ELECTRA backbone implementation in keras

keras_nlp/models/__init__.py

@@ -63,6 +63,7 @@
 from keras_nlp.models.distil_bert.distil_bert_tokenizer import (
     DistilBertTokenizer,
 )
+from keras_nlp.models.electra.electra_backbone import ElectraBackbone
 from keras_nlp.models.f_net.f_net_backbone import FNetBackbone
 from keras_nlp.models.f_net.f_net_classifier import FNetClassifier
 from keras_nlp.models.f_net.f_net_masked_lm import FNetMaskedLM

keras_nlp/models/electra/electra_backbone.py

@@ -0,0 +1,216 @@
+# 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.backend import keras
+from keras_nlp.layers.modeling.position_embedding import PositionEmbedding
+from keras_nlp.layers.modeling.reversible_embedding import ReversibleEmbedding
+from keras_nlp.layers.modeling.transformer_encoder import TransformerEncoder
+from keras_nlp.models.backbone import Backbone
+from keras_nlp.utils.keras_utils import gelu_approximate
+
+
+def electra_kernel_initializer(stddev=0.02):
+    return keras.initializers.TruncatedNormal(stddev=stddev)
+
+
+@keras_nlp_export("keras_nlp.models.ElectraBackbone")
+class ElectraBackbone(Backbone):
+    """A Electra encoder network.
+
+    This network implements a bidirectional Transformer-based encoder as
+    described in ["Electra: Pre-training Text Encoders as Discriminators Rather
+    Than Generators"](https://arxiv.org/abs/2003.10555). It includes the
+    embedding lookups and transformer layers, but not the masked language model
+    or classification task networks.
+
+    The default constructor gives a fully customizable, randomly initialized
+    Electra encoder with any number of layers, heads, and embedding
+    dimensions.
+
+    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://huggingface.co/docs/transformers/model_doc/electra#overview).
+
+    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.
+        embedding_dim: int. The size of the token embeddings.
+        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.
+
+    Examples:
+        ```python
+        input_data = {
+        "token_ids": np.ones(shape=(1, 12), dtype="int32"),
+        "segment_ids": np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0]]),
+        "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
+        }
+        # Randomly initialized Electra encoder
+        backbone = keras_nlp.models.ElectraBackbone(
+            vocabulary_size=1000,
+            num_layers=2,
+            num_heads=2,
+            hidden_dim=32,
+            intermediate_dim=64,
+            dropout=0.1,
+            max_sequence_length=512,
+            )
+        # Returns sequence and pooled outputs.
+        sequence_output, pooled_output = backbone(input_data)
+        ```
+    """
+
+    def __init__(
+        self,
+        vocab_size,
+        num_layers,
+        num_heads,
+        hidden_dim,
+        embedding_dim,
+        intermediate_dim,
+        dropout=0.1,
+        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 = ReversibleEmbedding(
+            input_dim=vocab_size,
+            output_dim=embedding_dim,
+            embeddings_initializer=electra_kernel_initializer(),
+            name="token_embedding",
+        )
+        token_embedding = token_embedding_layer(token_id_input)
+        position_embedding = PositionEmbedding(
+            initializer=electra_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=electra_kernel_initializer(),
+            name="segment_embedding",
+        )(segment_id_input)
+
+        # Add all embeddings together.
+        x = keras.layers.Add()(
+            (token_embedding, position_embedding, segment_embedding),
+        )
+        # Layer normalization
+        x = keras.layers.LayerNormalization(
+            name="embeddings_layer_norm",
+            axis=-1,
+            epsilon=1e-12,
+            dtype="float32",
+        )(x)
+        # Dropout
+        x = keras.layers.Dropout(
+            dropout,
+            name="embeddings_dropout",
+        )(x)
+        if hidden_dim != embedding_dim:
+            x = keras.layers.Dense(
+                hidden_dim,
+                kernel_initializer=electra_kernel_initializer(),
+                name="embeddings_projection",
+            )(x)
+
+        # Apply successive transformer encoder blocks.
+        for i in range(num_layers):
+            x = TransformerEncoder(
+                num_heads=num_heads,
+                intermediate_dim=intermediate_dim,
+                activation=gelu_approximate,
+                dropout=dropout,
+                layer_norm_epsilon=1e-12,
+                kernel_initializer=electra_kernel_initializer(),
+                name=f"transformer_layer_{i}",
+            )(x, padding_mask=padding_mask)
+
+        sequence_output = x
+        x = keras.layers.Dense(
+            hidden_dim,
+            kernel_initializer=electra_kernel_initializer(),
+            activation="tanh",
+            name="pooled_dense",
+        )(x)
+        pooled_output = 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.vocab_size = vocab_size
+        self.num_layers = num_layers
+        self.num_heads = num_heads
+        self.hidden_dim = hidden_dim
+        self.embedding_dim = embedding_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
+        self.token_embedding = token_embedding_layer
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "vocab_size": self.vocab_size,
+                "num_layers": self.num_layers,
+                "num_heads": self.num_heads,
+                "hidden_dim": self.hidden_dim,
+                "embedding_dim": self.embedding_dim,
+                "intermediate_dim": self.intermediate_dim,
+                "dropout": self.dropout,
+                "max_sequence_length": self.max_sequence_length,
+                "num_segments": self.num_segments,
+            }
+        )
+        return config

keras_nlp/models/electra/electra_backbone_test.py

@@ -0,0 +1,56 @@
+# 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 pytest
+
+from keras_nlp.backend import ops
+from keras_nlp.models.electra.electra_backbone import ElectraBackbone
+from keras_nlp.tests.test_case import TestCase
+
+
+class ElectraBackboneTest(TestCase):
+    def setUp(self):
+        self.init_kwargs = {
+            "vocab_size": 10,
+            "num_layers": 2,
+            "num_heads": 2,
+            "hidden_dim": 2,
+            "embedding_dim": 2,
+            "intermediate_dim": 4,
+            "max_sequence_length": 5,
+        }
+        self.input_data = {
+            "token_ids": ops.ones((2, 5), dtype="int32"),
+            "segment_ids": ops.zeros((2, 5), dtype="int32"),
+            "padding_mask": ops.ones((2, 5), dtype="int32"),
+        }
+
+    def test_backbone_basics(self):
+        self.run_backbone_test(
+            cls=ElectraBackbone,
+            init_kwargs=self.init_kwargs,
+            input_data=self.input_data,
+            expected_output_shape={
+                "sequence_output": (2, 5, 2),
+                "pooled_output": (2, 2),
+            },
+        )
+
+    @pytest.mark.large
+    def test_saved_model(self):
+        self.run_model_saving_test(
+            cls=ElectraBackbone,
+            init_kwargs=self.init_kwargs,
+            input_data=self.input_data,
+        )