def take_top_p_logits()

in lm_human_preferences/utils/core.py [0:0]

• 15 lines of code
• 1 McCabe index (conditional complexity)

def take_top_p_logits(logits, p):
    """Nucleus sampling"""
    batch, sequence, _ = logits.shape.as_list()
    sorted_logits = tf.sort(logits, direction='DESCENDING', axis=-1)
    cumulative_probs = tf.cumsum(tf.nn.softmax(sorted_logits, axis=-1), axis=-1)
    indices = tf.stack([
        tf.range(0, batch)[:, tf.newaxis],
        tf.range(0, sequence)[tf.newaxis, :],
        # number of indices to include
        tf.maximum(tf.reduce_sum(tf.cast(cumulative_probs <= p, tf.int32), axis=-1) - 1, 0),
    ], axis=-1)
    min_values = tf.gather_nd(sorted_logits, indices)
    return tf.where(
        logits < min_values,
        tf.ones_like(logits) * -1e10,
        logits,
    )