Source code for hierarchical_parameter_server.core.lookup_layer

"""
 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.
"""

import tensorflow as tf
from hierarchical_parameter_server.core import lookup_ops


[docs]class LookupLayer(tf.keras.layers.Layer): """ Abbreviated as ``hps.LookupLayer(*args, **kwargs)``. This is a wrapper class for HPS lookup layer, which basically performs the same function as ``tf.nn.embedding_lookup``. Note that ``ps_config_file`` and ``global_batch_size`` should be specified in the constructor if you want to use implicit HPS initialization. Parameters ---------- model_name: str The name of the model that has embedding tables. table_id: int The index of the embedding table for the model specified by model_name. emb_vec_size: int The embedding vector size for the embedding table specified by model_name and table_id. emb_vec_dtype: The data type of embedding vectors which must be ``tf.float32``. ps_config_file: str The JSON configuration file for HPS initialization. global_batch_size: int The global batch size for HPS that is deployed on multiple GPUs. Examples -------- .. code-block:: python import hierarchical_parameter_server as hps lookup_layer = hps.LookupLayer(model_name = args.model_name, table_id = args.table_id, emb_vec_size = args.embed_vec_size, emb_vec_dtype = tf.float32, ps_config_file = args.ps_config_file, global_batch_size = args.global_batch_size) @tf.function def _infer_step(inputs): embedding_vector = lookup_layer(inputs) ... for i, (inputs, labels) in enumerate(dataset): _infer_step(inputs) """ def __init__( self, model_name, table_id, emb_vec_size, emb_vec_dtype, ps_config_file="", global_batch_size=1, **kwargs ): super(LookupLayer, self).__init__(**kwargs) self.model_name = model_name self.table_id = table_id self.emb_vec_size = emb_vec_size self.emb_vec_dtype = emb_vec_dtype self.ps_config_file = ps_config_file self.global_batch_size = global_batch_size
[docs] def call(self, inputs): """ The forward logic of this wrapper class. Parameters ---------- inputs: Keys are stored in Tensor. The data type must be ``tf.int64``. Returns ------- emb_vector: ``tf.Tensor`` of int32 the embedding vectors for the input keys. Its shape is *inputs.get_shape() + emb_vec_size*. """ emb_vector = lookup_ops.lookup( values=inputs, model_name=self.model_name, table_id=self.table_id, emb_vec_size=self.emb_vec_size, emb_vec_dtype=self.emb_vec_dtype, ps_config_file=self.ps_config_file, global_batch_size=self.global_batch_size, ) output_shape = inputs.get_shape() + self.emb_vec_size emb_vector.set_shape(output_shape) return emb_vector