Function embed

LanguageNetwork/GPT2/scripts/modeling.py:258–324

reur and position embeddings :param input_ids: int Tensor of shape [batch_size, seq_length]. :param vocab_size: number of words in vocab :param embedding_size: dimensionality of the embedding :param position_offset: aka number of cached tokens. :param initializer_range: float. Ra

(input_ids,
          vocab_size,
          embedding_size,
          position_offset=0,
          initializer_range=0.02,
          max_position_embeddings=512,
          use_one_hot_embeddings=True)

def embed(input_ids,
          vocab_size,
          embedding_size,
          position_offset=0,
          initializer_range=0.02,
          max_position_embeddings=512,
          use_one_hot_embeddings=True):
    """reur and position embeddings
    :param input_ids: int Tensor of shape [batch_size, seq_length].
    :param vocab_size: number of words in vocab
    :param embedding_size: dimensionality of the embedding
    :param position_offset: aka number of cached tokens.
    :param initializer_range: float. Range of the weight initialization.
    :param max_position_embeddings: int. Maximum sequence length.
    :param use_one_hot_embeddings: probably want this to be true
    :return: [batch_size, seq_length, embedding_size] embedded tensor
    """
    (batch_size, seq_length) = get_shape_list(input_ids, expected_rank=2)

    embedding_table = tf.compat.v1.get_variable(
        name='word_embed',
        shape=[vocab_size, embedding_size],
        initializer=create_initializer(initializer_range),
    )

    assert_op = tf.compat.v1.assert_less_equal(tf.reduce_max(input_ids), vocab_size - 1)
    with tf.control_dependencies([assert_op]):
        if use_one_hot_embeddings:
            flat_input_ids = tf.reshape(input_ids, [-1])
            one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)
            output_flat = tf.matmul(one_hot_input_ids, embedding_table)
        else:
            output_flat = tf.nn.embedding_lookup(embedding_table, input_ids)

        embedded_input = tf.reshape(output_flat, [batch_size, seq_length, embedding_size])

    assert_op = tf.compat.v1.assert_less_equal(seq_length, max_position_embeddings)

    with tf.control_dependencies([assert_op]):
        full_position_embeddings = tf.compat.v1.get_variable(
            name='pos_embed',
            shape=[max_position_embeddings, embedding_size],
            initializer=create_initializer(initializer_range),
        )
        # Since the position embedding table is a learned variable, we create it
        # using a (long) sequence length `max_position_embeddings`. The actual
        # sequence length might be shorter than this, for faster training of
        # tasks that do not have long sequences.
        #
        # So `full_position_embeddings` is effectively an embedding table
        # for position [0, 1, 2, ..., max_position_embeddings-1], and the current
        # sequence has positions [0, 1, 2, ... seq_length-1], so we can just
        # perform a slice.
        if position_offset == 0:
            embedded_input += tf.slice(full_position_embeddings, [0, 0], [seq_length, -1])[None]
        else:
            # Tensorflow is too stupid to allow slicing
            flat_pos_ids = (tf.range(seq_length, dtype=tf.int32) + position_offset)