Default compilation for Albert, Distilbert, Roberta MaskedLM (#833)

* adding compilation defaults

* Update albert_masked_lm.py

* Update roberta_masked_lm.py

* Update distil_bert_masked_lm.py

* Update distil_bert_masked_lm.py

* Update distil_bert_masked_lm.py

Author: kanpuriyanawab

keras_nlp/models/albert/albert_masked_lm.py

@@ -27,6 +27,7 @@
 )
 from keras_nlp.models.albert.albert_presets import backbone_presets
 from keras_nlp.models.task import Task
+from keras_nlp.utils.keras_utils import is_xla_compatible
 from keras_nlp.utils.python_utils import classproperty
 
 
@@ -142,6 +143,13 @@ def __init__(self, backbone, preprocessor=None, **kwargs):
         self.backbone = backbone
         self.preprocessor = preprocessor
 
+        self.compile(
+            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+            optimizer=keras.optimizers.Adam(5e-5),
+            weighted_metrics=keras.metrics.SparseCategoricalAccuracy(),
+            jit_compile=is_xla_compatible(self),
+        )
+
     @classproperty
     def backbone_cls(cls):
         return AlbertBackbone

keras_nlp/models/albert/albert_masked_lm_test.py

@@ -114,6 +114,9 @@ def test_albert_masked_lm_predict_no_preprocessing(self, jit_compile):
         self.masked_lm_no_preprocessing.compile(jit_compile=jit_compile)
         self.masked_lm_no_preprocessing.predict(self.preprocessed_batch)
 
+    def test_albert_masked_lm_fit_default_compile(self):
+        self.masked_lm.fit(self.raw_dataset)
+
     @parameterized.named_parameters(
         ("jit_compile_false", False), ("jit_compile_true", True)
     )

keras_nlp/models/distil_bert/distil_bert_masked_lm.py

@@ -28,6 +28,7 @@
 )
 from keras_nlp.models.distil_bert.distil_bert_presets import backbone_presets
 from keras_nlp.models.task import Task
+from keras_nlp.utils.keras_utils import is_xla_compatible
 from keras_nlp.utils.python_utils import classproperty
 
 
@@ -143,6 +144,13 @@ def __init__(
         self.backbone = backbone
         self.preprocessor = preprocessor
 
+        self.compile(
+            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+            optimizer=keras.optimizers.Adam(5e-5),
+            weighted_metrics=keras.metrics.SparseCategoricalAccuracy(),
+            jit_compile=is_xla_compatible(self),
+        )
+
     @classproperty
     def backbone_cls(cls):
         return DistilBertBackbone

keras_nlp/models/distil_bert/distil_bert_masked_lm_test.py

@@ -89,6 +89,9 @@ def test_distilbert_masked_lm_predict_no_preprocessing(self, jit_compile):
         self.masked_lm_no_preprocessing.compile(jit_compile=jit_compile)
         self.masked_lm_no_preprocessing.predict(self.preprocessed_batch)
 
+    def test_distil_bert_masked_lm_fit_default_compile(self):
+        self.masked_lm.fit(self.raw_dataset)
+
     @parameterized.named_parameters(
         ("jit_compile_false", False), ("jit_compile_true", True)
     )

keras_nlp/models/roberta/roberta_masked_lm.py

@@ -26,6 +26,7 @@
 )
 from keras_nlp.models.roberta.roberta_presets import backbone_presets
 from keras_nlp.models.task import Task
+from keras_nlp.utils.keras_utils import is_xla_compatible
 from keras_nlp.utils.python_utils import classproperty
 
 
@@ -141,6 +142,13 @@ def __init__(
         self.backbone = backbone
         self.preprocessor = preprocessor
 
+        self.compile(
+            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+            optimizer=keras.optimizers.Adam(5e-5),
+            weighted_metrics=keras.metrics.SparseCategoricalAccuracy(),
+            jit_compile=is_xla_compatible(self),
+        )
+
     @classproperty
     def backbone_cls(cls):
         return RobertaBackbone

keras_nlp/models/roberta/roberta_masked_lm_test.py

@@ -103,6 +103,9 @@ def test_roberta_masked_lm_predict_no_preprocessing(self, jit_compile):
         self.masked_lm_no_preprocessing.compile(jit_compile=jit_compile)
         self.masked_lm_no_preprocessing.predict(self.preprocessed_batch)
 
+    def test_roberta_masked_lm_fit_default_compile(self):
+        self.masked_lm.fit(self.raw_dataset)
+
     @parameterized.named_parameters(
         ("jit_compile_false", False), ("jit_compile_true", True)
     )