# Copyright 2020 The TensorFlow Authors. All Rights Reserved. # # 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. # ============================================================================== """Companion classes for mid level API for TPU Embeddings in TF2.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import abc import math import typing from typing import Any, Dict, Callable, List, Optional, Text, Tuple, TypeVar, Union import six from tensorflow.core.protobuf.tpu import optimization_parameters_pb2 from tensorflow.python.distribute import sharded_variable from tensorflow.python.framework import ops from tensorflow.python.ops import init_ops_v2 from tensorflow.python.ops import variables as tf_variables from tensorflow.python.tpu.ops import tpu_ops from tensorflow.python.types import core from tensorflow.python.util.tf_export import tf_export TableVariable = TypeVar("TableVariable", sharded_variable.ShardedVariable, tf_variables.Variable) SlotVarCreationFnType = Callable[ [TableVariable, List[Text], List[init_ops_v2.Initializer]], Dict[Text, TableVariable]] ClipValueType = Union[Tuple[float, float], float] @six.add_metaclass(abc.ABCMeta) class _Optimizer(object): """Base class for all optimizers, with common parameters.""" def __init__( self, learning_rate: Union[float, Callable[[], float]], use_gradient_accumulation: bool, clip_weight_min: Optional[float], clip_weight_max: Optional[float], weight_decay_factor: Optional[float], multiply_weight_decay_factor_by_learning_rate: bool, clipvalue: Optional[ClipValueType] = None, slot_variable_creation_fn: Optional[SlotVarCreationFnType] = None): self.learning_rate = learning_rate self.use_gradient_accumulation = use_gradient_accumulation self.clip_weight_min = clip_weight_min self.clip_weight_max = clip_weight_max if not use_gradient_accumulation and clipvalue is not None: raise ValueError("Received non-None gradient clipping limit {} but " "use_gradient_accumulation is not set to True.".format( clipvalue)) if clipvalue is None: clipvalue = (None, None) elif not isinstance(clipvalue, tuple): clipvalue = (-1. * clipvalue, clipvalue) self.clip_gradient_min, self.clip_gradient_max = clipvalue self.weight_decay_factor = weight_decay_factor self.multiply_weight_decay_factor_by_learning_rate = ( multiply_weight_decay_factor_by_learning_rate) if (slot_variable_creation_fn is not None and not callable(slot_variable_creation_fn)): raise ValueError("slot_variable_creation_fn must be either None or a " "callable.") self.slot_variable_creation_fn = slot_variable_creation_fn @abc.abstractmethod def _slot_names(self) -> List[Text]: """Returns the name of all the slot variables. This does not include the 'parameters' variable and these names must match the names of the slots variables as used in the corresponding `tpu_ops.load_tpu_embedding_*` ops. """ raise NotImplementedError @abc.abstractmethod def _slot_initializers(self) -> List[init_ops_v2.Initializer]: """Returns initializers for slot variables. This returns a parallel list to self._slot_names(). """ raise NotImplementedError def _set_optimization_parameters( self, parameters: optimization_parameters_pb2.OptimizationParameters): """Sets the optimizer fields in the OptimizationParameters.""" if self.use_gradient_accumulation: parameters.gradient_accumulation_status = ( optimization_parameters_pb2.GradientAccumulationStatus.ENABLED) else: parameters.gradient_accumulation_status = ( optimization_parameters_pb2.GradientAccumulationStatus.DISABLED) if self.clip_weight_min is not None: parameters.clipping_limits.lower.value = self.clip_weight_min if self.clip_weight_max is not None: parameters.clipping_limits.upper.value = self.clip_weight_max if self.clip_gradient_min is not None: parameters.gradient_clipping_limits.lower.value = self.clip_gradient_min if self.clip_gradient_max is not None: parameters.gradient_clipping_limits.upper.value = self.clip_gradient_max if self.weight_decay_factor: parameters.weight_decay_factor = self.weight_decay_factor if self.multiply_weight_decay_factor_by_learning_rate: parameters.multiply_weight_decay_factor_by_learning_rate = True @abc.abstractmethod def _load(self) -> Callable[..., ops.Operation]: """Returns the load function for the optimizer.""" raise NotImplementedError @abc.abstractmethod def _retrieve(self) -> Callable[..., core.Tensor]: """Returns the retrieve function for the optimizer.""" raise NotImplementedError def _create_slots( self, table: "TableConfig", variable_creator: Callable[[Text, init_ops_v2.Initializer], tf_variables.Variable] ) -> Dict[Text, tf_variables.Variable]: """Creates slot variables for table. Args: table: The table variable to create slots for. variable_creator: A function which creates variables. Takes parameters 'name', 'initializer'. Returns: A dict of variables, keyed by self._slot_names(). """ if self.slot_variable_creation_fn is not None: return self.slot_variable_creation_fn(table, self._slot_names(), self._slot_initializers()) else: slots = {} for slot, initializer in zip(self._slot_names(), self._slot_initializers()): slots[slot] = variable_creator(slot, initializer) return slots @tf_export("tpu.experimental.embedding.SGD") class SGD(_Optimizer): """Optimization parameters for stochastic gradient descent for TPU embeddings. Pass this to `tf.tpu.experimental.embedding.TPUEmbedding` via the `optimizer` argument to set the global optimizer and its parameters: ``` embedding = tf.tpu.experimental.embedding.TPUEmbedding( ... optimizer=tf.tpu.experimental.embedding.SGD(0.1)) ``` This can also be used in a `tf.tpu.experimental.embedding.TableConfig` as the optimizer parameter to set a table specific optimizer. This will override the optimizer and parameters for global embedding optimizer defined above: ``` table_one = tf.tpu.experimental.embedding.TableConfig( vocabulary_size=..., dim=..., optimizer=tf.tpu.experimental.embedding.SGD(0.2)) table_two = tf.tpu.experimental.embedding.TableConfig( vocabulary_size=..., dim=...) feature_config = ( tf.tpu.experimental.embedding.FeatureConfig( table=table_one), tf.tpu.experimental.embedding.FeatureConfig( table=table_two)) embedding = tf.tpu.experimental.embedding.TPUEmbedding( feature_config=feature_config, batch_size=... optimizer=tf.tpu.experimental.embedding.SGD(0.1)) ``` In the above example, the first feature will be looked up in a table that has a learning rate of 0.2 while the second feature will be looked up in a table that has a learning rate of 0.1. See 'tensorflow/core/protobuf/tpu/optimization_parameters.proto' for a complete description of these parameters and their impacts on the optimizer algorithm. """ def __init__(self, learning_rate: Union[float, Callable[[], float]] = 0.01, clip_weight_min: Optional[float] = None, clip_weight_max: Optional[float] = None, weight_decay_factor: Optional[float] = None, multiply_weight_decay_factor_by_learning_rate: bool = None, clipvalue: Optional[ClipValueType] = None): """Optimization parameters for stochastic gradient descent. Args: learning_rate: The learning rate. It should be a floating point value or a callable taking no arguments for a dynamic learning rate. clip_weight_min: the minimum value to clip by; None means -infinity. clip_weight_max: the maximum value to clip by; None means +infinity. weight_decay_factor: amount of weight decay to apply; None means that the weights are not decayed. Weights are decayed by multiplying the weight by this factor each step. multiply_weight_decay_factor_by_learning_rate: if true, `weight_decay_factor` is multiplied by the current learning rate. clipvalue: Controls clipping of the gradient. Set to either a single positive scalar value to get clipping or a tiple of scalar values (min, max) to set a separate maximum or minimum. If one of the two entries is None, then there will be no clipping that direction. Note if this is set, you may see a decrease in performance as gradient accumulation will be enabled (it is normally off for SGD as it has no affect on accuracy). See 'tensorflow/core/protobuf/tpu/optimization_parameters.proto' for more information on gradient accumulation and its impact on tpu embeddings. """ use_gradient_accumulation = clipvalue is not None super(SGD, self).__init__( learning_rate, use_gradient_accumulation, clip_weight_min, clip_weight_max, weight_decay_factor, multiply_weight_decay_factor_by_learning_rate, clipvalue) def _slot_names(self) -> List[Text]: return [] def _slot_initializers(self) -> List[init_ops_v2.Initializer]: return [] def _set_optimization_parameters( self, parameters: optimization_parameters_pb2.OptimizationParameters): super(SGD, self)._set_optimization_parameters(parameters) parameters.stochastic_gradient_descent.SetInParent() def _load(self) -> Callable[..., ops.Operation]: return tpu_ops.load_tpu_embedding_stochastic_gradient_descent_parameters def _retrieve(self) -> Callable[..., core.Tensor]: return tpu_ops.retrieve_tpu_embedding_stochastic_gradient_descent_parameters @tf_export("tpu.experimental.embedding.Adagrad") class Adagrad(_Optimizer): """Optimization parameters for Adagrad with TPU embeddings. Pass this to `tf.tpu.experimental.embedding.TPUEmbedding` via the `optimizer` argument to set the global optimizer and its parameters: ```python embedding = tf.tpu.experimental.embedding.TPUEmbedding( ... optimizer=tf.tpu.experimental.embedding.Adagrad(0.1)) ``` This can also be used in a `tf.tpu.experimental.embedding.TableConfig` as the optimizer parameter to set a table specific optimizer. This will override the optimizer and parameters for global embedding optimizer defined above: ```python table_one = tf.tpu.experimental.embedding.TableConfig( vocabulary_size=..., dim=..., optimizer=tf.tpu.experimental.embedding.Adagrad(0.2)) table_two = tf.tpu.experimental.embedding.TableConfig( vocabulary_size=..., dim=...) feature_config = ( tf.tpu.experimental.embedding.FeatureConfig( table=table_one), tf.tpu.experimental.embedding.FeatureConfig( table=table_two)) embedding = tf.tpu.experimental.embedding.TPUEmbedding( feature_config=feature_config, batch_size=... optimizer=tf.tpu.experimental.embedding.Adagrad(0.1)) ``` In the above example, the first feature will be looked up in a table that has a learning rate of 0.2 while the second feature will be looked up in a table that has a learning rate of 0.1. See 'tensorflow/core/protobuf/tpu/optimization_parameters.proto' for a complete description of these parameters and their impacts on the optimizer algorithm. """ def __init__( self, learning_rate: float = 0.001, initial_accumulator_value: float = 0.1, use_gradient_accumulation: bool = True, clip_weight_min: Optional[float] = None, clip_weight_max: Optional[float] = None, weight_decay_factor: Optional[float] = None, multiply_weight_decay_factor_by_learning_rate: bool = None, slot_variable_creation_fn: Optional[SlotVarCreationFnType] = None, clipvalue: Optional[ClipValueType] = None): """Optimization parameters for Adagrad. Args: learning_rate: The learning rate. It should be a floating point value or a callable taking no arguments for a dynamic learning rate. initial_accumulator_value: initial accumulator for Adagrad. use_gradient_accumulation: setting this to `False` makes embedding gradients calculation less accurate but faster. clip_weight_min: the minimum value to clip by; None means -infinity. clip_weight_max: the maximum value to clip by; None means +infinity. weight_decay_factor: amount of weight decay to apply; None means that the weights are not decayed. multiply_weight_decay_factor_by_learning_rate: if true, `weight_decay_factor` is multiplied by the current learning rate. slot_variable_creation_fn: If you wish do directly control the creation of the slot variables, set this to a callable taking three parameters: a table variable, a list of slot names to create for it, and a list of initializers. This function should return a dict with the slot names as keys and the created variables as values with types matching the table variable. When set to None (the default), uses the built-in variable creation. clipvalue: Controls clipping of the gradient. Set to either a single positive scalar value to get clipping or a tuple of scalar values (min, max) to set a separate maximum or minimum. If one of the two entries is None, then there will be no clipping that direction. """ super(Adagrad, self).__init__( learning_rate, use_gradient_accumulation, clip_weight_min, clip_weight_max, weight_decay_factor, multiply_weight_decay_factor_by_learning_rate, clipvalue, slot_variable_creation_fn) if initial_accumulator_value <= 0: raise ValueError("Adagrad initial_accumulator_value must be positive") self.initial_accumulator_value = initial_accumulator_value def _slot_names(self) -> List[Text]: return ["accumulators"] def _slot_initializers(self) -> List[init_ops_v2.Initializer]: return [init_ops_v2.Constant(self.initial_accumulator_value)] def _set_optimization_parameters( self, parameters: optimization_parameters_pb2.OptimizationParameters): super(Adagrad, self)._set_optimization_parameters(parameters) parameters.adagrad.SetInParent() def _load(self) -> Callable[..., ops.Operation]: return tpu_ops.load_tpu_embedding_adagrad_parameters def _retrieve(self) -> Callable[..., core.Tensor]: return tpu_ops.retrieve_tpu_embedding_adagrad_parameters @tf_export("tpu.experimental.embedding.Adam") class Adam(_Optimizer): """Optimization parameters for Adam with TPU embeddings. Pass this to `tf.tpu.experimental.embedding.TPUEmbedding` via the `optimizer` argument to set the global optimizer and its parameters: NOTE: By default this optimizer is lazy, i.e. it will not apply the gradient update of zero to rows that were not looked up. You can change this behavior by setting `lazy_adam` to `False`. ```python embedding = tf.tpu.experimental.embedding.TPUEmbedding( ... optimizer=tf.tpu.experimental.embedding.Adam(0.1)) ``` This can also be used in a `tf.tpu.experimental.embedding.TableConfig` as the optimizer parameter to set a table specific optimizer. This will override the optimizer and parameters for global embedding optimizer defined above: ```python table_one = tf.tpu.experimental.embedding.TableConfig( vocabulary_size=..., dim=..., optimizer=tf.tpu.experimental.embedding.Adam(0.2)) table_two = tf.tpu.experimental.embedding.TableConfig( vocabulary_size=..., dim=...) feature_config = ( tf.tpu.experimental.embedding.FeatureConfig( table=table_one), tf.tpu.experimental.embedding.FeatureConfig( table=table_two)) embedding = tf.tpu.experimental.embedding.TPUEmbedding( feature_config=feature_config, batch_size=... optimizer=tf.tpu.experimental.embedding.Adam(0.1)) ``` In the above example, the first feature will be looked up in a table that has a learning rate of 0.2 while the second feature will be looked up in a table that has a learning rate of 0.1. See 'tensorflow/core/protobuf/tpu/optimization_parameters.proto' for a complete description of these parameters and their impacts on the optimizer algorithm. """ def __init__( self, learning_rate: Union[float, Callable[[], float]] = 0.001, beta_1: float = 0.9, beta_2: float = 0.999, epsilon: float = 1e-07, lazy_adam: bool = True, sum_inside_sqrt: bool = True, use_gradient_accumulation: bool = True, clip_weight_min: Optional[float] = None, clip_weight_max: Optional[float] = None, weight_decay_factor: Optional[float] = None, multiply_weight_decay_factor_by_learning_rate: bool = None, slot_variable_creation_fn: Optional[SlotVarCreationFnType] = None, clipvalue: Optional[ClipValueType] = None): """Optimization parameters for Adam. See 'tensorflow/core/protobuf/tpu/optimization_parameters.proto' for a complete description of these parameters and their impacts on the optimizer algorithm. Args: learning_rate: The learning rate. It should be a floating point value or a callable taking no arguments for a dynamic learning rate. beta_1: A float value. The exponential decay rate for the 1st moment estimates. beta_2: A float value. The exponential decay rate for the 2nd moment estimates. epsilon: A small constant for numerical stability. lazy_adam: Use lazy Adam instead of Adam. Lazy Adam trains faster. sum_inside_sqrt: When this is true, the Adam update formula is changed from `m / (sqrt(v) + epsilon)` to `m / sqrt(v + epsilon**2)`. This option improves the performance of TPU training and is not expected to harm model quality. use_gradient_accumulation: Setting this to `False` makes embedding gradients calculation less accurate but faster. clip_weight_min: the minimum value to clip by; None means -infinity. clip_weight_max: the maximum value to clip by; None means +infinity. weight_decay_factor: amount of weight decay to apply; None means that the weights are not decayed. multiply_weight_decay_factor_by_learning_rate: if true, `weight_decay_factor` is multiplied by the current learning rate. slot_variable_creation_fn: If you wish do directly control the creation of the slot variables, set this to a callable taking three parameters: a table variable, a list of slot names to create for it, and a list of initializers. This function should return a dict with the slot names as keys and the created variables as values with types matching the table variable. When set to None (the default), uses the built-in variable creation. clipvalue: Controls clipping of the gradient. Set to either a single positive scalar value to get clipping or a tiple of scalar values (min, max) to set a separate maximum or minimum. If one of the two entries is None, then there will be no clipping that direction. """ super(Adam, self).__init__( learning_rate, use_gradient_accumulation, clip_weight_min, clip_weight_max, weight_decay_factor, multiply_weight_decay_factor_by_learning_rate, clipvalue, slot_variable_creation_fn) if beta_1 < 0. or beta_1 >= 1.: raise ValueError("beta1 must be in the range [0, 1), but received {}." .format(beta_1)) if beta_2 < 0. or beta_2 >= 1.: raise ValueError("beta2 must be in the range [0, 1), but received {}." .format(beta_2)) if epsilon <= 0.: raise ValueError("epsilon must be positive; got {}.".format(epsilon)) if not use_gradient_accumulation and not lazy_adam: raise ValueError( "When disabling Lazy Adam, gradient accumulation must be used.") self.beta_1 = beta_1 self.beta_2 = beta_2 self.epsilon = epsilon self.lazy_adam = lazy_adam self.sum_inside_sqrt = sum_inside_sqrt def _slot_names(self) -> List[Text]: return ["momenta", "velocities"] def _slot_initializers(self) -> List[init_ops_v2.Initializer]: return [init_ops_v2.Constant(), init_ops_v2.Constant()] def _set_optimization_parameters( self, parameters: optimization_parameters_pb2.OptimizationParameters): super(Adam, self)._set_optimization_parameters(parameters) parameters.adam.beta1 = self.beta_1 parameters.adam.beta2 = self.beta_2 parameters.adam.epsilon = self.epsilon parameters.adam.use_non_lazy_adam = not self.lazy_adam parameters.adam.use_sum_inside_sqrt = self.sum_inside_sqrt def _load(self) -> Callable[..., ops.Operation]: return tpu_ops.load_tpu_embedding_adam_parameters def _retrieve(self) -> Callable[..., core.Tensor]: return tpu_ops.retrieve_tpu_embedding_adam_parameters @tf_export("tpu.experimental.embedding.TableConfig") class TableConfig(object): """Configuration data for one embedding table. This class holds the configuration data for a single embedding table. It is used as the `table` parameter of a `tf.tpu.experimental.embedding.FeatureConfig`. Multiple `tf.tpu.experimental.embedding.FeatureConfig` objects can use the same `tf.tpu.experimental.embedding.TableConfig` object. In this case a shared table will be created for those feature lookups. ```python table_config_one = tf.tpu.experimental.embedding.TableConfig( vocabulary_size=..., dim=...) table_config_two = tf.tpu.experimental.embedding.TableConfig( vocabulary_size=..., dim=...) feature_config = { 'feature_one': tf.tpu.experimental.embedding.FeatureConfig( table=table_config_one), 'feature_two': tf.tpu.experimental.embedding.FeatureConfig( table=table_config_one), 'feature_three': tf.tpu.experimental.embedding.FeatureConfig( table=table_config_two)} embedding = tf.tpu.experimental.embedding.TPUEmbedding( feature_config=feature_config, batch_size=... optimizer=tf.tpu.experimental.embedding.Adam(0.1)) ``` The above configuration has 2 tables, and three features. The first two features will be looked up in the first table and the third feature will be looked up in the second table. """ def __init__(self, vocabulary_size: int, dim: int, initializer: Optional[Callable[[Any], None]], optimizer: Optional[_Optimizer] = None, combiner: Text = "mean", name: Optional[Text] = None): """Embedding table configuration. Args: vocabulary_size: Size of the table's vocabulary (number of rows). dim: The embedding dimension (width) of the table. initializer: A callable initializer taking one parameter, the shape of the variable that will be initialized. Will be called once per task, to initialize that task's shard of the embedding table. If not specified, defaults to `truncated_normal_initializer` with mean `0.0` and standard deviation `1/sqrt(dim)`. optimizer: An optional instance of an optimizer parameters class, instance of one of `tf.tpu.experimental.embedding.SGD`, `tf.tpu.experimental.embedding.Adagrad` or `tf.tpu.experimental.embedding.Adam`. It set will override the global optimizer passed to `tf.tpu.experimental.embedding.TPUEmbedding`. combiner: A string specifying how to reduce if there are multiple entries in a single row. Currently 'mean', 'sqrtn', 'sum' are supported, with 'mean' the default. 'sqrtn' often achieves good accuracy, in particular with bag-of-words columns. For more information, see `tf.nn.embedding_lookup_sparse`. name: An optional string used to name the table. Useful for debugging. Returns: `TableConfig`. Raises: ValueError: if `vocabulary_size` is not a positive integer. ValueError: if `dim` is not a positive integer. ValueError: if `initializer` is specified and is not callable. ValueError: if `combiner` is not supported. """ if not isinstance(vocabulary_size, int) or vocabulary_size < 1: raise ValueError("Invalid vocabulary_size {}.".format(vocabulary_size)) if not isinstance(dim, int) or dim < 1: raise ValueError("Invalid dim {}.".format(dim)) if (initializer is not None) and (not callable(initializer)): raise ValueError("initializer must be callable if specified.") if initializer is None: initializer = init_ops_v2.TruncatedNormal(mean=0.0, stddev=1/math.sqrt(dim)) if combiner not in ("mean", "sum", "sqrtn"): raise ValueError("Invalid combiner {}".format(combiner)) self.vocabulary_size = vocabulary_size self.dim = dim self.initializer = initializer self.optimizer = optimizer self.combiner = combiner self.name = name def __repr__(self): # If using the default initializer, just print "None" for clarity. initializer = self.initializer if isinstance(initializer, init_ops_v2.TruncatedNormal): # PY2 type checking can't infer type of initializer even after if. initializer = typing.cast(init_ops_v2.TruncatedNormal, initializer) if (initializer.mean == 0.0 and math.isclose(initializer.stddev, 1/math.sqrt(self.dim))): # pytype: disable=module-attr (math.isclose not in PY2) initializer = None return ( "TableConfig(vocabulary_size={vocabulary_size!r}, dim={dim!r}, " "initializer={initializer!r}, optimizer={optimizer!r}, " "combiner={combiner!r}, name={name!r})".format( vocabulary_size=self.vocabulary_size, dim=self.dim, initializer=initializer, optimizer=self.optimizer, combiner=self.combiner, name=self.name,) ) @tf_export("tpu.experimental.embedding.FeatureConfig") class FeatureConfig(object): """Configuration data for one embedding feature. This class holds the configuration data for a single embedding feature. The main use is to assign features to `tf.tpu.experimental.embedding.TableConfig`s via the table parameter: ```python table_config_one = tf.tpu.experimental.embedding.TableConfig( vocabulary_size=..., dim=...) table_config_two = tf.tpu.experimental.embedding.TableConfig( vocabulary_size=..., dim=...) feature_config = { 'feature_one': tf.tpu.experimental.embedding.FeatureConfig( table=table_config_one), 'feature_two': tf.tpu.experimental.embedding.FeatureConfig( table=table_config_one), 'feature_three': tf.tpu.experimental.embedding.FeatureConfig( table=table_config_two)} embedding = tf.tpu.experimental.embedding.TPUEmbedding( feature_config=feature_config, batch_size=... optimizer=tf.tpu.experimental.embedding.Adam(0.1)) ``` The above configuration has 2 tables, and three features. The first two features will be looked up in the first table and the third feature will be looked up in the second table. When feeding features into `embedding.enqueue` they can be `tf.Tensor`s, `tf.SparseTensor`s or `tf.RaggedTensor`s. When the argument `max_sequence_length` is 0, the default, you should expect a output of `embedding.dequeue` for this feature of shape `(batch_size, dim)`. If `max_sequence_length` is greater than 0, the feature is embedded as a sequence and padded up to the given length. The shape of the output for this feature will be `(batch_size, max_sequence_length, dim)`. """ def __init__(self, table: TableConfig, max_sequence_length: int = 0, name: Optional[Text] = None): """Feature configuration. Args: table: An instance of `tf.tpu.experimental.embedding.TableConfig`, describing the table in which this feature should be looked up. max_sequence_length: If positive, the feature is a sequence feature with the corresponding maximum sequence length. If the sequence is longer than this, it will be truncated. If 0, the feature is not a sequence feature. name: An optional name for the feature, useful for debugging. Returns: `FeatureConfig`. Raises: ValueError: if `table` is not an instance of `tf.tpu.experimental.embedding.TableConfig`. ValueError: if `max_sequence_length` not an integer or is negative. """ if not isinstance(table, TableConfig): raise ValueError("table is type {}, expected " "`tf.tpu.experimental.embedding.TableConfig`".format( type(table))) if not isinstance(max_sequence_length, int) or max_sequence_length < 0: raise ValueError("Invalid max_sequence_length {}.".format( max_sequence_length)) self.table = table self.max_sequence_length = max_sequence_length self.name = name def __repr__(self): return ( "FeatureConfig(table={table!r}, " "max_sequence_length={max_sequence_length!r}, name={name!r})" .format( table=self.table, max_sequence_length=self.max_sequence_length, name=self.name) )