Add methods to update and verify out_features out_indices (huggingfac…

…e#23031)

* Add methods to update and verify out_features out_indices

* Safe update for config attributes

* Fix function names

* Save config correctly

* PR comments - use property setters

* PR comment - directly set attributes

* Update test

* Add updates to recently merged focalnet backbone

src/transformers/modeling_utils.py

@@ -1006,32 +1006,6 @@ def floating_point_ops(
         return 6 * self.estimate_tokens(input_dict) * self.num_parameters(exclude_embeddings=exclude_embeddings)
 
 
-class BackboneMixin:
-    @property
-    def out_feature_channels(self):
-        # the current backbones will output the number of channels for each stage
-        # even if that stage is not in the out_features list.
-        return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names)}
-
-    @property
-    def channels(self):
-        return [self.out_feature_channels[name] for name in self.out_features]
-
-    def forward_with_filtered_kwargs(self, *args, **kwargs):
-        signature = dict(inspect.signature(self.forward).parameters)
-        filtered_kwargs = {k: v for k, v in kwargs.items() if k in signature}
-        return self(*args, **filtered_kwargs)
-
-    def forward(
-        self,
-        pixel_values: Tensor,
-        output_hidden_states: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ):
-        raise NotImplementedError("This method should be implemented by the derived class.")
-
-
 class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMixin):
     r"""
     Base class for all models.

src/transformers/models/bit/configuration_bit.py

@@ -16,6 +16,7 @@
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
 
 
 logger = logging.get_logger(__name__)
@@ -25,7 +26,7 @@
 }
 
 
-class BitConfig(PretrainedConfig):
+class BitConfig(BackboneConfigMixin, PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`BitModel`]. It is used to instantiate an BiT
     model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
@@ -128,35 +129,6 @@ def __init__(
         self.width_factor = width_factor
 
         self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
-
-        if out_features is not None and out_indices is not None:
-            if len(out_features) != len(out_indices):
-                raise ValueError("out_features and out_indices should have the same length if both are set")
-            elif out_features != [self.stage_names[idx] for idx in out_indices]:
-                raise ValueError("out_features and out_indices should correspond to the same stages if both are set")
-
-        if out_features is None and out_indices is not None:
-            out_features = [self.stage_names[idx] for idx in out_indices]
-        elif out_features is not None and out_indices is None:
-            out_indices = [self.stage_names.index(feature) for feature in out_features]
-        elif out_features is None and out_indices is None:
-            out_features = [self.stage_names[-1]]
-            out_indices = [len(self.stage_names) - 1]
-
-        if out_features is not None:
-            if not isinstance(out_features, list):
-                raise ValueError("out_features should be a list")
-            for feature in out_features:
-                if feature not in self.stage_names:
-                    raise ValueError(
-                        f"Feature {feature} is not a valid feature name. Valid names are {self.stage_names}"
-                    )
-        if out_indices is not None:
-            if not isinstance(out_indices, (list, tuple)):
-                raise ValueError("out_indices should be a list or tuple")
-            for idx in out_indices:
-                if idx >= len(self.stage_names):
-                    raise ValueError(f"Index {idx} is not a valid index for a list of length {len(self.stage_names)}")
-
-        self.out_features = out_features
-        self.out_indices = out_indices
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
+        )

src/transformers/models/bit/modeling_bit.py

@@ -31,14 +31,15 @@
     BaseModelOutputWithPoolingAndNoAttention,
     ImageClassifierOutputWithNoAttention,
 )
-from ...modeling_utils import BackboneMixin, PreTrainedModel
+from ...modeling_utils import PreTrainedModel
 from ...utils import (
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     logging,
     replace_return_docstrings,
 )
+from ...utils.backbone_utils import BackboneMixin, get_aligned_output_features_output_indices
 from .configuration_bit import BitConfig
 
 
@@ -848,12 +849,10 @@ def __init__(self, config):
         self.stage_names = config.stage_names
         self.bit = BitModel(config)
 
-        self.out_features = config.out_features if config.out_features is not None else [self.stage_names[-1]]
         self.num_features = [config.embedding_size] + config.hidden_sizes
-        if config.out_indices is not None:
-            self.out_indices = config.out_indices
-        else:
-            self.out_indices = tuple(i for i, layer in enumerate(self.stage_names) if layer in self.out_features)
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            config.out_features, config.out_indices, self.stage_names
+        )
 
         # initialize weights and apply final processing
         self.post_init()

src/transformers/models/convnext/configuration_convnext.py

@@ -22,6 +22,7 @@
 from ...configuration_utils import PretrainedConfig
 from ...onnx import OnnxConfig
 from ...utils import logging
+from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
 
 
 logger = logging.get_logger(__name__)
@@ -32,7 +33,7 @@
 }
 
 
-class ConvNextConfig(PretrainedConfig):
+class ConvNextConfig(BackboneConfigMixin, PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`ConvNextModel`]. It is used to instantiate an
     ConvNeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
@@ -119,38 +120,9 @@ def __init__(
         self.drop_path_rate = drop_path_rate
         self.image_size = image_size
         self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.depths) + 1)]
-
-        if out_features is not None and out_indices is not None:
-            if len(out_features) != len(out_indices):
-                raise ValueError("out_features and out_indices should have the same length if both are set")
-            elif out_features != [self.stage_names[idx] for idx in out_indices]:
-                raise ValueError("out_features and out_indices should correspond to the same stages if both are set")
-
-        if out_features is None and out_indices is not None:
-            out_features = [self.stage_names[idx] for idx in out_indices]
-        elif out_features is not None and out_indices is None:
-            out_indices = [self.stage_names.index(feature) for feature in out_features]
-        elif out_features is None and out_indices is None:
-            out_features = [self.stage_names[-1]]
-            out_indices = [len(self.stage_names) - 1]
-
-        if out_features is not None:
-            if not isinstance(out_features, list):
-                raise ValueError("out_features should be a list")
-            for feature in out_features:
-                if feature not in self.stage_names:
-                    raise ValueError(
-                        f"Feature {feature} is not a valid feature name. Valid names are {self.stage_names}"
-                    )
-        if out_indices is not None:
-            if not isinstance(out_indices, (list, tuple)):
-                raise ValueError("out_indices should be a list or tuple")
-            for idx in out_indices:
-                if idx >= len(self.stage_names):
-                    raise ValueError(f"Index {idx} is not a valid index for a list of length {len(self.stage_names)}")
-
-        self.out_features = out_features
-        self.out_indices = out_indices
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
+        )
 
 
 class ConvNextOnnxConfig(OnnxConfig):

src/transformers/models/convnext/modeling_convnext.py

@@ -29,14 +29,15 @@
     BaseModelOutputWithPoolingAndNoAttention,
     ImageClassifierOutputWithNoAttention,
 )
-from ...modeling_utils import BackboneMixin, PreTrainedModel
+from ...modeling_utils import PreTrainedModel
 from ...utils import (
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     logging,
     replace_return_docstrings,
 )
+from ...utils.backbone_utils import BackboneMixin, get_aligned_output_features_output_indices
 from .configuration_convnext import ConvNextConfig
 
 
@@ -485,16 +486,14 @@ def __init__(self, config):
         self.embeddings = ConvNextEmbeddings(config)
         self.encoder = ConvNextEncoder(config)
 
-        self.out_features = config.out_features if config.out_features is not None else [self.stage_names[-1]]
         self.num_features = [config.hidden_sizes[0]] + config.hidden_sizes
-        if config.out_indices is not None:
-            self.out_indices = config.out_indices
-        else:
-            self.out_indices = tuple(i for i, layer in enumerate(self.stage_names) if layer in self.out_features)
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            config.out_features, config.out_indices, self.stage_names
+        )
 
         # Add layer norms to hidden states of out_features
         hidden_states_norms = {}
-        for stage, num_channels in zip(self.out_features, self.channels):
+        for stage, num_channels in zip(self._out_features, self.channels):
             hidden_states_norms[stage] = ConvNextLayerNorm(num_channels, data_format="channels_first")
         self.hidden_states_norms = nn.ModuleDict(hidden_states_norms)
 

src/transformers/models/convnextv2/configuration_convnextv2.py

@@ -17,6 +17,7 @@
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
 
 
 logger = logging.get_logger(__name__)
@@ -26,7 +27,7 @@
 }
 
 
-class ConvNextV2Config(PretrainedConfig):
+class ConvNextV2Config(BackboneConfigMixin, PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`ConvNextV2Model`]. It is used to instantiate an
     ConvNeXTV2 model according to the specified arguments, defining the model architecture. Instantiating a
@@ -109,35 +110,6 @@ def __init__(
         self.drop_path_rate = drop_path_rate
         self.image_size = image_size
         self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.depths) + 1)]
-
-        if out_features is not None and out_indices is not None:
-            if len(out_features) != len(out_indices):
-                raise ValueError("out_features and out_indices should have the same length if both are set")
-            elif out_features != [self.stage_names[idx] for idx in out_indices]:
-                raise ValueError("out_features and out_indices should correspond to the same stages if both are set")
-
-        if out_features is None and out_indices is not None:
-            out_features = [self.stage_names[idx] for idx in out_indices]
-        elif out_features is not None and out_indices is None:
-            out_indices = [self.stage_names.index(feature) for feature in out_features]
-        elif out_features is None and out_indices is None:
-            out_features = [self.stage_names[-1]]
-            out_indices = [len(self.stage_names) - 1]
-
-        if out_features is not None:
-            if not isinstance(out_features, list):
-                raise ValueError("out_features should be a list")
-            for feature in out_features:
-                if feature not in self.stage_names:
-                    raise ValueError(
-                        f"Feature {feature} is not a valid feature name. Valid names are {self.stage_names}"
-                    )
-        if out_indices is not None:
-            if not isinstance(out_indices, (list, tuple)):
-                raise ValueError("out_indices should be a list or tuple")
-            for idx in out_indices:
-                if idx >= len(self.stage_names):
-                    raise ValueError(f"Index {idx} is not a valid index for a list of length {len(self.stage_names)}")
-
-        self.out_features = out_features
-        self.out_indices = out_indices
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
+        )

src/transformers/models/convnextv2/modeling_convnextv2.py

@@ -29,14 +29,15 @@
     BaseModelOutputWithPoolingAndNoAttention,
     ImageClassifierOutputWithNoAttention,
 )
-from ...modeling_utils import BackboneMixin, PreTrainedModel
+from ...modeling_utils import PreTrainedModel
 from ...utils import (
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     logging,
     replace_return_docstrings,
 )
+from ...utils.backbone_utils import BackboneMixin, get_aligned_output_features_output_indices
 from .configuration_convnextv2 import ConvNextV2Config
 
 
@@ -508,16 +509,14 @@ def __init__(self, config):
         self.embeddings = ConvNextV2Embeddings(config)
         self.encoder = ConvNextV2Encoder(config)
 
-        self.out_features = config.out_features if config.out_features is not None else [self.stage_names[-1]]
         self.num_features = [config.hidden_sizes[0]] + config.hidden_sizes
-        if config.out_indices is not None:
-            self.out_indices = config.out_indices
-        else:
-            self.out_indices = tuple(i for i, layer in enumerate(self.stage_names) if layer in self.out_features)
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            config.out_features, config.out_indices, self.stage_names
+        )
 
         # Add layer norms to hidden states of out_features
         hidden_states_norms = {}
-        for stage, num_channels in zip(self.out_features, self.channels):
+        for stage, num_channels in zip(self._out_features, self.channels):
             hidden_states_norms[stage] = ConvNextV2LayerNorm(num_channels, data_format="channels_first")
         self.hidden_states_norms = nn.ModuleDict(hidden_states_norms)
 

src/transformers/models/dinat/configuration_dinat.py

@@ -16,6 +16,7 @@
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
 
 
 logger = logging.get_logger(__name__)
@@ -26,7 +27,7 @@
 }
 
 
-class DinatConfig(PretrainedConfig):
+class DinatConfig(BackboneConfigMixin, PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`DinatModel`]. It is used to instantiate a Dinat
     model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
@@ -145,35 +146,6 @@ def __init__(
         self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
         self.layer_scale_init_value = layer_scale_init_value
         self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
-
-        if out_features is not None and out_indices is not None:
-            if len(out_features) != len(out_indices):
-                raise ValueError("out_features and out_indices should have the same length if both are set")
-            elif out_features != [self.stage_names[idx] for idx in out_indices]:
-                raise ValueError("out_features and out_indices should correspond to the same stages if both are set")
-
-        if out_features is None and out_indices is not None:
-            out_features = [self.stage_names[idx] for idx in out_indices]
-        elif out_features is not None and out_indices is None:
-            out_indices = [self.stage_names.index(feature) for feature in out_features]
-        elif out_features is None and out_indices is None:
-            out_features = [self.stage_names[-1]]
-            out_indices = [len(self.stage_names) - 1]
-
-        if out_features is not None:
-            if not isinstance(out_features, list):
-                raise ValueError("out_features should be a list")
-            for feature in out_features:
-                if feature not in self.stage_names:
-                    raise ValueError(
-                        f"Feature {feature} is not a valid feature name. Valid names are {self.stage_names}"
-                    )
-        if out_indices is not None:
-            if not isinstance(out_indices, (list, tuple)):
-                raise ValueError("out_indices should be a list or tuple")
-            for idx in out_indices:
-                if idx >= len(self.stage_names):
-                    raise ValueError(f"Index {idx} is not a valid index for a list of length {len(self.stage_names)}")
-
-        self.out_features = out_features
-        self.out_indices = out_indices
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
+        )