Load sub-configs from composite configs (#34410)

* save/load sub-configs

* nit forgot these

* fix copies

* move test to common

* use dict for sub-configs

* add load-save-laod test

* clean up modeling check

* oops this are correct keys

* fix some tests, missed some composite configs

* this model was missed

Author: zucchini-nlp

src/transformers/configuration_utils.py

@@ -190,6 +190,8 @@ class PretrainedConfig(PushToHubMixin):
     """
 
     model_type: str = ""
+    base_config_key: str = ""
+    sub_configs: Dict[str, "PretrainedConfig"] = {}
     is_composition: bool = False
     attribute_map: Dict[str, str] = {}
     _auto_class: Optional[str] = None
@@ -543,11 +545,22 @@ def from_pretrained(
         cls._set_token_in_kwargs(kwargs, token)
 
         config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+        if cls.base_config_key and cls.base_config_key in config_dict:
+            config_dict = config_dict[cls.base_config_key]
+
         if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
+            # sometimes the config has no `base_config_key` if the config is used in several composite models
+            # e.g. LlamaConfig. In that case we try to see if there is match in `model_type` before raising a warning
+            for k, v in config_dict.items():
+                if isinstance(v, dict) and v.get("model_type") == cls.model_type:
+                    config_dict = v
+
+            # raise warning only if we still can't see a match in `model_type`
+            if config_dict["model_type"] != cls.model_type:
+                logger.warning(
+                    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+                )
 
         return cls.from_dict(config_dict, **kwargs)
 

src/transformers/modeling_utils.py

@@ -1608,15 +1608,14 @@ def _autoset_attn_implementation(
         # Below we check if a config is composite and manually prepare a dict of attn impl if not already passed as a dict.
         # Later each sub-module will dispatch with its own attn impl, by calling `XXXModel._from_config(config.text_config)`
         # If any of sub-modules doesn't support requested attn, an error will be raised. See https://github.com/huggingface/transformers/pull/32238
-        for key in config:
-            if isinstance(getattr(config, key), PretrainedConfig):
-                sub_config = getattr(config, key)
-                curr_attn_implementation = (
-                    requested_attn_implementation
-                    if not isinstance(requested_attn_implementation, dict)
-                    else requested_attn_implementation.get(key, None)
-                )
-                sub_config._attn_implementation_internal = curr_attn_implementation
+        for key in config.sub_configs.keys():
+            sub_config = getattr(config, key)
+            curr_attn_implementation = (
+                requested_attn_implementation
+                if not isinstance(requested_attn_implementation, dict)
+                else requested_attn_implementation.get(key, None)
+            )
+            sub_config._attn_implementation_internal = curr_attn_implementation
 
         if use_flash_attention_2:
             logger.warning_once(

src/transformers/models/align/configuration_align.py

@@ -14,8 +14,7 @@
 # limitations under the License.
 """ALIGN model configuration"""
 
-import os
-from typing import TYPE_CHECKING, List, Union
+from typing import TYPE_CHECKING, List
 
 
 if TYPE_CHECKING:
@@ -95,6 +94,7 @@ class AlignTextConfig(PretrainedConfig):
     ```"""
 
     model_type = "align_text_model"
+    base_config_key = "text_config"
 
     def __init__(
         self,
@@ -133,24 +133,6 @@ def __init__(
         self.use_cache = use_cache
         self.pad_token_id = pad_token_id
 
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        # get the text config dict if we are loading from AlignConfig
-        if config_dict.get("model_type") == "align":
-            config_dict = config_dict["text_config"]
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
 
 class AlignVisionConfig(PretrainedConfig):
     r"""
@@ -223,6 +205,7 @@ class AlignVisionConfig(PretrainedConfig):
     ```"""
 
     model_type = "align_vision_model"
+    base_config_key = "vision_config"
 
     def __init__(
         self,
@@ -272,24 +255,6 @@ def __init__(
         self.drop_connect_rate = drop_connect_rate
         self.num_hidden_layers = sum(num_block_repeats) * 4
 
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        # get the vision config dict if we are loading from AlignConfig
-        if config_dict.get("model_type") == "align":
-            config_dict = config_dict["vision_config"]
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
 
 class AlignConfig(PretrainedConfig):
     r"""
@@ -340,6 +305,7 @@ class AlignConfig(PretrainedConfig):
     ```"""
 
     model_type = "align"
+    sub_configs = {"text_config": AlignTextConfig, "vision_config": AlignVisionConfig}
 
     def __init__(
         self,

src/transformers/models/altclip/configuration_altclip.py

@@ -14,9 +14,6 @@
 # limitations under the License.
 """AltCLIP model configuration"""
 
-import os
-from typing import Union
-
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
 
@@ -199,6 +196,7 @@ class AltCLIPVisionConfig(PretrainedConfig):
     ```"""
 
     model_type = "altclip_vision_model"
+    base_config_key = "vision_config"
 
     def __init__(
         self,
@@ -233,24 +231,6 @@ def __init__(
         self.layer_norm_eps = layer_norm_eps
         self.hidden_act = hidden_act
 
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        # get the vision config dict if we are loading from AltCLIPConfig
-        if config_dict.get("model_type") == "altclip":
-            config_dict = config_dict["vision_config"]
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
 
 class AltCLIPConfig(PretrainedConfig):
     r"""
@@ -298,6 +278,7 @@ class AltCLIPConfig(PretrainedConfig):
     ```"""
 
     model_type = "altclip"
+    sub_configs = {"text_config": AltCLIPTextConfig, "vision_config": AltCLIPVisionConfig}
 
     def __init__(
         self, text_config=None, vision_config=None, projection_dim=768, logit_scale_init_value=2.6592, **kwargs

src/transformers/models/bark/configuration_bark.py

@@ -14,12 +14,11 @@
 # limitations under the License.
 """BARK model configuration"""
 
-import os
-from typing import Dict, Optional, Union
+from typing import Dict
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import add_start_docstrings, logging
-from ..auto import CONFIG_MAPPING
+from ..auto import CONFIG_MAPPING, AutoConfig
 
 
 logger = logging.get_logger(__name__)
@@ -64,7 +63,6 @@
 
 
 class BarkSubModelConfig(PretrainedConfig):
-    model_type = "bark_module"
     keys_to_ignore_at_inference = ["past_key_values"]
 
     attribute_map = {
@@ -101,38 +99,6 @@ def __init__(
 
         super().__init__(**kwargs)
 
-    @classmethod
-    def from_pretrained(
-        cls,
-        pretrained_model_name_or_path: Union[str, os.PathLike],
-        cache_dir: Optional[Union[str, os.PathLike]] = None,
-        force_download: bool = False,
-        local_files_only: bool = False,
-        token: Optional[Union[str, bool]] = None,
-        revision: str = "main",
-        **kwargs,
-    ) -> "PretrainedConfig":
-        kwargs["cache_dir"] = cache_dir
-        kwargs["force_download"] = force_download
-        kwargs["local_files_only"] = local_files_only
-        kwargs["revision"] = revision
-
-        cls._set_token_in_kwargs(kwargs, token)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        # get the config dict if we are loading from Bark
-        if config_dict.get("model_type") == "bark":
-            config_dict = config_dict[f"{cls.model_type}_config"]
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
 
 @add_start_docstrings(
     BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkSemanticConfig", model="BarkSemanticModel"),
@@ -154,6 +120,7 @@ def from_pretrained(
 )
 class BarkSemanticConfig(BarkSubModelConfig):
     model_type = "semantic"
+    base_config_key = "semantic_config"
 
 
 @add_start_docstrings(
@@ -176,6 +143,7 @@ class BarkSemanticConfig(BarkSubModelConfig):
 )
 class BarkCoarseConfig(BarkSubModelConfig):
     model_type = "coarse_acoustics"
+    base_config_key = "coarse_acoustics_config"
 
 
 @add_start_docstrings(
@@ -203,6 +171,7 @@ class BarkCoarseConfig(BarkSubModelConfig):
 )
 class BarkFineConfig(BarkSubModelConfig):
     model_type = "fine_acoustics"
+    base_config_key = "fine_acoustics_config"
 
     def __init__(self, tie_word_embeddings=True, n_codes_total=8, n_codes_given=1, **kwargs):
         self.n_codes_total = n_codes_total
@@ -265,6 +234,12 @@ class BarkConfig(PretrainedConfig):
     """
 
     model_type = "bark"
+    sub_configs = {
+        "semantic_config": BarkSemanticConfig,
+        "coarse_acoustics_config": BarkCoarseConfig,
+        "fine_acoustics_config": BarkFineConfig,
+        "codec_config": AutoConfig,
+    }
 
     def __init__(
         self,

src/transformers/models/blip/configuration_blip.py

@@ -14,9 +14,6 @@
 # limitations under the License.
 """Blip model configuration"""
 
-import os
-from typing import Union
-
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
 
@@ -96,6 +93,7 @@ class BlipTextConfig(PretrainedConfig):
     ```"""
 
     model_type = "blip_text_model"
+    base_config_key = "text_config"
 
     def __init__(
         self,
@@ -146,24 +144,6 @@ def __init__(
         self.use_cache = use_cache
         self.label_smoothing = label_smoothing
 
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        # get the text config dict if we are loading from BlipConfig
-        if config_dict.get("model_type") == "blip":
-            config_dict = config_dict["text_config"]
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
 
 class BlipVisionConfig(PretrainedConfig):
     r"""
@@ -215,6 +195,7 @@ class BlipVisionConfig(PretrainedConfig):
     ```"""
 
     model_type = "blip_vision_model"
+    base_config_key = "vision_config"
 
     def __init__(
         self,
@@ -245,24 +226,6 @@ def __init__(
         self.layer_norm_eps = layer_norm_eps
         self.hidden_act = hidden_act
 
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        # get the vision config dict if we are loading from BlipConfig
-        if config_dict.get("model_type") == "blip":
-            config_dict = config_dict["vision_config"]
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
 
 class BlipConfig(PretrainedConfig):
     r"""
@@ -316,6 +279,7 @@ class BlipConfig(PretrainedConfig):
     ```"""
 
     model_type = "blip"
+    sub_configs = {"text_config": BlipTextConfig, "vision_config": BlipVisionConfig}
 
     def __init__(
         self,