Add AlbertBackbone

keras_nlp/models/__init__.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from keras_nlp.models.albert.albert_backbone import AlbertBackbone
 from keras_nlp.models.bert.bert_backbone import BertBackbone
 from keras_nlp.models.bert.bert_classifier import BertClassifier
 from keras_nlp.models.bert.bert_preprocessor import BertPreprocessor

keras_nlp/models/albert/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2022 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.

keras_nlp/models/albert/albert_backbone.py

@@ -0,0 +1,248 @@
+# Copyright 2022 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.
+
+"""ALBERT backbone model."""
+
+import tensorflow as tf
+from tensorflow import keras
+
+from keras_nlp.layers.position_embedding import PositionEmbedding
+from keras_nlp.models.albert.albert_group_layer import AlbertGroupLayer
+from keras_nlp.utils.python_utils import classproperty
+
+
+def albert_kernel_initializer(stddev=0.02):
+    return keras.initializers.TruncatedNormal(stddev=stddev)
+
+
+@keras.utils.register_keras_serializable(package="keras_nlp")
+class AlbertBackbone(keras.Model):
+    """ALBERT encoder network.
+
+    This class implements a bi-directional Transformer-based encoder as
+    described in
+    ["ALBERT: A Lite BERT for Self-supervised Learning of Language Representations"](https://arxiv.org/abs/1909.11942).
+    It includes the embedding lookups and transformer layers, but not the masked
+    language model or next sentence prediction heads.
+
+    The default constructor gives a fully customizable, randomly initialized
+    ALBERT encoder with any number of layers, heads, and embedding dimensions.
+    To load preset architectures and weights, use the `from_preset` constructor.
+
+    Disclaimer: Pre-trained models are provided on an "as is" basis, without
+    warranties or conditions of any kind.
+
+    Args:
+        vocabulary_size: int. The size of the token vocabulary.
+        num_layers: int. The number of transformer layers.
+        num_heads: int. The number of attention heads for each transformer.
+            The hidden size must be divisible by the number of attention heads.
+        hidden_dim: int. The size of the transformer encoding and pooler layers.
+        intermediate_dim: int. The output dimension of the first Dense layer in
+            a two-layer feedforward network for each transformer.
+        dropout: float. Dropout probability for the Transformer encoder.
+        max_sequence_length: int. The maximum sequence length that this encoder
+            can consume. If None, `max_sequence_length` uses the value from
+            sequence length. This determines the variable shape for positional
+            embeddings.
+        num_segments: int. The number of types that the 'segment_ids' input can
+            take.
+
+    Examples:
+    ```python
+    input_data = {
+        "token_ids": tf.ones(shape=(1, 12), dtype=tf.int64),
+        "segment_ids": tf.constant(
+            [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0], shape=(1, 12)
+        ),
+        "padding_mask": tf.constant(
+            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0], shape=(1, 12)
+        ),
+    }
+
+    # Randomly initialized ALBERT encoder
+    model = keras_nlp.models.AlbertBackbone(
+        vocabulary_size=30552,
+        num_layers=12,
+        num_heads=12,
+        hidden_dim=768,
+        intermediate_dim=3072,
+        max_sequence_length=12,
+    )
+    output = model(input_data)
+    ```
+    """
+
+    def __init__(
+        self,
+        vocabulary_size,
+        num_layers,
+        num_heads,
+        num_hidden_groups,
+        num_layers_per_group,
+        embedding_dim,
+        hidden_dim,
+        intermediate_dim,
+        dropout=0.0,
+        max_sequence_length=512,
+        num_segments=2,
+        **kwargs,
+    ):
+
+        # Index of classification token in the vocabulary
+        cls_token_index = 0
+        # Inputs
+        token_id_input = keras.Input(
+            shape=(None,), dtype="int32", name="token_ids"
+        )
+        segment_id_input = keras.Input(
+            shape=(None,), dtype="int32", name="segment_ids"
+        )
+        padding_mask = keras.Input(
+            shape=(None,), dtype="int32", name="padding_mask"
+        )
+
+        # Embed tokens, positions, and segment ids.
+        token_embedding_layer = keras.layers.Embedding(
+            input_dim=vocabulary_size,
+            output_dim=embedding_dim,
+            embeddings_initializer=albert_kernel_initializer(),
+            name="token_embedding",
+        )
+        token_embedding = token_embedding_layer(token_id_input)
+        position_embedding = PositionEmbedding(
+            initializer=albert_kernel_initializer(),
+            sequence_length=max_sequence_length,
+            name="position_embedding",
+        )(token_embedding)
+        segment_embedding = keras.layers.Embedding(
+            input_dim=num_segments,
+            output_dim=embedding_dim,
+            embeddings_initializer=albert_kernel_initializer(),
+            name="segment_embedding",
+        )(segment_id_input)
+
+        # Sum, normalize and apply dropout to embeddings.
+        x = keras.layers.Add()(
+            (token_embedding, position_embedding, segment_embedding)
+        )
+        x = keras.layers.LayerNormalization(
+            name="embeddings_layer_norm",
+            axis=-1,
+            epsilon=1e-12,
+            dtype=tf.float32,
+        )(x)
+        x = keras.layers.Dropout(
+            dropout,
+            name="embeddings_dropout",
+        )(x)
+
+        # Project the embedding to `hidden_dim`.
+        x = keras.layers.Dense(
+            hidden_dim,
+            kernel_initializer=albert_kernel_initializer(),
+            name="embedding_projection",
+        )(x)
+
+        albert_group_layers = [
+            AlbertGroupLayer(
+                num_layers=num_layers_per_group,
+                num_heads=num_heads,
+                intermediate_dim=intermediate_dim,
+                activation=lambda x: keras.activations.gelu(
+                    x, approximate=True
+                ),
+                dropout=dropout,
+                kernel_initializer=albert_kernel_initializer(),
+                name=f"group_layer_{i}",
+            )
+            for i in range(num_hidden_groups)
+        ]
+
+        # Apply successive transformer encoder blocks.
+        for i in range(num_layers):
+            # Index of the hidden group
+            group_idx = int(i / (num_layers / num_hidden_groups))
+            x = albert_group_layers[group_idx](x, padding_mask)
+
+        # Construct the two ALBERT outputs. The pooled output is a dense layer on
+        # top of the [CLS] token.
+        sequence_output = x
+        pooled_output = keras.layers.Dense(
+            hidden_dim,
+            kernel_initializer=albert_kernel_initializer(),
+            activation="tanh",
+            name="pooled_dense",
+        )(x[:, cls_token_index, :])
+
+        # Instantiate using Functional API Model constructor
+        super().__init__(
+            inputs={
+                "token_ids": token_id_input,
+                "segment_ids": segment_id_input,
+                "padding_mask": padding_mask,
+            },
+            outputs={
+                "sequence_output": sequence_output,
+                "pooled_output": pooled_output,
+            },
+            **kwargs,
+        )
+        # All references to `self` below this line
+        self.vocabulary_size = vocabulary_size
+        self.num_layers = num_layers
+        self.num_heads = num_heads
+        self.num_hidden_groups = num_hidden_groups
+        self.num_layers_per_group = num_layers_per_group
+        self.embedding_dim = embedding_dim
+        self.hidden_dim = hidden_dim
+        self.intermediate_dim = intermediate_dim
+        self.dropout = dropout
+        self.max_sequence_length = max_sequence_length
+        self.num_segments = num_segments
+        self.cls_token_index = cls_token_index
+
+    def get_config(self):
+        return {
+            "vocabulary_size": self.vocabulary_size,
+            "num_layers": self.num_layers,
+            "num_heads": self.num_heads,
+            "num_hidden_groups": self.num_hidden_groups,
+            "num_layers_per_group": self.num_layers_per_group,
+            "embedding_dim": self.embedding_dim,
+            "hidden_dim": self.hidden_dim,
+            "intermediate_dim": self.intermediate_dim,
+            "dropout": self.dropout,
+            "max_sequence_length": self.max_sequence_length,
+            "num_segments": self.num_segments,
+            "name": self.name,
+            "trainable": self.trainable,
+        }
+
+    @classmethod
+    def from_config(cls, config):
+        return cls(**config)
+
+    @classproperty
+    def presets(cls):
+        return {}
+
+    @classmethod
+    def from_preset(
+        cls,
+        preset,
+        load_weights=True,
+        **kwargs,
+    ):
+        raise NotImplementedError

keras_nlp/models/albert/albert_group_layer.py

@@ -0,0 +1,73 @@
+# Copyright 2022 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 tensorflow import keras
+
+from keras_nlp.layers.transformer_encoder import TransformerEncoder
+
+
+class AlbertGroupLayer(keras.layers.Layer):
+    def __init__(
+        self,
+        num_layers,
+        num_heads,
+        intermediate_dim,
+        activation,
+        dropout,
+        kernel_initializer,
+        name,
+        **kwargs,
+    ):
+        super().__init__(name=name, **kwargs)
+
+        self.num_layers = num_layers
+        self.num_heads = num_heads
+        self.intermediate_dim = intermediate_dim
+        self.activation = keras.activations.get(activation)
+        self.dropout = dropout
+        self.kernel_initializer = keras.initializers.get(kernel_initializer)
+
+        # Define Transformer blocks.
+        self.transformer_layers = [
+            TransformerEncoder(
+                num_heads=num_heads,
+                intermediate_dim=intermediate_dim,
+                activation=activation,
+                dropout=dropout,
+                kernel_initializer=kernel_initializer,
+                name=f"transformer_layer_{i}",
+            )
+            for i in range(num_layers)
+        ]
+
+    def call(self, inputs, padding_mask):
+        x = inputs
+        for transformer_layer in self.transformer_layers:
+            x = transformer_layer(x, padding_mask=padding_mask)
+        return x
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "num_layers": self.num_layers,
+                "num_heads": self.num_heads,
+                "intermediate_dim": self.intermediate_dim,
+                "activation": keras.activations.serialize(self.activation),
+                "dropout": self.dropout,
+                "kernel_initializer": keras.initializers.serialize(
+                    self.kernel_initializer
+                ),
+            }
+        )
+        return config