# Copyright 2020 The PEGASUS Authors..
#
# 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.

"""Embedding layers.

Notations:
  B: batch_size, I: max_input_len, D: hidden_size, V: vocab_size
"""
#
# pylint: disable=invalid-name

import tensorflow as tf


class Embedding(object):
    """Embedding layer supporting shared input/output weights."""

    def __init__(self, vocab_size, hidden_size, name, dtype):
        self._vocab_size = vocab_size
        self._hidden_size = hidden_size
        self._name = name
        self._dtype = dtype

    def __call__(self, tensor, is_input_layer):
        if is_input_layer:
            return self._ids_to_weights(tensor)
        else:
            return self._weights_to_logits(tensor)

    def _ids_to_weights(self, ids_BxI):
        """Maps IDs to embedding weights."""
        weights_BxIxD = tf.nn.embedding_lookup(self.weights_VxD, ids_BxI)
        weights_BxIxD *= self._hidden_size ** 0.5
        return weights_BxIxD

    def _weights_to_logits(self, states_BxIxD):
        B = tf.shape(states_BxIxD)[0]
        I = tf.shape(states_BxIxD)[1]
        D = tf.shape(states_BxIxD)[2]
        states_BIxD = tf.reshape(states_BxIxD, [-1, D])
        states_BIxV = tf.matmul(
            states_BIxD, self.weights_VxD, transpose_b = True
        )
        states_BxIxV = tf.reshape(states_BIxV, [B, I, self._vocab_size])
        return states_BxIxV

    @property
    def weights_VxD(self):
        """Gets embedding weights."""
        with tf.variable_scope('embeddings', reuse = tf.AUTO_REUSE):
            # Initialization is important here, and a normal distribution with stdev
            # equal to rsqrt hidden_size is significantly better than the default
            # initialization used for other layers (fan in / out avg).
            embeddings_VxD = tf.get_variable(
                self._name,
                [self._vocab_size, self._hidden_size],
                initializer = tf.random_normal_initializer(
                    stddev = self._hidden_size ** -0.5, dtype = self._dtype
                ),
                dtype = self._dtype,
            )
        return embeddings_VxD

# Copyright 2020 The PEGASUS Authors..
#
# 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.

"""Embedding layers.

Notations:
  B: batch_size, I: max_input_len, D: hidden_size, V: vocab_size
"""
#
# pylint: disable=invalid-name

import tensorflow as tf


class Embedding(object):
    """Embedding layer supporting shared input/output weights."""

    def __init__(self, vocab_size, hidden_size, name, dtype):
        self._vocab_size = vocab_size
        self._hidden_size = hidden_size
        self._name = name
        self._dtype = dtype

    def __call__(self, tensor, is_input_layer):
        if is_input_layer:
            return self._ids_to_weights(tensor)
        else:
            return self._weights_to_logits(tensor)

    def _ids_to_weights(self, ids_BxI):
        """Maps IDs to embedding weights."""
        weights_BxIxD = tf.nn.embedding_lookup(self.weights_VxD, ids_BxI)
        weights_BxIxD *= self._hidden_size ** 0.5
        return weights_BxIxD

    def _weights_to_logits(self, states_BxIxD):
        B = tf.shape(states_BxIxD)[0]
        I = tf.shape(states_BxIxD)[1]
        D = tf.shape(states_BxIxD)[2]
        states_BIxD = tf.reshape(states_BxIxD, [-1, D])
        states_BIxV = tf.matmul(
            states_BIxD, self.weights_VxD, transpose_b = True
        )
        states_BxIxV = tf.reshape(states_BIxV, [B, I, self._vocab_size])
        return states_BxIxV

    @property
    def weights_VxD(self):
        """Gets embedding weights."""
        with tf.variable_scope('embeddings', reuse = tf.AUTO_REUSE):
            # Initialization is important here, and a normal distribution with stdev
            # equal to rsqrt hidden_size is significantly better than the default
            # initialization used for other layers (fan in / out avg).
            embeddings_VxD = tf.get_variable(
                self._name,
                [self._vocab_size, self._hidden_size],
                initializer = tf.random_normal_initializer(
                    stddev = self._hidden_size ** -0.5, dtype = self._dtype
                ),
                dtype = self._dtype,
            )
        return embeddings_VxD

# Copyright 2020 The PEGASUS Authors.. # # 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. Embedding layers.  Notations:   B: batch_size, I: max_input_len, D: hidden_size, V: vocab_size # # pylint: disable=invalid-name Embedding layer supporting shared input/output weights. Maps IDs to embedding weights. Gets embedding weights. # Initialization is important here, and a normal distribution with stdev # equal to rsqrt hidden_size is significantly better than the default # initialization used for other layers (fan in / out avg).