Add a preprocessor for the Mistral backbone

keras_nlp/models/mistral/mistral_preprocessor.py

@@ -0,0 +1,175 @@
+# 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.
+
+from keras_nlp.api_export import keras_nlp_export
+from keras_nlp.layers.preprocessing.start_end_packer import StartEndPacker
+from keras_nlp.models.mistral.mistral_tokenizer import MistralTokenizer
+from keras_nlp.models.preprocessor import Preprocessor
+from keras_nlp.utils.keras_utils import (
+    convert_inputs_to_list_of_tensor_segments,
+)
+from keras_nlp.utils.keras_utils import pack_x_y_sample_weight
+from keras_nlp.utils.python_utils import classproperty
+
+
+@keras_nlp_export("keras_nlp.models.MistralPreprocessor")
+class MistralPreprocessor(Preprocessor):
+    """A Mistral preprocessing layer which tokenizes and packs inputs.
+
+    This preprocessing layer will do three things:
+
+     1. Tokenize any number of input segments using the `tokenizer`.
+     2. Pack the inputs together using a `keras_nlp.layers.StartEndPacker`.
+       with the appropriate tokens.
+     3. Construct a dictionary with keys `"token_ids"`, and `"padding_mask"`
+       that can be passed directly to `keras_nlp.models.MistralBackbone`.
+
+    This layer can be used directly with `tf.data.Dataset.map` to preprocess
+    string data in the `(x, y, sample_weight)` format used by
+    `keras.Model.fit`.
+
+    Args:
+        tokenizer: A `keras_nlp.models.MistralTokenizer` instance.
+        sequence_length: The length of the packed inputs.
+        add_start_token: If `True`, the preprocessor will prepend the tokenizer
+            start token to each input sequence. Default is `True`.
+        add_end_token: If `True`, the preprocessor will append the tokenizer
+            end token to each input sequence. Default is `False`.
+
+    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: Any label data. Will be passed through unaltered.
+        sample_weight: Any label weight data. Will be passed through unaltered.
+        sequence_length: Pass to override the configured `sequence_length` of
+            the layer.
+
+    Examples:
+
+    Directly calling the from_preset().
+    ```python
+    preprocessor = keras_nlp.models.MistralPreprocessor.from_preset(
+        "mistral_base_en"
+    )
+
+    # Tokenize and pack a single sentence.
+    preprocessor("The quick brown fox jumped.")
+
+    # Tokenize and a batch of single sentences.
+    preprocessor(["The quick brown fox jumped.", "Call me Ishmael."])
+
+    # Preprocess a batch of sentence pairs.
+    # When handling multiple sequences, always convert to tensors first!
+    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.MistralPreprocessor.from_preset(
+        "mistral_base_en"
+    )
+    first = tf.constant(["The quick brown fox jumped.", "Call me Ishmael."])
+    second = tf.constant(["The fox tripped.", "Oh look, a whale."])
+    label = tf.constant([1, 1])
+
+    # Map labeled single sentences.
+    ds = tf.data.Dataset.from_tensor_slices((first, label))
+    ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)
+
+    # Map unlabeled single sentences.
+    ds = tf.data.Dataset.from_tensor_slices(first)
+    ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)
+
+    # Map labeled sentence pairs.
+    ds = tf.data.Dataset.from_tensor_slices(((first, second), label))
+    ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)
+
+    # Map unlabeled 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=1024,
+        add_start_token=True,
+        add_end_token=False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.tokenizer = tokenizer
+        self.add_start_token = add_start_token
+        self.add_end_token = add_end_token
+        self.sequence_length = sequence_length
+        self.packer = StartEndPacker(
+            start_value=self.tokenizer.start_token_id,
+            end_value=self.tokenizer.end_token_id,
+            sequence_length=sequence_length,
+            return_padding_mask=True,
+        )
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "sequence_length": self.sequence_length,
+                "add_start_token": self.add_start_token,
+                "add_end_token": self.add_end_token,
+            }
+        )
+        return config
+
+    def call(
+        self,
+        x,
+        y=None,
+        sample_weight=None,
+        sequence_length=None,
+    ):
+        x = convert_inputs_to_list_of_tensor_segments(x)
+        if len(x) != 1:
+            raise ValueError(
+                "Mistral requires each input feature to contain only "
+                f"one segment, but received {len(x)}. If you are using Mistral"
+                " for a multi-segment classification task, please refer to "
+                "classification models like BERT or RoBERTa."
+            )
+        sequence_length = sequence_length or self.sequence_length
+        token_ids, padding_mask = self.packer(
+            self.tokenizer(x[0]),
+            sequence_length=sequence_length,
+            add_start_value=self.add_start_token,
+            add_end_value=self.add_end_token,
+        )
+        x = {
+            "token_ids": token_ids,
+            "padding_mask": padding_mask,
+        }
+        return pack_x_y_sample_weight(x, y, sample_weight)
+
+    @classproperty
+    def tokenizer_cls(cls):
+        return MistralTokenizer

keras_nlp/models/mistral/mistral_preprocessor_test.py

@@ -0,0 +1,59 @@
+# 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.
+
+import os
+
+from keras_nlp.models.mistral.mistral_preprocessor import MistralPreprocessor
+from keras_nlp.models.mistral.mistral_tokenizer import MistralTokenizer
+from keras_nlp.tests.test_case import TestCase
+
+
+class MistralPreprocessorTest(TestCase):
+    def setUp(self):
+        self.tokenizer = MistralTokenizer(
+            # Generated using create_mistral_test_proto.py
+            proto=os.path.join(
+                self.get_test_data_dir(), "mistral_test_vocab.spm"
+            )
+        )
+        self.init_kwargs = {
+            "tokenizer": self.tokenizer,
+            "sequence_length": 8,
+        }
+        self.input_data = (
+            ["the quick brown fox"],
+            [1],  # Pass through labels.
+            [1.0],  # Pass through sample_weights.
+        )
+
+    def test_preprocessor_basics(self):
+        self.run_preprocessing_layer_test(
+            cls=MistralPreprocessor,
+            init_kwargs=self.init_kwargs,
+            input_data=self.input_data,
+            expected_output=(
+                {
+                    "token_ids": [[1, 3, 8, 4, 6, 0, 0, 0]],
+                    "padding_mask": [[1, 1, 1, 1, 1, 0, 0, 0]],
+                },
+                [1],  # Pass through labels.
+                [1.0],  # Pass through sample_weights.
+            ),
+        )
+
+    def test_errors_for_2d_list_input(self):
+        preprocessor = MistralPreprocessor(**self.init_kwargs)
+        ambiguous_input = [["one", "two"], ["three", "four"]]
+        with self.assertRaises(ValueError):
+            preprocessor(ambiguous_input)