+        }
+
+
+class TestCogView3PlusTransformer(CogView3PlusTransformerTesterConfig, ModelTesterMixin):
+    pass
+
+
+class TestCogView3PlusTransformerTraining(CogView3PlusTransformerTesterConfig, TrainingTesterMixin):
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"CogView3PlusTransformer2DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
+
+
+class TestCogView3PlusTransformerCompile(CogView3PlusTransformerTesterConfig, TorchCompileTesterMixin):
+    pass

tests/models/transformers/test_models_transformer_cogview4.py

@@ -12,59 +12,46 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import torch
 
 from diffusers import CogView4Transformer2DModel
+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,
+    TorchCompileTesterMixin,
+    TrainingTesterMixin,
+)
 
 
 enable_full_determinism()
 
 
-class CogView3PlusTransformerTests(ModelTesterMixin, unittest.TestCase):
-    model_class = CogView4Transformer2DModel
-    main_input_name = "hidden_states"
-    uses_custom_attn_processor = True
+class CogView4TransformerTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return CogView4Transformer2DModel
 
     @property
-    def dummy_input(self):
-        batch_size = 2
-        num_channels = 4
-        height = 8
-        width = 8
-        embedding_dim = 8
-        sequence_length = 8
+    def main_input_name(self) -> str:
+        return "hidden_states"
 
-        hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
-        original_size = torch.tensor([height * 8, width * 8]).unsqueeze(0).repeat(batch_size, 1).to(torch_device)
-        target_size = torch.tensor([height * 8, width * 8]).unsqueeze(0).repeat(batch_size, 1).to(torch_device)
-        crop_coords = torch.tensor([0, 0]).unsqueeze(0).repeat(batch_size, 1).to(torch_device)
-        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
+    @property
+    def output_shape(self) -> tuple:
+        return (4, 8, 8)
 
+    @property
+    def input_shape(self) -> tuple:
+        return (4, 8, 8)
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self) -> dict:
         return {
-            "hidden_states": hidden_states,
-            "encoder_hidden_states": encoder_hidden_states,
-            "timestep": timestep,
-            "original_size": original_size,
-            "target_size": target_size,
-            "crop_coords": crop_coords,
-        }
-
-    @property
-    def input_shape(self):
-        return (4, 8, 8)
-
-    @property
-    def output_shape(self):
-        return (4, 8, 8)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
             "patch_size": 2,
             "in_channels": 4,
             "num_layers": 2,
@@ -75,9 +62,37 @@ class CogView3PlusTransformerTests(ModelTesterMixin, unittest.TestCase):
             "time_embed_dim": 8,
             "condition_dim": 4,
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
 
+    def get_dummy_inputs(self, batch_size: int = 2) -> dict[str, torch.Tensor]:
+        num_channels = 4
+        height = 8
+        width = 8
+        embedding_dim = 8
+        sequence_length = 8
+
+        return {
+            "hidden_states": randn_tensor(
+                (batch_size, num_channels, height, width), generator=self.generator, device=torch_device
+            ),
+            "encoder_hidden_states": randn_tensor(
+                (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
+            ),
+            "timestep": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
+            "original_size": torch.tensor([height * 8, width * 8]).unsqueeze(0).repeat(batch_size, 1).to(torch_device),
+            "target_size": torch.tensor([height * 8, width * 8]).unsqueeze(0).repeat(batch_size, 1).to(torch_device),
+            "crop_coords": torch.tensor([0, 0]).unsqueeze(0).repeat(batch_size, 1).to(torch_device),
+        }
+
+
+class TestCogView4Transformer(CogView4TransformerTesterConfig, ModelTesterMixin):
+    pass
+
+
+class TestCogView4TransformerTraining(CogView4TransformerTesterConfig, TrainingTesterMixin):
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"CogView4Transformer2DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
+
+
+class TestCogView4TransformerCompile(CogView4TransformerTesterConfig, TorchCompileTesterMixin):
+    pass

tests/models/transformers/test_models_transformer_glm_image.py

@@ -1,99 +0,0 @@
-# coding=utf-8
-# Copyright 2025 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import torch
-
-from diffusers import GlmImageTransformer2DModel
-from diffusers.utils.torch_utils import randn_tensor
-
-from ...testing_utils import enable_full_determinism, torch_device
-from ..testing_utils import (
-    BaseModelTesterConfig,
-    ModelTesterMixin,
-    TorchCompileTesterMixin,
-    TrainingTesterMixin,
-)
-
-
-enable_full_determinism()
-
-
-class GlmImageTransformerTesterConfig(BaseModelTesterConfig):
-    @property
-    def model_class(self):
-        return GlmImageTransformer2DModel
-
-    @property
-    def main_input_name(self) -> str:
-        return "hidden_states"
-
-    @property
-    def output_shape(self) -> tuple:
-        return (16, 8, 8)
-
-    @property
-    def input_shape(self) -> tuple:
-        return (4, 8, 8)
-
-    @property
-    def generator(self):
-        return torch.Generator("cpu").manual_seed(0)
-
-    def get_init_dict(self) -> dict:
-        return {
-            "patch_size": 2,
-            "in_channels": 4,
-            "out_channels": 4,
-            "num_layers": 1,
-            "attention_head_dim": 8,
-            "num_attention_heads": 2,
-            "text_embed_dim": 32,
-            "time_embed_dim": 16,
-            "condition_dim": 8,
-            "prior_vq_quantizer_codebook_size": 64,
-        }
-
-    def get_dummy_inputs(self, batch_size: int = 1) -> dict[str, torch.Tensor]:
-        num_channels = 4
-        height = width = 8
-        sequence_length = 12
-
-        return {
-            "hidden_states": randn_tensor(
-                (batch_size, num_channels, height, width), generator=self.generator, device=torch_device
-            ),
-            "encoder_hidden_states": randn_tensor(
-                (batch_size, sequence_length, 32), generator=self.generator, device=torch_device
-            ),
-            "prior_token_id": torch.randint(0, 64, size=(batch_size,), generator=self.generator).to(torch_device),
-            "prior_token_drop": torch.zeros(batch_size, dtype=torch.bool, device=torch_device),
-            "timestep": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
-            "target_size": torch.tensor([[height, width]] * batch_size, dtype=torch.float32).to(torch_device),
-            "crop_coords": torch.tensor([[0, 0]] * batch_size, dtype=torch.float32).to(torch_device),
-        }
-
-
-class TestGlmImageTransformer(GlmImageTransformerTesterConfig, ModelTesterMixin):
-    pass
-
-
-class TestGlmImageTransformerTraining(GlmImageTransformerTesterConfig, TrainingTesterMixin):
-    def test_gradient_checkpointing_is_applied(self):
-        expected_set = {"GlmImageTransformer2DModel"}
-        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
-
-
-class TestGlmImageTransformerCompile(GlmImageTransformerTesterConfig, TorchCompileTesterMixin):
-    pass