Fix re-compilation bugs (keras-team#1541)

The primary bug this is trying to fix is to get rid of weird
re-compilation behaviors with our default compilation. E.g. create a
`GemmaCausalLM`, generate some text without specifying a sampler,
`compile()` again without a sampler, generation will have switched from
`"greedy"` -> `"top_k"`. Create a `BertClassifier`, `fit()`, `compile()`
again without specifying an optimizer, optimizer will have switch from
`"adam"` to `"rmsprop"`.

The way I am trying to fix this is by leaning a little more heavily on
the `"auto"` style option we introduced for `jit_compile`. KerasNLP
tasks will by default use `loss="auto"` and `optimizer="auto"`, which
resolve to a default for a given task.

Since we override compile with these in the signature, recompilation
will not silently change behavior.

keras_nlp/models/albert/albert_classifier.py

@@ -186,17 +186,6 @@ def __init__(
         self.activation = keras.activations.get(activation)
         self.dropout = dropout
 
-        # === Default compilation ===
-        logit_output = self.activation == keras.activations.linear
-        self.compile(
-            loss=keras.losses.SparseCategoricalCrossentropy(
-                from_logits=logit_output
-            ),
-            optimizer=keras.optimizers.Adam(5e-5),
-            metrics=[keras.metrics.SparseCategoricalAccuracy()],
-            jit_compile=True,
-        )
-
     def get_config(self):
         config = super().get_config()
         config.update(

keras_nlp/models/albert/albert_masked_lm.py

@@ -124,11 +124,3 @@ def __init__(self, backbone, preprocessor=None, **kwargs):
             outputs=outputs,
             **kwargs,
         )
-
-        # === Default compilation ===
-        self.compile(
-            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
-            optimizer=keras.optimizers.Adam(5e-5),
-            weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],
-            jit_compile=True,
-        )

keras_nlp/models/bart/bart_seq_2_seq_lm.py

@@ -14,7 +14,6 @@
 
 
 from keras_nlp.api_export import keras_nlp_export
-from keras_nlp.backend import keras
 from keras_nlp.backend import ops
 from keras_nlp.models.bart.bart_backbone import BartBackbone
 from keras_nlp.models.bart.bart_seq_2_seq_lm_preprocessor import (
@@ -200,14 +199,6 @@ def __init__(
             **kwargs,
         )
 
-        # === Default compilation ===
-        self.compile(
-            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
-            optimizer=keras.optimizers.Adam(2e-5),
-            metrics=[keras.metrics.SparseCategoricalAccuracy()],
-            jit_compile=True,
-        )
-
     def call_decoder_with_cache(
         self,
         encoder_hidden_states,
@@ -460,7 +451,7 @@ def repeat_tensor(x):
                 cache,
             )
 
-        decoder_token_ids = self._sampler(
+        decoder_token_ids = self.sampler(
             next=next,
             prompt=decoder_token_ids,
             cache=self_attention_cache,

keras_nlp/models/bert/bert_classifier.py

@@ -170,17 +170,6 @@ def __init__(
         self.activation = keras.activations.get(activation)
         self.dropout = dropout
 
-        # === Default compilation ===
-        logit_output = self.activation == keras.activations.linear
-        self.compile(
-            loss=keras.losses.SparseCategoricalCrossentropy(
-                from_logits=logit_output
-            ),
-            optimizer=keras.optimizers.Adam(5e-5),
-            metrics=[keras.metrics.SparseCategoricalAccuracy()],
-            jit_compile=True,
-        )
-
     def get_config(self):
         config = super().get_config()
         config.update(

keras_nlp/models/bert/bert_masked_lm.py

@@ -128,13 +128,3 @@ def __init__(
             outputs=outputs,
             **kwargs,
         )
-
-        # === Default compilation ===
-        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=True,
-        )

keras_nlp/models/bloom/bloom_causal_lm.py

@@ -14,7 +14,6 @@
 
 
 from keras_nlp.api_export import keras_nlp_export
-from keras_nlp.backend import keras
 from keras_nlp.backend import ops
 from keras_nlp.models.bloom.bloom_backbone import BloomBackbone
 from keras_nlp.models.bloom.bloom_causal_lm_preprocessor import (
@@ -167,15 +166,6 @@ def __init__(
             **kwargs,
         )
 
-        # === Default compilation ===
-        self.compile(
-            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
-            optimizer=keras.optimizers.Adam(2e-5),
-            metrics=[keras.metrics.SparseCategoricalAccuracy()],
-            sampler="greedy",
-            jit_compile=True,
-        )
-
     def call_with_cache(
         self,
         token_ids,
@@ -273,7 +263,7 @@ def next(prompt, cache, index):
                 cache,
             )
 
-        token_ids = self._sampler(
+        token_ids = self.sampler(
             next=next,
             prompt=token_ids,
             cache=cache,

keras_nlp/models/causal_lm.py

@@ -68,23 +68,74 @@ class CausalLM(Task):
     ```
     """
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # Default compilation.
+        self.compile()
+
     def compile(
         self,
-        *args,
-        run_eagerly=False,
-        jit_compile=True,
+        optimizer="auto",
+        loss="auto",
+        *,
+        weighted_metrics="auto",
         sampler="top_k",
         **kwargs,
     ):
-        xla_compatible = True
+        """Configures the `CausalLM` task for training and generation.
+
+        The `CausalLM` task extends the default compilation signature of
+        `keras.Model.compile` with defaults for `optimizer`, `loss`, and
+        `weighted_metrics`. To override these defaults, pass any value
+        to these arguments during compilation.
+
+        The `CausalLM` task adds a new `sampler` to `compile`, which can be used
+        to control the sampling strategy used with the `generate` function.
+
+        Note that because training inputs include padded tokens which are
+        excluded from the loss, it is almost always a good idea to compile with
+        `weighted_metrics` and not `metrics`.
+
+        Args:
+            optimizer: `"auto"`, an optimizer name, or a `keras.Optimizer`
+                instance. Defaults to `"auto"`, which uses the default optimizer
+                for the given model and task. See `keras.Model.compile` and
+                `keras.optimizers` for more info on possible `optimizer` values.
+            loss: `"auto"', a loss name, or a `keras.losses.Loss` instance.
+                Defaults to `"auto"`, where a
+                `keras.losses.SparseCategoricalCrossentropy` loss will be
+                applied for the token classification `CausalLM` task. See
+                `keras.Model.compile` and `keras.losses` for more info on
+                possible `loss` values.
+            weighted_metrics: `"auto"`, or a list of metrics to be evaluated by
+                the model during training and testing. Defaults to `"auto"`,
+                where a `keras.metrics.SparseCategoricalAccuracy` will be
+                applied to track the accuracy of the model at guessing masked
+                token values. See `keras.Model.compile` and `keras.metrics` for
+                more info on possible `weighted_metrics` values.
+            sampler: A sampler name, or a `keras_nlp.samplers.Sampler` instance.
+                Configures the sampling method used during `generate()` calls.
+                See `keras_nlp.samplers` for a full list of built-in sampling
+                strategies.
+            **kwargs: See `keras.Model.compile` for a full list of arguments
+                supported by the compile method.
+        """
+        if optimizer == "auto":
+            optimizer = keras.optimizers.Adam(2e-5)
+        if loss == "auto":
+            loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+        if weighted_metrics == "auto":
+            weighted_metrics = [keras.metrics.SparseCategoricalAccuracy()]
+        # Keras 2 does not jit_compile by default.
+        if not config.keras_3():
+            kwargs["jit_compile"] = True
         super().compile(
-            *args,
-            run_eagerly=run_eagerly,
-            # Only `jit_compile` if not eager and in a compatible environment.
-            jit_compile=jit_compile and xla_compatible and not run_eagerly,
+            optimizer=optimizer,
+            loss=loss,
+            weighted_metrics=weighted_metrics,
             **kwargs,
         )
-        self._sampler = get_sampler(sampler)
+        self.sampler = get_sampler(sampler)
         # Clear the compiled generate function.
         self.generate_function = None
 
@@ -127,7 +178,7 @@ def compiled_generate_function(inputs, stop_token_ids, state):
                     non_trainable_variables,
                 ) = state
                 mapping = itertools.chain(
-                    zip(self._sampler.variables, sampler_variables),
+                    zip(self.sampler.variables, sampler_variables),
                     zip(self.trainable_variables, trainable_variables),
                     zip(self.non_trainable_variables, non_trainable_variables),
                 )
@@ -137,7 +188,7 @@ def compiled_generate_function(inputs, stop_token_ids, state):
 
                 # Get updated sampler variables from the stateless scope.
                 sampler_variables = []
-                for v in self._sampler.variables:
+                for v in self.sampler.variables:
                     new_v = scope.get_current_value(v)
                     sampler_variables.append(new_v if new_v is not None else v)
                 return outputs, sampler_variables
@@ -151,7 +202,7 @@ def wrapped_generate_function(
 
                 # Create an explicit tuple of all variable state.
                 state = (
-                    self._sampler.variables,
+                    self.sampler.variables,
                     # Use the explicit variable.value to preserve the
                     # sharding spec of distribution.
                     [v.value for v in self.trainable_variables],
@@ -165,7 +216,7 @@ def wrapped_generate_function(
                 )
                 # Only assign the sampler variables (random seeds), as other
                 # model variables should never be updated in generation.
-                for ref_v, v in zip(self._sampler.variables, sampler_variables):
+                for ref_v, v in zip(self.sampler.variables, sampler_variables):
                     ref_v.assign(v)
                 return outputs
 

keras_nlp/models/classifier.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from keras_nlp.api_export import keras_nlp_export
+from keras_nlp.backend import config
+from keras_nlp.backend import keras
 from keras_nlp.models.task import Task
 
 
@@ -49,3 +51,62 @@ class Classifier(Task):
     classifier.predict(["What an amazing movie!", "A total waste of my time."])
     ```
     """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # Default compilation.
+        self.compile()
+
+    def compile(
+        self,
+        optimizer="auto",
+        loss="auto",
+        *,
+        metrics="auto",
+        **kwargs,
+    ):
+        """Configures the `Classifier` task for training.
+
+        The `Classifier` task extends the default compilation signature of
+        `keras.Model.compile` with defaults for `optimizer`, `loss`, and
+        `metrics`. To override these defaults, pass any value
+        to these arguments during compilation.
+
+        Args:
+            optimizer: `"auto"`, an optimizer name, or a `keras.Optimizer`
+                instance. Defaults to `"auto"`, which uses the default optimizer
+                for the given model and task. See `keras.Model.compile` and
+                `keras.optimizers` for more info on possible `optimizer` values.
+            loss: `"auto"', a loss name, or a `keras.losses.Loss` instance.
+                Defaults to `"auto"`, where a
+                `keras.losses.SparseCategoricalCrossentropy` loss will be
+                applied for the classification task. See
+                `keras.Model.compile` and `keras.losses` for more info on
+                possible `loss` values.
+            metrics: `"auto"`, or a list of metrics to be evaluated by
+                the model during training and testing. Defaults to `"auto"`,
+                where a `keras.metrics.SparseCategoricalAccuracy` will be
+                applied to track the accuracy of the model during training.
+                See `keras.Model.compile` and `keras.metrics` for
+                more info on possible `metrics` values.
+            **kwargs: See `keras.Model.compile` for a full list of arguments
+                supported by the compile method.
+        """
+        if optimizer == "auto":
+            optimizer = keras.optimizers.Adam(5e-5)
+        if loss == "auto":
+            activation = getattr(self, "activation", None)
+            activation = keras.activations.get(activation)
+            from_logits = activation != keras.activations.softmax
+            loss = keras.losses.SparseCategoricalCrossentropy(from_logits)
+        if metrics == "auto":
+            metrics = [keras.metrics.SparseCategoricalAccuracy()]
+        # Keras 2 does not jit_compile by default.
+        if not config.keras_3():
+            kwargs["jit_compile"] = True
+        super().compile(
+            optimizer=optimizer,
+            loss=loss,
+            metrics=metrics,
+            **kwargs,
+        )

keras_nlp/models/deberta_v3/deberta_v3_classifier.py

@@ -212,17 +212,6 @@ def __init__(
         self.hidden_dim = hidden_dim
         self.dropout = dropout
 
-        # === Default compilation ===
-        logit_output = self.activation == keras.activations.linear
-        self.compile(
-            loss=keras.losses.SparseCategoricalCrossentropy(
-                from_logits=logit_output
-            ),
-            optimizer=keras.optimizers.Adam(5e-5),
-            metrics=[keras.metrics.SparseCategoricalAccuracy()],
-            jit_compile=True,
-        )
-
     def get_config(self):
         config = super().get_config()
         config.update(

keras_nlp/models/deberta_v3/deberta_v3_masked_lm.py

@@ -130,11 +130,3 @@ def __init__(
             outputs=outputs,
             **kwargs,
         )
-
-        # === Default compilation ===
-        self.compile(
-            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
-            optimizer=keras.optimizers.Adam(5e-5),
-            weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],
-            jit_compile=True,
-        )

keras_nlp/models/distil_bert/distil_bert_classifier.py

@@ -192,17 +192,6 @@ def __init__(
         self.hidden_dim = hidden_dim
         self.dropout = dropout
 
-        # === Default compilation ===
-        logit_output = self.activation == keras.activations.linear
-        self.compile(
-            loss=keras.losses.SparseCategoricalCrossentropy(
-                from_logits=logit_output
-            ),
-            optimizer=keras.optimizers.Adam(5e-5),
-            metrics=[keras.metrics.SparseCategoricalAccuracy()],
-            jit_compile=True,
-        )
-
     def get_config(self):
         config = super().get_config()
         config.update(

keras_nlp/models/distil_bert/distil_bert_masked_lm.py

@@ -132,11 +132,3 @@ def __init__(
             outputs=outputs,
             **kwargs,
         )
-
-        # === Default compilation ===
-        self.compile(
-            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
-            optimizer=keras.optimizers.Adam(5e-5),
-            weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],
-            jit_compile=True,
-        )

keras_nlp/models/f_net/f_net_classifier.py

@@ -141,17 +141,6 @@ def __init__(
         self.activation = keras.activations.get(activation)
         self.dropout = dropout
 
-        # === Default compilation ===
-        logit_output = self.activation == keras.activations.linear
-        self.compile(
-            loss=keras.losses.SparseCategoricalCrossentropy(
-                from_logits=logit_output
-            ),
-            optimizer=keras.optimizers.Adam(5e-5),
-            metrics=[keras.metrics.SparseCategoricalAccuracy()],
-            jit_compile=True,
-        )
-
     def get_config(self):
         config = super().get_config()
         config.update(