rename modeling code

src/diffusers/__init__.py

@@ -4,7 +4,7 @@
 
 __version__ = "0.0.1"
 
-from .modeling_utils import PreTrainedModel
+from .modeling_utils import ModelMixin
 from .models.unet import UNetModel
 from .pipeline_utils import DiffusionPipeline
 from .schedulers.gaussian_ddpm import GaussianDDPMScheduler

src/diffusers/configuration_utils.py

@@ -13,7 +13,7 @@
 # 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.
-""" Configuration base class and utilities."""
+""" ConfigMixinuration base class and utilities."""
 
 
 import copy
@@ -44,7 +44,7 @@ logger = logging.get_logger(__name__)
 _re_configuration_file = re.compile(r"config\.(.*)\.json")
 
 
-class Config:
+class ConfigMixin:
     r"""
     Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as
     methods for loading/downloading/saving configurations.
@@ -71,7 +71,7 @@ class Config:
     def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
         """
         Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the
-        [`~Config.from_config`] class method.
+        [`~ConfigMixin.from_config`] class method.
 
         Args:
             save_directory (`str` or `os.PathLike`):
@@ -88,7 +88,7 @@ class Config:
         output_config_file = os.path.join(save_directory, self.config_name)
 
         self.to_json_file(output_config_file)
-        logger.info(f"Configuration saved in {output_config_file}")
+        logger.info(f"ConfigMixinuration saved in {output_config_file}")
 
     @classmethod
     def get_config_dict(

src/diffusers/modeling_utils.py

@@ -122,11 +122,11 @@ def _load_state_dict_into_model(model_to_load, state_dict):
     return error_msgs
 
 
-class PreTrainedModel(torch.nn.Module):
+class ModelMixin(torch.nn.Module):
     r"""
     Base class for all models.
 
-    [`PreTrainedModel`] takes care of storing the configuration of the models and handles methods for loading,
+    [`ModelMixin`] takes care of storing the configuration of the models and handles methods for loading,
     downloading and saving models as well as a few methods common to all models to:
 
         - resize the input embeddings,
@@ -134,13 +134,13 @@ class PreTrainedModel(torch.nn.Module):
 
     Class attributes (overridden by derived classes):
 
-        - **config_class** ([`Config`]) -- A subclass of [`Config`] to use as configuration class
+        - **config_class** ([`ConfigMixin`]) -- A subclass of [`ConfigMixin`] to use as configuration class
           for this model architecture.
         - **load_tf_weights** (`Callable`) -- A python *method* for loading a TensorFlow checkpoint in a PyTorch model,
           taking as arguments:
 
-            - **model** ([`PreTrainedModel`]) -- An instance of the model on which to load the TensorFlow checkpoint.
-            - **config** ([`PreTrainedConfig`]) -- An instance of the configuration associated to the model.
+            - **model** ([`ModelMixin`]) -- An instance of the model on which to load the TensorFlow checkpoint.
+            - **config** ([`PreTrainedConfigMixin`]) -- An instance of the configuration associated to the model.
             - **path** (`str`) -- A path to the TensorFlow checkpoint.
 
         - **base_model_prefix** (`str`) -- A string indicating the attribute associated to the base model in derived
@@ -163,7 +163,7 @@ class PreTrainedModel(torch.nn.Module):
     ):
         """
         Save a model and its configuration file to a directory, so that it can be re-loaded using the
-        `[`~PreTrainedModel.from_pretrained`]` class method.
+        `[`~ModelMixin.from_pretrained`]` class method.
 
         Arguments:
             save_directory (`str` or `os.PathLike`):
@@ -231,20 +231,20 @@ class PreTrainedModel(torch.nn.Module):
                       Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
                       user or organization name, like `dbmdz/bert-base-german-cased`.
                     - A path to a *directory* containing model weights saved using
-                      [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
+                      [`~ModelMixin.save_pretrained`], e.g., `./my_model_directory/`.
 
-            config (`Union[Config, str, os.PathLike]`, *optional*):
+            config (`Union[ConfigMixin, str, os.PathLike]`, *optional*):
                 Can be either:
 
-                    - an instance of a class derived from [`Config`],
-                    - a string or path valid as input to [`~Config.from_pretrained`].
+                    - an instance of a class derived from [`ConfigMixin`],
+                    - a string or path valid as input to [`~ConfigMixin.from_pretrained`].
 
-                Configuration for the model to use instead of an automatically loaded configuration. Configuration can
+                ConfigMixinuration for the model to use instead of an automatically loaded configuration. ConfigMixinuration can
                 be automatically loaded when:
 
                     - The model is a model provided by the library (loaded with the *model id* string of a pretrained
                       model).
-                    - The model was saved using [`~PreTrainedModel.save_pretrained`] and is reloaded by supplying the
+                    - The model was saved using [`~ModelMixin.save_pretrained`] and is reloaded by supplying the
                       save directory.
                     - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
                       configuration JSON file named *config.json* is found in the directory.
@@ -295,7 +295,7 @@ class PreTrainedModel(torch.nn.Module):
                       underlying model's `__init__` method (we assume all relevant updates to the configuration have
                       already been done)
                     - If a configuration is not provided, `kwargs` will be first passed to the configuration class
-                      initialization function ([`~Config.from_pretrained`]). Each key of `kwargs` that
+                      initialization function ([`~ConfigMixin.from_pretrained`]). Each key of `kwargs` that
                       corresponds to a configuration attribute will be used to override said attribute with the
                       supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
                       will be passed to the underlying model's `__init__` function.

src/diffusers/models/unet.py

@@ -29,8 +29,8 @@ from torchvision import transforms, utils
 from PIL import Image
 from tqdm import tqdm
 
-from ..configuration_utils import Config
-from ..modeling_utils import PreTrainedModel
+from ..configuration_utils import ConfigMixin
+from ..modeling_utils import ModelMixin
 
 
 def get_timestep_embedding(timesteps, embedding_dim):
@@ -175,7 +175,7 @@ class AttnBlock(nn.Module):
         return x + h_
 
 
-class UNetModel(PreTrainedModel, Config):
+class UNetModel(ModelMixin, ConfigMixin):
     def __init__(
         self,
         ch=128,

src/diffusers/pipeline_utils.py

@@ -22,7 +22,7 @@ from huggingface_hub import snapshot_download
 # CHANGE to diffusers.utils
 from transformers.utils import logging
 
-from .configuration_utils import Config
+from .configuration_utils import ConfigMixin
 
 
 INDEX_FILE = "diffusion_model.pt"
@@ -33,16 +33,16 @@ logger = logging.get_logger(__name__)
 
 LOADABLE_CLASSES = {
     "diffusers": {
-        "PreTrainedModel": ["save_pretrained", "from_pretrained"],
+        "ModelMixin": ["save_pretrained", "from_pretrained"],
         "GaussianDDPMScheduler": ["save_config", "from_config"],
     },
     "transformers": {
-        "PreTrainedModel": ["save_pretrained", "from_pretrained"],
+        "ModelMixin": ["save_pretrained", "from_pretrained"],
     },
 }
 
 
-class DiffusionPipeline(Config):
+class DiffusionPipeline(ConfigMixin):
 
     config_name = "model_index.json"
 

src/diffusers/schedulers/gaussian_ddpm.py

@@ -14,7 +14,7 @@
 import torch
 from torch import nn
 
-from ..configuration_utils import Config
+from ..configuration_utils import ConfigMixin
 
 
 SAMPLING_CONFIG_NAME = "scheduler_config.json"
@@ -24,7 +24,7 @@ def linear_beta_schedule(timesteps, beta_start, beta_end):
     return torch.linspace(beta_start, beta_end, timesteps, dtype=torch.float64)
 
 
-class GaussianDDPMScheduler(nn.Module, Config):
+class GaussianDDPMScheduler(nn.Module, ConfigMixin):
 
     config_name = SAMPLING_CONFIG_NAME
 

utils/check_config_docstrings.py

@@ -40,13 +40,13 @@ _re_checkpoint = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
 
 
 CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK = {
-    "CLIPConfig",
-    "DecisionTransformerConfig",
-    "EncoderDecoderConfig",
-    "RagConfig",
-    "SpeechEncoderDecoderConfig",
-    "VisionEncoderDecoderConfig",
-    "VisionTextDualEncoderConfig",
+    "CLIPConfigMixin",
+    "DecisionTransformerConfigMixin",
+    "EncoderDecoderConfigMixin",
+    "RagConfigMixin",
+    "SpeechEncoderDecoderConfigMixin",
+    "VisionEncoderDecoderConfigMixin",
+    "VisionTextDualEncoderConfigMixin",
 }
 
 

utils/check_repo.py

@@ -87,7 +87,7 @@ IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [
     "ReformerForMaskedLM",  # Needs to be setup as decoder.
     "Speech2Text2DecoderWrapper",  # Building part of bigger (tested) model.
     "TFDPREncoder",  # Building part of bigger (tested) model.
-    "TFElectraMainLayer",  # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?)
+    "TFElectraMainLayer",  # Building part of bigger (tested) model (should it be a TFModelMixin ?)
     "TFRobertaForMultipleChoice",  # TODO: fix
     "TrOCRDecoderWrapper",  # Building part of bigger (tested) model.
     "SeparableConv1D",  # Building part of bigger (tested) model.
@@ -271,7 +271,7 @@ def get_model_modules():
 def get_models(module, include_pretrained=False):
     """Get the objects in module that are models."""
     models = []
-    model_classes = (transformers.PreTrainedModel, transformers.TFPreTrainedModel, transformers.FlaxPreTrainedModel)
+    model_classes = (transformers.ModelMixin, transformers.TFModelMixin, transformers.FlaxModelMixin)
     for attr_name in dir(module):
         if not include_pretrained and ("Pretrained" in attr_name or "PreTrained" in attr_name):
             continue
@@ -372,7 +372,7 @@ def find_tested_models(test_file):
 
 def check_models_are_tested(module, test_file):
     """Check models defined in module are tested in test_file."""
-    # XxxPreTrainedModel are not tested
+    # XxxModelMixin are not tested
     defined_models = get_models(module)
     tested_models = find_tested_models(test_file)
     if tested_models is None:
@@ -625,9 +625,9 @@ def ignore_undocumented(name):
     # Constants uppercase are not documented.
     if name.isupper():
         return True
-    # PreTrainedModels / Encoders / Decoders / Layers / Embeddings / Attention are not documented.
+    # ModelMixins / Encoders / Decoders / Layers / Embeddings / Attention are not documented.
     if (
-        name.endswith("PreTrainedModel")
+        name.endswith("ModelMixin")
         or name.endswith("Decoder")
         or name.endswith("Encoder")
         or name.endswith("Layer")

utils/check_table.py

@@ -94,7 +94,7 @@ def get_model_table_from_auto_modules():
         for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
         if code in config_maping_names
     }
-    model_name_to_prefix = {name: config.replace("Config", "") for name, config in model_name_to_config.items()}
+    model_name_to_prefix = {name: config.replace("ConfigMixin", "") for name, config in model_name_to_config.items()}
 
     # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
     slow_tokenizers = collections.defaultdict(bool)
@@ -190,7 +190,7 @@ def has_onnx(model_type):
     for part in config_module.split(".")[1:]:
         module = getattr(module, part)
     config_name = config.__name__
-    onnx_config_name = config_name.replace("Config", "OnnxConfig")
+    onnx_config_name = config_name.replace("ConfigMixin", "OnnxConfigMixin")
     return hasattr(module, onnx_config_name)