Add an XLMRobertaMaskedLM task model

keras_nlp/models/__init__.py

@@ -90,6 +90,12 @@
 from keras_nlp.models.xlm_roberta.xlm_roberta_classifier import (
     XLMRobertaClassifier,
 )
+from keras_nlp.models.xlm_roberta.xlm_roberta_masked_lm import (
+    XLMRobertaMaskedLM,
+)
+from keras_nlp.models.xlm_roberta.xlm_roberta_masked_lm_preprocessor import (
+    XLMRobertaMaskedLMPreprocessor,
+)
 from keras_nlp.models.xlm_roberta.xlm_roberta_preprocessor import (
     XLMRobertaPreprocessor,
 )

keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm.py

@@ -0,0 +1,159 @@
+# Copyright 2023 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""XLM-RoBERTa masked lm model."""
+
+import copy
+
+from tensorflow import keras
+
+from keras_nlp.api_export import keras_nlp_export
+from keras_nlp.layers.masked_lm_head import MaskedLMHead
+from keras_nlp.models.roberta.roberta_backbone import roberta_kernel_initializer
+from keras_nlp.models.task import Task
+from keras_nlp.models.xlm_roberta.xlm_roberta_backbone import XLMRobertaBackbone
+from keras_nlp.models.xlm_roberta.xlm_roberta_masked_lm_preprocessor import (
+    XLMRobertaMaskedLMPreprocessor,
+)
+from keras_nlp.models.xlm_roberta.xlm_roberta_presets import backbone_presets
+from keras_nlp.utils.keras_utils import is_xla_compatible
+from keras_nlp.utils.python_utils import classproperty
+
+
+@keras_nlp_export("keras_nlp.models.XLMRobertaMaskedLM")
+class XLMRobertaMaskedLM(Task):
+    """An end-to-end XLM-RoBERTa model for the masked language modeling task.
+
+    This model will train XLM-RoBERTa on a masked language modeling task.
+    The model will predict labels for a number of masked tokens in the
+    input data. For usage of this model with pre-trained weights, see the
+    `from_preset()` method.
+
+    This model can optionally be configured with a `preprocessor` layer, in
+    which case inputs can be raw string features during `fit()`, `predict()`,
+    and `evaluate()`. Inputs will be tokenized and dynamically masked during
+    training and evaluation. This is done by default when creating the model
+    with `from_preset()`.
+
+    Disclaimer: Pre-trained models are provided on an "as is" basis, without
+    warranties or conditions of any kind. The underlying model is provided by a
+    third party and subject to a separate license, available
+    [here](https://github.com/facebookresearch/fairseq).
+
+    Args:
+        backbone: A `keras_nlp.models.XLMRobertaBackbone` instance.
+        preprocessor: A `keras_nlp.models.XLMRobertaMaskedLMPreprocessor` or
+            `None`. If `None`, this model will not apply preprocessing, and
+            inputs should be preprocessed before calling the model.
+
+    Example usage:
+
+    Raw string inputs and pretrained backbone.
+    ```python
+    # Create a dataset with raw string features. Labels are inferred.
+    features = ["The quick brown fox jumped.", "I forgot my homework."]
+
+    # Pretrained language model
+    # on an MLM task.
+    masked_lm = keras_nlp.models.XLMRobertaMaskedLM.from_preset(
+        "xlm_roberta_base_multi",
+    )
+    masked_lm.fit(x=features, batch_size=2)
+    ```
+
+    # Re-compile (e.g., with a new learning rate).
+    masked_lm.compile(
+        loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+        optimizer=keras.optimizers.Adam(5e-5),
+        jit_compile=True,
+    )
+    # Access backbone programatically (e.g., to change `trainable`).
+    masked_lm.backbone.trainable = False
+    # Fit again.
+    masked_lm.fit(x=features, batch_size=2)
+    ```
+
+    Preprocessed integer data.
+    ```python
+    # Create a preprocessed dataset where 0 is the mask token.
+    features = {
+        "token_ids": tf.constant(
+            [[1, 2, 0, 4, 0, 6, 7, 8]] * 2, shape=(2, 8)
+        ),
+        "padding_mask": tf.constant(
+            [[1, 1, 1, 1, 1, 1, 1, 1]] * 2, shape=(2, 8)
+        ),
+        "mask_positions": tf.constant([[2, 4]] * 2, shape=(2, 2))
+    }
+    # Labels are the original masked values.
+    labels = [[3, 5]] * 2
+
+    masked_lm = keras_nlp.models.XLMRobertaMaskedLM.from_preset(
+        "xlm_roberta_base_multi",
+        preprocessor=None,
+    )
+
+    masked_lm.fit(x=features, y=labels, batch_size=2)
+    ```
+    """
+
+    def __init__(
+        self,
+        backbone,
+        preprocessor=None,
+        **kwargs,
+    ):
+        inputs = {
+            **backbone.input,
+            "mask_positions": keras.Input(
+                shape=(None,), dtype="int32", name="mask_positions"
+            ),
+        }
+        backbone_outputs = backbone(backbone.input)
+        outputs = MaskedLMHead(
+            vocabulary_size=backbone.vocabulary_size,
+            embedding_weights=backbone.token_embedding.embeddings,
+            intermediate_activation="gelu",
+            kernel_initializer=roberta_kernel_initializer(),
+            name="mlm_head",
+        )(backbone_outputs, inputs["mask_positions"])
+
+        # Instantiate using Functional API Model constructor.
+        super().__init__(
+            inputs=inputs,
+            outputs=outputs,
+            include_preprocessing=preprocessor is not None,
+            **kwargs,
+        )
+        # All references to `self` below this line
+        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 XLMRobertaBackbone
+
+    @classproperty
+    def preprocessor_cls(cls):
+        return XLMRobertaMaskedLMPreprocessor
+
+    @classproperty
+    def presets(cls):
+        return copy.deepcopy(backbone_presets)

keras_nlp/models/xlm_roberta/xlm_roberta_masked_lm_preprocessor.py

@@ -0,0 +1,184 @@
+# Copyright 2023 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""XLM-RoBERTa masked language model preprocessor layer."""
+
+from absl import logging
+
+from keras_nlp.api_export import keras_nlp_export
+from keras_nlp.layers.masked_lm_mask_generator import MaskedLMMaskGenerator
+from keras_nlp.models.xlm_roberta.xlm_roberta_preprocessor import (
+    XLMRobertaPreprocessor,
+)
+from keras_nlp.utils.keras_utils import pack_x_y_sample_weight
+
+
+@keras_nlp_export("keras_nlp.models.XLMRobertaMaskedLMPreprocessor")
+class XLMRobertaMaskedLMPreprocessor(XLMRobertaPreprocessor):
+    """XLM-RoBERTa preprocessing for the masked language modeling task.
+
+    This preprocessing layer will prepare inputs for a masked language modeling
+    task. It is primarily intended for use with the
+    `keras_nlp.models.XLMRobertaMaskedLM` task model. Preprocessing will occur in
+    multiple steps.
+
+    1. Tokenize any number of input segments using the `tokenizer`.
+    2. Pack the inputs together with the appropriate `"<s>"`, `"</s>"` and
+      `"<pad>"` tokens, i.e., adding a single `"<s>"` at the start of the
+      entire sequence, `"</s></s>"` between each segment,
+      and a `"</s>"` at the end of the entire sequence.
+    3. Randomly select non-special tokens to mask, controlled by
+      `mask_selection_rate`.
+    4. Construct a `(x, y, sample_weight)` tuple suitable for training with a
+      `keras_nlp.models.XLMRobertaMaskedLM` task model.
+
+    Args:
+        tokenizer: A `keras_nlp.models.XLMRobertaTokenizer` instance.
+        sequence_length: int. The length of the packed inputs.
+        truncate: string. The algorithm to truncate a list of batched segments
+            to fit within `sequence_length`. The value can be either
+            `round_robin` or `waterfall`:
+                - `"round_robin"`: Available space is assigned one token at a
+                    time in a round-robin fashion to the inputs that still need
+                    some, until the limit is reached.
+                - `"waterfall"`: The allocation of the budget is done using a
+                    "waterfall" algorithm that allocates quota in a
+                    left-to-right manner and fills up the buckets until we run
+                    out of budget. It supports an arbitrary number of segments.
+        mask_selection_rate: float. The probability an input token will be
+            dynamically masked.
+        mask_selection_length: int. The maximum number of masked tokens
+            in a given sample.
+        mask_token_rate: float. The probability the a selected token will be
+            replaced with the mask token.
+        random_token_rate: float. The probability the a selected token will be
+            replaced with a random token from the vocabulary. A selected token
+            will be left as is with probability
+            `1 - mask_token_rate - random_token_rate`.
+
+    Call arguments:
+        x: A tensor of single string sequences, or a tuple of multiple
+            tensor sequences to be packed together. Inputs may be batched or
+            unbatched. For single sequences, raw python inputs will be converted
+            to tensors. For multiple sequences, pass tensors directly.
+        y: Label data. Should always be `None` as the layer generates labels.
+        sample_weight: Label weights. Should always be `None` as the layer
+            generates label weights.
+
+    Examples:
+    ```python
+    # Load the preprocessor from a preset.
+    preprocessor = keras_nlp.models.XLMRobertaMaskedLMPreprocessor.from_preset(
+        "xlm_roberta_base_multi"
+    )
+
+    # Tokenize and mask a single sentence.
+    preprocessor("The quick brown fox jumped.")
+    # Tokenize and mask a batch of single sentences.
+    preprocessor(["The quick brown fox jumped.", "Call me Ishmael."])
+    # Tokenize and mask sentence pairs.
+    # In this case, always convert input to tensors before calling the layer.
+    first = tf.constant(["The quick brown fox jumped.", "Call me Ishmael."])
+    second = tf.constant(["The fox tripped.", "Oh look, a whale."])
+    preprocessor((first, second))
+    ```
+
+    Mapping with `tf.data.Dataset`.
+    ```python
+    preprocessor = keras_nlp.models.XLMRobertaMaskedLMPreprocessor.from_preset(
+        "xlm_roberta_base_multi"
+    )
+    first = tf.constant(["The quick brown fox jumped.", "Call me Ishmael."])
+    second = tf.constant(["The fox tripped.", "Oh look, a whale."])
+
+    # Map single sentences.
+    ds = tf.data.Dataset.from_tensor_slices(first)
+    ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)
+
+    # Map sentence pairs.
+    ds = tf.data.Dataset.from_tensor_slices((first, second))
+    # Watch out for tf.data's default unpacking of tuples here!
+    # Best to invoke the `preprocessor` directly in this case.
+    ds = ds.map(
+        lambda first, second: preprocessor(x=(first, second)),
+        num_parallel_calls=tf.data.AUTOTUNE,
+    )
+    ```
+    ```
+    """
+
+    def __init__(
+        self,
+        tokenizer,
+        sequence_length=512,
+        truncate="round_robin",
+        mask_selection_rate=0.15,
+        mask_selection_length=96,
+        mask_token_rate=0.8,
+        random_token_rate=0.1,
+        **kwargs,
+    ):
+        super().__init__(
+            tokenizer,
+            sequence_length=sequence_length,
+            truncate=truncate,
+            **kwargs,
+        )
+
+        self.masker = MaskedLMMaskGenerator(
+            mask_selection_rate=mask_selection_rate,
+            mask_selection_length=mask_selection_length,
+            mask_token_rate=mask_token_rate,
+            random_token_rate=random_token_rate,
+            vocabulary_size=tokenizer.vocabulary_size(),
+            mask_token_id=tokenizer.mask_token_id,
+            unselectable_token_ids=[
+                tokenizer.start_token_id,
+                tokenizer.end_token_id,
+                tokenizer.pad_token_id,
+            ],
+        )
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "mask_selection_rate": self.masker.mask_selection_rate,
+                "mask_selection_length": self.masker.mask_selection_length,
+                "mask_token_rate": self.masker.mask_token_rate,
+                "random_token_rate": self.masker.random_token_rate,
+            }
+        )
+        return config
+
+    def call(self, x, y=None, sample_weight=None):
+        if y is not None or sample_weight is not None:
+            logging.warning(
+                f"{self.__class__.__name__} generates `y` and `sample_weight` "
+                "based on your input data, but your data already contains `y` "
+                "or `sample_weight`. Your `y` and `sample_weight` will be "
+                "ignored."
+            )
+
+        x = super().call(x)
+        token_ids, padding_mask = x["token_ids"], x["padding_mask"]
+        masker_outputs = self.masker(token_ids)
+        x = {
+            "token_ids": masker_outputs["token_ids"],
+            "padding_mask": padding_mask,
+            "mask_positions": masker_outputs["mask_positions"],
+        }
+        y = masker_outputs["mask_ids"]
+        sample_weight = masker_outputs["mask_weights"]
+        return pack_x_y_sample_weight(x, y, sample_weight)