Class detection works for huggingface checkpoints

keras_nlp/src/models/albert/albert_preprocessor.py

@@ -18,6 +18,7 @@
 from keras_nlp.src.layers.preprocessing.multi_segment_packer import (
     MultiSegmentPacker,
 )
+from keras_nlp.src.models.albert.albert_backbone import AlbertBackbone
 from keras_nlp.src.models.albert.albert_tokenizer import AlbertTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
 from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
@@ -144,6 +145,7 @@ class AlbertPreprocessor(Preprocessor):
     ```
     """
 
+    backbone_cls = AlbertBackbone
     tokenizer_cls = AlbertTokenizer
 
     def __init__(

keras_nlp/src/models/albert/albert_tokenizer.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.albert.albert_backbone import AlbertBackbone
 from keras_nlp.src.tokenizers.sentence_piece_tokenizer import (
     SentencePieceTokenizer,
 )
@@ -84,6 +85,8 @@ class AlbertTokenizer(SentencePieceTokenizer):
     ```
     """
 
+    backbone_cls = AlbertBackbone
+
     def __init__(self, proto, **kwargs):
         self.cls_token = "[CLS]"
         self.sep_token = "[SEP]"

keras_nlp/src/models/backbone.py

@@ -20,17 +20,12 @@
 from keras_nlp.src.utils.keras_utils import assert_quantization_support
 from keras_nlp.src.utils.preset_utils import CONFIG_FILE
 from keras_nlp.src.utils.preset_utils import MODEL_WEIGHTS_FILE
-from keras_nlp.src.utils.preset_utils import check_config_class
-from keras_nlp.src.utils.preset_utils import check_format
-from keras_nlp.src.utils.preset_utils import get_file
-from keras_nlp.src.utils.preset_utils import jax_memory_cleanup
+from keras_nlp.src.utils.preset_utils import get_preset_loader
 from keras_nlp.src.utils.preset_utils import list_presets
 from keras_nlp.src.utils.preset_utils import list_subclasses
-from keras_nlp.src.utils.preset_utils import load_serialized_object
 from keras_nlp.src.utils.preset_utils import save_metadata
 from keras_nlp.src.utils.preset_utils import save_serialized_object
 from keras_nlp.src.utils.python_utils import classproperty
-from keras_nlp.src.utils.transformers.convert import load_transformers_backbone
 
 
 @keras_nlp_export("keras_nlp.models.Backbone")
@@ -200,25 +195,15 @@ class like `keras_nlp.models.Backbone.from_preset()`, or from
         )
         ```
         """
-        format = check_format(preset)
-
-        if format == "transformers":
-            return load_transformers_backbone(cls, preset, load_weights)
-
-        preset_cls = check_config_class(preset)
-        if not issubclass(preset_cls, cls):
+        loader = get_preset_loader(preset)
+        backbone_cls = loader.check_backbone_class()
+        if not issubclass(backbone_cls, cls):
             raise ValueError(
-                f"Preset has type `{preset_cls.__name__}` which is not a "
+                f"Saved preset has type `{backbone_cls.__name__}` which is not "
                 f"a subclass of calling class `{cls.__name__}`. Call "
-                f"`from_preset` directly on `{preset_cls.__name__}` instead."
+                f"`from_preset` directly on `{backbone_cls.__name__}` instead."
             )
-
-        backbone = load_serialized_object(preset, CONFIG_FILE, **kwargs)
-        if load_weights:
-            jax_memory_cleanup(backbone)
-            backbone.load_weights(get_file(preset, MODEL_WEIGHTS_FILE))
-
-        return backbone
+        return loader.load_backbone(backbone_cls, load_weights, **kwargs)
 
     def save_to_preset(self, preset_dir):
         """Save backbone to a preset directory.

keras_nlp/src/models/backbone_test.py

@@ -24,7 +24,7 @@
 from keras_nlp.src.utils.preset_utils import METADATA_FILE
 from keras_nlp.src.utils.preset_utils import MODEL_WEIGHTS_FILE
 from keras_nlp.src.utils.preset_utils import check_config_class
-from keras_nlp.src.utils.preset_utils import load_config
+from keras_nlp.src.utils.preset_utils import load_json
 
 
 class TestBackbone(TestCase):
@@ -68,7 +68,7 @@ def test_from_preset_errors(self):
             GPT2Backbone.from_preset("bert_tiny_en_uncased", load_weights=False)
         with self.assertRaises(ValueError):
             # No loading on a non-keras model.
-            Backbone.from_preset("hf://google-bert/bert-base-uncased")
+            Backbone.from_preset("hf://spacy/en_core_web_sm")
 
     @pytest.mark.large
     def test_save_to_preset(self):
@@ -84,12 +84,12 @@ def test_save_to_preset(self):
         self.assertTrue(os.path.exists(os.path.join(save_dir, METADATA_FILE)))
 
         # Check the backbone config (`config.json`).
-        backbone_config = load_config(save_dir, CONFIG_FILE)
+        backbone_config = load_json(save_dir, CONFIG_FILE)
         self.assertTrue("build_config" not in backbone_config)
         self.assertTrue("compile_config" not in backbone_config)
 
         # Try config class.
-        self.assertEqual(BertBackbone, check_config_class(save_dir))
+        self.assertEqual(BertBackbone, check_config_class(backbone_config))
 
         # Try loading the model from preset directory.
         restored_backbone = Backbone.from_preset(save_dir)

keras_nlp/src/models/bart/bart_preprocessor.py

@@ -17,6 +17,7 @@
 
 from keras_nlp.src.api_export import keras_nlp_export
 from keras_nlp.src.layers.preprocessing.start_end_packer import StartEndPacker
+from keras_nlp.src.models.bart.bart_backbone import BartBackbone
 from keras_nlp.src.models.bart.bart_tokenizer import BartTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
 from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
@@ -127,6 +128,7 @@ class BartPreprocessor(Preprocessor):
     ```
     """
 
+    backbone_cls = BartBackbone
     tokenizer_cls = BartTokenizer
 
     def __init__(

keras_nlp/src/models/bart/bart_tokenizer.py

@@ -14,6 +14,7 @@
 
 
 from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.bart.bart_backbone import BartBackbone
 from keras_nlp.src.tokenizers.byte_pair_tokenizer import BytePairTokenizer
 
 
@@ -73,6 +74,8 @@ class BartTokenizer(BytePairTokenizer):
     ```
     """
 
+    backbone_cls = BartBackbone
+
     def __init__(
         self,
         vocabulary=None,

keras_nlp/src/models/bert/bert_preprocessor.py

@@ -18,6 +18,7 @@
 from keras_nlp.src.layers.preprocessing.multi_segment_packer import (
     MultiSegmentPacker,
 )
+from keras_nlp.src.models.bert.bert_backbone import BertBackbone
 from keras_nlp.src.models.bert.bert_tokenizer import BertTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
 from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
@@ -122,6 +123,7 @@ class BertPreprocessor(Preprocessor):
     ```
     """
 
+    backbone_cls = BertBackbone
     tokenizer_cls = BertTokenizer
 
     def __init__(

keras_nlp/src/models/bert/bert_tokenizer.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.bert.bert_backbone import BertBackbone
 from keras_nlp.src.tokenizers.word_piece_tokenizer import WordPieceTokenizer
 
 
@@ -68,6 +69,8 @@ class BertTokenizer(WordPieceTokenizer):
     ```
     """
 
+    backbone_cls = BertBackbone
+
     def __init__(
         self,
         vocabulary=None,

keras_nlp/src/models/bloom/bloom_preprocessor.py

@@ -17,6 +17,7 @@
 
 from keras_nlp.src.api_export import keras_nlp_export
 from keras_nlp.src.layers.preprocessing.start_end_packer import StartEndPacker
+from keras_nlp.src.models.bloom.bloom_backbone import BloomBackbone
 from keras_nlp.src.models.bloom.bloom_tokenizer import BloomTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
 from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
@@ -103,6 +104,7 @@ class BloomPreprocessor(Preprocessor):
     ```
     """
 
+    backbone_cls = BloomBackbone
     tokenizer_cls = BloomTokenizer
 
     def __init__(

keras_nlp/src/models/bloom/bloom_tokenizer.py

@@ -14,6 +14,7 @@
 
 
 from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.bloom.bloom_backbone import BloomBackbone
 from keras_nlp.src.tokenizers.byte_pair_tokenizer import BytePairTokenizer
 
 
@@ -65,6 +66,8 @@ class BloomTokenizer(BytePairTokenizer):
     ```
     """
 
+    backbone_cls = BloomBackbone
+
     def __init__(
         self,
         vocabulary=None,

keras_nlp/src/models/deberta_v3/deberta_v3_preprocessor.py

@@ -19,6 +19,9 @@
 from keras_nlp.src.layers.preprocessing.multi_segment_packer import (
     MultiSegmentPacker,
 )
+from keras_nlp.src.models.deberta_v3.deberta_v3_backbone import (
+    DebertaV3Backbone,
+)
 from keras_nlp.src.models.deberta_v3.deberta_v3_tokenizer import (
     DebertaV3Tokenizer,
 )
@@ -145,6 +148,7 @@ class DebertaV3Preprocessor(Preprocessor):
     ```
     """
 
+    backbone_cls = DebertaV3Backbone
     tokenizer_cls = DebertaV3Tokenizer
 
     def __init__(

keras_nlp/src/models/deberta_v3/deberta_v3_tokenizer.py

@@ -14,6 +14,9 @@
 
 
 from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.deberta_v3.deberta_v3_backbone import (
+    DebertaV3Backbone,
+)
 from keras_nlp.src.tokenizers.sentence_piece_tokenizer import (
     SentencePieceTokenizer,
 )
@@ -94,6 +97,8 @@ class DebertaV3Tokenizer(SentencePieceTokenizer):
     ```
     """
 
+    backbone_cls = DebertaV3Backbone
+
     def __init__(self, proto, **kwargs):
         self.cls_token = "[CLS]"
         self.sep_token = "[SEP]"

keras_nlp/src/models/distil_bert/distil_bert_preprocessor.py

@@ -19,6 +19,9 @@
 from keras_nlp.src.layers.preprocessing.multi_segment_packer import (
     MultiSegmentPacker,
 )
+from keras_nlp.src.models.distil_bert.distil_bert_backbone import (
+    DistilBertBackbone,
+)
 from keras_nlp.src.models.distil_bert.distil_bert_tokenizer import (
     DistilBertTokenizer,
 )
@@ -114,6 +117,7 @@ class DistilBertPreprocessor(Preprocessor):
     ```
     """
 
+    backbone_cls = DistilBertBackbone
     tokenizer_cls = DistilBertTokenizer
 
     def __init__(

keras_nlp/src/models/distil_bert/distil_bert_tokenizer.py

@@ -14,6 +14,9 @@
 
 
 from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.distil_bert.distil_bert_backbone import (
+    DistilBertBackbone,
+)
 from keras_nlp.src.tokenizers.word_piece_tokenizer import WordPieceTokenizer
 
 
@@ -70,6 +73,8 @@ class DistilBertTokenizer(WordPieceTokenizer):
     ```
     """
 
+    backbone_cls = DistilBertBackbone
+
     def __init__(
         self,
         vocabulary,

keras_nlp/src/models/electra/electra_preprocessor.py

@@ -18,6 +18,7 @@
 from keras_nlp.src.layers.preprocessing.multi_segment_packer import (
     MultiSegmentPacker,
 )
+from keras_nlp.src.models.electra.electra_backbone import ElectraBackbone
 from keras_nlp.src.models.electra.electra_tokenizer import ElectraTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
 from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
@@ -111,6 +112,7 @@ class ElectraPreprocessor(Preprocessor):
     ```
     """
 
+    backbone_cls = ElectraBackbone
     tokenizer_cls = ElectraTokenizer
 
     def __init__(

keras_nlp/src/models/electra/electra_tokenizer.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.electra.electra_backbone import ElectraBackbone
 from keras_nlp.src.tokenizers.word_piece_tokenizer import WordPieceTokenizer
 
 
@@ -60,6 +61,8 @@ class ElectraTokenizer(WordPieceTokenizer):
     ```
     """
 
+    backbone_cls = ElectraBackbone
+
     def __init__(
         self,
         vocabulary,

keras_nlp/src/models/f_net/f_net_preprocessor.py

@@ -19,6 +19,7 @@
 from keras_nlp.src.layers.preprocessing.multi_segment_packer import (
     MultiSegmentPacker,
 )
+from keras_nlp.src.models.f_net.f_net_backbone import FNetBackbone
 from keras_nlp.src.models.f_net.f_net_tokenizer import FNetTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
 from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
@@ -116,6 +117,7 @@ class FNetPreprocessor(Preprocessor):
     ```
     """
 
+    backbone_cls = FNetBackbone
     tokenizer_cls = FNetTokenizer
 
     def __init__(

keras_nlp/src/models/f_net/f_net_tokenizer.py

@@ -14,6 +14,7 @@
 
 
 from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.f_net.f_net_backbone import FNetBackbone
 from keras_nlp.src.tokenizers.sentence_piece_tokenizer import (
     SentencePieceTokenizer,
 )
@@ -61,6 +62,8 @@ class FNetTokenizer(SentencePieceTokenizer):
     ```
     """
 
+    backbone_cls = FNetBackbone
+
     def __init__(self, proto, **kwargs):
         self.cls_token = "[CLS]"
         self.sep_token = "[SEP]"

keras_nlp/src/models/falcon/falcon_preprocessor.py

@@ -17,6 +17,7 @@
 
 from keras_nlp.src.api_export import keras_nlp_export
 from keras_nlp.src.layers.preprocessing.start_end_packer import StartEndPacker
+from keras_nlp.src.models.falcon.falcon_backbone import FalconBackbone
 from keras_nlp.src.models.falcon.falcon_tokenizer import FalconTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
 from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
@@ -105,6 +106,7 @@ class FalconPreprocessor(Preprocessor):
     ```
     """
 
+    backbone_cls = FalconBackbone
     tokenizer_cls = FalconTokenizer
 
     def __init__(

keras_nlp/src/models/falcon/falcon_tokenizer.py

@@ -14,6 +14,7 @@
 
 
 from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.falcon.falcon_backbone import FalconBackbone
 from keras_nlp.src.tokenizers.byte_pair_tokenizer import BytePairTokenizer
 
 
@@ -65,6 +66,8 @@ class FalconTokenizer(BytePairTokenizer):
     ```
     """
 
+    backbone_cls = FalconBackbone
+
     def __init__(
         self,
         vocabulary=None,