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Class to keep track of the specification for TPU embeddings.
tf.compat.v1.estimator.tpu.experimental.EmbeddingConfigSpec(
feature_columns=None,
optimization_parameters=None,
clipping_limit=None,
pipeline_execution_with_tensor_core=False,
experimental_gradient_multiplier_fn=None,
feature_to_config_dict=None,
table_to_config_dict=None,
partition_strategy='div',
profile_data_directory=None
)
Migrate to TF2
TPU Estimator manages its own TensorFlow graph and session, so it is not
compatible with TF2 behaviors. We recommend that you migrate to the newer
tf.distribute.TPUStrategy. See the
TPU guide for details.
Description
Pass this class to tf.estimator.tpu.TPUEstimator via the
embedding_config_spec parameter. At minimum you need to specify
feature_columns and optimization_parameters. The feature columns passed
should be created with some combination of
tf.tpu.experimental.embedding_column and
tf.tpu.experimental.shared_embedding_columns.
TPU embeddings do not support arbitrary Tensorflow optimizers and the
main optimizer you use for your model will be ignored for the embedding table
variables. Instead TPU embeddigns support a fixed set of predefined optimizers
that you can select from and set the parameters of. These include adagrad,
adam and stochastic gradient descent. Each supported optimizer has a
Parameters class in the tf.tpu.experimental namespace.
column_a = tf.feature_column.categorical_column_with_identity(...)
column_b = tf.feature_column.categorical_column_with_identity(...)
column_c = tf.feature_column.categorical_column_with_identity(...)
tpu_shared_columns = tf.tpu.experimental.shared_embedding_columns(
[column_a, column_b], 10)
tpu_non_shared_column = tf.tpu.experimental.embedding_column(
column_c, 10)
tpu_columns = [tpu_non_shared_column] + tpu_shared_columns
...
def model_fn(features):
dense_features = tf.keras.layers.DenseFeature(tpu_columns)
embedded_feature = dense_features(features)
...
estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn,
...
embedding_config_spec=tf.estimator.tpu.experimental.EmbeddingConfigSpec(
column=tpu_columns,
optimization_parameters=(
tf.estimator.tpu.experimental.AdagradParameters(0.1))))
Args | ||
|---|---|---|
feature_columns
|
All embedding FeatureColumns used by model.
|
|
optimization_parameters
|
An instance of AdagradParameters,
AdamParameters or StochasticGradientDescentParameters. This
optimizer will be applied to all embedding variables specified by
feature_columns.
|
|
clipping_limit
|
(Optional) Clipping limit (absolute value). | |
pipeline_execution_with_tensor_core
|
setting this to True makes training
faster, but trained model will be different if step N and step N+1
involve the same set of embedding IDs. Please see
tpu_embedding_configuration.proto for details.
|
|
experimental_gradient_multiplier_fn
|
(Optional) A Fn taking global step as input returning the current multiplier for all embedding gradients. | |
feature_to_config_dict
|
A dictionary mapping feature names to instances of
the class FeatureConfig. Either features_columns or the pair of
feature_to_config_dict and table_to_config_dict must be specified.
|
|
table_to_config_dict
|
A dictionary mapping feature names to instances of
the class TableConfig. Either features_columns or the pair of
feature_to_config_dict and table_to_config_dict must be specified.
|
|
partition_strategy
|
A string, determining how tensors are sharded to the
tpu hosts. See tf.nn.safe_embedding_lookup_sparse for more details.
Allowed value are "div" and "mod"'. If"mod"is used, evaluation
and exporting the model to CPU will not work as expected.
</td>
</tr><tr>
<td>profile_data_directory`
|
Directory where embedding lookup statistics are stored. These statistics summarize information about the inputs to the embedding lookup operation, in particular, the average number of embedding IDs per example and how well the embedding IDs are load balanced across the system. The lookup statistics are used during TPU initialization for embedding table partitioning. Collection of lookup statistics is done at runtime by profiling the embedding inputs, only a small fraction of input samples are profiled to minimize host CPU overhead. Once a suitable number of samples are profiled, the lookup statistics are saved to table-specific files in the profile data directory generally at the end of a TPU training loop. The filename corresponding to each table is obtained by hashing table specific parameters (e.g., table name and number of features) and global configuration parameters (e.g., sharding strategy and task count). The same profile data directory can be shared among several models to reuse embedding lookup statistics. |