Source code for parts.transformer.utils

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"""Transformer model helper methods."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math

import tensorflow as tf

_NEG_INF = -1e9
#_NEG_INF_FP16 = -1e4

[docs]def get_position_encoding( length, hidden_size, min_timescale=1.0, max_timescale=1.0e4): """Return positional encoding. Calculates the position encoding as a mix of sine and cosine functions with geometrically increasing wavelengths. Defined and formulized in Attention is All You Need, section 3.5. Args: length: Sequence length. hidden_size: Size of the min_timescale: Minimum scale that will be applied at each position max_timescale: Maximum scale that will be applied at each position Returns: Tensor with shape [length, hidden_size] """ position = tf.cast(tf.range(length),dtype=tf.float32) num_timescales = hidden_size // 2 log_timescale_increment = ( math.log(float(max_timescale) / float(min_timescale)) / (tf.cast((num_timescales) - 1, dtype=tf.float32))) inv_timescales = min_timescale * tf.exp( tf.cast(tf.range(num_timescales),dtype=tf.float32 ) * -log_timescale_increment) scaled_time = tf.expand_dims(position, 1) * tf.expand_dims(inv_timescales, 0) signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1) return signal
[docs]def get_decoder_self_attention_bias(length, dtype=tf.float32): """Calculate bias for decoder that maintains model's autoregressive property. Creates a tensor that masks out locations that correspond to illegal connections, so prediction at position i cannot draw information from future positions. Args: length: int length of sequences in batch. Returns: float tensor of shape [1, 1, length, length] """ #print("get_decoder_self_attention_bias", dtype) with tf.name_scope("decoder_self_attention_bias"): #valid_locs = tf.matrix_band_part(tf.ones([length, length], dtype=dtype), -1, 0) valid_locs = tf.matrix_band_part(tf.ones([length, length], dtype=tf.float32), -1, 0) valid_locs = tf.reshape(valid_locs, [1, 1, length, length]) neg_inf=_NEG_INF #if (dtype==tf.float32) else _NEG_INF_FP16 bias = neg_inf * (1.0 - valid_locs) #bias=tf.saturate_cast(bias, dtype=dtype) return bias
[docs]def get_padding(x, padding_value=0, dtype=tf.float32): """Return float tensor representing the padding values in x. Args: x: int tensor with any shape padding_value: int value that dtype: type of the output Returns: float tensor with same shape as x containing values 0 or 1. 0 -> non-padding, 1 -> padding """ #print("get_padding", dtype) with tf.name_scope("padding"): return tf.cast(tf.equal(x, padding_value), dtype=dtype)
[docs]def get_padding_bias(x, res_rank=4, pad_sym=0, dtype=tf.float32): """Calculate bias tensor from padding values in tensor. Bias tensor that is added to the pre-softmax multi-headed attention logits, which has shape [batch_size, num_heads, length, length]. The tensor is zero at non-padding locations, and -1e9 (negative infinity) at padding locations. Args: x: int tensor with shape [batch_size, length] res_rank: int indicates the rank of attention_bias. dtype: type of the output attention_bias pad_sym: int the symbol used for padding Returns: Attention bias tensor of shape [batch_size, 1, 1, length] if res_rank = 4 - for Transformer or [batch_size, 1, length] if res_rank = 3 - for ConvS2S """ #print("get_padding_bias", dtype) with tf.name_scope("attention_bias"): padding = get_padding(x, padding_value=pad_sym, dtype=tf.float32) # padding = get_padding(x, padding_value=pad_sym, dtype=dtype) neg_inf=_NEG_INF #if dtype==tf.float32 else _NEG_INF_FP16 attention_bias = padding * neg_inf if res_rank == 4: attention_bias = tf.expand_dims(tf.expand_dims(attention_bias, axis=1), axis=1) elif res_rank == 3: attention_bias = tf.expand_dims(attention_bias, axis=1) else: raise ValueError("res_rank should be 3 or 4 but got {}".format(res_rank)) return attention_bias