Add FeatureCross Layer

keras_rs/api/layers/__init__.py

@@ -4,6 +4,7 @@
 since your modifications would be overwritten.
 """
 
+from keras_rs.src.layers.modeling.feature_cross import FeatureCross
 from keras_rs.src.layers.retrieval.brute_force_retrieval import (
     BruteForceRetrieval,
 )

keras_rs/src/layers/modeling/feature_cross.py

@@ -0,0 +1,204 @@
+from typing import Any, Optional, Text, Union
+
+import keras
+from keras import ops
+
+from keras_rs.src import types
+from keras_rs.src.api_export import keras_rs_export
+from keras_rs.src.utils.keras_utils import clone_initializer
+
+
+@keras_rs_export("keras_rs.layers.FeatureCross")
+class FeatureCross(keras.layers.Layer):
+    """FeatureCross layer in Deep & Cross Network (DCN).
+
+    A layer that creates explicit and bounded-degree feature interactions
+    efficiently. The `call` method accepts two inputs: `x0` contains the
+    original features; the second input `xi` is the output of the previous
+    `FeatureCross` layer in the stack, i.e., the i-th `FeatureCross` layer.
+    For the first `FeatureCross` layer in the stack, `x0 = xi`.
+
+    The output is `x_{i+1} = x0 .* (W * x_i + bias + diag_scale * x_i) + x_i`,
+    where .* denotes element-wise multiplication. W could be a full-rank
+    matrix, or a low-rank matrix `U*V` to reduce the computational cost, and
+    `diag_scale` increases the diagonal of W to improve training stability (
+    especially for the low-rank case).
+
+    Args:
+        projection_dim: int. Dimension for down-projecting the input to reduce
+            computational cost. If `None` (default), the full matrix, `W`
+            (with shape `(input_dim, input_dim)`) is used. Otherwise, a low-rank
+            matrix `W = U*V` will be used, where `U` is of shape
+            `(input_dim, projection_dim)` and `V` is of shape
+            `(projection_dim, input_dim)`. `projection_dim` need to be smaller
+            than `input_dim//2` to improve the model efficiency. In practice,
+            we've observed that `projection_dim = input_dim//4` consistently
+            preserved the accuracy of a full-rank version.
+        diag_scale: non-negative float. Used to increase the diagonal of the
+            kernel W by `diag_scale`, i.e., `W + diag_scale * I`, where I is the
+            identity matrix. Defaults to `None`.
+        use_bias: bool. Whether to add a bias term for this layer. Defaults to
+            `True`.
+        pre_activation: string or `keras.activations`. Activation applied to
+            output matrix of the layer, before multiplication with the input.
+            Can be used to control the scale of the layer's outputs and
+            improve stability. Defaults to `None`.
+        kernel_initializer: string or `keras.initializers` initializer.
+            Initializer to use for the kernel matrix. Defaults to
+            `"glorot_uniform"`.
+        bias_initializer: string or `keras.initializers` initializer.
+            Initializer to use for the bias vector. Defaults to `"ones"`.
+        kernel_regularizer: string or `keras.regularizer` regularizer.
+            Regularizer to use for the kernel matrix.
+        bias_regularizer: string or `keras.regularizer` regularizer.
+            Regularizer to use for the bias vector.
+
+    Example:
+
+    ```python
+    # after embedding layer in a functional model
+    input = keras.Input(shape=(), name='indices', dtype="int64")
+    x0 = keras.layers.Embedding(input_dim=32, output_dim=6)(x0)
+    x1 = FeatureCross()(x0, x0)
+    x2 = FeatureCross()(x0, x1)
+    logits = keras.layers.Dense(units=10)(x2)
+    model = keras.Model(input, logits)
+    ```
+
+    References:
+    - [R. Wang et al.](https://arxiv.org/abs/2008.13535)
+    - [R. Wang et al.](https://arxiv.org/abs/1708.05123)
+    """
+
+    def __init__(
+        self,
+        projection_dim: Optional[int] = None,
+        diag_scale: Optional[float] = 0.0,
+        use_bias: bool = True,
+        pre_activation: Optional[Union[str, keras.layers.Activation]] = None,
+        kernel_initializer: Union[
+            Text, keras.initializers.Initializer
+        ] = "glorot_uniform",
+        bias_initializer: Union[Text, keras.initializers.Initializer] = "zeros",
+        kernel_regularizer: Union[
+            Text, None, keras.regularizers.Regularizer
+        ] = None,
+        bias_regularizer: Union[
+            Text, None, keras.regularizers.Regularizer
+        ] = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(**kwargs)
+
+        # Passed args.
+        self.projection_dim = projection_dim
+        self.diag_scale = diag_scale
+        self.use_bias = use_bias
+        self.pre_activation = keras.activations.get(pre_activation)
+        self.kernel_initializer = keras.initializers.get(kernel_initializer)
+        self.bias_initializer = keras.initializers.get(bias_initializer)
+        self.kernel_regularizer = keras.regularizers.get(kernel_regularizer)
+        self.bias_regularizer = keras.regularizers.get(bias_regularizer)
+
+        # Other args.
+        self.supports_masking = True
+
+        if self.diag_scale is not None and self.diag_scale < 0.0:
+            raise ValueError(
+                "`diag_scale` should be non-negative. Received: "
+                f"`diag_scale={self.diag_scale}`"
+            )
+
+    def build(self, input_shape: types.TensorShape) -> None:
+        last_dim = input_shape[-1]
+
+        if self.projection_dim is not None:
+            self.down_proj_dense = keras.layers.Dense(
+                units=self.projection_dim,
+                use_bias=False,
+                kernel_initializer=clone_initializer(self.kernel_initializer),
+                kernel_regularizer=self.kernel_regularizer,
+                dtype=self.dtype_policy,
+            )
+
+        self.dense = keras.layers.Dense(
+            units=last_dim,
+            activation=self.pre_activation,
+            use_bias=self.use_bias,
+            kernel_initializer=clone_initializer(self.kernel_initializer),
+            bias_initializer=clone_initializer(self.bias_initializer),
+            kernel_regularizer=self.kernel_regularizer,
+            bias_regularizer=self.bias_regularizer,
+            dtype=self.dtype_policy,
+        )
+
+        self.built = True
+
+    def call(
+        self, x0: types.Tensor, x: Optional[types.Tensor] = None
+    ) -> types.Tensor:
+        """Forward pass of the cross layer.
+
+        Args:
+            x0: a Tensor. The input to the cross layer. N-rank tensor
+                with shape `(batch_size, ..., input_dim)`.
+            x: a Tensor. Optional. If provided, the layer will compute
+                crosses between x0 and x. Otherwise, the layer will
+                compute crosses between x0 and itself. Should have the same
+                shape as `x0`.
+
+        Returns:
+            Tensor of crosses, with the same shape as `x0`.
+        """
+
+        if x is None:
+            x = x0
+
+        if x0.shape != x.shape:
+            raise ValueError(
+                "`x0` and `x` should have the same shape. Received: "
+                f"`x.shape` = {x.shape}, `x0.shape` = {x0.shape}"
+            )
+
+        # Project to a lower dimension.
+        if self.projection_dim is None:
+            output = x
+        else:
+            output = self.down_proj_dense(x)
+
+        output = self.dense(output)
+
+        output = ops.cast(output, self.compute_dtype)
+
+        if self.diag_scale:
+            output = ops.add(output, ops.multiply(self.diag_scale, x))
+
+        return ops.add(ops.multiply(x0, output), x)
+
+    def get_config(self) -> dict[str, Any]:
+        config: dict[str, Any] = super().get_config()
+
+        config.update(
+            {
+                "projection_dim": self.projection_dim,
+                "diag_scale": self.diag_scale,
+                "use_bias": self.use_bias,
+                "pre_activation": keras.activations.serialize(
+                    self.pre_activation
+                ),
+                "kernel_initializer": keras.initializers.serialize(
+                    self.kernel_initializer
+                ),
+                "bias_initializer": keras.initializers.serialize(
+                    self.bias_initializer
+                ),
+                "kernel_regularizer": keras.regularizers.serialize(
+                    self.kernel_regularizer
+                ),
+                "bias_regularizer": keras.regularizers.serialize(
+                    self.bias_regularizer
+                ),
+            }
+        )
+
+        return config

keras_rs/src/layers/modeling/feature_cross_test.py

@@ -0,0 +1,96 @@
+import keras
+from absl.testing import parameterized
+from keras import ops
+from keras.layers import deserialize
+from keras.layers import serialize
+
+from keras_rs.src import testing
+from keras_rs.src.layers.modeling.feature_cross import FeatureCross
+
+
+class FeatureCrossTest(testing.TestCase, parameterized.TestCase):
+    def setUp(self):
+        self.x0 = ops.array([[0.1, 0.2, 0.3]], dtype="float32")
+        self.x = ops.array([[0.4, 0.5, 0.6]], dtype="float32")
+        self.exp_output = ops.array([[0.55, 0.8, 1.05]])
+
+        self.one_inp_exp_output = ops.array([[0.16, 0.32, 0.48]])
+
+    def test_full_layer(self):
+        layer = FeatureCross(projection_dim=None, kernel_initializer="ones")
+        output = layer(self.x0, self.x)
+
+        # Test output.
+        self.assertAllClose(self.exp_output, output)
+
+        # Test which layers have been initialised and their shapes.
+        # Kernel, bias terms corresponding to dense layer.
+        self.assertLen(layer.weights, 2, msg="Unexpected number of `weights`")
+        self.assertEqual(layer.weights[0].shape, (3, 3))
+        self.assertEqual(layer.weights[1].shape, (3,))
+
+    def test_low_rank_layer(self):
+        layer = FeatureCross(projection_dim=1, kernel_initializer="ones")
+        output = layer(self.x0, self.x)
+
+        # Test output.
+        self.assertAllClose(self.exp_output, output)
+
+        # Test which layers have been initialised and their shapes.
+        # Kernel term corresponding to down projection layer, and kernel,
+        # bias terms corresponding to dense layer.
+        self.assertLen(layer.weights, 3, msg="Unexpected number of `weights`")
+        self.assertEqual(layer.weights[0].shape, (3, 1))
+        self.assertEqual(layer.weights[1].shape, (1, 3))
+        self.assertEqual(layer.weights[2].shape, (3,))
+
+    def test_one_input(self):
+        layer = FeatureCross(projection_dim=None, kernel_initializer="ones")
+        output = layer(self.x0)
+        self.assertAllClose(self.one_inp_exp_output, output)
+
+    def test_invalid_input_shapes(self):
+        x0 = ops.ones((12, 5))
+        x = ops.ones((12, 7))
+
+        layer = FeatureCross()
+
+        with self.assertRaises(ValueError):
+            layer(x0, x)
+
+    def test_invalid_diag_scale(self):
+        with self.assertRaises(ValueError):
+            FeatureCross(diag_scale=-1.0)
+
+    def test_serialization(self):
+        sampler = FeatureCross(projection_dim=None, pre_activation="swish")
+        restored = deserialize(serialize(sampler))
+        self.assertDictEqual(sampler.get_config(), restored.get_config())
+
+    def test_diag_scale(self):
+        layer = FeatureCross(
+            projection_dim=None, diag_scale=1.0, kernel_initializer="ones"
+        )
+        output = layer(self.x0, self.x)
+
+        self.assertAllClose(ops.array([[0.59, 0.9, 1.23]]), output)
+
+    def test_pre_activation(self):
+        layer = FeatureCross(projection_dim=None, pre_activation=ops.zeros_like)
+        output = layer(self.x0, self.x)
+
+        self.assertAllClose(self.x, output)
+
+    def test_model_saving(self):
+        def get_model():
+            x0 = keras.layers.Input(shape=(3,))
+            x1 = FeatureCross(projection_dim=None)(x0, x0)
+            x2 = FeatureCross(projection_dim=None)(x0, x1)
+            logits = keras.layers.Dense(units=1)(x2)
+            model = keras.Model(x0, logits)
+            return model
+
+        self.run_model_saving_test(
+            model=get_model(),
+            input_data=self.x0,
+        )

keras_rs/src/testing/test_case.py

@@ -1,4 +1,7 @@
+import os
+import tempfile
 import unittest
+from typing import Any
 
 import keras
 import numpy as np
@@ -54,3 +57,22 @@ def assertAllEqual(
         if not isinstance(desired, np.ndarray):
             desired = keras.ops.convert_to_numpy(desired)
         np.testing.assert_array_equal(actual, desired, err_msg=msg)
+
+    def run_model_saving_test(
+        self,
+        model: Any,
+        input_data: Any,
+        atol: float = 1e-6,
+        rtol: float = 1e-6,
+    ) -> None:
+        """Save and load a model from disk and assert output is unchanged."""
+        model_output = model(input_data)
+
+        with tempfile.TemporaryDirectory() as temp_dir:
+            path = os.path.join(temp_dir, "model.keras")
+            model.save(path, save_format="keras_v3")
+            restored_model = keras.models.load_model(path)
+
+        # # Check that output matches.
+        restored_output = restored_model(input_data)
+        self.assertAllClose(model_output, restored_output, atol=atol, rtol=rtol)

keras_rs/src/utils/keras_utils.py

@@ -0,0 +1,18 @@
+from typing import Text, Union
+
+import keras
+
+
+def clone_initializer(
+    initializer: Union[Text, keras.initializers.Initializer],
+) -> keras.initializers.Initializer:
+    """Clones an initializer to ensure a new seed.
+
+    As of tensorflow 2.10, we need to clone user passed initializers when
+    invoking them twice to avoid creating the same randomized initialization.
+    """
+    # If we get a string or dict, just return as we cannot and should not clone.
+    if not isinstance(initializer, keras.initializers.Initializer):
+        return initializer
+    config = initializer.get_config()
+    return initializer.__class__.from_config(config)