#
# Copyright (c) 2021, NVIDIA CORPORATION.
#
# 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
#
# http://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 typing import List, Optional, Union
import tensorflow as tf
from merlin.models.tf.core.base import Block
from merlin.models.tf.core.combinators import ResidualBlock, SequentialBlock, TabularAggregationType
from merlin.models.tf.core.tabular import Filter, tabular_aggregation_registry
from merlin.models.tf.utils.tf_utils import (
maybe_deserialize_keras_objects,
maybe_serialize_keras_objects,
)
from merlin.models.utils.misc_utils import filter_kwargs
from merlin.schema import Schema, Tags
InitializerType = Union[str, tf.keras.initializers.Initializer]
RegularizerType = Union[str, tf.keras.regularizers.Regularizer]
[docs]def MLPBlock(
dimensions: List[int],
activation: Union[str, List[str]] = "relu",
use_bias: bool = True,
kernel_initializer: InitializerType = "glorot_uniform",
bias_initializer: InitializerType = "zeros",
kernel_regularizer: Optional[RegularizerType] = None,
bias_regularizer: Optional[RegularizerType] = None,
activity_regularizer: Optional[RegularizerType] = None,
dropout: Optional[float] = None,
normalization: Optional[Union[str, tf.keras.layers.Layer]] = None,
filter: Optional[Union[Schema, Tags, List[str], "Filter"]] = None,
no_activation_last_layer: bool = False,
block_name: str = "MLPBlock",
**kwargs,
) -> SequentialBlock:
"""
A block that applies a multi-layer perceptron to the input.
Example usage::
mlp = ml.InputBlock(schema).connect(ml.MLPBlock([64, 32]))
Parameters
----------
dimensions: List[int]
The number of units in each layer of the MLP.
activation: str
The activation function to use.
use_bias: bool
Whether to use a bias in the MLP.
kernel_initializer: InitializerType
Initializer for the kernel weights matrix. Defaults to "glorot_uniform".
bias_initializer: InitializerType
Initializer for the bias vector. Default to "zeros".
kernel_regularizer: Optional[RegularizerType]
Regularizer function applied to the kernel weights matrix. Default to None.
bias_regularizer: Optional[RegularizerType]
Regularizer function applied to the bias vector. Default to None.
activity_regularizer: Optional[RegularizerType]
Regularizer function applied to the output of the layer (its "activation").
Default to None.
dropout: float
The dropout rate to use.
normalization: str or Layer
The normalization layer to use.
filter: Schema, Tag, List[str], or Filter
The filter to apply to the inputs of the MLP.
no_activation_last_layer: bool
Ensures that no activation function (i.e. 'linear') or droptout is used in the
output of the last MLP layer
block_name: str
The name of the block.
"""
if isinstance(activation, list) and len(activation) != len(dimensions):
raise ValueError(
f"Activation and Dimensions length mismatch. \
Activation length: {len(activation)}, Dimensions length: {len(dimensions)}"
)
block_layers = []
for idx, dim in enumerate(dimensions):
dropout_layer = None
activation_idx = activation if isinstance(activation, str) else activation[idx]
if no_activation_last_layer and idx == len(dimensions) - 1:
activation_idx = "linear"
else:
if dropout:
if activation_idx in ["selu", tf.keras.activations.selu]:
# Best practice for SeLU. It is also recommended
# kernel_initializer="lecun_normal"
dropout_layer = tf.keras.layers.AlphaDropout(dropout)
else:
dropout_layer = tf.keras.layers.Dropout(dropout)
block_layers.append(
_Dense(
dim,
activation=activation_idx,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
)
if dropout_layer:
block_layers.append(dropout_layer)
if normalization:
if normalization == "batch_norm":
block_layers.append(tf.keras.layers.BatchNormalization())
elif isinstance(normalization, tf.keras.layers.Layer):
block_layers.append(normalization)
else:
raise ValueError("Normalization needs to be an instance `Layer` or " "`batch_norm`")
return SequentialBlock(block_layers, filter=filter, block_name=block_name, **kwargs)
[docs]def DenseResidualBlock(
low_rank_dim: Optional[int] = None,
activation: Optional[Union[str, tf.keras.layers.Layer]] = "relu",
use_bias: bool = True,
dropout: Optional[float] = None,
normalization: Optional[Union[str, tf.keras.layers.Layer]] = "batch_norm",
depth: int = 1,
**dense_kwargs,
) -> Block:
"""A block that applies a dense residual block to the input.
The residual consists in the input summed element-wise with the
output of the dense block.
The dense block projects the inputs using dense layers to the same output dim.
If the input dim is very high dimensional, the low_rank_dim can
be used to create a low rank matrix and reduce the necessary number
of parameters for this projection.
Example usage::
block = ml.DenseResidualBlock(depth=3).connect(ml.MLPBlock([1]))
Parameters
----------
low_rank_dim: int, optional
The dimension of the low rank matrix. If set, it projects the input to the
low_rank_dim (`LR`) and then back to the input dim (`I`) as output.
That requires much less parameters (`I*LR + LR*I`) than projecting the
inputs dim directly to the same dim (`I*I`). By default None
activation: Union[str, tf.keras.layers.Layer], optional
The activation function to use. By default "relu"
use_bias: bool
Whether to use a bias in the MLP. By default True
dropout: float
The dropout rate to use. By default 0.0
normalization: Union[str, tf.keras.layers.Layer], optional
The normalization layer to use. By the default None.
depth: int
The number of residual blocks to stack. By default 1
"""
block_layers = []
block_layers.append(
DenseMaybeLowRank(low_rank_dim, activation=None, use_bias=use_bias, **dense_kwargs)
)
if dropout:
block_layers.append(tf.keras.layers.Dropout(dropout))
if normalization:
if normalization == "batch_norm":
block_layers.append(tf.keras.layers.BatchNormalization())
elif isinstance(normalization, tf.keras.layers.Layer):
block_layers.append(normalization)
else:
raise ValueError("Normalization needs to be an instance `Layer` or " "`batch_norm`")
output = ResidualBlock(
SequentialBlock(block_layers, block_name="DenseResidual"), activation=activation
)
if depth > 1:
return output.repeat(depth - 1)
elif depth < 1:
raise ValueError(
"The depth (number of stacked residual blocks) needs " "to be equal or greater than 1."
)
return output
@tf.keras.utils.register_keras_serializable(package="merlin.models")
class _Dense(tf.keras.layers.Layer):
def __init__(
self,
units,
activation=None,
use_bias=True,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
pre_aggregation="concat",
dense=None,
**kwargs,
):
super(_Dense, self).__init__(**kwargs)
self.dense = dense or tf.keras.layers.Dense(
units,
activation,
use_bias,
kernel_initializer,
bias_initializer,
kernel_regularizer,
bias_regularizer,
activity_regularizer,
kernel_constraint,
bias_constraint,
**kwargs,
)
self.pre_aggregation = pre_aggregation
self.units = units
def call(self, inputs, **kwargs):
if isinstance(inputs, dict):
inputs = tabular_aggregation_registry.parse(self.pre_aggregation)(inputs)
filtered_kwargs = filter_kwargs(kwargs, self.dense)
return self.dense(inputs, **filtered_kwargs)
def compute_output_shape(self, input_shape):
if isinstance(input_shape, dict):
agg = tabular_aggregation_registry.parse(self.pre_aggregation)
input_shape = agg.compute_output_shape(input_shape)
return self.dense.compute_output_shape(input_shape)
def get_config(self):
config = super(_Dense, self).get_config()
config["pre_aggregation"] = self.pre_aggregation
config["units"] = self.units
return maybe_serialize_keras_objects(self, config, ["dense"])
@classmethod
def from_config(cls, config):
config = maybe_deserialize_keras_objects(config, {"dense": tf.keras.layers.deserialize})
return cls(**config)
@tf.keras.utils.register_keras_serializable(package="merlin.models")
class DenseMaybeLowRank(tf.keras.layers.Layer):
def __init__(
self,
low_rank_dim: Optional[int] = None,
use_bias: bool = True,
activation: Optional[Union[str, tf.keras.layers.Layer]] = None,
kernel_initializer: InitializerType = "truncated_normal",
bias_initializer: InitializerType = "zeros",
kernel_regularizer: Optional[RegularizerType] = None,
bias_regularizer: Optional[RegularizerType] = None,
pre_aggregation: Optional[TabularAggregationType] = "concat",
dense: Optional[tf.keras.layers.Dense] = None,
dense_u: Optional[tf.keras.layers.Dense] = None,
**kwargs,
):
"""A block that projects the inputs the same input dim.
If the input dim is very high dimensional, the low_rank_dim can
be used to create a low rank matrix and reduce the necessary number
of parameters for this projection.
Parameters
----------
low_rank_dim : Optional[int], optional
The dimension of the low rank matrix. If set, it projects the input to the
low_rank_dim (`LR`) and then back to the input dim (`I`) as output.
That requires much less parameters (`I*LR + LR*I`) than projecting the
inputs dim directly to the same dim (`I*I`), by default None.
use_bias : bool, optional
Whether to use a bias in the MLP, by default True
activation : Optional[Union[str,tf.keras.layers.Layer]], optional
The activation function to use. By default None
kernel_initializer: InitializerType
Initializer for the kernel weights matrix. Defaults to "glorot_uniform".
bias_initializer: InitializerType
Initializer for the bias vector. Default to "zeros".
kernel_regularizer: Optional[RegularizerType]
Regularizer function applied to the kernel weights matrix. Default to None.
bias_regularizer: Optional[RegularizerType]
Regularizer function applied to the bias vector. Default to None.
pre_aggregation : Optional[TabularAggregationType], optional
Aggregation to be done before projection, by default "concat"
dense : Optional[tf.keras.layers.Dense], optional
An optional dense layer to be used for projection,
by default None. If not set it is created internally.
dense_u : Optional[tf.keras.layers.Dense], optional
An optional dense layer to be called first if low_rank_dim is set,
by default None. If not set it is created internally.
"""
super().__init__(**kwargs)
self.low_rank_dim = low_rank_dim
self.use_bias = use_bias
self.activation = activation
self.kernel_initializer = kernel_initializer
self.bias_initializer = bias_initializer
self.kernel_regularizer = kernel_regularizer
self.bias_regularizer = bias_regularizer
self.pre_aggregation = pre_aggregation
self.dense = dense
self.dense_u = dense_u
def build(self, input_shape):
last_dim = input_shape[-1]
if self.dense is None:
self.dense = _Dense(
last_dim,
activation=self.activation,
kernel_initializer=self.kernel_initializer,
bias_initializer=self.bias_initializer,
kernel_regularizer=self.kernel_regularizer,
bias_regularizer=self.bias_regularizer,
use_bias=self.use_bias,
)
if self.low_rank_dim is not None and self.dense_u is None:
self.dense_u = _Dense(
self.low_rank_dim,
activation=self.activation,
kernel_initializer=self.kernel_initializer,
kernel_regularizer=self.kernel_regularizer,
use_bias=False,
)
super(DenseMaybeLowRank, self).build(input_shape)
def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
if isinstance(inputs, dict):
inputs = tabular_aggregation_registry.parse(self.pre_aggregation)(inputs)
if self.low_rank_dim is None:
return self.dense(inputs) # type: ignore
return self.dense(self.dense_u(inputs)) # type: ignore
def compute_output_shape(self, input_shape):
if isinstance(input_shape, dict):
agg = tabular_aggregation_registry.parse(self.pre_aggregation)
input_shape = agg.compute_output_shape(input_shape)
return input_shape
def get_config(self):
config = dict(
low_rank_dim=self.low_rank_dim,
use_bias=self.use_bias,
activation=self.activation,
pre_aggregation=self.pre_aggregation,
)
config.update(super(DenseMaybeLowRank, self).get_config())
config = maybe_serialize_keras_objects(
self,
config,
[
"dense",
"dense_u",
],
)
return config
@classmethod
def from_config(cls, config):
config = maybe_deserialize_keras_objects(
config,
[
"dense",
"dense_u",
],
)
return cls(**config)