Merge branch 'main' into cosmos-test-refactor

.github/workflows/claude_review.yml

@@ -32,9 +32,6 @@ jobs:
       )
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
       - uses: anthropics/claude-code-action@v1
         with:
           anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}

src/diffusers/quantizers/quantization_config.py

@@ -470,8 +470,8 @@ class TorchAoConfig(QuantizationConfigMixin):
         self.post_init()
 
     def post_init(self):
-        if is_torchao_version("<", "0.15.0"):
-            raise ValueError("TorchAoConfig requires torchao >= 0.15.0. Please upgrade with `pip install -U torchao`.")
+        if is_torchao_version("<=", "0.9.0"):
+            raise ValueError("TorchAoConfig requires torchao > 0.9.0. Please upgrade with `pip install -U torchao`.")
 
         from torchao.quantization.quant_api import AOBaseConfig
 
@@ -495,8 +495,8 @@ class TorchAoConfig(QuantizationConfigMixin):
     @classmethod
     def from_dict(cls, config_dict, return_unused_kwargs=False, **kwargs):
         """Create configuration from a dictionary."""
-        if not is_torchao_version(">=", "0.15.0"):
-            raise NotImplementedError("TorchAoConfig requires torchao >= 0.15.0 for construction from dict")
+        if not is_torchao_version(">", "0.9.0"):
+            raise NotImplementedError("TorchAoConfig requires torchao > 0.9.0 for construction from dict")
         config_dict = config_dict.copy()
         quant_type = config_dict.pop("quant_type")
 

src/diffusers/quantizers/torchao/torchao_quantizer.py

@@ -113,7 +113,7 @@ if (
     is_torch_available()
     and is_torch_version(">=", "2.6.0")
     and is_torchao_available()
-    and is_torchao_version(">=", "0.15.0")
+    and is_torchao_version(">=", "0.7.0")
 ):
     _update_torch_safe_globals()
 
@@ -168,10 +168,10 @@ class TorchAoHfQuantizer(DiffusersQuantizer):
             raise ImportError(
                 "Loading a TorchAO quantized model requires the torchao library. Please install with `pip install torchao`"
             )
-        torchao_version = version.parse(importlib.metadata.version("torchao"))
-        if torchao_version < version.parse("0.15.0"):
+        torchao_version = version.parse(importlib.metadata.version("torch"))
+        if torchao_version < version.parse("0.7.0"):
             raise RuntimeError(
-                f"The minimum required version of `torchao` is 0.15.0, but the current version is {torchao_version}. Please upgrade with `pip install -U torchao`."
+                f"The minimum required version of `torchao` is 0.7.0, but the current version is {torchao_version}. Please upgrade with `pip install -U torchao`."
             )
 
         self.offload = False

tests/models/transformers/test_models_transformer_cosmos.py

@@ -12,60 +12,46 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import torch
 
 from diffusers import CosmosTransformer3DModel
+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,
+    MemoryTesterMixin,
+    ModelTesterMixin,
+    TrainingTesterMixin,
+)
 
 
 enable_full_determinism()
 
 
-class CosmosTransformer3DModelTests(ModelTesterMixin, unittest.TestCase):
-    model_class = CosmosTransformer3DModel
-    main_input_name = "hidden_states"
-    uses_custom_attn_processor = True
+class CosmosTransformerTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return CosmosTransformer3DModel
 
     @property
-    def dummy_input(self):
-        batch_size = 1
-        num_channels = 4
-        num_frames = 1
-        height = 16
-        width = 16
-        text_embed_dim = 16
-        sequence_length = 12
-        fps = 30
+    def output_shape(self) -> tuple[int, ...]:
+        return (4, 1, 16, 16)
 
-        hidden_states = torch.randn((batch_size, num_channels, num_frames, height, width)).to(torch_device)
-        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, text_embed_dim)).to(torch_device)
-        attention_mask = torch.ones((batch_size, sequence_length)).to(torch_device)
-        padding_mask = torch.zeros(batch_size, 1, height, width).to(torch_device)
+    @property
+    def input_shape(self) -> tuple[int, ...]:
+        return (4, 1, 16, 16)
 
+    @property
+    def main_input_name(self) -> str:
+        return "hidden_states"
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self) -> dict[str, int | list | tuple | float | bool | str]:
         return {
-            "hidden_states": hidden_states,
-            "timestep": timestep,
-            "encoder_hidden_states": encoder_hidden_states,
-            "attention_mask": attention_mask,
-            "fps": fps,
-            "padding_mask": padding_mask,
-        }
-
-    @property
-    def input_shape(self):
-        return (4, 1, 16, 16)
-
-    @property
-    def output_shape(self):
-        return (4, 1, 16, 16)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
             "in_channels": 4,
             "out_channels": 4,
             "num_attention_heads": 2,
@@ -80,57 +66,68 @@ class CosmosTransformer3DModelTests(ModelTesterMixin, unittest.TestCase):
             "concat_padding_mask": True,
             "extra_pos_embed_type": "learnable",
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
 
-    def test_gradient_checkpointing_is_applied(self):
-        expected_set = {"CosmosTransformer3DModel"}
-        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
-
-
-class CosmosTransformer3DModelVideoToWorldTests(ModelTesterMixin, unittest.TestCase):
-    model_class = CosmosTransformer3DModel
-    main_input_name = "hidden_states"
-    uses_custom_attn_processor = True
-
-    @property
-    def dummy_input(self):
-        batch_size = 1
+    def get_dummy_inputs(self, batch_size: int = 1) -> dict[str, torch.Tensor]:
         num_channels = 4
         num_frames = 1
         height = 16
         width = 16
         text_embed_dim = 16
         sequence_length = 12
-        fps = 30
-
-        hidden_states = torch.randn((batch_size, num_channels, num_frames, height, width)).to(torch_device)
-        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, text_embed_dim)).to(torch_device)
-        attention_mask = torch.ones((batch_size, sequence_length)).to(torch_device)
-        condition_mask = torch.ones(batch_size, 1, num_frames, height, width).to(torch_device)
-        padding_mask = torch.zeros(batch_size, 1, height, width).to(torch_device)
 
         return {
-            "hidden_states": hidden_states,
-            "timestep": timestep,
-            "encoder_hidden_states": encoder_hidden_states,
-            "attention_mask": attention_mask,
-            "fps": fps,
-            "condition_mask": condition_mask,
-            "padding_mask": padding_mask,
+            "hidden_states": randn_tensor(
+                (batch_size, num_channels, num_frames, height, width), generator=self.generator, device=torch_device
+            ),
+            "timestep": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
+            "encoder_hidden_states": randn_tensor(
+                (batch_size, sequence_length, text_embed_dim), generator=self.generator, device=torch_device
+            ),
+            "attention_mask": torch.ones((batch_size, sequence_length)).to(torch_device),
+            "fps": 30,
+            "padding_mask": torch.zeros(batch_size, 1, height, width).to(torch_device),
         }
 
+
+class TestCosmosTransformer(CosmosTransformerTesterConfig, ModelTesterMixin):
+    """Core model tests for Cosmos Transformer."""
+
+
+class TestCosmosTransformerMemory(CosmosTransformerTesterConfig, MemoryTesterMixin):
+    """Memory optimization tests for Cosmos Transformer."""
+
+
+class TestCosmosTransformerTraining(CosmosTransformerTesterConfig, TrainingTesterMixin):
+    """Training tests for Cosmos Transformer."""
+
+    def test_gradient_checkpointing_is_applied(self):
+        expected_set = {"CosmosTransformer3DModel"}
+        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
+
+
+class CosmosTransformerVideoToWorldTesterConfig(BaseModelTesterConfig):
     @property
-    def input_shape(self):
+    def model_class(self):
+        return CosmosTransformer3DModel
+
+    @property
+    def output_shape(self) -> tuple[int, ...]:
         return (4, 1, 16, 16)
 
     @property
-    def output_shape(self):
+    def input_shape(self) -> tuple[int, ...]:
         return (4, 1, 16, 16)
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
+    @property
+    def main_input_name(self) -> str:
+        return "hidden_states"
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self) -> dict[str, int | list | tuple | float | bool | str]:
+        return {
             "in_channels": 4 + 1,
             "out_channels": 4,
             "num_attention_heads": 2,
@@ -145,8 +142,40 @@ class CosmosTransformer3DModelVideoToWorldTests(ModelTesterMixin, unittest.TestC
             "concat_padding_mask": True,
             "extra_pos_embed_type": "learnable",
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
+
+    def get_dummy_inputs(self, batch_size: int = 1) -> dict[str, torch.Tensor]:
+        num_channels = 4
+        num_frames = 1
+        height = 16
+        width = 16
+        text_embed_dim = 16
+        sequence_length = 12
+
+        return {
+            "hidden_states": randn_tensor(
+                (batch_size, num_channels, num_frames, height, width), generator=self.generator, device=torch_device
+            ),
+            "timestep": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
+            "encoder_hidden_states": randn_tensor(
+                (batch_size, sequence_length, text_embed_dim), generator=self.generator, device=torch_device
+            ),
+            "attention_mask": torch.ones((batch_size, sequence_length)).to(torch_device),
+            "fps": 30,
+            "condition_mask": torch.ones(batch_size, 1, num_frames, height, width).to(torch_device),
+            "padding_mask": torch.zeros(batch_size, 1, height, width).to(torch_device),
+        }
+
+
+class TestCosmosTransformerVideoToWorld(CosmosTransformerVideoToWorldTesterConfig, ModelTesterMixin):
+    """Core model tests for Cosmos Transformer (Video-to-World)."""
+
+
+class TestCosmosTransformerVideoToWorldMemory(CosmosTransformerVideoToWorldTesterConfig, MemoryTesterMixin):
+    """Memory optimization tests for Cosmos Transformer (Video-to-World)."""
+
+
+class TestCosmosTransformerVideoToWorldTraining(CosmosTransformerVideoToWorldTesterConfig, TrainingTesterMixin):
+    """Training tests for Cosmos Transformer (Video-to-World)."""
 
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"CosmosTransformer3DModel"}

tests/quantization/torchao/test_torchao.py

@@ -14,11 +14,13 @@
 # limitations under the License.
 
 import gc
+import importlib.metadata
 import tempfile
 import unittest
 from typing import List
 
 import numpy as np
+from packaging import version
 from parameterized import parameterized
 from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer, T5EncoderModel
 
@@ -80,17 +82,18 @@ if is_torchao_available():
         Float8WeightOnlyConfig,
         Int4WeightOnlyConfig,
         Int8DynamicActivationInt8WeightConfig,
-        Int8DynamicActivationIntxWeightConfig,
         Int8WeightOnlyConfig,
-        IntxWeightOnlyConfig,
     )
     from torchao.quantization.linear_activation_quantized_tensor import LinearActivationQuantizedTensor
     from torchao.utils import get_model_size_in_bytes
 
+    if version.parse(importlib.metadata.version("torchao")) >= version.Version("0.10.0"):
+        from torchao.quantization import Int8DynamicActivationIntxWeightConfig, IntxWeightOnlyConfig
+
 
 @require_torch
 @require_torch_accelerator
-@require_torchao_version_greater_or_equal("0.15.0")
+@require_torchao_version_greater_or_equal("0.14.0")
 class TorchAoConfigTest(unittest.TestCase):
     def test_to_dict(self):
         """
@@ -125,7 +128,7 @@ class TorchAoConfigTest(unittest.TestCase):
 # Slices for these tests have been obtained on our aws-g6e-xlarge-plus runners
 @require_torch
 @require_torch_accelerator
-@require_torchao_version_greater_or_equal("0.15.0")
+@require_torchao_version_greater_or_equal("0.14.0")
 class TorchAoTest(unittest.TestCase):
     def tearDown(self):
         gc.collect()
@@ -524,7 +527,7 @@ class TorchAoTest(unittest.TestCase):
         inputs = self.get_dummy_inputs(torch_device)
         _ = pipe(**inputs)
 
-    @require_torchao_version_greater_or_equal("0.15.0")
+    @require_torchao_version_greater_or_equal("0.9.0")
     def test_aobase_config(self):
         quantization_config = TorchAoConfig(Int8WeightOnlyConfig())
         components = self.get_dummy_components(quantization_config)
@@ -537,7 +540,7 @@ class TorchAoTest(unittest.TestCase):
 # Slices for these tests have been obtained on our aws-g6e-xlarge-plus runners
 @require_torch
 @require_torch_accelerator
-@require_torchao_version_greater_or_equal("0.15.0")
+@require_torchao_version_greater_or_equal("0.14.0")
 class TorchAoSerializationTest(unittest.TestCase):
     model_name = "hf-internal-testing/tiny-flux-pipe"
 
@@ -647,7 +650,7 @@ class TorchAoSerializationTest(unittest.TestCase):
         self._check_serialization_expected_slice(quant_type, expected_slice, device)
 
 
-@require_torchao_version_greater_or_equal("0.15.0")
+@require_torchao_version_greater_or_equal("0.14.0")
 class TorchAoCompileTest(QuantCompileTests, unittest.TestCase):
     @property
     def quantization_config(self):
@@ -693,7 +696,7 @@ class TorchAoCompileTest(QuantCompileTests, unittest.TestCase):
 # Slices for these tests have been obtained on our aws-g6e-xlarge-plus runners
 @require_torch
 @require_torch_accelerator
-@require_torchao_version_greater_or_equal("0.15.0")
+@require_torchao_version_greater_or_equal("0.14.0")
 @slow
 @nightly
 class SlowTorchAoTests(unittest.TestCase):
@@ -851,7 +854,7 @@ class SlowTorchAoTests(unittest.TestCase):
 
 @require_torch
 @require_torch_accelerator
-@require_torchao_version_greater_or_equal("0.15.0")
+@require_torchao_version_greater_or_equal("0.14.0")
 @slow
 @nightly
 class SlowTorchAoPreserializedModelTests(unittest.TestCase):